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Goal conflicts, self-regulation, and course completion

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Title:
Goal conflicts, self-regulation, and course completion a comparison of web-based learners to traditional classroom learners
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English
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Moore, Barbara
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University of South Florida
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Subjects / Keywords:
Education
Learning
Instruction
Distance learning
Self-efficacy
Dissertations, Academic -- Secondary Education -- Doctoral -- USF
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bibliography   ( marcgt )
theses   ( marcgt )
non-fiction   ( marcgt )

Notes

Abstract:
ABSTRACT: The purpose of this study was to examine the goal conflicts, self-regulation, and course completion of post-secondary learners and to compare these factors in distance and traditional learners. Participants completed a self-report survey given on-line to those who had Internet access and administered in paper format to students in traditional classrooms. Procrastination, socializing, and employment were the most common goal conflicts reported by participants. Significantly more web-based students than traditional students were employed and were employed more average hours. Web-based students also had more children under the age of 12 than did traditional students. A significantly greater percentage of web-based participants than traditional students passed the courses included in this study. Web-based participants reported a significantly greater amount of self-regulation than did traditional students. Contacting the instructor for help and analyzing assignments contributed significantly to passing courses included in this study. Distinctions between distance learners and traditional learners are becoming less clear since some traditional courses have begun to offer web completion as an option. Many students who live on or near campus and who are otherwise traditional students now include web-based courses in their schedule.
Thesis:
Dissertation (Ph.D.)--University of South Florida, 2006.
Bibliography:
Includes bibliographical references.
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System requirements: World Wide Web browser and PDF reader.
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Mode of access: World Wide Web.
Statement of Responsibility:
by Barbara Moore.
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Title from PDF of title page.
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Document formatted into pages; contains 233 pages.
General Note:
Includes vita.

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aleph - 001796807
oclc - 156879923
usfldc doi - E14-SFE0001608
usfldc handle - e14.1608
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SFS0025926:00001


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Goal Conflicts, Self-Regulation, and Course Completion: A Comparison of Web-Based Learners to Traditional Classroom Learners by Barbara Moore A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Secondary Education College of Education University of South Florida Major Professor: James White, Ph.D. Ann Barron, Ph.D. Darrel Bostow, Ph.D. John Ferron, Ph.D. Date of Approval: April 19, 2006 Keywords: education, learning, instruction, distance learning, self-efficac y Copyright 2006, Barbara Moore

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i Table of Contents List of Tables..................................................................................................................viiiList of Figures...................................................................................................................xiAbstract............................................................................................................................xiiChapter One: Introduction.................................................................................................1 Web-Based Learners May Require Special Consideration...................................1Self-Regulation and Goal Conflicts in Traditional Classrooms............................2Self-Regulation and Goal Conflicts for Web-Based Learners..............................3Completion Rates of Traditional and Web-based Learners...................................4Results of a Pilot Study..........................................................................................4The Essential Ideas of This Study..........................................................................8The Research Questions for This Study................................................................8Answering the Research Questions.......................................................................9Subjects................................................................................................................10Definitions...........................................................................................................10 Chapter Two: Literature Review.....................................................................................13 Topics Covered in This Review of the Literature...............................................13Self-Regulation....................................................................................................14 Self-Regulated Learning..........................................................................14Demographic Impact on Self-Regulated Learning..................................16

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ii Goals and Goal Orientation.................................................................................17 Goal Conflicts..........................................................................................21 Procrastination.....................................................................................................22Course Completion..............................................................................................23Self-Efficacy........................................................................................................24Using the Internet for Distance Education...........................................................25 Asynchronous Communication Environment..........................................26Variation in Learning Environment.........................................................28Rapid Retrieval of Information................................................................28Hypertext/Hyperlinking...........................................................................29Virtual Reality..........................................................................................30The Internet and Constructivist Learning Theory....................................31Problems With Instructional Use of the Internet.....................................33 Summary of Pertinent Literature.........................................................................36Suggested Future Research..................................................................................37Implications for This Study.................................................................................38Research Questions..............................................................................................39 Chapter Three: Method....................................................................................................40 Study Overview...................................................................................................40Time Table...........................................................................................................40Included Courses.................................................................................................41Subjects................................................................................................................41 Inclusion of Participants..........................................................................44

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iii Categorizing Students as Web-Based or Traditional...............................44Inclusion and Exclusion of Certain Participants......................................47The Number of Participants.....................................................................48 Procedures............................................................................................................52 Instrumentation........................................................................................52Development of the Survey.....................................................................54Administration of the Survey...................................................................56 Development of Goal Conflict Measures............................................................58 Goal Conflict Questions Included on the Survey....................................58Non-Likert Goal Conflict Questions Included on the Survey.................59 Validity of Goal Conflict Measures.....................................................................60 Logical Content Analysis........................................................................60Construct Validity of Goal Conflict Measures........................................61 Reliability of the Goal Conflict Questions..........................................................62 Internal Consistency................................................................................62 Self-Regulation Scale Development....................................................................63Self-Regulation Items Included in the Survey.....................................................63Validity of the Self-Regulation Items..................................................................64 Logical Content Analysis........................................................................64Construct Validity....................................................................................65 Reliability of Self-Regulation Measures.............................................................66 Internal Consistency................................................................................66 Development of Self-Efficacy Measures.............................................................66

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iv Validity of Self-Efficacy Questions.....................................................................67Reliability of Self-Efficacy Measures.................................................................67Other Factors Concerning Validity and Reliability of the Survey.......................68 External Validity......................................................................................68Predictive Validity of This Survey..........................................................68Consistency Across Time........................................................................68 Analysis of Study Data........................................................................................69 Factor Analysis for Likert-Response Questions......................................69Analysis of Data for Research Question One..........................................71Analysis of Data for Research Question Two.........................................71Analysis of Data for Research Question Three.......................................74Analysis of Data for Research Question Four.........................................74Analysis of Data for Research Question Five..........................................76Analysis of Data for Research Question Six...........................................76 Chapter Four: Results......................................................................................................78 Demographics of Participants..............................................................................78Descriptive Statistics of the Likert Response Questions.....................................83 Descriptive Statistics for the Construct Self-Efficacy.............................83Descriptive Statistics for the Construct Self-Regulation.........................85Descriptive Statistics for the Construct Goal Conflicts...........................88 Comparing Traditional Classroom Students to Web-Based Students.................90Comparing the students of Course Nine to Other Web-BasedStudents..............................................................................................................102

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v Answering the Research Questions for This Study...........................................114 What Goal Conflicts Commonly Arise for Post-SecondaryLearners?................................................................................................114Are There Differences Between Post-Secondary DistanceLearners and Traditional Learners in the Number andPerceived Intensity of Goal Conflicts?..................................................123Is There a Difference in the Course Completion Rates ofPost-Secondary Distance Learners and Traditional Learners? .............134What is the Relationship Between Goal Conflicts and CourseCompletion of Post-Secondary Learners?.............................................135Is There A Difference in the Instructional Self-Regulation ofPost-Secondary Distance Learners and Traditional Learners?..............141What is the Relationship Between the Instructional Self-Regulation and Course Completion of Post-SecondaryLearners?................................................................................................144 Chapter Five: Discussion...............................................................................................147 Major Findings in This Study............................................................................147 What Goal Conflicts Commonly Arise for Post-SecondaryLearners?................................................................................................147Are There Differences Between Post-Secondary DistanceLearners and Traditional Learners in the Number andPerceived Intensity of Goal Conflicts?..................................................148

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vi Is There a Difference in the Course Completion Rates ofPost-Secondary Distance Learners and Traditional Learners?..............150What is the Relationship Between Goal Conflicts andCourse Completion of Post-Secondary Learners?.................................150Is There a Difference in the Instructional Self-Regulation ofPost-Secondary Distance Learners and Traditional Learners?..............150What is the Relationship Between the Instructional Self-Regulation and Course Completion of Post-SecondaryLearners?................................................................................................151 Implications of Study Findings..........................................................................152Conclusions........................................................................................................156Suggestions for Further Studies.........................................................................157Limitations to This Study..................................................................................159 Generalizability of the Study.................................................................159Elevated Type 1 Error Rate...................................................................160Possible Bias Caused by Course Nine Participants...............................160Assumption of Equality of Instruction..................................................161Mechanical Problem with Web Version of the Survey.........................161Classifying Participants as Web-Based or Traditional..........................162Unequal Incentives for Study Participation...........................................163Calculating the Number of Children of Participants.............................163Missing Completion Data .....................................................................164Low Participation by Course Two Students..........................................164Differences Between Paper and Web Versions of the Survey .............165

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vii Assumption of Truthfulness in Participants..........................................166 References......................................................................................................................167Appendices....................................................................................................................183 Appendix A: Pilot Study....................................................................................184Appendix B: Learning Factors Survey..............................................................194Appendix C: Survey Questions Sorted by Demographics andConstructs..........................................................................................................200Appendix D: Survey Announcements to Students............................................207Appendix E: Pilot Participant Responses Regarding Other GoalConflicts.............................................................................................................211Appendix F: Informed Consent.........................................................................213Appendix G: Rotated Factor Pattern..................................................................217Appendix H: Number of Participants Living With Children.............................220Appendix I: Comparing Goal Conflict Likert Responses by Format................222Appendix J: Frequency of Participants' College Majors Sorted byFormat................................................................................................................226Appendix K: The Likert-scored Conflict Measures of Course NineStudents Compared to All Other Web-Based Students.....................................228Appendix L: Logistic Regression Tables..........................................................230 About the Author..................................................................................................End Page

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viii List of Tables Table 1 Summary of Significant Findings in Pilot Study.................................................6 Table 2 Pilot Study: Course Level, Instructors, and Numbers of Participants.................7Table 3 Time and Place Categories in Distance Education............................................28Table 4 Courses and Instructors, Enrollment, Participation, and Completion...............42Table 5 Sample Sizes Needed to Obtain Varying Power...............................................50Table 6 Gender of Participants Compared to Course Format........................................78Table 7 Number of Participants in Courses by Format..................................................80Table 8 Summary of Demographics Reported by Students...........................................82Table 9 Descriptive Statistics for the Construct Self-efficacy.......................................84Table 10 Descriptive Statistics for the Construct Self-Regulation.................................86Table 11 Descriptive Statistics for Construct Goal Conflicts........................................89Table 12 Comparison of Web-based to Traditional Student Demographics..................91Table 13 Frequency of Year in School or Status by Format..........................................94Table 14 Marital Status by Format.................................................................................95Table 15 Partial Listing of Frequency of College Majors Sorted by Format................. 96 Table 16 Frequency of Race by Format.........................................................................97Table 17 Distance From Home to School by Format.....................................................98Table 18 Primary Reason Participant Enrolled in the Class...........................................99Table 19 Participant Household Size Sorted by Format ..............................................101

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ix Table 20 Gender of Course Nine Participants Compared to Other Participants...........103Table 21 Comparison of Course Nine to All Other Web Student Demographics.........104Table 22 Comparison of Levels of Course Nine Students to Other Students...............107Table 23 Frequency of Participants' College Majors Sorted by Format.......................108Table 24 Race of Course Nine Participants Compared to All Other Participants......... 109 Table 25 Primary Reason Course Nine Participants Enrolled in This Class.................111Table 26 Course Nine Participant Lifestyles Compared to Other Participant s.............113 Table 27 Participants Who Experienced Some Degree of Goal Conflicts....................115Table 28 Other Conflicts Mentioned by Participants....................................................116Table 29 Goal Conflicts Reported by Gender...............................................................118Table 30 Number of Children Sorted by Gender of Participants.................................120Table 31 Total Number of People in Participant’s Household......................................122Table 32 Some conflicts of Web Students Compared to Traditional Students.............124Table 33 Likert-Scored Conflicts of Web-based Compared to Traditional Students... 126 Table 34 Other Conflicts Reported by Traditional and Web-based Students...............128Table 35 Frequencies and Ages of Children Living with Participants.........................130Table 36 Responsibility for Child or Other Person Who Needs Assistance.................132Table 37 Comparison of Reported Responsibility for Others.......................................133Table 38 Course Completion of Traditional Students Compared to Web Students......135Table 39 Logistic Regression for Possible Predictors of Passing Course.....................137Table 40 Logistic Regression for Possible Predictors of Failing Course......................230Table 41 Logistic Regression for Possible Predictors of Withdrawing from Course...232 Table 42 Self-Regulation of Web-based Compared to Traditional Students................142

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x Table 43 The Construct Self-Regulation as Predictor of Course Completion..............146Table 44 Pilot Study: Course Level, Participants, and Instructor in Each Class........... 185 Table 45 Summary of Significant Findings in Pilot Study............................................187Table 46 Goal Conflict Questions and Their Variable Names......................................203Table 47 Self-Regulation Questions and Their Variable Names..................................205Table 48 Self-Efficacy Questions and Their Variable Names......................................206Table 49 Rotated Factor Pattern (Standardized Regression Coefficients)....................217Table 50 Number of Participants Living with Children................................................220Table 51 Comparing Goal Conflict Likert Responses by Format.................................222Table 52 Frequency of Participants' College Majors Sorted by Format.......................226Table 53 Likert-Scored Conflicts of Course Nine Students Compared to All Other Web-Based Students.......................................................................................228Table 54 F Ratios of Likert-Scored Conflict Measures Comparing Course Nine Students to All Other Web-Based Student.....................................................229

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xi List of Figures Figure 1 Self-regulation Habits That May affect Learning..........................................2Figure 2 Goal Conflicts That May Affect Learning.....................................................3Figure 3 Factors That May Contribute to Course Completion..................................76

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xii Goal Conflicts, Self-Regulation, and Course Completion: A Comparison of Web-Based Learners to Traditional Classroom Learners Barbara Moore ABSTRACT The purpose of this study was to examine the goal conflicts, self-regulation, and course completion of post-secondary learners and to compare these factors in distanc e and traditional learners. Participants completed a self-report survey gi ven on-line to those who had Internet access and administered in paper format to students intraditional classrooms. Procrastination, socializing, and employment were t he most common goal conflicts reported by participants. Significantly more web-ba sed students than traditional students were employed and were employed more average hours. We bbased students also had more children under the age of 12 than did traditional students.A significantly greater percentage of web-based participants than tradit ional students passed the courses included in this study. Web-based participants reported asignificantly greater amount of self-regulation than did traditional students Contacting the instructor for help and analyzing assignments contributed significantly to pa ssing courses included in this study. Distinctions between distance learners and tradi tional learners are becoming less clear since some traditional courses have beg un to offer web completion as an option. Many students who live on or near campus and who areotherwise traditional students now include web-based courses in their schedule.

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1 Chapter One Introduction Web-Based Learners May Require Special Consideration While distance education courses meet the needs of many students, web-based classes make special demands not required of traditional classroom students. Th e webbased learner must be responsible for instructional time management and technica l access to instruction. This student must arrange for learning space within t he home or work environment. The web-based student may need to practice more instructional self-regulation habits than does a traditional learner. Instruction takes place at hom e or in the workplace; therefore, the web-based learner may encounter more instructiona l goal conflicts or may feel their impact more than the traditional learner. This stud y will focus on the instructional self-regulation, instructional goal conflicts, and the coursecompletion of post-secondary students. Additionally, it will compare these factors in traditional students to those of web-based students. Instructional self-regulation is the pattern of behaviors or habits that student s use to inquire information. Highlighting or outlining text information, making flashcar ds, and self-quizzing before an exam are examples of instructional self-regula tion. Instructional goal conflicts are factors that may negatively affect s tudent achievement because they conflict with or detract from learning goals. For example a student may list one goal as the completion of a college degree. That same student may also ha ve a

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2 goal of putting the family first. The illness of a family member may become an instructional goal conflict for the student if he or she is required to spend considera ble time caring for the family member. Corno, (1989), Schunk, (1998), Pintrich (2000), andothers examined the effects of self-regulation and goal conflicts on traditional students. Instructional self-regulation and instructional goal conflicts for web-base d learners may differ from those of traditional students.Self-Regulation and Goal Conflicts in Traditional Classrooms Research reveals that students in traditional classroom settings follow certain patterns of self-regulation in completion of tasks (Baum, 1997; Pintrich, 2000; Garcia,1995; Zimmerman, 1990). Figure 1 illustrates some self-regulation habits that mayaffect course completion. The self-regulation process consists primarily of goal-setting, goal pursuance, and monitoring of progress toward goals (Vancouver, 2000; Butler and Winne, 1995;Kerlin, 2000). Goals often conflict with one another (Nichols, 1998; Carver and Scheier,2000; Hammer, 1998). Instructional goal conflicts are those conditions that hinderachievement because they conflict with student learning goals. Traditional students often encounter goal conflicts such as family problems, jobs, financial difficult ies and Figure 1 Self-regulation habits that may affect course completion. Set Goals Manage Time Take Notes Self-Quiz Classify Info Course Completion SelfRegulation

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3 other factors (eCollege, 2000; Carver and Scheier, 2000). Figure 2 illustrates a few of the instructional goal conflicts that ma y affect achievement and/or course completion.Self-Regulation and Goal Conflicts for Web-Based Learners Literature is sparse concerning self-regulation and goal conflicts in webbased learners. For distance education courses some self-regulation tasks are si milar to those in traditional classrooms; however, differences exist due to the format of web-ba sed learning. In addition to performing the self-regulation behaviors of traditional learners, web-based learners must also: Acquire appropriate access to technology Make schedules for learning at home or work Put aside people and activities at home or work during learning time Ask for help or check on grade standing via e-mail, or form virtual study teams via listserv, chat, email The quality of instructional goal conflicts for web-based learners may be sim ilar to Figure 2 Goal conflicts that may affect course completion. Course Completion Number of children Course load Hours worked Stress Illness

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4 those of traditional classroom learners. For example, both traditional and web-bas ed learners may have jobs, carry a heavy credit load, experience personal illnes s or illness of a family member, or have children that require their attention. However, due to t he nature of their learning environment, usually home or work, web-based learners'perceptions of goal conflict magnitude may be greater than those same perce ptions in classroom learners. Hence, web-based learners’ family or job commitment s may greatly impact their learning experience because they are learning in the home or w orkplace. However, web-based learners may enroll in distance courses because of additiona l goal conflicts that preclude taking traditional courses.Completion Rates of Traditional and Web-based Learners Findings regarding course completion rates and achievement for distance and traditional learners remain inconsistent. While Cohen, Ebeling, and Kulik (1981) revea l no variations in completion rates for visually based computer learners compared t o traditional learners, Searcy (1993) and Hogan (1997) report that web-based lear ners exhibited higher course completion rates than traditional students. However, thesestudies were completed several years ago, and distance learning formats have evolved rapidly; findings regarding completion rates may vary today.Results of a Pilot Study In the fall of 2001, a pilot study was conducted at a major urban research university in the southeastern United States. Using a five-point Likert scale, pa rticipants completed a self-report survey of perceived goal conflicts and self-regul ation. Both undergraduates and graduate students participated in the study. These included 171traditional learners and 126 web-based learners in the College of Education in the st udy

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5 university. Students who met with their instructors one or more times a week wereconsidered traditional learners, while those who met with their instructors sole ly at the beginning and/or end of the course were deemed web-based learners. Participa nts were given the option of either completing the survey online or employing an identical pape r and pencil survey. Data regarding student achievement or completion of the course was not collected for this pilot. Using the SAS system, an analysis of variance using a general linear model (because the cells were unequal) compared the goal conflicts and self-regul ation of webbased learners and traditional learners. Several significant variations between the two groups were revealed in areas that were considered goal conflicts or impedi ments to learning, as shown in Table 1. A more extensive description of the pilot study iscontained in Appendix A. Based on pilot study results various changes were made in the survey. Several goal conflicts were added at the suggestion of pilot study participants; some questions were omitted as they appeared either redundant or non-relevant when employing anexploratory factor analysis using SAS.

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6 Table 1Summary of Significant Findings in Pilot Study ___________________________________________________________________________ Traditional Web-based Students reported: nFMSDMSD ___________________________________________________________________________ Goal Conflicts Course-related stress29616.85***2.871.293.501.35Worried about demands of course296 8.68***2.681.273.101.71Impact on coursework when illness ordisability of friend/family member existed 295 7.45**1.941.292.351.26 Self-Regulated Learning Related new information to old295 5.81*4.210.764.000.77Re-read or studied notes prior to quiz or test2967.50**4.560.774.31Joined study teams or virtual study teams29612.23***2.261.231.791.02Set daily or weekly goals as they worked29610.86**3.321.193.750.99___________________________________________________________________________ p < .05 ** p < .01 *** p < .001 A design problem was encountered in the pilot study in that 117 of the 126 webbased learners were enrolled in four different educational psychology course s taught by one instructor, listed as Instructor I in Table 2. As also shown in Table 2, of the 171traditional learners, 73 participants were in one undergraduate education course usingthe same content but having four different instructors. To overcome this design flaw this study was planned to include web-based and traditional courses matched accor ding to content. In addition, it was planned that each group would consist of a minimum of500 participants and would contain a variety of courses.

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7 Table 2Pilot Study: Course Level, Instructors, and Numbers of Participants_____________________________________________________________________ Traditional learners Web-based learners Undergraduate ________________ Graduate _______________ Undergraduate ______________ Graduate ____________ CseInstrnCseInstrnCseInstrnCseInstrn _____________________________________________________________________ T1A,B,C,D73T2E38 T3F16T4G28T5H16D1H2D2H7 D3I38D4I17D5I34D6I28 _____________________________________________________________________

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8 The Essential Ideas of This Study The purpose of this study was to expand knowledge about the post-secondary learner by examining certain aspects of student learning experiences. The outcome measures included the following: 1. Number and perceived intensity of goal conflicts 2. Self-regulation 3. Course Completion Also examined were the following: 1. The relationship of goal conflicts to course completion 2. The relationship of self-regulation to course completion 3. Differences between web-based students and traditional students in allcategories of this list The Research Questions for This Study:1. What goal conflicts commonly arise for post-secondary learners? 2. Are there differences between post-secondary web-based learners and traditi onal learners in the number and perceived intensity of goal conflicts? 3. Is there a difference in the course completion rates of post-secondary web-basedlearners and traditional learners? 4. What is the relationship between goal conflicts and course completion of post-secondary learners? 5. Is there a difference in the instructional self-regulation of post-secondary we b-based learners and traditional learners?

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9 6. What is the relationship between the instructional self-regulation and coursecompletion of post-secondary learners? Answering the Research Questions This study utilized a self-report questionnaire designed to identify learner perceptions of their own instructional goal conflicts and instructional self-re gulation. The survey collected information about the number and types of goal conflicts, and theintensity of internal conflict experienced by students as a result of these goal conflicts. The instrument also allowed students to input self-regulation information such aswhether they made schedules for assignment completion, used flashcards or pra ctice quizzes, and contacted other students or the instructor for help. A paper survey was administered in class to traditional learners, and an online version was made available for web-based learners. Traditional learners a lso had the option of participating online. Data was used only if the student's enrollment in a cours e included in this study could be verified. The questionnaire was administered during th e seventh and eighth weeks after the course began. It was believed that participa nts would thus have had time to experience the factors in question and adjust to problems that aroseearly in the course. The questionnaire administration time period was also prior t o the last date to withdraw without penalty. At the end of the semester, course completion data was obtained from the instructors and each student’s completion data was recorded as one the following: Completion with passing grade (P) Completion with failing grade (F) Withdrawal (or drop) (W)

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10 Incomplete granted by the instructor (I) Subjects The initial study data was gathered from self-reports of post-secondary undergraduate students enrolled in a major urban research university in the southeas tern United States. Limited to students enrolled in undergraduate courses, the study incl uded 604 web-based students and 540 traditional classroom students. Participants included826 females and 318 males. Web-based participants were students who received theirprimary course instruction via the Internet. Traditional students were those taki ng courses in which the instructor met with students in person periodically.Definitions Asynchronous instruction Instruction that occurs while the instructor is separated from the student by physical distance and time difference. Distance education Instruction that takes place with the instructor and student separated by physical distance and in some cases separated by time differ ence. For purposes of this study, distance education and distance learning are the same as we bbased learning. Distance learner A student who is separated from his or her instructor by physical distance and in some cases separated by time difference. For thi s study, the distance learner is separated from the instructor by both time and physical dis tance. Either a very small amount or no real time communication occurs between instruct or and students, with the exception of “chat” or chat-room meetings. For purposes of thisstudy, the distance learner is the same as the web-based learner.

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11 Goal conflicts Factors that conflict with actions that an individual should be performing in order to achieve a goal. Goal orientation The tendency of individuals to be either task-oriented (carry out activities based on enjoyment of the task or learning) or performance oriented ( carry out activities to win approval of others or gain extrinsic rewards such as degre e or grades). Most individuals have some traits of each but will exhibit primarily one or the other. Instructional goal conflicts Factors that conflict with actions that an individual ordinarily performs to achieve an instructional goal such as course completi on. Instructional self-regulation Self-management activities that an individual conducts to achieve an instructional goal such as course completion. Motivation Factors that drive or lead an individual to perform certain tasks or acquire a particular thought process. Self-regulation Self-management activities that an individual performs to set goals, implement them, and monitor ongoing progress. Self-regulated learning Self-management activities deliberately employed by a student to perform to learning tasks or acquire ideas. Traditional classroom A classroom in which instruction occurs with both teacher and students present periodically throughout the semester. Web-based learner. A student who is enrolled in a course in which instruction is provided via the Internet; the student does not physically meet with an instructor ex cept at the beginning and/or end of the course. For this study, the web-based learner isseparated from the instructor by both time and physical distance. Either a very s mall

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12 amount or no real time communication occurs between instructor and students, with theexception of “chat” or chat-room meetings. For purposes of this study, the web-bas ed learner is the same as the distance learner. Web-based learning A type of Distance Learning Instruction that occurs via the Internet. Web-based learning in this study refers to courses in which the student doe s not physically meet with an instructor except perhaps for an orientation meet ing and/or examinations. For purposes of this study web-based learning is the same as distance learning.

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13 Chapter Two Literature Review Topics Covered in This Review of the Literature Goal conflicts, self-regulation, and course completion were of specific interest in this study. Provided with a particular learning environment, why does one studentcomplete a course while another fails at this task? What are the conditions that prevent a student from successfully completing a course at certain times? Past research on goal conflicts, self-regulation, and course completion all gave direction for this study. Distance education brings special considerations for the learner. The defining characteristic of distance education is physical separation of the student f rom the teacher. Distance or web-based education, in the context of this study, is instruction that em ploys the Internet for primary instructor-learner interaction and does not utilize the traditional classroom setting except for orientation meetings and/or examinations. As t here is little or no face-to-face teacher-learner interaction, the ability of the learner to self-regulate and the ensuing goal conflicts encountered are of great interest. A review of the literature began with an examination of self-regulation and selfregulated learning. Included was literature concerning goals, goal conf licts, selfefficacy, procrastination, and task completion. This was followed by a review ofresearch concerning distance or web-based education and a discussion of theinstructional usefulness of distance learning. This chapter ends with a summar y of

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14 literature leading to the research questions of this study. Important consid erations for survey research are reviewed in Chapter Three: Method.Self-regulation Self-regulation includes the process of behaviors that an individual follows in setting, monitoring, adjusting and achieving goals (Carver & Scheier, 1982; Ja ckson, MacKenzie, and Hobfoll, 2000; Demetriou, 2000). Self-regulation of the individualtakes place within communities of individuals (Demetriou, 1996; Jackson, MacKenzie,and Hobfoll, 2000). Each person operates within communities consisting of families,co-workers, peers, and classmates, and each is influenced by those communities Codevelopment of self-regulation occurs because of interactions within these communi ties (Demetriou, 1996; Jackson, MacKenzie, and Hobfoll, 2000). Self-regulated learning. Self-regulation plays an important role in academic success (Baum, 1997; Pintrich, 2000; Garcia, 2000; Zimmerman, 2000). Pintrich andDeGroot (1990) studied 173 seventh graders from eight science and seven Englishclasses. Using regression analysis they found that the significant predictor s of the average grade ( r 2 = .22) were self-efficacy (partial r = .18, p < .02) and self-regulation (partial r = .22, p < .005). Shih (l997) examined the motivators of 99 students enrolled in two web-based non-major introductory courses, zoology and biology, through a Midwestern universityin 1997. Thirty-two of the participants enrolled in these university courses were hig h school students. In a self-report survey using a five-point scale, students indica ted their highest rated motivator was wanting to get better grades than other students ( M = 4.21, SD = 1.01). The second highest rated motivator was expecting to do well in the class

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15 ( M = 3.77, SD = 0.84). Students also believed that they could do better if they studied in appropriate ways ( M = 3.70, SD = 0.89). The study found that the most important factors in Web-based learning were motivation and learning strategies. Thes e two factors accounted for more than one-third of student achievement and they correlat ed significantly with student achievement. Students who scored high on motivation and useof learning strategies scored higher in overall achievement. Self-regulated learning includes metacognitive and behavioral strategi es deliberately employed by students to enable task completion, including maintaini ng awareness of their learning processes and selecting and employing usef ul strategies. (Bandura, 1986; Zimmerman, 1989; Pintrich, 1995). Academic self-regulators choosepractice techniques, memory aids, plan study time and place, ask relevant questions and set goals (Baum, 1997). Self-regulated learning includes three features: goals, actions, and as sessment (Vancouver, 2000). In self-regulated learning, the learner creates new goa ls, creates means to attain or maintain the goals, and creates or changes ways to asse ss or perceive his or her current state. Self-regulated learners inspect situations, set g oals, monitor progress, and provide internal feedback (Butler and Winne, 1995; Kerlin, 2000).Pintrich and DeGroot (1990) found that self-regulated learning consists of: "1) Stude nt metacognitive strategies for planning, monitoring, and modifying their cogni tion… 2) Students' management and control of their effort on classroom academic tasks…3) the cognitive strategies students use to learn, remember, and understand the mat erial for example: rehearsal, elaboration, or organizational strategies" (pg. 33). Self-regulated learning is not easy to induce in the classroom context because

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16 students expect teachers to set goals and follow up with motivation and monitoring.Boekarts and Niemivirta (2000) described characteristics of natural contex t learning, wherein self-regulation occurs simply and easily: First, natural learning episodes are often self-initiated or occurspontaneously. Second, they are cumulative, thus creating ongoing andunfolding learning experiences. Third, this type of learning is alwayssocially situated. Fourth, it is driven by personal goals and thereforeconsequential in nature and affectively charged (p. 418). Self-regulated learning contains the primary elements of goal setti ng and goal striving initiated by the learner. In traditional classrooms, self-regul ation includes such practices as repeating information aloud, taking notes, rewriting notes, outl ining text information, forming study teams, asking for instructor help, setting goals f or time and tasks, scheduling assignments, self-quizzing or using available quizzes for pra ctice (Winne & Perry, 2000; Pintrich & DeGroot, 1990). Self-regulation tasks for distancelearners include similar tasks plus several that vary somewhat. They include s uch behaviors as acquiring appropriate access to technology, arranging time a nd place to “attend class” at home or workplace, making schedules for completion of tasks, askingfor help or checking on grade standing via email, forming virtual study teams via listserv, chat, email, and using self-quizzes or automated online quizzes. Demographic impact on self-regulated learning. Strage (1998) examined student-reported family backgrounds of university undergraduates and their self-regulation behaviors. Results suggested that the quality of students' relations hips with their parents is predictive of their attitudes and behaviors regarding self-r egulated

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17 learning. Students who experienced secure, authoritative parenting were bett er at selfregulating behaviors than students who experienced insecure-ambivalent, authorita rian parenting. Strage's work pointed out that effects of family influence persi st after students are no longer in close contact with parents. Purdie, Douglas, and Hattie (1996) studied the differences in self-reported learning strategies used by Australian and Japanese high school students. The Aus tralian students included 122 men and 126 women; Japanese students consisted of 98 men and117 women. The researchers found cultural differences affected the students'conceptions of learning as well as their use of self-regulated learning stra tegies. Japanese students used memorization and rehearsal significantly more than theirAustralian counterparts. However, Japanese students were less likely to view l earning as memorizing and reproducing. They used rote learning as a desirable route tounderstanding. Hannifin (1984 in Williams, 1996) implied that older students shouldhave acquired more clearly developed learning strategies, therefore should displ ay greater benefit of learner control (self-regulation) than younger students.Goals and Goal Orientation Goals are entities that guide the behaviors of individuals (Boekaerts & Niemivirta, 2000; Carver and Scheier, 2000; Barnhart, 1962; Meece, 1994; Hagen &Weinstein, 1995). Goals are generally regarded as attracting targets tow ard which efforts are directed (Sheldon, 1998; Hagen & Weinstein, 1995; Carver and Scheier,2000; Meece, 1994). Goal orientation, the quality of inner, often unstated goals that motivate students in their learning processes, was studied by Ames (1992), Dweck and Leggett (1988),

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18 Dweck (1990), Harackiewicz and Elliot (1996, 1998), Pintrich & DeGroot (1990),Garcia (1995), Meece (1994), and others. Individual students exhibit a variety of goalsand each student may be influenced by several goals at once. Examples of student goals include enjoying the material, scoring higher than everyone else in the c lass, enjoying the learning process, not failing the class, impressing one's family and/ or friends, appearing smart, avoiding embarrassment due to ignorance, obtaining a better j ob, or hoping to comprehend the material. These goals and others, in some combinationunique to the individual, compile the goal orientation of each learner. Learner goal factors most recently were grouped into two primary divisions and, although given different labels by various researchers, they were similar i n context (Boekaerts & Niemivirta, 2000; Pintrich, 2000; Hagen & Weinstein, 1995; Meece,1994). The two major goal orientation classification groups are learning goa ls and performance goals. Learning goals are often called “mastery” or “ task” goals and performance goals are also referred to as “ego centered” goals. Meece ( 1994) described two types of achievement goals. He first addressed learning-oriented or t ask-oriented goals, similar to Hagen and Weinstein's (1990) mastery goals, in which the learni ng process is valued. Secondly, he described performance-oriented or ego-oriented goa ls, which are similar to Hagen and Weinstein's (1990) performance goals, in which s tudents seek to demonstrate high ability or gain favorable judgments of others. Learning g oals are generally intrinsic motivators while performance goals are norm ally extrinsic (Dweck, 1990; Burns, 1998). Learning goals acknowledge the student's value of learning the material, understanding ideas, learning new things, and valuing the information (Dweck, 1990;

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19 Meece, 1994; Hagen and Weinstein, 1995). Performance goals are usually ego-cent ered goals, e.g. getting a good grade, attaining the top score, positively impress ing family or friends (Dweck, 1990; Meece, 1994; Hagen & Weinstein, 1995). Learning goal andperformance goal orientations are not mutually exclusive. While most students exhibit a combination of both mastery and performance goals, their predominant motivation factorwill usually be one or the other (Hagen & Weinstein, 1995). Each of the two major goal groups, learning and performance goals, can be further divided into two major groups called approach focus and avoidance focus goals(Carver & Scheier, 2000). Approach orientation goals pursue goals while avoidanc e orientation goals avoid failure. An example of an approach focus goal is the studentgoal to complete the course with a B or better. An example of an avoidance focus goalis the student goal not to fail the course. In identifying and quantifying goal orientation, researchers employed var ious instruments, the most popular being the Motivated Strategies for LearningQuestionnaire, referred to as MSLQ (Pintrich & De Groot, 1990). This instrument as ks students to respond to a self-report questionnaire and quantifies the data using a Likertscale. Pintich, DeGroot and others have studied goal orientation as it relates to selfregulation for traditional students. Their work revealed that students who are learni ng goal oriented acquire more efficient patterns of self-regulation compared to thos e who are performance goal oriented (Pintrich, 2000; Hagen & Weinstein, 1995). The higherthe degree of self-regulation, the greater the learning achievement (Pintri ch & De Groot, 1990). Meece (1994) asserted that the study methods students employ and what they

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20 remember are influenced by achievement goals. She discovered that students learn best "when they focus on mastering the task at hand rather than competing with others f or grades and teacher approval" (p 41). For students in traditional classrooms, both young children and college students with mastery goals remain on task longer and use advanced strategies compa red to those with performance goals (Hagen & Weinstein, 1995). Burley, Turner, & Vitulli (1999) examined the relationship between age and goal orientation in undergraduate students enrolled in a southern university. They analyze d the data of 199 participants, whose ages ranged from 17 to 59 years, in two age groups.The younger group, mean age = 19.7 years, ( SD = 1.7), included 117 participants and the older group, mean age = 36.2 years, ( SD = 8.8) included 82 participants. These researchers found a significant correlation between age and learning orientati on, r (199) = .23, p < .001. Their findings indicated that the older students tended to have higher learning-orientation scores than the younger students. Although the rela tionship was not as strong for performance orientation, r (199) = -.13, p = .08, it indicated that the younger students had higher performance-orientation scores than the older student s. Using age (younger or older) as the independent variable and learning orientation a s the dependent variable, the researchers conducted an analysis of variance. The mea n score for learning-orientation in the younger group was 3.8 and the mean score for learningorientation in the older group was 4.0, indicating a significant difference, F (1, 197) = 4.75, p = .03. When the researchers examined age and performance orientation, they found no significant main effect for age, F (1, 197) = 1.02, p = .31. Emotions such as task anxiety, test anxiety, and anger directed at the task or the

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21 instructor may negatively impact student motivation (Ames C., 1992; Hatzigeorg iadis & Biddle,1999; Ntoumanis, 1998; Boekaerts, 1993). Emotions drive, determine, andpredict goal orientation, rather than goal orientation preceding or determini ng emotion (Boekart, 1993; Seifert, 1995; Ntoumanis, 1998). Student value of the task is the component of motivation in which the student consciously or unconsciously asks, "Why am I doing this task?" Student value of anacademic task has been shown to have significant effect on student learning behavior s (Dweck & Elliott, 1983; Paris & Oka, 1986). Students who retain high value for the taskand are oriented toward mastery of content are likely to persist in the task, enga ge in metacognition, and employ cognitive strategies (Ames & Archer, 1988; Dwe ck & Elliott, 1983; Paris & Oka, 1986). Goal conflicts. Goals sometimes conflict with one another (Nichols, 1998; Carver & Scheier, 2000; Hammer, 1998). A goal to master a learning task may conf lict with a goal to maintain family bonds. A goal to earn the highest grade in a course may conflict with a goal to please peers. If one considers multiple goals, conflicting or non-conflicting, as variables affecting behaviors of individuals, a person may imagine that an event in pursuit of onegoal may affect pursuit of another goal. The goal to complete college may con flict with the goal to keep a job. In 1995-96, over 50 percent of all undergraduates worked anaverage of 25 hours per week to pay school expenses (NCES, 1998). The report alsostates that the greater the number of hours worked, the more likely students reported t hat working negatively affected their grades. Sixty-eight percent of the student s enrolled in distance learning courses work more than 30 hours per week (eCollege, 2000).

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22 Hammer (1998) mailed a survey to 1000 part-time and full-time students whoattended an urban university in the western United States for at least two yea rs but no longer than four years and who were at least 22 years old. Participants comple ted and returned 375 of the surveys. High degrees of work-school conflict correlated withhigher numbers of hours worked, lower levels of perceived effectiveness of supportservices (tutorial services, child-care, student legal services, etc.), a nd lower levels of satisfaction with educational experience. High levels of family-school confl ict correlated with higher numbers of children and higher numbers of credits taken. Tubre (1985) completed a meta-analysis of the relationships between role ambiguity, role conflict, and job performance. His research revealed a negativ e relationship between role ambiguity (expectations surrounding the job role) and jobperformance, but only a negligible relationship between role conflict (incompat ibility of job demands) and job performance.Procrastination Upon viewing a series of studies concerning personalized systems of instructi on (PSI), Ferrari, Johnson, and Williams (1995) reported that when left entirely to the ir own time schedule, students tended to procrastinate to the detriment of their completi on rate or retention score. Majchrzak (2001) studied deadline contingencies in 181 pre-serviceteachers who participated in a content-on-demand course, similar to PSI. She report ed that students who have contingency deadlines (bonus and penalty points for early or lat e submission) for assignments have higher posttest achievement ( M = 47.22, SD = 22.65) than those who have only one deadline for all assignments ( M = 39.13, SD = 22.16). A high degree of procrastination existed in students who needed to have all assignments

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23 turned in at the end of the course with no bonus for early or late submission. In a study of 104 college students, Saddler and Buley (1999) discovered the following predictors of procrastination: test anxiety, socially prescrib ed perfectionism, beliefs that outcomes are contingent on one's own efforts, fear of negative eval uation, and low personal standards for achievement. Haycock, McCarthy, and Skay, (1998)studied the relationship of self-efficacy, anxiety, age, and gender to procras tination in college students. They realized that procrastination was significantly and inver sely related to self-efficacy. Additionally, while apparently not related to age or gender, procrastination was significantly and positively related to both state and trai t anxiety. Ferrari, Johnson, and Williams (1995) examined theory and research concerningprocrastination. They discovered that academic anxiety, irrational beliefs(inappropriately high standards), and low self-esteem were all positively r elated to procrastination.Course Completion There are mixed findings about course completion rates for distance and traditional learners. In 1981, Cohen, Ebeling, and Kulik reported no difference incompletion rates for visually based computer learners when compared to traditionallearners. Searcy (1993) stated that course completion rates may be higher f or distance learners than traditional learners. Hogan (1997), in a study of 11 courses involving 220distance learners and 457 traditional learners, discovered distance learner s had a higher course completion rate than traditional students (75% for distance learners compa red to 72% for traditional learners). However, withdrawal rates were higher for dista nce learners (21%) when compared to traditional learners (19%).

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24 Sinclair Community College (1999) compared the completion and grades of 651 traditional students to those of 651 distance learning students. The groups were matche d according to demographics and course. More traditional learners (70%) than distanc e learners (55%) completed with a passing grade. More distance learners (21% ) than traditional learners (15%) withdrew. Distance learners in this study incl uded web-based students and students who attended live-interactive off-campus distance classr ooms via satellite. Hara and Kling (2000) did a qualitative study in which they observed graduate students enrolled in a text-based distance learning course. In this study stude nts experienced distress due to the format of the course on several occasions. Of the e ight who started the course, two dropped out due to technical difficulties. This studyaddressed the difference in course-related distress and frustration experi enced by distance learners compared to that of traditional learners, and the relations hip of distress and frustration to the course completion rate.Self-Efficacy Self-efficacy is perceived capability to perform a given task (Bandura, 2001) Self-efficacy often presents a positive correlation with achievement (Sc hunk, 1985; Paris & Oka, 1986; Andrew & Viale, 1998; Zimmerman & Pons, 1990; Pintrich & DeGroot,1990; Pajares & Schunk, 2001). Additionally, several researchers suggest that self-efficacy in distance learners is positively correlated with achievement (Miltiadou, 1999; Zhang et al, 2001). Self-efficacy may represent a major predictor of using selfregulatory learning strategies (Zimmerman & Pons, 1990). Hagen and Weinstein ( 1995) established that instructions to students are vital: those who believe that a task i s do-able

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25 with effort maintained high efficacy, set challenging goals, and employed appr opriate learning strategies. The self-efficacy beliefs of an individual are based on mastery experience the vicarious experience of the effects produced by the actions of others, verbal persua sions of others, and the physiological state (Pajares, 2001). The measurement of selfefficacy for a given task involves three dimensions: level of task, strength of belief, andgenerality, that is, how closely the particular belief corresponds to the pa rticular outcome (Pajares, 2001). Items in a self-efficacy instrument should be worded in terms of "can", which indicates capability, rather than "will", which indicates intention (Pa jares, 2001; Bandura, 2001).Using the Internet for Distance Education Internet use promotes learning despite the physical separation of student and instructor, and each can participate or interact at separate times. Therefor e, it appears an ideal medium for wide distribution of learning tools in a variety of circumstances Concurrently, this format introduces special requirements for student motivati on and self-regulation. Although there remains slight documented evidence in favor of webbased instruction (Reeves & Reeves, 1997), "Distance education … is regardedinternationally as a viable and cost effective way of providing individualizedinstruction." (McIsaac & Gunawardena, 1996). Cavanaugh (1998) performed a meta-analysis of data from 19 studies of the effects of interactive distance education on K-12 learning. In comparing the achi evement of 929 participants, she revealed a small effect size (0.147 with a ninety-five perc ent confidence interval from -1.113 to 1.407) in favor of distance education. She did,

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26 however, discover a large negative effect size (-0.801) for language instr uction via distance education. When she analyzed the data without the effects of language studies the overall effect size of interactive distance learning on K-12 learners wa s 0.344 (with a ninety-five percent confidence interval from -0.686 to 1.374), a positive effect in favorof interactive distance learning. There are several particular features of the Internet that impact g reatly on education. These include an asynchronous communication environment (referring toboth time separation and physical distance), rapid retrieval of information, hypert ext (hyperlinking), virtual reality, and variation in format. While these features promote significant diversity in learning dynamics, they also introduce special cir cumstances that impact student motivation and interaction. Because the defining characteristi c of distance education is the physical separation of the student from the instructor, andbecause a time differential in student-teacher participation is possible, t he crucial feature of distance education is this asynchronous communication environment. Asynchronous communication environment. The term asynchronous formerly referenced time differences but the advent of the Internet elicits new uses f or the term, uses that refer to physical distance as well as time separation. Asynchronouscommunication environments exist when interaction events occur at various times a nd places. Asynchronous communication allows instruction to occur while student andinstructor are in different places and interacting at separate times (C artwright, G. P., 1994). When communication events on the Internet occur simultaneously, they are saidto occur in real time. A person watching a live television broadcast is seeing it in real time. When people are speaking to one another face to face or on the telephone or in a

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27 chat room, they are communicating in real time. Movies, taped television shows, lett ers sent in the mail, e-mail, and web pages are all forms of communication that are not in real time. Instruction can occur in both modes. Instruction can also occur when teacher and student are in the same place, suchas a traditional classroom, or not in the same place, as in televised courses or c ourses posted on the Web. Additionally, the Internet allows instruction to occur long after theinstructor posted it on the Internet. The Internet makes possible four categor ies of time and place communication relationships: Same time-same place, same time-di fferent place, different time-same place, and different time-different place (Mc Isaac and Gunawardena, 1996). Table 3 lists major variations of distance education in these fourtime-place categories. Asynchronous instruction via the Internet offers tremendous potential for changin g the structure of education. While correspondence courses available through the ma il previously offered asynchronous instruction, the variation in learning formats off ered by the Internet illustrates a marked improvement over the limited possibilitie s of the past.

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28 Table 3Time and Place Categories in Distance Education___________________________________________________ ______________________________________ Time-Place Relationship Examples ___________________________________________________ ______________________________________ Real Time ------Same PlaceWeb enhanced instruction (in classroom) Real Time text-based (chat, moo, mush, mud)Television-transmitted distance education Real Time ----Different Place Teleconferencing (audio, visual) Different Time ------Same PlaceWeb enhanced instr (same room, differe nt times) Television-transmitted distance educationE-mail or other correspondence schoolWeb-managed coursesWeb-delivered instruction Different Time ----Different Place (Asynchronous) Web-managed, web-delivered instruction __________________________________________________________________________ Variation in learning environment. There are numerous formats for delivery of instruction via the Internet. Examples include web-enhanced instruction (using t he Internet to extend traditional classroom instruction), teleconferencing, web-m anaged instruction, web-delivered instruction, and web-managed/web-delivered instruction.One of the most practical ways of sorting and classifying the formats for distance education is to examine time and place relationships of the primary human participant s: teacher and learner (See Table 3). Rapid retrieval of information. The Internet offers momentary retrieval of information from a wide variety of resources. Research data and other informa tion are

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29 available through the efforts of government agencies, universities, libra ries, private and public organizations, corporations, and individuals. Additionally, numerous sites offerfree services such as e-mail, search engines, web site space, and chat rooms One can locate information on prescription medicines and insurance rates, locate and print outmaps, and find courses offered on-line through universities. Many other services areoffered at low cost: banking services and on-line stock market purchases are av ailable on the Internet. Search engines, which are services for searching the Internet, make loc ating information or services fairly simple. E-commerce, or sales through the Int ernet, represents a growing part of American commerce. Accessing nearly unl imited information through the Internet is a vast improvement over physically going to a library or a series of libraries for needed data or others’ research material. Hypertext/hyperlinking. One of the most powerful features of the Internet is hypertext or hyperlink. Hypertext allows the user to navigate from place to pla ce within a document, from document to document, and from computer to computer. If a link isavailable, it appears as underlined text or a navigational object, such as a button or ic on. By clicking on underlined text or an icon or button, the user moves to another area in thedocument or to another document, which may reside in another computer. Hypertextallows users to seek information on an as-needed basis. This feature is particula rly useful to students and researchers. Henry and Worthington (1999) reported that hypertext provides positive cognitive benefits for learners. Students learn the following information:1.There is more information than appears on the immediate page.

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30 2.There is complexity in information.3.Their immediate knowledge is part of the complexity.4.Their knowledge is context dependent. With hypertext, there are inexhaustible amounts of pertinent information. Hypertext allows otherwise linear text to become flexible and adaptive. Usi ng linear text in instruction, the information is presented in the amount and quality decided by theteacher; the teacher decides the correctness. With hypertext, there is not always one correct answer: the answer is context dependent. Multiple understandings are possi ble (Henry & Worthington, 1999). A drawback to educational use of hypertext includes student distraction from the initial topic. Unlimited use of hypertext may introduce time constraint proble ms and specific parameter issues with an instructional unit. The student may not be able tocomplete needed objectives if all hypertext links prove engaging. Virtual reality. Virtual reality is "an interactive environment in which the learner is projected into a complete computer-generated world which responds to individualmovement and actions" (Sims, 1995). Virtual reality environments constructed fromanimation or photography are commonly available on the Internet. The computermonitor may reveal a simulated or photographed location and the user experiences asense of existing in that other place. By manipulating the keyboard or mouse, the usermay seem to “turn” around and view the surroundings. The educational use of virtual reality remains unexplored, yet the potential is unlimited. A quick search for the term "virtual reality" with an Internet search engine produces several sites. Web sites now offer virtual tours of famous buildings, sceni c

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31 landmarks, and homes that are for sale. Instructional sequences using virtual re ality place learners in environments that closely resemble reality and allow them to manipulate and explore these surroundings. Other examples of virtual reality sit es on the Internet include museums, art galleries, flight simulators, and scienc e lab projects. Virtual reality offers a high degree of learner control, interactivity, and an open-ended learning environment, blending well with constructivist learning theory. The Internet and constructivist learning theory. “Constructivists believe that our personal world is constructed in our minds and that these personal constructions defineour personal realities” (Jonassen, 1995). A paradigm shift occurred recently in lea rning theories. That shift was enhanced by the advent of the Internet and its advantages f or distance learning. Previously, the quality of learning was a function of how well t he student reproduced the thinking of the instructor (Jonassen, 1995). In recent years,constructivist learning theories have emerged: quality learning consists of that which is discovered by the learner – knowledge is constructed. Constructivist learning theo ries focus on discovery learning that is specific to each instructional situation (Brun er, 1966; Jonassen, 1998). Piaget’s work represents a constructivist nature: consider hisdevelopmental stages and his belief that cognition develops in an ongoing fashion as thelearner develops and interacts with the environment (Kearsley, 1998). Jonassen, in an interview with Gibson (1998), lists the primary concepts of constructivism as follows: 1.Knowledge is constructed. 2.Reality is in the mind of the knower. 3.There are multiple perspectives on the world.

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32 4.Knowledge is built from interactions with the environment. 5.Knowledge is anchored in and indexed by relevant contexts. 6.Knowledge cannot be transmitted. “Knowledge is not an external entity that is in the physical world to be transmitted by teachers and acquired by learners, but rat her it is a conscious, intentional act of meaning-making” (Gibson, 1998 p. 69). 7.A problem, question, need, or desire to know stimulates knowledge construction. People can memorize ideas that others reveal, but to construct meaning requiresdesire or need to understand information given by others. 8.Meaning is also socially negotiated and co-constructed. As the physical wor ld is shared by everyone, so is some of the meaning that people interpret from it. Humansare social creatures who rely on feedback from other humans to determine their ownexistence and the veridicality of their personal beliefs. 9.Meaning and thinking are distributed among the culture and community.10.Not all meaning is created equally. Nor is all meaning equally valid. The lit mus test for the knowledge that is constructed by individuals is its viability in the communityof practice in which people are engaged (Gibson interview with Jonassen, 1998, p.68-69). According to constructivist theory, knowledge equals nonspecific information that the learner constructs: it varies according to the learner and is rel ative to the particular situation. The learner, presented with a problem to solve, works individuallyor in collaboration with cohorts and constructs knowledge from the environment. Thespecific body of knowledge acquired is particular to the individual and remains largelydependent on the learner’s cognitive developmental state.

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33 Activity theory (Jonassen, 1998) emulates constructivism, as learning and activity are interrelated. Learning occurs as a result of the activiti es in which people engage. As an example, Jonassen suggests that people who are engaged in workactivities will learn from experiences and tools they encounter while try ing to work more effectively (1998). Several features of the Internet that coordinate with constructivist lear ning theories include hypertext and massive amounts of readily available informati on. Henry and Worthington (1999) assert that traditional education consists of certain definedstructures of knowledge that must be mastered by the student. Using hypertextintroduces evolving comprehension in which the users realize that a knowledge struct ure is a simple intellectual jumping-off point from which the learner must construct unique and personal knowledge, building context-dependent meaning (Henry & Worthington,1999). Open-ended learning processes require vast resources to ensure that learners not experience unnecessary restrictions. Readily available information on demogr aphics, history, law, health, education, government, social and cultural concerns define theInternet as the world's largest “library.” The teaching task, accordin g to constructivist theory, creates environments in which the student may discover and construct usefulinformation. Constructivist learning theories require new instructional designs The distance learner must identify and use information deemed helpful, yet, the lack ofspecific direction may be detrimental. Problems with instructional use of the Internet. Educational use of the Internet is not problem free. This study addresses problems incurred or magnified by async hronous

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34 communication. Web-based classes create special demands on learners because students are separated from their instructor by both time and physical distance. The dis tance learner must cope with various aspects of motivation, self-regulation, and goal conf licts to a somewhat greater degree than the traditional student. McIsaac and Gunawarde na (1996) state that "Although adults possess a high degree of motivation, the technologyassociated with distance education, coupled with the distance separating the student and instructor, leads to high degrees of anxiety." (p. 424). As previously mentioned,another problem with educational use of the Internet emerges in the form of studentdistraction. Unlimited use of hyperlinks may lure students away from required re adings. Further, the distance learner may incur more circumstances that interfe re with learning motivation than does the traditional classroom student. For example, the distancelearner may experience computer equipment problems, live in a noisy household, or notmanage time well. Several studies in the 1980s revealed that over 60% of adult dista nce learners were married, over 70% had full time jobs, and over 60% were paying for thei r own education (McIsaac & Gunawardena, 1996). While these factors may also be truefor the traditional learner, they may particularly impact distance learn ers because learning occurs amid home or work distractions rather than in the traditional cl assroom. One of the greatest social problems today is the “digital divide,” the growing disparity in access to technology between social and racial classes in Ame rica. Fewer people living below the poverty level have computers and Internet access in their homes than those living at or above the poverty level. Employing 1997 Census Bureaustatistics, the National Telecommunications and Information Administration ( NTIA) released a report titled "Falling through the net II: New data on the digita l divide"

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35 (NTIA, 1999). Compared to 1994 statistics, the income variation between high and low-income families greatly increased over time. Those who could most benefit from a ccess to the Internet, minorities, those with low incomes, and individuals lacking a high schooldiploma, are least likely to have Internet access (NTIA, 1999). Yet, lack of infor mation services forces people to live continually in poverty. A particularly deleterious aspect of the Internet includes dangers for i mmature people. According to 125 researchers and developers who met to pool ideas concerningthe Internet, young people with unguarded Internet access may encounter ha rmful situations. Roschelle and Pea (1999) described the primary issues discussed at t he workshop:1.Pedophiles who make contact with children through the Internet create physical danger. 2.Access to unlimited information represents a common dilemma.3.A steady stream of unedited advertising accompanies most public web pages. The Internet workshop was a project of the Center for Innovative Learning Technologies (CILT), which is funded by the National Science Foundation. The CILTworkshop participants identified other Internet problems related to education:1.Most Internet educational resources have not matched or integrated well wi th existing K-12 curricula, state or national standards. There is need for a uniformmetadata (system of descriptors) standard. 2.One may easily find information on the Web, but it is not easy to construct knowledge using today's Web tools. 3.Hardware and software are expensive.

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36 4.Teachers experience difficulties when integrating Web collaborati on environments and fostering higher order thinking skills using the Web under current teachingconditions (Roschelle and Pea, 1999). Several researchers investigated motivation in on-line learning. Results reve aled that the highest motivator for Web-based courses include high performance expe ctations (Shih, 1997). Recall that in his study ( n = 99), students indicated their highest rated motivator was that they wanted to get better grades than other students ( M = 4.21, SD = 1.01). The second highest rated motivator was that they expected to do well in the class ( M = 3.77, SD = 0.84). Hara and Kling (2000) studied students' distress in a webbased distance education course and found the two main sources of student distress weretechnological problems and confusing teacher instructions.Summary of Pertinent Literature The Internet offers a wide variety of instructional delivery formats. S tudents may complete courses from their homes or other location at convenient times. Dista nce learners’ requirements differ from those of students who participate in tradit ional classroom settings. Distance learners must be responsible for self-motiva tion and selfregulation, and must resolve goal conflicts and technical equipment problems. Goal conflicts often interfere with student ability to achieve learning goal s. Several studies supplied information describing the goal conflicts of traditional students. Few studies are available concerning the goal conflicts of distance learne rs. The quality and importance of goal conflicts for distance learners may differ from the qua lity and importance of goal conflicts for traditional classroom students. Self-regulation remains a crucial factor affecting student achievement Self-

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37 regulation for distance learners resembles that of traditional learners However, it includes additional factors: acquiring appropriate access to technology, arr anging time and place to access coursework, forming virtual study teams via listserv, cha t, email, etc., and using self-quizzes or automated quizzes. Additional self-regulation fac tors for distance learners may exist, but few studies in distance learner self-re gulation were reported. The effect of self-regulation on achievement may not affect distanc e learners as traditional learners; additional studies of distance learners and their s elf-regulation will expand this knowledge base.Suggested Future Research Boekarts and Niemvirta (2000) suggested the following future research for s elfregulated learning: (1) Investigation of differences between self-regul ated learners and learners who are not self-regulated. (2) Investigation of how multiple feedback l oops operate and interact in learners. (3) Investigation of learners' interact ing control systems… "the nature of conflicting goal processes in classrooms…and… the e ffect of social forces (social control) on the individual's learning" (p. 446). Zeidner, Boekae rts, and Pintrich (2000) suggested clarifying self-regulation structure and proc esses, exploring interactions between the environment and self-regulation and examiningindividual differences in self-regulatory skills. Rheinberg, Vollmeyer and Roll ett (2000) proposed two aims for further research in self-regulated learning: a searc h for mediating variables in various situations and learning tasks, and a search for ways to overcomeaversive learning activities. Covington (2000) suggested further research on the impact of cultural values on the goals of schooling and pathways to personal excellence. He also recommended

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38 further research addressing the various motives that operate simultaneously i n any achievement setting those constantly changing motives and the effects of their relative strengths as they impact achievement. Additionally, Covington suggested furthe r research into the learner's valuation and appreciation of learning tasks and mot ivation present in individual academic pursuits. Seifert (1995) recommended further research into clarifying goals stude nts pursue, the emotions associated with learning experiences, and the relationshi p between those emotions and learning goals. Pintrich (2000) proposed defining these goals andmeasuring the self-regulating processes. He further proposed researc h defining personal characteristics and potential moderator relationships as well as the role of multiple goals. These investigations would enhance research of both traditional educational setting s and distance learning. The rapid growth of distance education courses requires examina tion of distance learners’ goal conflicts; this expanded knowledge base will improve the quality of on-line courses and increase the likelihood of student success.Implications for This Study Perhaps surroundings and circumstances affect learner self-regulati on. This study clarifies self-regulation processes and explores interactions be tween the environment and self-regulation. This inquiry also addresses the effects of inst ructional self-regulation and instructional goal conflicts on course completion. Previous investigation of learner motivation, goal conflicts, and learner self-regulation centered mainly on the traditional classroom student. The growth of dis tance education in recent years has been exponential; therefore, it is appropriate to e xtend the knowledge base concerning the distance learner. As the learning environment of the

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39 distance learner varies greatly from that of the traditional student, the f actors that affect course completion may differ from those of the traditional student. Examination of the literature and suggestions offered by past researchers led to the following research questions, those that have been pursued by this study. ChapterThree: Method of this document describes the procedures followed for answering th ese questions.Research Questions1. What goal conflicts commonly arise for post-secondary learners? 2. Are there differences between post-secondary distance learners and traditi onal learners in the number and perceived intensity of goal conflicts? 3. Is there a difference in the course completion rates of post-secondary distancelearners and traditional learners? 4. What is the relationship between goal conflicts and course completion of post-secondary learners? 5. Is there a difference in the instructional self-regulation of post-secondary dis tance learners and traditional learners? 6. What is the relationship between the instructional self-regulation and coursecompletion of post-secondary learners?

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40 Chapter Three Method Study Overview Data gathered in this study of post-secondary students contained information regarding the instructional self-regulation, instructional goal conflict s, and course completion of post-secondary distance and traditional learners. Course completionconsisted of completion with passing grade (P), completion with failing grade (F ), withdrawal (W), or the granting of an incomplete (I) by the instructor. Participants completed a self-report questionnaire designed to ascertai n perceptions of their own instructional goal conflicts and instructional self-re gulation. Traditional learners completed a paper survey in class and distance learne rs employed an online version of the same survey. The study included 540 traditional participantsand 604 web based participants.Time Table May – Aug. 2003. Made arrangements for courses, contacted instructorsAug 25. First day of classesSept 4. Obtained enrollment numbersSept 29 Oct 2. Instructors announced survey to occur in weeks seven and eightOct 3. Made online survey available; notice sent to web-based studentsOct 6 Oct. 17. Survey given to traditional classes

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41 Oct 13. Reminded web-based students of survey deadline (start of 8 th week) Oct 20. Made online survey unavailableOct 31. Drew names of winners in office of Secondary EdDec 15, 03 Jan, 04. Obtained completion information: P, F, W, I Included Courses The study included eleven courses that were taught simultaneously as web base d and traditional courses. The courses existed within the Colleges of Education, Ar ts and Science, Business Administration, and Nursing. Video-conferencing courses w ere not included because it was believed that video conferencing students might perceive close contact with their instructors and might possibly participate in classroom sett ings similar to traditional classroom settings. Table 4 shows the number of students enrolled in thecourse two weeks after classes began, the number of study participants for ea ch instructor in each course, and the number for whom completion data was available.Subjects Subjects for this study were post-secondary undergraduate students enrolled in traditional and web-based distance learning courses in a major urban research uni versity in the southeastern United States. Distance learning subjects included only students who were receiving their primary course instruction via the Internet and who did not meet with their instructor in person except for an orientation meeting and requiredexaminations. Traditional students were those who were enrolled in courses in whichthe instructor physically met with students periodically throughout the course. S everal courses that were scheduled to be traditional courses had the potential to becomedistance courses since students were not required to attend, except for the final e xam,

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42 Table 4Courses and Instructors, Enrollment, Participation, and Completion ___________________________________________________ ________________ Number of Participants ________________________________________ Classroom Based ____________________ Web-based __________________ CourseInstructor Enrollment Participation Completion Enrollment Participation Completion ___________________________________________________ ________________ 1A19897597523152B63980002C28220002D30330002E00038772F00036873G9070690003H3117170003I3016162012124I5551390004J0002817125K4839390005L00030656M0002513136N3018180007O15440007P0002477 ___________________________________________________ ________________

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43 Table 4 (continued).___________________________________________________ ________________ Number of Participants ________________________________________ Classroom Based ____________________ Web-based __________________ CourseInstructor Enrollment Participation Completion Enrollment Participation Completion ___________________________________________________ ________________ 8Q90434390559R2516160009S5037370009T3231310009U000880477477 10V112686000010W145666300010X00028211911X251616282625 Totals:10976035401302622604 ___________________________________________________ ________________

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44 and were able to submit assignments via the Internet. Students were categori zed as traditional or web-based students according to their answers to several quest ions within the survey and instructor answers to questions regarding their course. Inclusion of participants. To be included in the study, participants were required to be enrolled in courses included in the study. In both cases, traditional and distancelearning, data was included only if the student's enrollment in a course included in thisstudy could be verified by cross-checking ID numbers with those enrolled in include d courses. The ID numbers employed were the last five numbers of students’ univers ity identification numbers. Traditional classroom students participated in the survey using pencil and paper in the classroom but had the option of submitting the survey via theInternet. Categorizing students as web-based or traditional. It was necessary to identify students as belonging to one group or the other in order to make comparisons betweenweb-based students and traditional students. The university in which the study tookplace categorized courses selected for this study as web-based or traditional and listed them as such in their course offerings. However, for this study additional criter ia for included web-based courses were that the instructor and students not meet in personexcept at the start and end of the course and all assignments except final exa ms were to be submitted via the Internet. Traditional courses were those in which the instructo r and students met periodically throughout the course.

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45 There were, however, potentially two groups of students comprising a mixed category: 1. Students enrolled in traditional courses but who treated them as web-based by non-attendance and submission of assignments by e-mail with permission of theinstructor. 2. Students enrolled in web-based courses but who met with their instructors periodically in person for assistance. These students were identified using the following survey questions: How often do you physically attend class in a traditional classroom for this cours e? a) Not at all. The entire course is online. b) I attend class only once or twice per semester (orientation and final exam). c) The class meets weekly, but I can do most of the work without attending classes in person, so I rarely attend class. d) I attend class only for proctored exams. e) I attend class one or more times a month. f) I attend class one or more times a week. 2. For this course, what face-to-face, real-time contact have you had with the instructor or course assistant in scheduled class meetings? a) Once or twice at most. b) More than twice but less than weekly. c) At least weekly.

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46 For question 1, point values of a = 1, b = 1, c = 1, d = 1, e = 3, f = 3 were assigned to responses of the participants. For question 2, point values of a = 1, b = 2,c = 3 were assigned to responses The points were then summed and students assigned tocategories as follows: Web-based students were those whose sum equals two. Traditional students were those whose sum equals five or six. Mixed category students were those whose sum is three or four. When a student appeared in the mixed category, the situation was examined separately by comparing responses of the instructor to the following questions asked of all instructors via email during the course: 1. How often does this course normally meet? a) Never in person – entirely web-based. b) Once or twice at most – at start and/or end of course. c) More than twice during the semester but less than once a week. d) One or more times weekly. e) Other: ______________________ 2. What screening or permitting was done before students were allowed toenroll in this course? (What questions were asked of students, if any?) 3. If this is a traditional course (that is, not categorized by the university asdistance learning or web-based), can students do most of the courseassignments for this class without attending class in person? (Yes/No) If you answered “yes” to the previous question, how do students submit theassignments?

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47 Students who were in traditional courses but treated them as web-based by nonattendance and submission of assignments by e-mail with permission of the instruc tor became web-based students. This occurred with one student. His responses werecategorized with web-based students. If participants did not have permission of theinstructor they remained traditional students who were not attending class. T his occurred with six students. The responses of students enrolled in web-based courses but who met with their instructors for personal assistance were not to be considered as web-based or tradi tional when answering questions in which web-based students were compared to traditionalstudents. There were no students in this category. Two students enrolled in web-basedcourses reported that they attended classes regularly. Since the instructor in each case taught a classroom-based section of the course and since class attendance wa s allowed or encouraged by the instructor these students were categorized as traditi onal students for web versus traditional comparison basis. Establishing comparability of the groups is discussed in the section of this chapter titled Analysis of Data for Research Question Two. Inclusion and Exclusion of Certain Participants. In twelve cases two different participants had same ID numbers. Based on confirmation of enrollment in courses,availability of completion data, and comparison of demographic information, it wasdetermined that in these cases participants were indeed two different people wit h the same last five ID numbers. Both were included in the study in each case. In nine cases participants with duplicate numbers were probably the sameperson, based on comparison of demographic responses, but since each entry was

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48 attributed to a different course and since completion data was available in each ca se, both entries were included in the studies. Responses regarding attitude toward thecourses were different and in one case the participant passed one course and failedanother. In twenty cases pairs of entries with the same ID number appeared to be the same person enrolled in the same course. Entries were nearly identical but the se cond entry was more complete, thus the entries were assumed to be from the same indivi dual and the second entry was assumed to be correct and the first entry was elimi nated. The number of participants. The sample size needed for this study was determined by evaluating the research questions individually because the questionsrequired different types of analysis. Analysis of Research Question One, t he investigation of number and intensity of goal conflicts commonly experienced by postsecondary learners, required that goal conflicts of many students be exami ned. The plan for this study was to examine the goal conflicts of 500 traditional learners and 500distance learners. It was believed that this sample should display an accura te estimation of the goal conflicts experienced by students enrolled in the Colleges of Educati on, Arts and Science, Business Administration, and Nursing in the study university. The studyoutcome included the responses of 540 traditional students and 604 web-based studentsfor the answer to this question. Research Question Two investigated variations between post-secondary dista nce learners and traditional learners in the quantity and perceived intensity of goal conflicts. An analysis of variance using the general linear model was used for this investi gation. Using Cohen's (1992) tables to estimate an appropriate sample size, at Power = .80 for a

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49 = .05, a small effect size would result with 393 participants in each group, while amedium effect size would require 64 in each group. The study included the responses of540 traditional students and 604 web-based students for the answer to this question. Analysis of Research Question Three, "Is there a difference in the course completion rates of post-secondary distance learners and traditional learners ?" was examined using correlation of the variables. As both variables, course format andcompletion rate, were dichotomous variables, final results included calculation of the phi coefficient. Using Cohen's (1992) tables to detect a medium sized difference be tween two populations, to estimate an appropriate sample size at Power = .80 for a = .05, a medium effect size required 177 in each group. Analysis of Research Question Four, "What is the relationship between goal conflicts and course completion for post-secondary learners?" employed logisti c regression. Course completion was considered as a dichotomous variable, while thepredictor variable, goal conflicts, as a continuous variable. Goal conflicts we re examined in several ways. First, the number of conflicts each student experien ced were addressed by indicating whether each goal conflict was present or not by the indi cators 1 or 0. To calculate the sample size needed for this part of the analysis, Powerlog w as used (Friendly, 1998), a SAS macro for calculating necessary sample size for a logistic regression model using a quantitative predictor. Using an estimation that the c ompletion rate would be .80, and estimating that there might be .5 R Square (squared multiplecorrelation of goal conflicts with all other predictors), if there were a one standard deviation change in the predictor goal conflicts, the sample size required to dete ct a five percent change in the completion rate with alpha = .05, seeking a Power = .8, the sample

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50 size should be 569. A sample size of 144 would be needed to detect a ten percentincrease in the completion rate with alpha = .05, seeking a Power = .8. Table 5illustrates the sample sizes needed to detect .3, .5, and .10 increases in the completionrate.Table 5Sample Sizes Needed to Obtain Varying Power___________________________________________________________________ Probability of completion at X_mean + 1 std dev. _________________________________________ Power 0.832 (.04% inc.) 0.84 (.05% inc.) 0.88 (.10% inc.)R**2 (X, other Xs) R**2 (X, other Xs) R**2 (X, other Xs)0.3 0.5 0.7 0.3 0.5 0.7 0.3 0.5 0.7 ___________________________________________________________________ 0.7494 6911152311435 725 80112186 0.75564 7891316355496 827 91127211 0.8647 9061510407569 949103144241 0.85752105317544726611102119167278 0.9895125220875617861310140197328 ___________________________________________________________________

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51 The quantitative value of each goal conflict (for example, the number of children present, or the number of hours worked) was also examined as well as the impact ofeach conflict on the rate of course completion, holding others constant, using logisticregression. Table 5 shows that estimating the completion rate at .80, and estimat ing that there might be .5 R Square (squared multiple correlation of the goal conflicts with all other predictors), if there is a one standard deviation change in the predictor goalconflict, the sample size that would be required to detect a ten percent change in thecompletion rate with alpha = .05, seeking a Power = .8, the sample size should be 144. Research Question Five, "Is there a difference in the instructional self -regulation of post-secondary distance learners and traditional learners?" was exami ned by comparing the self-regulation habits of distance learners to the total selfregulation habits of traditional learners. For each self-regulation question an analysis of variance was carried out using a general linear model. Using Cohen's (1992) tables to esti mate an appropriate sample size, at Power = .80 for a = .05, a small effect size would be shown with 393 participants in each group, while a medium effect size required 64 in eachgroup. The data for Research Question Six, "What is the relationship between the instructional self-regulation and course completion of post-secondary learners? was analyzed using logistic regression with the dependent variable, course completion,viewed as a dichotomous variable for each of the four cases, pass, fail, withdraw, o r incomplete. The predictor variable, self-regulation, was calculated using the t otal selfregulation habits of participants by adding the scores of the self-regulati on responses. For the calculation of this sample size, Powerlog was used (Friendly, 1998), a SAS

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52 macro for predicting sample size for a logistic regression model using a quantitative predictor. Using an estimation that the passing completion rate is .80 with an aver age instructional self-regulation, examining the difference in non-completion (withdr aw, fail or incomplete) that would exist if there is a one standard deviation change in self-regulation, the sample size that would be required to detect a 5 percent change in thecompletion rate with alpha=.05, seeking a Power=.8, the sample size should be 569, ifthere is a .5 R Square, which is the squared multiple correlation of self-regulation with all other predictors. Table 5 shows sample sizes needed to obtain varying amounts ofpower with a .04 increase and with a .05 and with .10 increase in the probability ofcompletion when self-regulation changes by 1 standard deviation. The goal was to include 1000 subjects for this study, five hundred in each course format, distance and traditional learning, numbers sufficient for analysis of each of the research questions. The total number of participants in the study was 1,135. Since ninestudents were enrolled in two different included courses, the total when examiningdifferences in the two groups, traditional classes and web-based courses, was 1144: 540 traditional and 604 web-based students.Procedures Instrumentation. A self-report questionnaire was administered to university students enrolled in several traditional classroom courses and the corresponding di stance learning courses at a major urban research university in the southeastern Unite d States. The survey was comprised of questions adapted from several other studies for web-based learning, plus questions derived from results of a small pilot study ( n = 10) and

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53 later a larger pilot study ( n = 297), and researcher interviews with students. The questionnaire, called The Learning Factors Survey, is shown in Appendix B. The survey allowed each participant to quantify information such as whether he or she created a schedule for assignment completion, whether the participant c ontacted other students or the instructor for help, and whether the student had an illness ordisability. These factors were self-scored by the participants yie lding information regarding goal conflicts, self-regulation, and self-efficacy regar ding course completion. Additionally, the questionnaire recorded minimal demographic information such as ag e, gender, college major, and race or ethnicity. Appendix C lists the survey questi ons sorted by demographics, goal conflicts, self-regulation, and self-efficac y regarding completion of the course. The questionnaire was administered online over a two week period, seven and eight weeks after the course began. This allowed time for participants to e xperience the factors in question and correct problems that arose early in the course. It was be lieved that if the survey was administered earlier, respondents may not have engaged i n coursework to a significant degree, may not have established self-regulatory pa tterns for a particular course, and would not have experienced some of the problems that mightarise during the class. If the survey was administered later in the course students may not have accurately recalled the problems they experienced early in the cour se. Furthermore, if administered after the last date to drop the course, most of those w ho choose to drop the course would have done so and their input would not have beenobtained.

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54 Development of the survey. Some of the questions on this survey were adapted from several other questionnaires designed for web-based learners. Also, some que stions were derived from researcher interviews with students and from the response to tw o pilot studies. Creation of the instrument required research of correct survey cons truction. Surveys are a viable way to collect data in educational research (Holloway 1996; Dillman, 2000). When trying to quantify educational constructs using questionnaires,one should not reduce the information gathered at the sacrifice of rich, completeresearch (Holloway, 1996). Several volunteer graduate students examined the initial survey and offered thei r feedback about survey readability and whether the instructions and questions were cl ear. Later subjects in a small pilot study ( n = 10) completed the survey and were also asked for feedback about comprehension factors. Following revision several questionssupplied more useful answers and provided improved readability. Analysis of feedbackin a larger pilot study ( n = 297) led to further revision. For example, participants in the larger pilot ( n = 297) suggested that their social life conflicted with their studies and that trauma made it difficult to study (the destruction of the World Trade Center Tow ers in New York City had occurred several months prior to the pilot study). Appendix Acontains primary results of that larger pilot ( n = 297) and the questions used in that study. The following recommendations of Dillman (2000) were followed in the construction of the web survey:

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55 The welcome screen should be short, contain easy instructions, bemotivational, emphasize survey simplicity and provide instructions aboutnavigation Make the first question easy, interesting, and fully visible on screen Present questions in familiar format, similar to paper survey Don’t force the respondent to answer every question Restrain use of color so that consistency and readability are maintained,navigation is unimpeded and measurement properties of questions aremaintained Avoid differences due to various screen configurations, operating systems,browsers, partial screen displays, and wrap-around text Provide specific instructions for handling drop-down menus, open-endedanswers, radio buttons, check boxes Use drop-down mode sparingly; consider the mode implications and identifyeach with a "click here" instruction Use scrolling questionnaire rather than screen-to-screen for each questi on gives user the chance to review other questions and answers For long answer list that won't fit in one screen, double-bank the answers,eliminating excess scrolling Provide completion information to user such as scroll bar, percent complete,or "you're almost done" messages Dillman also suggests not to use "Check all that apply" questions that may cause considerable measurement problems or give biased responses caused by order of

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56 possibilities. Participants may choose those at top of list more often (Israe l & Taylor, 1990; Krosnick, Narayan, & Smith, 1996 both in Dillman, 2000). Participants alsosometimes check answers until they think the question has been satisfactorily ans wered. Instead, include "yes/no" response for each item. Open-ended questions receivenotoriously short or poor answers on paper surveys; e-mail surveys elicited moredetailed responses than paper (Schaefer & Dillman, 1998). Open-ended questions maybe suitable for web surveys however no conclusive information exists at this time. The paper version of the survey provided to traditional classes was nearly identical to the web survey. Chapter Five of this document contains a description of thedifferences between the paper survey and the web survey. In both versions partic ipants were encouraged but not required to answer all survey questions. If a participant us ing the web version of the survey left questions unanswered, this fact was mentioned uponsubmission; numbers identified those questions that remained unanswered. This enabledthe student to fill in those responses. However, if they preferred, students could stil l submit the survey with some unanswered questions. Administration of the survey. To minimize sampling error and coverage error, all students in included courses were strongly encouraged to participate. Verbalintroduction to traditional students and email introduction sent to web-based studentsenthusiastically described the study and the survey. Instructors’ encourag ement enhanced student participation as well. Several instructors offered extra cr edit for participation in the survey. However, because all instructors did not offer extra cr edit for participation, a cash drawing offered incentive to all students. Participant s who completed the survey in either format could enter a drawing for cash prizes of $25, $50,

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57 or $100. Traditional students received a verbal invitation to enter the drawing and adrawing entry form when they received the survey. Instructors for web-base d courses received an email notice that the survey was about to begin along with a suggeste d announcement to students. Complete announcements to instructors and students arecontained in Appendix D. An informed consent form was given to participants in both web-based and traditional formats. Traditional students received the informed consent when theyreceived the survey and web-based students were presented with the informed cons ent prior to their taking the survey. The Institutional Review Board of the study univer sity did not require participants to sign the informed consent form. Appendix F contains theinformed consent form used in this study. Traditional classes received an oral explanation of the study and refreshments during and after the survey. Most traditional students accepted the survey and drawin g entry form and returned them as soon as they had completed them. When participantsfinished filling out the survey and drawing entry form they placed them in separat e boxes provided to prevent identification connection between the two forms. When web-based students completed the survey they were invited to fill out an entry form for the drawing. Drawing entries went into a file that was sepa rated from the survey responses so that participant identities could not be linked to their response data.At the end of the eighth week of the semester, after all surveys had been complet ed, the web-based drawing entries were printed out and placed in a box along with the entries ofthe traditional participants. The entries were mixed and the drawing took place in t he

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58 office of the College of Education at the university. Winners were notified by phoneand their prizes were mailed to them.Development of Goal Conflict Measures The survey included questions designed to identify and quantify goal conflicts. Several sources, including a survey by Dailey, Carey, and White (2000) called theDistance Learning Dimensions (DLD) survey (summer 1999) and the survey byeCollege.com (2000), called the Distance Learning Survey, yielded insight to potential goal conflicts. Many of the respondents in the larger pilot survey ( n = 297) suggested additional goal conflicts. The current study survey added the following potential goal conflict suggestions by participants in the pilot study: Procrastination Social life conflicts The effect of trauma (past or present) on schoolwork Possible responsibility for a senior citizen or other person who needsassistance The current study survey presented a list of possible conflicts and allowed students to quantify, using a four point Likert scale, the degree of intensity experie nced for each conflict. The study survey also offered space for participants to identi fy other possible interfering factors. Goal conflict questions included on the survey: Likert – 4 point scale (not true) (rarely) (sometimes) (often) 1. I do other things when I should be studying. 2. It is difficult to study because I have other things on my mind.

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59 3. I am under stress due to circumstances that conflict with my studies. 4. One or more distracting factors interfere with my learning. 5. Someone close to me disapproves of my taking classes. 6. My social life affects my study time. 7. World affairs or thoughts of war affect my current schoolwork. 8. I have an illness or disability that affects my schoolwork. 9. Someone close to me has an illness or disability that affects my schoolwork. 10. Intentionally or not, someone close to me sabotages my studies. 11. I procrastinate. 12. The technology needed for this course causes problems for me. Non-Likert goal conflict questions included on the survey:1. Including yourself, how many people live in your household or dorm room?____ 2. How many children live with you? Age 0 – 3 __ ; Age 4 – 7 __; Age 8 – 11 __; Age 12 – 18 ___. 3. Are you responsible for a senior citizen, child, or other person who needs assistance? Never [] Rarely [] Sometimes [] Usually [] Always [] 4. How many hours per week are you employed? _________ 5. How many credit hours are you enrolled in this semester? ____________ 6. Please add any information not previously mentioned if it affects you and yourtaking this course. ________________________________________

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60 Validity of Goal Conflict Measures Logical content analysis. Four experts in distance learning and behavior analysis examined the survey items for logical analysis of content. One is an instruc tor and researcher in educational psychology, two are instructors and researchers i n instructional technology, and one is a behavioral psychologist retired from private clinical pra ctice who also did ergonomic research for NASA. While they wrote no formal documentsaddressing the survey, they provided valuable input about the content. Following theirsuggestions, several questions were either altered or eliminated. The followin g questions were included in the original survey: It is difficult to choose between spending time with my family and spending time on my course assignments Sometimes I choose to be with my family when I would rather do my homework I feel that I should spend more time with my family Sometimes I choose to do homework when I would rather be with my familyThe experts stressed the similarity among these questions; therefore the questions were narrowed to the following two in the revision: I do other things when I should be studying It is difficult to study because I have other things on my mindThe experts also suggested that questions about stress due to financial problems could be reduced by asking the participants how many hours per week they work. A small pilot study was conducted ( n = 10). Analysis of subject input led to shortening of the survey due to question similarity and the excessive time requir ed to complete the survey. The original survey consisted of 95 questions that were reduced to

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61 65 questions for the larger pilot study. Following feedback analysis in a larger pi lot study ( n = 297), the questions were further revised. Participants in the larger pilot study considered the logical content, possible confusion by any of the questions, or perceivedomissions in any questions. Appendix E contains two of eleven total pages of pilot studyparticipant input in response to the question "Briefly list sources of stress or othe r factors not mentioned previously that could impact your time, emotions, or attitudewhile taking this course." Pilot participant responses elicited changes to the wording of several questions, the addition of several questions, and the elimination of severalquestions from the current study survey. Construct validity of goal conflict measures. Several goal conflict questions were suggested by the Distance Learning Dimensions (DLD) survey of Dail ey, Carey, and White (2000) and the survey by eCollege.com (2000), called the Distance LearningSurvey. Participants in the two pilot studies for this research described other goa l conflicts that are included in this instrument (social life and mental trauma) See also Appendix E, which contains two of eleven total pages of pilot study participant input inresponse to the question "Briefly list sources of stress or other factors not ment ioned previously that could impact your time, emotions, or attitude while taking this cours e." Participants in the pilot study suggested the following possible goal conflicts that were included in the survey in the proposed study survey: My social life affects my study time World affairs or thoughts of war affect my current schoolwork I procrastinate. Are you responsible for a senior citizen or other person who needs assistance?

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62 Using data from the larger pilot study ( n = 297), internal survey structure was examined by a factor analysis on the Likert response questions using the SAS sys tem. Only Likert scale questions were included in this factor analysis because other potential goal conflicts, such as number of children or hours worked, employed varied scales.The factor analysis was run with the number of factors were forced to three, and a n orthogonal rotational procedure, Varimax, was used. This resulted in a pattern of thre e distinct factors, self-regulation, goal conflicts, and goal-orientation. The g oal conflict questions had standardized regression coefficients ranging from .378 to .675, with theexception of question 31: “My spouse/friends/family approve of my taking classes,”which had a coefficient of .133. This question was replaced in the study survey by thefollowing question: “Someone close to me disapproves of my taking classes.”Reliability of the Goal Conflict Questions Internal consistency. Cronbach's (1951) reliability coefficient, alpha, carried out on the Likert response questions pertaining to goal conflicts, revealed genera l reliability scores for the Likert response goal conflicts. Cronbach's Alpha for this cl uster of questions in the pilot study was 0.70. This is not an extremely high value, possibly dueto the low number of items in this cluster. Chronbach’s Alpha for the Likert-respons e goal conflict questions in the current study was 0.75. It was not practical to administer this survey more than once to establish reliability via test-retest or parallel-form techniques. Second administ ration of the survey in a test-retest technique might result in changes in variable magnitude c aused by situational changes or student withdrawal from the course.

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63 Self-Regulation Scale Development Pintrich and DeGroot (1990) developed the Motivated Strategies for Learning Questionnaire (MSLQ), an instrument designed to identify self-regulation in seve nth graders. The study survey contained several similar questions, revised for pos tsecondary students. Four experts in distance learning and behavior analysis offered input on selfregulation, particularly in distance learners. These individuals included an instr uctor and researcher in educational psychology, two instructors and researchers ininstructional technology, and a behavioral psychologist retired from private clini cal practice who also carried out ergonomic research for NASA. The experts wr ote no formal documents for this survey. Yet, in discussions concerning the survey, theyprovided valuable input regarding the content. Following their suggestions, thefollowing self-regulation entries were added: I e-mail or see my instructor for help when I don't understand I join study teams or virtual study teams via listserv, or chat, or e-mail The Distance Learning Dimensions (DLD) survey of Dailey, Carey, and White (2000) contained several questions that were adapted and added to the current studysurvey: I complete my assignments days or weeks before they are due I arrange to have the technology needed for this class Self-Regulation Items Included in the Survey: Likert 4 point scale (not true) (rarely) (often) (almost always)1. I use flashcards to study course material.

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64 2. I arrange to have the technology needed for this course. 3. When I study I intentionally categorize and classify things in my mind. 4. I deliberately block out distractions when I study. 5. I practice saying important facts over and over to myself. 6. I try to relate new information to what I already know. 7. I underline, take notes, or outline new information as I read. 8. I reread or study my notes prior to a quiz or test. 9. I do practice quizzes before taking a test. 10. I e-mail or see my instructor for help when I don't understand. 11. I make schedules for doing my assignments. 12. I analyze assignments to determine what I need to do. 13. I try to estimate the amount of time needed for each assignment. 14. I do my course assignments first, before I do other things. 15. I complete my assignments days or weeks before they are due. 16. I set daily or weekly goals for myself as I work on assignments. 17. I join study teams or virtual study teams via listserv, chat, email, etc. 18. If I don't understand one source, I get the information another way. Validity of the Self-Regulation Items Logical content analysis. The four previously mentioned experts in distance learning and behavior analysis examined the survey items for logical anal ysis of content. The experts included an instructor and researcher in educational psychology, twoinstructors and researchers in instructional technology, and a behavioral psychologi st retired from private clinical practice, who also carried out ergonomic resear ch for

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65 NASA. While these experts wrote no formal documents about the survey, they suppliedvaluable input regarding the content. Following their suggestions the following self-regulation items were added: I e-mail or see my instructor for help when I don't understand I join study teams or virtual study teams via listserv, or chat, or e-mail Analysis of subject input from small pilot study (n = 10) led to item revisions that supported participant comprehension. Participants in the larger pilot study(n = 297) offered suggestions about the logical content of questions, identified questionsthat confused them, and pointed out perceived omissions. The current study surveycontained several questions that were derived from analysis of these data. Construct validity. Many of the self-regulation questions are modifications of the self-regulation portion of Pintrich and DeGroot's (1989) Motivated Strategies f or Learning Questionnaire (MSLQ). Since that instrument addressed seventh gra de students some questions were irrelevant. Others required modification for post-secondary students. The Distance Learning Dimensions (DLD) survey of Daile y, Carey, and White (2000), suggested other self-regulation questions. Some questions wereparticipant suggestions from pilot studies. As previously described, a factor analysis was run on the Likert-scored quest ions of the larger pilot study (n = 297). The rotated (Varimax) analysis forced the f actors to three readily identifiable groups of questions: self-regulation, goal conflict s, and goalorientation. The self-regulation questions, Questions 41 through 56, showed coefficientvalues ranging from 0.22 to 0.63.

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66 Reliability of Self-Regulation Measures Internal consistency. Cronbach's (1951) reliability coefficient, alpha, was run on the self-regulation questions in the pilot study (numbered as Questions 41 throughQuestions 56 in the pilot study) to establish general score reliability for the construct goal conflicts. Cronbach's Coefficient Alpha for this cluster of questions in t he pilot study was 0.79. For this current study Chronbach’s Alpha was 0.84. It was not practical to administer this survey more than once to establish reliability via test-retest or parallel-form techniques. Additionally, t he second administration of the survey in a test-retest technique might result in magnit ude changes of variables due to situational changes or student withdrawal from the course. Development of Self-Efficacy Measures Pintrich and DeGroot's (1990) Motivated Strategies for Learning Questionna ire (MSLQ) contained several items designed to reflect self-efficacy. T hat instrument, however, was developed for seventh graders and questions were revised for post-secondary students. The self-efficacy scale development guides of Bandura ( 2001) and Pajares (1996) suggested other self-efficacy questions. As a result, the following items designed to reflect self-efficacy are included in the survey: Likert-response questions, 4 point scale (probably not) (maybe I can) (probably Ican) (definitely I can) 1. I can perform the tasks that are necessary to pass this course 2. I can do the assignments required to complete this course. 3. I can complete this course.

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67 4. I believe I can pass this course this semester/term. 5. I can complete this course this term with a satisfactory grade. Technology plays a crucial role in some courses, distance learning courses i n particular; therefore the study survey contained the following self-effic acy statements concerning technology capability: 1. I can acquire and use the technology needed for this course. 2. I can master the technology necessary to complete this course. Validity of Self-Efficacy Questions The questions concerning course completion were specific to the student's selfefficacy concerning the ability to complete the course. They also provided s everal indicators of self-efficacy concerning the significant variable course completion. They specifically asked if the student believed he or she could complete the course, indi cating capability. The self-efficacy questions concerning technology addresse d the student's beliefs concerning the ability to master technology, not simply perform requir ed academic tasks to ensure course completion. Technology mastery and studentperception of technology competence are vital to the distance learner and to tra ditional learners in certain courses. The survey also contained technology management as a potential goal conflict and also as part of self-regulation.Reliability of the Self-Efficacy Measures Course completion was not part of the pilot study; therefore, participant selfefficacy concerning course completion was not included in the pilot study. Moreover no reliability data exists for these measures. For the study Cronbach's ( 1951) reliability coefficient, alpha, was 0.90 for the Likert-response self-efficacy questions.

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68 It was not practical to administer this survey more than once to establish reliability via test-retest or parallel-form techniques. Here again, a second survey administration in a test-retest technique might result in magnitude changes of variables from situational changes or student course withdrawal. Other Factors Concerning Validity and Reliability of the Survey External validity. The study results are not expected to be generalizable beyond the limits of this particular sample. The instrument may or may not supply simila r results if used with similar participants in another post-secondary institution a t the 7 8 week period following the semester’s onset. However, this point might be verified byadministration of the instrument in alternative settings. Further discourse on thegeneralizability of the findings from this study is found in Chapter Five: Di scussion. Predictive validity of this survey. No completion data was collected in the pilot study; thus, there exists no predictive validity of the relationship between self -regulation and course completion established for this instrument. Consistency across time. This instrument may reveal similar results if used with similar participants in identical courses at the same institution in the 7 8 we ek period following another semester’s onset. However, as Buley (2000) suggests, individuals maybe differentially attentive to their surroundings. If administered to the sam e participants at alternate times, the survey is not expected to produce similar results; indi viduals may respond differently as their environment changes. If the survey had been admini stered earlier, the respondents may not have significantly engaged in the coursewor k, established self-regulatory patterns for this course, or may not have experie nced

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69 problems that arise during the class. If the survey occurred later in the course students may not accurately recall prior problems.Analysis of Study Data Following revision and collection of the data, descriptive statistics were obta ined using the SAS system. Descriptive statistics included the number of observations, t he mean, the standard deviation, the variance, skewness, kurtosis, range, and plots showingthe distribution of each indicator. Additionally, each construct’s items were al so analyzed as a group for descriptive statistics. These included the number ofobservations, the mean, the standard deviation, the variance, skewness, kurtosis, range,and plots revealing each scale’s distribution. Factor analysis for Likert-response questions. As previously described, Likertscored questions from the pilot study (n = 297) had been analyzed by factor analysis foridentification of factors. However, the final study survey used contained seve ral questions that had been revised from the most recent pilot survey and several questionshad been added for the goal conflicts construct. Further, a third construct, se lf efficacy (not present in the pilot survey), was added. Therefore it was appropriate to exam ine the data with a factor analysis using the SAS system. Internal survey structure of the data obtained in this study was examined by an initial factor analysis on the Likert response questions using the SAS system Identifiable factors consisted of goal conflicts, self-efficacy, sel f-regulation, technology, and several sub-sets of self-regulation, including study preparation and reaching out for assistance. Several other factors were not readily identifiable. A scre e plot of the

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70 eigenvalues revealed a large drop between the first four factors and a marked le veling off following the fourth factor. The factor analysis was run again, limiting factors to those having eigenvalues greater than 1.0, using the squared multiple correlation between the variable and allother variables as the estimate of communality (PRIORS = SMC), and rotate d by the Varimax method. This time when the factor analysis was run four distinct constr uct patterns emerged. Factor one loadings were greatest for the construct sel f-regulation, ranging from 0.3274 to 0.6643; factor two loadings were greatest for self-effica cy, ranging from 0.7412 to 0.9100; factor three loadings were greatest for goal conflic t questions, ranging from 0.1522 to 0.6845; factor four emerged as greatest fortechnology-related questions, factor loadings ranging from -0.2960 to 0.6380. Allrotated factor loadings are shown in Appendix G. The statement "Someone close to medisapproves of my taking classes," had a relatively low factor loading (0.152), ther efore responses from this statement were analyzed for descriptive statistic s and comparison analyses, but were not included in the summed goal conflict construct. Since the student may feel different self-efficacy regarding the abi lity to complete two different courses, information from all 1,144 participant questionnaireswas used for factor analysis. That data file that contained both response sets fr om the nine participants who were enrolled in two courses. Further, goal conflicts and se lfregulation habits may differ in two different courses for the same participant s o the data file containing both responses of the nine participants ( n = 1,144) was used.

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71 Internal consistency for measures of each construct was checked by corre lation studies including Cronbach's alpha. Data was then analyzed using procedures desi gned to answer the individual research questions as follows: Analysis of data for research question one. The data for research question one, "What are the goal conflicts that commonly arise for post-secondary learne rs?" exists as a result of participant survey responses to items about goal conflicts. Students w ere presented with a series of possible goal conflicts and indicated the presence and t he degree of presence for each factor. For example, they answered whether t hey have children or not, and if so, reply how many children are in the household. They wereasked whether they work and how many hours per week they contribute to that job. SeeAppendix C for a complete list of items pertaining to goal conflicts. Partici pants also described other situations that conflict with their studying or learning. The pe rcentage of students who experience various goal conflicts could then be identified. Analysis of data for research question two. The data for research question two, "Are there differences between post-secondary distance learners and tradit ional learners in the quantity and perceived intensity of goal conflicts?" were analyzed in se veral ways. Analysis of the data included identification of participant goal conflicts a nd calculation of the intensity of each conflict if present. For example, if the student was li ving with children, the data contained information regarding the number of children and their a ges. It also contained information about the student's course load and the number of hoursworked weekly. To examine the goal conflicts, completion rate, and self-regulation of distance learners and traditional learners it was necessary establish the compara bility of the two

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72 groups. Tables comparing web-based and traditional students are located in Chapte r Four: Results. The following survey items were used to assess the comparabili ty of webbased and traditional students: 1. Age 1. Gender 2. Number of children living at home 3. Hours worked per week 4. Credit hours 5. Student illness or disability 6. Family member illness or disability 7. Prior academic achievement 8. Year in school 9. Race/ethnicity 10. Responsible for care of other individual 11. Self-efficacy 12. Marital status 13. Number of current web-based courses 14. Number of web-based courses students has completed in the past two years 15. Distance from student's residence to campus 16. Type of instructor contact outside of the classroom (Check all that apply) E-mail [] Phone [] Instructor’s office [] Other: _______ 17. Number of times student met with instructor outside of scheduled class

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73 time: ___ 18. The single most important reason that student registered for this particular format/section for this course 20. Other classes in which student is currently enrolledAfter analyzing these listed items for comparability, the two groups, di stance learners and traditional learners, were then analyzed for descriptive sta tistics as to each possible conflict. These analyses included the number of observations, the mean,standard deviation, variance, skewness, kurtosis, range, and plots revealing thedistribution of each possible conflict. Additionally, a frequency table was creat ed indicating the number of respondents participating in each course format and the numberin each group experiencing each particular goal conflict. In this initial conflict data analysis, presence of the conflict was indicated by a 1, absence by a 0. The quantity of goal conflicts is an extension of research question one, that is, identification of the conflicts. However, in this case the comparison of goal confl icts in the two groups, distance and traditional students, is important. Initial analysis of t he conflict data revealed presence of the conflict. Further analysis reveale d the intensity of the conflict. An analysis of variance using a general linear model reveale d differences in the two groups. The general linear model was appropriate in order to accommodatesize variations in the two groups. Investigations of the differences between distance learners and traditional learners included the intensity of particular goal conflicts, such as number of c hildren, work hours, and credit hours; the perceived intensity of conflict experienced wasanalyzed by totaling the Likert scale responses to feelings of conflict items.

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74 Next, an analysis of variance using the general linear model revealed t he total intensity of each conflict for each group, distance learners and traditional l earners. The analysis of variance using the general linear model included an F ratio and allowed for cell size variations, as the number of distance learners did not equal the number oftraditional learners. Analysis of data for research question three. Research Question Three is "Is there a difference in the course completion rates of post-secondary distance l earners and traditional learners?" An examination of the variable correlations was appr opriate for obtaining answer to this question. Both variables, course format and completion rate,are dichotomous; thus calculations included the Phi coefficient. Next, since courseformat may or may not be a predictor of course completion, and because other variablesmay contribute to course completion, logistic regression, holding the other predicti ng variables constant, was appropriate for answering Research Question Four. Analysis of data for research question four. The data for research question four, "What is the relationship between goal conflicts and course completion of post-secondary learners?" was analyzed by logistic regression using various possible goal conflicts, course format, self-efficacy, and previous academic achievement as predictors of course completion. The dependent variable, course completion, was viewed as adichotomous variable for each of the four cases: Did the student pass the course? Did the student fail the course? Did the student withdraw? Did the student receive an Incomplete?

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75 In each example, the answer was yes or no, thus creating a dichotomous dependent variable. Logistic regression was chosen instead of multiple regre ssion because logistic regression is appropriate when the dependent variable, or cour se completion, is a dichotomous variable. Logistic regression also sufficed instead ofdiscriminant function analysis, which is often used when the dependent variable isrepresented by a nominal scale. However, unlike discriminate function analysis, l ogistic regression does not assume that the independent variables are normally distri buted or that there are homogeneous variance-covariance matrices for all groups be ing contrasted (Glass & Hopkins, 1996). When examining the pilot data descriptive statistics, it wa s discovered that responses to some of the questions were not normally distributed. Note that many factors contribute to a student's completion of a course. Research reveals that past academic performance is often a good indicat or, as is selfefficacy. Therefore, these two indicators were included in the model, but were not pa rt of the research questions for this study. Figure 3 illustrates the relationshi p of the variables considered in Research Question Four.

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76 Analysis of data for research question five. The data for Research Question Five, "Is there a difference in the instructional self-regulation of post-sec ondary distance learners and traditional learners?" was examined by comparing the total s elf-regulation habits of distance learners to those of traditional learners. The descriptive statistics for self-regulation in the two groups included the number of observations, mean, standarddeviation, variance, skewness, kurtosis, and range. An analysis of variance using ageneral linear model included the F ratio and allowed for differences in cell numbers. Analysis of data for research question six. Research Question Six is "What is the relationship between the instructional self-regulation and course completi on of postFigure 3. Factors that may contribute to course completion. Number of children Hours worked Current credit hours Feelings of goal conflict Student illness/disability Disability of relative Self-efficacy Course Completion Prior academic history Course format Self-regulation Arrange for technology Analyze assignments Estimate time Schedule assignments Set goals for assignments Block distractions Categorize information Take practice quizzes Relate info to previous info Underline or outline info Use flashcards Reread or study notes Do assignments first Complete assignments early Join study team Contact instructor for help Get info another way if nec.

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77 secondary learners?" The predictor variable, self-regulation, was calcul ated using the total self-regulation habits of participants by adding the scores of the selfregulation responses. Next, since self-regulation may or may not be a predictor of coursecompletion and considering there are other variables that contribute to coursecompletion, logistic regression was again employed, holding the other predictingvariables constant. This analysis is similar to the one described in answering Re search Question Four, which examined the relationship of goal conflicts to course complet ion.

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78 Chapter Four Results Demographics of Participants Participants completed 1,144 surveys for which course completion data was available. Eighty-one others completed the survey but completion data was not availa ble either because the participant did not include an ID that matched instructor list s or the instructor did not supply completion data. Of those for whom completion data was available, 826 were female and 318 were male. Eight females and one male were registered for two included cou rses. Participants included 540 students who were registered in classes that were sch eduled to be traditional courses, those in which the instructor met with students in person on aregular basis. There were 604 students who had registered for web-based courses.Table 6 displays a comparison of participant gender to course format. Table 6Gender of Participants Compared to Course Format_____________________________________________________________________ Format _____________________________________ Gender Traditional Web-based Total _____________________________________________________________________ Female399427826Male141177318Total5406041,144 _____________________________________________________________________

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79 Table 7 displays the number of participants in each course in each format, traditional and web, for whom there was completion data available. Instructors of som e sections of included courses chose not to participate or did not respond to the request forparticipation. However all sections of Courses Two, Three, Eight, and Nine, as list ed in Table 7, were included in the study. Table 4, found in Chapter Three of this document,contains a complete list of courses and instructors, identified by numbers and letter s, total enrolled for each instructor, the number of participants for each instructor and the number for whom completion data was available.

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80 Table 7Number of Participants in Courses by Format_________________________________________________ Format ______________________________ CourseClassroomWebTotal _________________________________________________ 1.2.3.4.5.6.7.8.9. 10.11. Total 5913 102 393918 4 4384 123 16 540 15141212 5 13 75 477 1925 604 7427 114 5144311148 561142 41 1,144 _________________________________________________ The two web-based sections of Course Nine had 880 students enrolled, of which 477 participated in this study. The web-based participants enrolled in the two sect ions of this one course, both taught by the same instructor, comprised 79% of the web-based

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81 participants in this study. When the study was in the planning stages this course wa s selected because it was taught in both traditional classroom and web formats. Howe ver, it was not anticipated that there would be so many enrolled in the web sections of theclass. The demographics of the Course Nine web-based participants were compare d to those of the other web-based participants. The results of that comparison are found late r in this chapter at the end of the section comparing web-based students to traditiona l students. Proc Univariate via the SAS (SAS Institute, 2004) system yielded descripti ve statistics and Proc Frequency, also using the SAS system, yielded frequenci es of events in categorized groups such as gender, format, etc. Table 8 displays results of Pr oc Univariate on participant responses to questions regarding demographics such as age( M = 21.98, SD = 5.20), high school GPA ( M = 3.44, SD = 0.50), and hours worked per week ( M = 18.18, SD = 14.695). When Proc Univariate was first run on the number in each household, all households were included. However fourteen participants reportedliving in households of eighteen or more residents, including one who reported living ina household of 76. These households are assumed to be dorms or Greek housing. Theanalysis was repeated with those households removed. The statistics for both areincluded in Table 8, which follows.

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82 Table 8Summary of Demographics Reported by Students_______________________________________________________________________________ Demographics n*MSDSk*K* maxmin _______________________________________________________________________________Age of participants1,14121.9765.2023.16811.6855317High School GPA 1,1343.4410.500-0.8824.9065.8312.000 College GPA9943.1100.490-0.9954.4214.0002.000Number of current web courses1,1360.9630.9221.1352.22160Past web courses taken1,1391.0121.4812.1346.038100Times met instr outside class 1,1420.1660.6037.22883.985100 Number in all households**1,1383.3973.43611.466197.146761Number in household***1,1243.1801.2980.5140.69591Hours worked per week****1,14218.18114.6950.322-0.535750Current credit hours carried1,14413.222.890-0.7602.333243_______________________________________________________________________________ n refers to the number who responded to the question out of 1,144 participants, sk refers to the skewness, and K indicates kurtosis. ** Included all households, including those which appear to be dorms, Greekhousing.*** Proc Univariate run on households which did not appear by size to be dorms orGreek housing. Removed 14 values ranging from 18 to 76 in household.**** Effect of outlier removed (participant stated she worked 440 hours).

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83 Descriptive Statistics of the Likert Response Questions Descriptive statistics for the construct self-efficacy. Self-efficacy is important to this study because it is believed to contribute to task completion. Therefore parti cipants answered seven questions regarding their self-efficacy concerning course completion. Participants answered the self-efficacy questions by choosing one of four Lik erttype responses. The possible answers were "Probably Not," "Maybe I can," "P robably I can," and "Definitely I can," having corresponding point values of 1.0 (Probably Not ) to 4.0 (Definitely I can). The descriptive statistics for their responses are shown in Table 9. Most participants perceived themselves as capable of completing the course While very few did respond "Probably Not" to one or more of the questions, the mean scores rangedfrom 3.67 to 3.86 for these questions. The range for all responses was 1.0 to 4.0. The three planned constructs for this survey were self-efficacy, selfregulation, and goal conflicts. It is interesting to note that the mean score s of the two self-efficacy questions relating to technology were the lowest of the me ans for that group, 3.67 and 3.69. These two questions plus a third technology question "I arrange tohave the technology needed for my coursework," which was placed in the survey as partof self-regulation, formed a fourth factor as shown by the factor loadings in Appendi x G of this document.

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Table 9Descriptive Statistics for the Construct Self-efficacy_______________________________________________________________________________________ Likert scores ranging from 1 – 4 ___________________________ Item n M SD Sk K _______________________________________________________________________________________ I can acquire and use the technology needed for this course.I can master the technology necessary to complete this course.I can perform the tasks that are necessary to pass this course.I can do the assignments required to complete this course.I can complete this course.I believe I can pass this course this semester/term.I can complete this course this term with a satisfactory grade. 1,1441,1441.1431,1441,1441,1431,144 3.6933.6713.7573.8123.8573.8383.782 0.5840.5930.5190.4550.4340.4840.554 -2.018-1.781-2.262-2.531-3.478-3.417-2.764 4.1292.7375.2376.536 13.34812.527 7.561 _______________________________________________________________________________________84

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85 Descriptive statistics for the construct self-regulation. Table 10 shows the descriptive statistics for each question included in the construct self-regula tion. Most students responded to the self-regulation questions, n = 1,140 to 1,144. The possible answers for these questions were "Not true," "Rarely," "Often," a nd "Almost always." The corresponding answers were scored from 1.0 (Not true) to 4.0 (Almost always).Respondents' answers ranged from 1.0 to 4.0 on the four point Likert scale. As shown in Table 10, the highest mean score for the self-regulation questions was in response to the statement I reread or study my notes prior to a quiz or tes t," ( M = 3.57, SD = 0.67). Another relatively high mean score was in response to the statement “I analyze assignments to determine what I need to do,” ( M = 3.36, SD = 0.67). The lowest mean score for the self-regulation questions was in response to thestatement I join study teams or virtual study teams via listserv, chat or e-mail, etc," ( M = 1.77, SD = 0.84). Other relatively low mean scores were in response to the statements I complete my assignments days or weeks before they are due, ( M = 2.25, SD = 0.84), and I use flashcards to study course material," ( M = 2.32, SD = 1.04).

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Table 10Descriptive Statistics for the Construct Self-Regulation_________________________________________________________________________________________________ Likert scores 1 – 4 ______________________________ Item nMSDSkK _________________________________________________________________________________________________ I arrange to have the technology needed for my coursework.1,1433.221.01-1.100.01I analyze assignments to determine what I need to do.1,1433.360.67-0.750.15I try to estimate the amount of time needed for each assignment.1,1423.110.79-0.55-0.28I make schedules for doing my assignments.1,1442.760.93-0.18-0.91I set daily or weekly goals for myself as I work on assignments.1,1442.800.950.28-0.91 I deliberately block out distractions when I study.1,1422.580.80-0.11-0.45When I study I intentionally categorize and classify things in my mind.1,1422.80 0.84-0.34-0.34 I practice saying important facts over and over to myself.1,1442.900.86-0.49-0.32I try to relate new information to what I already know.1,1403.160.72-0.570.13 _________________________________________________________________________________________________86

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Table 10 (continued).__________________________________________________________________________________________________ Likert scores 1 – 4 ______________________________ Item n M SD Sk K __________________________________________________________________________________________________ I underline, take notes, or outline new information as I read.1,1443.140.87-0.71-0.35I use flashcards to study course material.1,1432.321.04-0.27-1.08I reread or study my notes prior to a quiz or test.1,1443.570.67-1.602.36I do practice quizzes before taking a test.1,1402.700.93-0.19-0.85I do my course assignments first, before I do other things.1,1442.720.78-0.22-0.30I complete my assignments days or weeks before they are due.1,1442.250.840.22-0.56I join study teams or virtual study teams via listserv, chat or e-mail, etc. 1,1441.770.840.83-0.12 I e-mail or see my instructor for help when I don't understand.1,1442.600.92-0.05-0.85If I don't understand one source, I get the information another way.1,1432.950.76-0.42-0.08 __________________________________________________________________________________________________87

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88 Descriptive statistics for the construct goal conflicts. Table 11 shows the descriptive statistics for the Likert-response goal conflict questions. The possible responses for the Likert-scored goal conflict statements were "Not t rue," "Rarely," "Sometimes," and "Often," with corresponding score values from 1.0 to 4.0. Participa nt responses to these questions ranged from 1.0 to 4.0. The highest mean score was inresponse to the statement I do other things when I should be studying," ( M = 3.18, SD = 0.73). Other relatively high mean scores resulted from responses to the statem ent "I procrastinate," ( M = 3.10, SD = 0.89) and the statement "It is difficult to study because I have other things on my mind," ( M = 3.07, SD = 0.79). The lowest goal conflict mean score was indicated in response to the statement "Someone close to me disapproves of my taking classes, ( M = 1.11, SD = 0.44). Responses to this statement also exhibited skewness of 4.595 and kurtosis of 21.922,reflecting the fact that over 90% of respondents scored “1 ” for this statement indicting it was not true. It should be noted that the factor loading for this statement wasrelatively low, 0.152, when the statement was grouped with the goal conflicts factor (See Appendix G).

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Table 11Descriptive Statistics for Construct Goal Conflicts_____________________________________________________________________________________________ Likert scores 1 – 4 ____________________________ Item nMSD Sk K _____________________________________________________________________________________________ I do other things when I should be studying.1,1433.1770.725-0.6000.122It is difficult to study because I have other things on my mind.1,1423.0690.790-0.551-0.149I am under stress due to circumstances that conflict with my studies.1,1422.7810.9160.287-0.757 One or more distracting factors interfere with my learning.1,1422.6580.856-0.181-0.590Someone close to me disapproves of my taking classes.1,1431.1070.4404.59521.922My social life affects my study time.1,1412.2670.9620.220-0.938World affairs or thoughts of war affect my current schoolwork.1,1421.4230.6531.4591.624Intentionally or not, someone close to me sabotages my studies.1,1421.4960.8121.4951.186I procrastinate. 1,1423.0970.887-0.710-0.297 The technology needed for this course causes problems for me.1,1431.4720.7701.5231.401 ___________________________________________________ ___________________________________________________ _________89

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90 Comparing Traditional Classroom Students to Web-Based Students Demographics gathered to compare traditional classroom students to web-based students included age, number and ages of children, hours worked, high school andcollege GPA, and credit hours carried. These results are shown in Table 12. It shouldbe noted that the question regarding the number of past online surveys was only given toparticipants who completed the survey online. There were 139 traditional students whoanswered this question, indicating that they completed the survey online. One student,enrolled in a traditional course, responded that he had completed 100 online surveys, soresults for that question are given both with and without this outlier. As shown by Table 12, the mean age of web-based students ( M = 23.33, SD = 5.74) was several years older than traditional students ( M = 20.46, SD = 4.02). Course Nine fulfilled an exit requirement for many students and many who enrolled i n the web-based section of this course were third and fourth year students. Those student s nearing their bachelor's degree would be several years older than the many fr eshman and sophomore participants. In fact, the Course Nine web students were younger than theother web-based participants. The mean age of Course Nine web-based students ( M = 22.76, SD = 4.83) was several years younger than the other web-based students ( M = 25.14, SD = 7.74). The 477 web-based participants in Course Nine moderated several other differences between web-based participants and traditional students. Thes e differences will be discussed later in this chapter. For now let us examine thedifferences between traditional and web-based participants.

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Table 12Comparison of Web-based to Traditional Student Demo graphics ___________________________________________________ ___________________________________________________ _____________________________________ Survey Variable ________ Traditional students __________________ Web-based students ______________________ F Ratio _______________ n M SD n M SDFp ___________________________________________________ ___________________________________________________ ______________________________________ Number of online surveys completed in the past 1392.7699.1425961.1023.45412.33.0005 Number of online surveys completed in the past (eff ect of outlier who reported 100 past surveys removed) 1382.0653.8315961.1023.4541.440.2303 Age 53720.4564.01660423.3285.73993.68<.0001 High school GPA (on a 4.0 scale) 5343.5010.4396003.3880.54414.70<.0001 College undergraduate GPA (on a 4.0 scale) 3923.1570.5056023.0800.4785.840.0159 Total number of web-based courses currently taking5 320.4980.7996041.3730.824327.79<.0001 Prior to and not counting this semester, number of web-based courses taken in the past two years 5350.5761.0196041.3991.70394.92<.0001 Number of times met with the instructor in person o utside of scheduled class time5380.1900.5476040.146 0.6481.510.2193 Number of hours per week employed (effect of outlie r who reported working 440 hours weekly removed) 53914.19513.19160321.74515.06080.35<.0001 Number of credit hours enrolled in this semester 54013.3572.40260413.0963.2652.330.1270 Number of children aged 0 – 3 living with participa nt5400.0370.2086040.0940.35910.610.0012 Number of children aged 0 – 3 living with participa nt (effect of participants who reported never responsible for others removed) 5400.0260.1816040.0730.3129.380.0002 Number of children aged 0 – 7 living with participa nt5400.0700.3376040.1770.53415.91<.0001 Number of children aged 0 – 7 living with participa nt (effect of participants who reported never responsible for others removed) 5400.0410.2166040.1390.47219.75<.000191

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Table 12 (continued).___________________________________________________ ___________________________________________________ ______________________________________ Survey Variable ________ Traditional students ___________________ Web-based students ______________________ F Ratio ________________ n M SDn M SDFp ___________________________________________________ ___________________________________________________ ______________________________________ Number of children aged 0 – 11 living with particip ant5400.1040.4096040.2450.64619.02<.0001 Number of children aged 0 – 11 living with particip ant (effect of participants who reported never responsible for others removed) 5400.0610.3026040.1920.57822.27<.0001 Number of children aged 0 – 18 living with particip ant (effect of participants who reported never responsible for others removed) 5400.1150.4416040.2900.74923.92<.0001 ___________________________________________________ ___________________________________________________ ______________________________________92

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93 Table 12 shows that traditional students reported higher high school and college GPA than web-based students, carried more credit hours at the time of the survey, andmet with their instructors outside of scheduled class time more often than webbased students. Web-based students, however, were employed more hours and had morechildren than traditional students. Appendix H shows more detail regarding thefrequencies of participants living with specific numbers of children in certa in age brackets. The last cluster of children's ages shown in Appendix H, participants li ving with children ages 12 to 18, is in all likelihood biased because examination of individualparticipant data revealed that many who were aged 24 or less stated that they w ere living with one or more children aged 12 to 18 years of age, yet had no responsibility for thecare of a child or other person. It is believed that many of these participants m ay have been living at home with their parents and siblings or may have been referring toroommates who were aged 17 or 18. As previously mentioned, fourteen participantsreported living in households of eighteen or more residents, including one who reportedliving in a household of 76. These households are assumed to be dorms or Greekhousing. Table 13 displays the frequencies of web-based to traditional students as regards their year in school or school status. As shown, 31.30% of traditional participants werefreshmen, while only 10.74% were seniors. In contrast, only 1.49% of web-basedparticipants were freshmen, while 46.36% were seniors. This concurs with the agedifference between web-based and traditional students. Recall from Table 12 t hat the mean age of traditional students was 20.46 years while the mean age of web-basedparticipants was 23.33 years.

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94 Table 13 Frequency of Year in School or Status by Format ________________________________________________________________________ Traditional __________ Web-based __________ Total _________ n % of 540 n % of 604 n % of 1,144 ________________________________________________________________________ FreshmenSophomoreJuniorSeniorGraduateOther including those below Teacher certification Post-bachelor or 2 nd degree Misc. undergraduate Non-degree seeking 169138168 58 250111 31.3025.5631.1110.74 0.370.930.000.190.190.19 9 55 237280 8 15 5720 1.499.11 39.2446.36 0.322.480.831.160.330.00 178193405338 1020 5831 15.5616.8735.4029.55 0.871.750.440.700.260.09 ________________________________________________________________________ A higher percentage of web-based students (13.91%) were married than traditionalstudents (5.19%) as indicated by Table 14. Similarly, 29.64% of web-based studentswere living with a significant other while only 10.56% of traditional students were doing so. Most web-based students are older and further along in their college career t han traditional students; they are also more likely to have chosen a living or marita l mate.

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95 Table 14Marital Status by Format________________________________________________________________________ Traditional Web-based Total Status n % of 540 n % of 604 n % of 1,144 ________________________________________________________________________ MarriedLiving with significant other 2857 5.19 10.56 84 179 13.9129.64 112236 9.79 20.63 ________________________________________________________________________ Of the 540 participating traditional students 41.11% were education majors while only 15.40% of the 604 web students were education majors. Further, while only 8.15%of traditional students were majoring in business, 27.65% of the web-based studentswere business majors. Twenty-seven different majors were represented i n this study. Table 15 lists many of the college majors and the number of participants in each, sor ted by format (web or traditional). Once again differences in the age and year i n school were apparent in the number of participants in each format who were undecided about theirmajor. Of the 540 traditional students who participated 9.63% were undecided abouttheir major while only 1.66% of the 604 web-based participants were undecided abouttheir major. A complete listing of all majors indicated by participants is shown in Appendix J.

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96 Table 15 Partial Listing of Frequency of College Majors Sorted by Format ________________________________________________________________________ Traditional __________ Web-based __________ Total _________ Major n % of 540 n % of 604 n % of 1,144 ________________________________________________________________________ Education22241.119315.4031527.53Business448.1516727.6521118.44Bio Sciences, Pre-Med, Pre-dental7012.966911.4213912.15Communications, MIS, LIS173.156310.43806.99Undecided529.63101.66625.42Psychology112.04467.62574.98Nursing356.48172.81524.55Criminology91.67376.13464.02Engineering152.7871.16221.92Wellness, wellness educ, sports med142.5920.33161.40 ________________________________________________________________________ An examination of the data regarding race of the participants reveals that the majority of both web-based and traditional respondents were Caucasian. Table 16 showsthat 62.22% of traditional students and 58.61% of web-based students were Caucasian.A slightly greater percentage of web-based students (17.05%) than traditional stude nts (13.15%) were Black. The percentage of web-based Hispanic students (13.74%) was

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97 slightly higher than the percentage of Hispanic traditional (12.59%) participants Again, these figures were based on 540 traditional students and 604 web-based students whoparticipated.Table 16Frequency of Race by Format________________________________________________________________________ Traditional __________ Web-based __________ Total _________ Race n % of 540 n % of 604 n % of 1,144 ________________________________________________________________________American IndianAsianBlackCaucasianHispanicMiddle EasternOtherNo Response 3 2471 336 68 6 29 3 0.564.44 13.1562.2212.59 1.115.370.56 3 37 103354 83 4 20 0 0.506.13 17.0558.6113.74 0.663.310.00 6 61 174690151 1049 3 0.525.33 15.2160.3113.20 0.874.280.26 ________________________________________________________________________ The distance from home to campus was greater for web-based students than traditional students. Table 17 shows that 24.26% of traditional participants lived oncampus while only 6.62% of web-based respondents lived that close. However, agreater percentage of web-based students than traditional students were in each of t he other distance categories.

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98 Table 17Distance From Home to School by Format________________________________________________________________________ Traditional __________ Web-based __________ Total _________ Distance from home to school n % of 540 n % of 604 n % of 1,144 ________________________________________________________________________ Zero miles Live on Campus1 to 5 miles6 to 20 miles21 to 50 milesGreater than 50 miles 131189139 6912 24.2635.0025.7412.78 2.22 40 252176101 35 6.62 41.7229.1416.72 5.79 171441315170 47 14.9538.5527.5314.86 4.11 ________________________________________________________________________ Nevertheless, the primary reason students enrolled in the included distance courses was not location (or non-location) or avoidance of driving or parking problems.As shown in Table 18, the primary reason that both traditional (50.19%) and web-based(26.82%) participants chose the particular format of their class was that it fit their class schedule. However, the second most important reason that web-based students, 21.69%,chose the particular section of their course was that it fit their work schedule while only 6.85% of traditional students stated that their work schedule was the reason they chosetheir class format. The second most important reason that traditional students (13.33% ) enrolled in their particular included course section was that it was the only one ava ilable.

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99 Table 18Primary Reason Participant Enrolled in the Class________________________________________________________________________ Traditional __________ Web-based __________ Total _________ Reason n % of 540 n % of 604 n % of 1,144 ________________________________________________________________________ It fits my class scheduleIt fits my work scheduleIt was the only one availablePreference for web coursesFulfills major or exit requirementPreference for traditional classesConvenience for family obligationsConvenient location (or non-location)To avoid driving hasslesPreference for this instructorTo avoid parking hasslesTo accommodate physical disabilityOtherNo response 271 3772 8 6548 212531 22 3 50.19 6.85 13.33 1.48 12.04 8.890.370.190.370.930.560.194.070.56 162131 40 102 46 1 3025181314 0 22 0 26.8221.69 6.62 16.89 7.610.174.974.142.982.152.320.003.640.00 433168112110111 493226201817 1 44 3 38.8514.69 9.799.629.704.282.802.271.751.571.490.093.850.26 ________________________________________________________________________ Table 19 compares the household size of web-based participants to those of traditional participants. The majority of students in both formats lived in households oftwo to four people. A greater percentageof web-based students (28.64%) lived in

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100 two person households than any other size household. The largest percentage oftraditional students (32.59%) lived in four person households while fewer web-basedstudents (24.83%) lived in four person households. Twelve web-based students (1.99%)but no traditional students stated that they lived in households that consisted of 18 to 28persons. These were probably dorms or Greek housing or co-operative housing. Itshould be noted that six students did not respond to the question "Including yourself,how many people live in your household?" In addition, six other students responded thatzero people, including themselves, lived in their households. It is also interesting to not e that only 14 participants indicated that there are nine or more individuals residing intheir household while twenty-three participants indicated that they live in Greek hous ing in answer to the open response question that asked them to list other goal conflicts.

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101 Table 19Participant Household Size Sorted by Format__________________________________________________________________ Traditional __________ Web-based __________ Total _________ Participant household size,including participant n % of 540 n % of 604 n % of 1,144 __________________________________________________________________ 061.1100.0060.521407.41599.77998.65212523.1517328.6429825.14313224.4413522.3526723.34417632.5915024.8332628.505346.30508.28847.346152.78132.15282.45730.5620.33 50.44830.5610.1740.35900.0020.3320.1718 to 28*00.00121.99121.053910.1900.010.097610.1900.010.09No Response61.1100.060.52 __________________________________________________________________ No participant listed 10-17 or 29-38 persons in their household.

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102 Comparing the students of Course Nine to other web-based students. As previously mentioned, 477 out of 880 students who were enrolled in the two web-basedsections of Course Nine participated in this study. The participants enrolled i n these two sections of this one course, both taught by the same instructor, comprised 78.97% of theweb-based participants in this study. This does bias the results to some degree. Whe n the study was in the planning stages this course was selected because it was ta ught in traditional classroom and web-based formats. However, it was not anticipated that t here would be so many enrolled in the web sections of the class. Thus the demographics ofthe Course Nine web-based participants must be compared to those of other web-basedparticipants. The gender ratios of Course Nine web-based students, all other web-based students, and traditional students were similar in that all three groups contained m ore female than male participants. However, as shown by Table 20, the group consisting ofall other web-based participants had a greater percentage of females than di d either the Course Nine group or the traditional group. Females comprised 77.95% of all otherweb-based students, 73.89% of traditional students, and 68.76% of the 477 Course Nineweb-based students.

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103 Table 20Gender of Course Nine Participants Compared to Other Participants_____________________________________________________________________ Format ________________________________________ Course 9 ___________ All web except Course 9 ___________ Traditional ___________ Gender n % of 477 n % of 127 n % of 540 _____________________________________________________________________ Female32868.769977.9539973.89Male14931.242822.0514126.11Total477100.00127100.00540100.00 _____________________________________________________________________ Recall from Table 12 that the mean age of traditional students was 20.456 years ( SD = 4.016), while the mean age of web-based students was 23.328 years ( SD = 5.739). As shown in Table 21, the mean age of Course Nine we-based participants was 22.757years ( SD = 4.831) while that of the other web-based participants was 25.472 years ( SD = 7.973). Thus the difference in age between traditional and most web-based studentsmay in fact be greater than shown by this study since web-based students in Course Nine were significantly younger than other web-based students. Examination of data displayed in Table 21 shows other differences between Course Nine web-based participants ( n = 477) and the other web-based participants ( n = 127) in this study. As shown in this table, Course Nine web-based students were taking fewer web-based courses ( M = 1.308, SD = 0.767) than all other web-based

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Table 21Comparison of Course Nine to All Other Web Student Demographics ___________________________________________________ ___________________________________________________ _______________________________________ Course Nine web students ____________________ All other web-based students ________________________F Ratio _______________ Survey Question n M SD n M SDFp ___________________________________________________ ___________________________________________________ _______________________________________ Number of online surveys completed in the past4711. 0153.5221251.4323.1731.442.2303 Age 47722.7574.83112725.4727.97323.29<.0001 High school GPA (on a 4.0 scale) 4753.3870.5121253.3890.6530.000.9576 College undergraduate GPA (on a 4.0 scale)4763.0760 .4141263.0960.6670.180.6697 Total number of web-based courses currently taking4 771.3080.7671271.6140.97614.140.0002 Prior to and not counting this semester, number of web-based courses taken in the past two years 4771.2601.4721271.9212.31215.49<.0001 Number of times met with the instructor in person o utside of scheduled class time 4770.1200.6521270.2440.6263.730.0541 Number of hours per week employed (effect of outlie r who reported working 440 hours weekly removed) 47620.72314.64812725.57516.00380.35<.0001 Number of credit hours enrolled in this semester477 13.3122.97312712.2844.09810.110.0016 Number of children aged 0 – 3 living with participa nt4770.0570.2811270.2360.54126.19<.0001 Number of children aged 0 – 3 living with participa nt (effect of participants who reported never responsible for oth ers removed) 4770.0400.2261270.1970.50526.43<.0001 ___________________________________________________ ___________________________________________________ _______________________________________104

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Table 21 (continued).___________________________________________________ ___________________________________________________ _______________________________________ Survey Question ______________ Course Nine web students ____________________ All other web-based students ________________________ F Ratio _______________ n M SD n M SDFp ___________________________________________________ ___________________________________________________ _______________________________________ Number of children aged 0 – 7 living with participa nt4770.1130.4051270.4170.82134.31<.0001 Number of children aged 0 – 7 living with participa nt (effect of participants who reported never responsible for oth ers removed) 4770.0880.3501270.3310.74627.69<.0001 Number of children aged 0 – 11 living with particip ant4770.1660.5141270.5430.94136.28<.0001 Number of children aged 0 – 11 living with particip ant (effect of participants who reported never responsible for oth ers removed) 4770.1340.4621270.4090.85823.57<.0001 Number of children aged 0 – 18 living with particip ant (effect of participants who reported never responsible for oth ers removed) 4770.1530.5301270.5201.08328.83<.0001 ___________________________________________________ ___________________________________________________ _______________________________________105

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106 students ( M = 1.614, SD = 0.976) and had taken fewer web-based courses in the past ( M = 1.260, SD = 1.472) than had all other web-based students ( M = 1.921, SD = 2.312). The difference in both cases was significant, as shown in Table 21. As previously shown in Table 12, the mean hours worked per week by all webbased students is 21.745 ( SD = 15.06) and the mean hours worked per week by traditional students is 14.195 ( SD = 13.191), which is a significant difference, F (1, 1,142) = 80.35, p = <.0001. However, the effect of Course Nine web-based students is visible when we examine Table 21, which shows that Course Nine web-basedstudents worked fewer hours per week ( M = 20.723, SD = 14.648) than all other webbased students ( M = 25.575, SD = 16.00). Thus the value of hours worked by all webbased students ( M = 21.745, SD = 15.06) is very much moderated by the value of the hours worked by Course Nine students. As previously shown in Table 12, there was not a significant difference between the number of credit hours carried by traditional students ( M = 13.357, SD = 2.402) and the number of credit hours carried by all web-based students ( M = 13.096, SD = 3.265). There was, however, a significant difference F (1, 604) = 10.11, p = 0.0016, in credit hours carried by Course Nine web-based participants ( n = 477, M = 13.312, SD = 2.973) when compared to all other web-based participants ( n = 127, M = 12.284, SD = 4.098). Web-based students in this study had more children of all ages living with them than did traditional students as shown in Table 12. However, the 477 Course Nine web-based students heavily influenced the number of children of web-based students. Forexample the mean number of children under age 12 for traditional students was 0.061

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107 ( SD = 0.302) and the mean number of children under age 12 for all web-based participants was 0.192 ( SD = 0.578) which is a significant difference, as seen in Table 12. As Table 21 shows, all other web-based students had significantly more childrenunder 12 ( M = 0.409, SD = 0.858) than did Course Nine web-based students ( M = 0.134 SD = 0.462). Thus the mean number of children under 12 for all web-based participants shown in Table 12 ( M = 0.192, SD = 0.578) may not accurately represent the average for most web-based students since such a large percentage of web-based students consi sted of Course Nine students. As shown by Table 22, a large percentage of Course Nine web-based students were seniors (50.10%) while 32.28% of all other web-based students and only 10.74% oftraditional students were seniors.Table 22Comparison of Levels of Course Nine Students to Other Students________________________________________________________________________ Traditional __________ All Web __________ Course Nine Web _______________ All Other Web ____________ n % of540 n % of 604 n % of 477 n % of 127 ________________________________________________________________________ FreshmenSophomoreJuniorSeniorGraduateOther 169138168 58 25 31.3025.5631.1110.74 0.370.93 9 55 237280 8 15 1.499.11 39.2446.36 1.322.48 1 30 201239 15 0.216.29 42.1450.10 0.211.05 8 253641 7 10 6.3 19.6928.3532.28 5.517.87 ________________________________________________________________________

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108 Table 23 shows that more Course Nine web-based students were business majors (32.49%) than any other major, while 42.52% of all other web-based students wereeducation majors, which was the greatest percentage of any single major for this group. A greater percentage of traditional students (41.11%) were also education majors tha n any other major. The large number of education majors is reflected by the fact that most of the included courses in this study were education courses.Table 23Frequency of Participants' College Majors Sorted by Format________________________________________________________________________ Format _________________________________________________ Traditional ____________ Web-based _________ Course Nine Web __________ All other web____________ Major n % of 540 n % of 604 n % of 477 n % of 127 ________________________________________________________________________Education22241.119315.40387.975442.52Business448.1516727.6515532.49118.66Nursing356.48172.81132.7343.15Other24445.1932954.4727156.815845.67________________________________________________________________________ The majority of participants were Caucasian including 57.02% of Course Nine web-based students, 64.57% of all other web-based students, and 62.22% of traditionalstudents. Course Nine web-based participants consisted of 13.42% Hispanics, while14.96% of all other web-based students and 12.59% of traditional students wereHispanic. Course Nine web-based students consisted of 19.08% blacks while only

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109 9.45% of all other web-based students and 13.15% of traditional students were black.Table 24 shows the frequency of the race in the three groups: Course Nine web-based,all other web-based, and traditional classroom participants.Table 24Race of Course Nine Participants Compared to All Other Participants_______________________________________________________________________ Course Nine Web-based ____________ All Other Web-based ___________ Traditional __________ Race n % of 477 n % of 127 n % of 540 _______________________________________________________________________ American IndianAsianBlackCaucasianHispanicMiddle EasternOtherNo Response 0 3291 272 64 4 14 0 0.006.71 19.0857.0213.42 0.842.940.00 35 128219 060 2.363.949.45 64.5714.96 0.004.720.00 3 2471 336 68 6 29 3 0.564.44 13.1562.2212.59 1.115.370.56 _______________________________________________________________________ There were some differences in the reasons that Course Nine web-based participants enrolled in their particular class when compared to the reasons tha t all other web-based participants enrolled in their class. Table 25 is similar to Table 18 in t hat it compares the reasons participants enrolled in the particular class secti on they did, but in this case Course Nine web-based students are compared to all other web-based st udents

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110 and to traditional students. As shown in Table 25, many Course Nine web-basedstudents (29.77%) and traditional students (50.19%) stated that the primary reason theyenrolled in their particular course or course section was that it fit their cl ass schedule. Only 15.75% of all other web-based students chose their particular course because it fi t their class schedule. Many of all other web-based students (32.28%) stated that t he reason they chose the particular class and section was that it fit their work s chedule, while only 18.87% of Course Nine web-based students and 6.85% of traditional studentschose their course because of their work schedule.

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111 Table 25Primary Reason Course Nine Participants Enrolled in This Class________________________________________________________________________ Course Nine web-based __________ All other web-based ___________ Traditional participants _________ Primary reason stated by participant n % of 477 n % of 127 n % of 540 ________________________________________________________________________ It fits my class schedule14229.772015.7527150.19It fits my work schedule9018.874132.28376.85It was the only one available306.29107.877213.33Preference for web courses8417.611814.1781.48Fulfills major or exit requirement449.2221.576512.04Preference for traditional classes10.2100.0488.89Convenience for family obligations173.561310.2420.37Convenient location (or non-location)193.9864.7210.19To avoid driving hassles102.1086.3020.37Preference for this instructor132.7300.050.98To avoid parking hassles132.7310.7930.56To accommodate physical disability00.000.0010.19Other 142.9486.30224.07 No response00.000.0030.56________________________________________________________________________ Other demographic differences between Course Nine web-based students and al l other web-based students are shown in Table 26. As indicated in this table Course Nine

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112 web-based students were in several ways more like traditional participants t han all other web-based participants. For example, while 30.71% of all other web-based studentswere married, only 9.43% of Course Nine Web-based participants and 5.19% oftraditional participants were married. While 12.60% of all other web-based partici pants stated that they had a disability or illness, only 7.13% of Course Nine web-basedparticipants and 6.48% of traditional students reported disabilities or illness. Furthe r, 15.75% of all other web-based participants were always responsible for the care of a child or other person, while only 5.66% of Course Nine web-based participants and4.63% of traditional participants were responsible for another person. More traditional participants (24.26%) lived on campus than either Course Nine web-based participants (6.92%) or all other web-based participants (5.51%). Howe ver more Course Nine web-based participants (46.12%) lived one to five miles from campusthan all other web-based participants (25.20%) or traditional students (35.00%). Table26 shows, however, that a far greater percentage of all other web-based partic ipants (41.65%) lived 21 miles or more from campus than did Course Nine web-basedparticipants (17.40%) or traditional participants (15.00%).

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113 Table 26Course Nine Participant Lifestyles Compared to Other Participants________________________________________________________________________ Course Nine web-based _________ All other web-based __________ Traditional Participants _________ Demographic n % of 477 n % of 127 n % of 540 ________________________________________________________________________Married459.433930.71285.19Living with Significant Other12626.425341.735710.56Reported Disability or Illness347.131612.60356.48Affected Some or a Lot by Disability183.771310.24183.33Close Friend or Relative with Disability8417.612519.6910519.44Affected by Disability of Other Person398.1897.09519.44Always Responsible for Child or Person275.662015.75254.63Distance from home to campus 0 Lives on campus336.9275.5113124.26 1 to 5 miles22046.123225.2018935.00 6 to 20 miles14129.563527.5613925.74 21 to 50 miles6513.633628.356912.78 Greater than 50 miles183.771713.30122.22 ________________________________________________________________________

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114 Answering the Research Questions for This Study: What goal conflicts commonly arise for post-secondary learners? The survey for this study listed goal conflicts that had been gathered from several source s previously mentioned, including participant input from a pilot study. The listed goalconflicts included working, being responsible for the care of a child or senior citi zen, carrying a heavy load of credit hours, disabilities or illness, disabilitie s or illness of people close to the student, having other things on their mind, stress, distractions,disapproval of others, social life, concern about world affairs, intentional orunintentional sabotage of studies by others, procrastination, and problems withtechnology needed for a course. Table 11, shown previously, lists the questions pertaining to goal conflicts that were written in the Likert-response format on the survey and displays the des criptive statistics for responses to the goal conflict questions. Possible responses we re "Not true," "Rarely," Sometimes," and "Often." The highest mean scores for thi s set of questions were in response to the statements “I do other things when I should bestudying,” and “I procrastinate.” Table 27 lists the number of participants who stated that they experience to some degree those conflicts appearing as Likert-r esponse questions contained in the survey. To gather this information Proc Frequency wascarried out using the SAS system. The conflicts were keyed as present if the pa rticipant responded anything other than "Not true." Therefore, if the participant responded"Rarely," "Sometimes," or "Often True" the conflict was scored as p resent for that participant. The total number of completed surveys used to identify goal conflict s was 1,144.

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115 One free response portion of the survey requested that participants list any other conflicts not previously mentioned in the survey. The survey question was "Please addany other situations that affect your study time for this course." Table 28 li sts conflicts mentioned by participants in response to this open-ended question. Sixty-nineparticipants cited extracurricular activities, such as membership in orga nizations and volunteering, as taking time from studies. Twenty-seven students reported that Table 27Participants Who Experienced Some Degree of Goal Conflicts___________________________________________________________ Conflict n Experience some conflict % of 1,144 ___________________________________________________________ProcrastinationSocializingEmploymentConcern about world affairsSabotage by others (intentional or not)Technology problemsClose friend or relative with disabilityAlways/usually responsible for othersHaving disabilitySomeone close disapproves of school 1142 1141 1142 1142 1142 1141 1141 1141 1142 1143 1,073 856 826 389 368 370 214 104 85 76 93.7974.8372.2034.0032.1932.3418.71 9.097.436.64 ___________________________________________________________

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116 participation in sports competed for study time. Twenty-four stated that their r esidence in a Greek fraternity or sorority impacted their studies.Table 28Other Conflicts Mentioned by Participants___________________________________________________________________ Number of participants Percent Conflict who added this as a conflict of 1,144___________________________________________________________________Extra curricular activities696.03Participation in sports272.36Residence in Greek housing 242.10Family problems161.40Loud roommates141.22Television (noise from or watching)131.14Long commute to school131.14Telephone131.14Pregnancy100.87Sleep problems too much or too little110.96Planning a wedding100.87Activities of children 50.44Working out, fitness program 40.35Clinical depression 30.26_________________________________________________________________

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117 Table 29 lists goal conflicts reported according to gender of participant. A greater percentage of males (95.51%) agreed to some degree other than “Not tru e” with the statement “One or more distracting factors interfere with my lea rning,” while only 90.07% of females responded that this was true to any degree. In response to thestatement “My social life affects my study time,” 84.59 % of males agre ed to some degree other than “Not true,” while 71.07% of females responded that this was true to any degree. On the other hand, a greater percentage of females (20.70%) than ma les (11.32%) stated that they sometimes, usually, or always have responsibility for t he care of another individual. As shown later, this corresponds to the greater percentage offemales (12.71%) compared to males (9.12%) who stated that they have children aged11or under.

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Table 29Goal Conflicts Reported by Gender___________________________________________________ ___________________________________________________ ____________________________ Females _____________ Males _____________ Total ____________ Variable Item # n% of 826 n% of 318 n % of 1,144 ___________________________________________________ ___________________________________________________ ____________________________ I do other things when I should be studying. 3080797.7031398.431,12097.90 It is difficult to study because I have other thing s on my mind.3179996.7330395.281,10296.33 I am under stress due to circumstances that conflic t with my studies. 3274690.3128990.381,03590.47 One or more distracting factors interfere with my l earning.3374490.0729195.511,03590.47 Someone close to me disapproves of my taking classe s. 34506.05268.18766.64 My social life affects my study time. 3558771.0736984.5985674.83 World affairs or thoughts of war affect my current schoolwork. 3628434.3810533.0238934.00 Intentionally or not, someone close to me sabotages my studies.3727433.179429.5636832.17 I procrastinate. 3877193.3430294.971,07393.79 The technology needed for this course causes proble ms for me.3926832.4510232.0837032.34 Responsible for care of another person sometimes, u sually, or always2217120.703611.3220718.09 Have disability or illness 27678.11185.66857.43 Effected to some degree or a lot by disability or i llness27 a354.23144.40494.28 Disability or illness of someone close 2816519.984915.4021418.71 Effected to some degree or a lot by disability or illness of someone close28 a748.96257.86998.65 ___________________________________________________ ___________________________________________________ ____________________________118

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119 Table 30 lists the numbers and ages of children living with participants sorted by gender. Overall most students did not have children. For example, 87.29% of femalesand 90.88% of male participants had no children under the age of 12. For those who didhave children, however, in each category more female participants had children tha n male participants. For example, 9.32% of females had children ages 0 to 7, while only7.23% of males had children in this age bracket.

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120 Table 30Number of Children Sorted by Gender of Participants (n = 1,144 includessiblings and roommates aged 18 and under).______________________________________________________________ Gender of Participants _________________________ Male Female __________ __________ Participants living with children n % of 318 n % of 826 ______________________________________________________________ 1 child 0 to 3 9 2.83 38 4.60 2 children 0 to 3 5 1.57 10 1.21Total living with children ages 0 to 314 4.40 48 5.81Total living with no children ages 0 to 3 30495.60 77894.19 1 child aged 0 to 7103.14 53 6.42 2 children ages 0 to 7113.46 20 2.42 3 children ages 0 to 7 1 0.31 3 0.36 4 children ages 0 to 7 1 0.31 1 0.12Total living with children ages 0 to 7 23 7.23 77 9.32Total living with no children ages 0 to 729592.77 74990.68 1 child aged 0 to 11 14 4.40 67 8.11 2 children ages 0 to 11 9 2.83 30 3.63______________________________________________________________

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121 Table 30 (continued).______________________________________________________________ Gender of Participants _______________________ Male Female__________ __________ Participants living with children n % of 318 n % of 826 ______________________________________________________________ 3 children ages 0 to 11 5 1.57 6 0.73 4 children ages 0 to 11 1 0.31 2 0.24Total living with children ages 0 to 1129 9.1210512.71Total with no children ages 0 to 11 28990.8872187.29 1 child aged 0 to 18 2 children ages 0 to 18 3 children ages 0 to 18 4 children ages 0 to 18 More than 4 children under age 19Total living with children ages 0 to 18Total living with no children ages 0 to 18 2218 5 4 4 53 265 6.92 5.66 1.57 1.26 1.2616.6783.33 122 41 22 7 2 194 632 14.77 4.96 2.66 0.85 0.2423.4976.51 ______________________________________________________________ Participants were asked how many people live in their household, including themselves. Table 31 indicates the responses. Note that at least six participant s indicated that no one lived in their household, indicating that they, plus possibly others,misinterpreted the question and indicated the number in their household in addition tothemselves.

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122 Table 31 Total Number of People in Participant’s Household ____________________________________________________________ Number reported in participant household Number of participants who reported this __________________________________________________________Zero people reported in household 6Living alone 99Two in household 298Three in household 267Four in household 326Five in household 84Six in household 28Seven in household 5Eight in household 4Nine in household 2Eighteen or greater in household 11Participants who did not respond to question 6 _____________________________________________________________

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123 Are there differences between post-secondary distance learners and traditional learners in the number and perceived intensity of goal conflicts? The total of participants for this section was 1,144, which included both responses of nineparticipants who were enrolled in two included courses. Distance and traditionallearners were presented with possible goal conflicts such as employment, cr edit hours, children, and feelings of conflict. Participants indicated quantification when poss ible for demographic indicators such as hours worked and credit hours currently carried and use d a Likert scale to indicate feelings of conflict in other cases. The quantity or intensity of the goal conflicts was analyzed and the two groups were compared. Table 32 shows the descriptive statistics and comparison of the numbe r of hours worked, the number of credit hours carried, and the number of children underthe age of 12. There was no remarkable difference between web-based students andtraditional students in the number of credit hours carried. However web-based studentsworked more hours ( M = 21.745, SD = 15.060) than did traditional students ( M = 14.195, SD = 13.191). As shown in Table 32, an analysis of variance using the general linear model revealed this to be a significant difference, F (1, 1,142) = 80.35, p = <.0001. There was also a significant difference between web-based and traditi onal students in the average number of children under the age of 12 living with students.Children under the age of 12 were used for this analysis since there was indication thatsome respondents included roommates in next group, children aged 12 – 18. In addition,the effect of participants who reported that they never had responsibility for a child or other person was removed.

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Table 32Some conflicts of Web Students Compared to Traditio nal Students ___________________________________________________ ___________________________________________________ ________________________________________ Traditional students ___________________ Web-based students _______________________ F Ratio ______________ Variable n M SD n M SD n F p ___________________________________________________ ___________________________________________________ ________________________________________ Number of hours worked (effect of participant who r eported 440 hrs removed)53914.19513.19160321.74515. 0601,14280.35<.0001 Number of credit hours carried 54013.3572.40260413.0963.2651,1442.330.1270 Responsible for care of another person sometimes, u sually, or always4981.3090.7565421.4190.9121,0404.4 10.0361 Have responsibility for children under age 125400.0 610.3016040.1920.5781,14422.27<.0001 ___________________________________________________ ___________________________________________________ ________________________________________ Numerator degrees of freedom = 1 for all F tests. .124

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125 Web-based participants had an average of 0.192 ( SD = 0.578) children under the age of 12 while traditional students each had an average of 0.061 ( SD = 0.301) children under 12 living with them. Analysis of variance using the general linear model re vealed this to be a significant difference, F (1, 1,144) = 22.27, p = <.0001. Table 33 displays the descriptive statistics of the Likert scored feelings of conflict reported by participants, comparing web-based to traditional students. Web-base d students reported moderately higher Likert-scored feelings ( M = 2.842, SD = 0.910) than traditional students ( M = 2.712, SD = 0.919) when responding to the statement “I am under stress due to circumstances that conflict with my studies.” Analysis of variance revealed this was a modestly significant differen ce, F (1, 1,141) = 5.73, p = 0.0169. Web-based students felt somewhat more intensely ( M = 1.136, SD = 0.511) than did traditional students ( M = 1.074, SD = 0.342) the disapproval of someone close to them regarding their taking classes, F (1, 1,142) = 5.70, p = 0.0171. Traditional students, on the other hand, indicated that their social life affected their study time to a somewhat greater degree ( M = 2.333, SD = 0.973) than did web-based students ( M = 2.210, SD = 0.949). Analysis of variance on this Likert-scored question revealed an F ratio (1, 1,140) = 4.69, p = 0.0306. Both traditional and webbased students stated that they procrastinated. While both groups had mean scores over3.0, based on a Likert scale of 1 – 4, concerning feelings about the statements “I do othe r things when I should be studying,” and “I procrastinate,” there was not a signif icant difference between the two groups regarding either statement.

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Table 33Likert-Scored Conflicts of Web-based Compared to Tr aditional Students ___________________________________________________ ___________________________________________________ ______________________________________________ Traditional students __________________ Web-based students ______________________ F Ratio _______________ Survey Statement n M SD n M SD F*p ___________________________________________________ ___________________________________________________ ______________________________________________ I do other things when I should be studying.5403.16 70.7126023.1860.7380.200.6523 It is difficult to study because I have other thing s on my mind.5393.0760.7816023.0630.7990.080.7825 I am under stress due to circumstances that conflic t with my studies.5392.7120.9196022.8420.9105.730.0 169 One or more distracting factors interfere with my l earning.5402.6150.8506012.6990.8612.740.0981 Someone close to me disapproves of my taking classe s.5401.0740.3426021.1360.5115.700.0171 My social life affects my study time. 5402.3330.9736002.2100.9494.690.0306 World affairs or thoughts of war affect my current schoolwork.5401.3960.6206011.4480.6811.750.1856 Intentionally or not, someone close to me sabotages my studies.5391.4750.7816021.5170.8380.750.3870 I procrastinate. 5393.1150.8746023.0800.8990.450.5026 The technology needed for this course causes proble ms for me.5401.4910.7956021.4570.7470.550.4575 ___________________________________________________ ___________________________________________________ ______________________________________________ Numerator degrees of freedom = 1 for all F tests.126

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127 While there was not a significant difference between traditional students and web-based students regarding the statement “I do other things when I should bestudying,” there was a significant difference regarding this state ment between Course Nine web-based participants and all other web-based students, F (1, 602) = 12.27, p = 0.0005. Course Nine web students reported a mean of 3.240 ( SD = 0.707) on a Likert scale of 1 – 4, while all other web-based participants reported a mean of 2.984( SD = 0.816). Course Nine web respondents also reported higher scores ( M = 2.310, SD = 0.948) for the statement “My social life affects my study time,” than did al l other web-based participants ( M = 1.833, SD = 0.856). This was a significant difference, F (1, 600) = 26.18, p = <.0001. Appendix K shows the descriptive statistics of the Likertscored conflicts and the F ratios of the Likert-scored conflict measures of Course Nine web-based students compared to all other web-based students. Frequency tables were created, indicating the number of respondents in each course format and the number in each group experiencing each particular goal con flict. Table 34 shows the frequency of students who stated that they experienced variousconflicts to some degree. Likert scores were not considered in Table 34. Instead, t hese conflicts were scored as present if anything other than “never” was indicat ed. These are sorted by course format and by the percentage of students who stated they experi enced each conflict. A large percentage (93.79%) of participants stated that they procra stinated but as previously mentioned there was not a significant difference between web -based students and traditional students in procrastination. The majority (74.83%) of allparticipants stated that socializing interfered with their studies.

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128 Table 34Other Conflicts Reported by Traditional and Web-bas ed Students ___________________________________________________ _______________________________ Traditional Web-based Total Conflict n % of 540 n % of 604 n % of 1,144 ___________________________________________________ _______________________________ Procrastination present51294.8156192.88107393.79Socializing present41777.2243972.6885674.83Concern for world affairs present17933.1521034.7738 934.00 Sabotage by others present17131.6719732.6236832.17Technology problems present17632.5919432.1237032.34Friend or relative with disability10519.4410918.052 1418.71 Responsible for another at least some8115.0012620.8 620718.09 Rarely responsible for others519.44508.281018.83Having disability356.48508.28857.43Extra curricular activities234.26467.62696.03Participation in sports101.85172.81272.37Residence in Greek housing61.11182.98242.10Family problems71.3091.49161.40Planning a wedding50.9350.83100.87Loud roommates50.9391.49141.22Television (watching or noise from)40.7491.49131.14Long commute to school40.7491.49131.14Telephone40.7491.49131.14 ___________________________________________________ _______________________________

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129 In this case, the previously mentioned difference between web-based and traditional students was modestly significant, F (1, 1,141) = 4.84, p = 0.0280, in that a greater percentage of traditional students (77.22%) than web-based students (72.68%)stated that socializing interfered with their studies. Over 30 percent of all students stated that concern for world affairs, sabotage by others (intentional or not) and technologyproblems interfered to some degree with their studies. As previously mentioned, web-based students had significantly more children under the age of 12, F(1, 1,144) = 22.27, p = <.0001, than did traditional, classroombased students. Those are the figures for children of participants. However, others even those who reported that they never had responsibility for a child or other person reportedthat they lived with children. It is assumed that these were siblings or children of others in their home. Table 35 shows the frequencies of various ages of the children living withparticipants in both groups, even though the participant may not have had responsibilityfor the child. Table 34 (continued).___________________________________________________ _______________________________ TraditionalWeb-basedTotal Conflict n % of 540 n % of 604 n % of 1,144 ___________________________________________________ _______________________________ Pregnancy40.7460.99100.87Sleep problems61.1150.83110.96Depression10.1920.3330.26 ___________________________________________________ _______________________________

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130 Table 35Frequencies and Ages of Children Living with Participants (Includes siblings, roommates and children of roommates)________________________________________________________________________ Traditional ___________ Web-based __________ Total _________ Participants living with children n % of 540 n % of 604 n % of1,144 ________________________________________________________________________ 1 child 0 to 3 2 children 0 to 3Total living with children ages 0 to 3Total living with no children ages 0 to 3 1 child aged 0 to 7 2 children ages 0 to 7 3 children ages 0 to 7 4 children ages 0 to 7Total living with children ages 0 to 7Total living with no children ages 0 to 7 1 child aged 0 to 11 2 children ages 0 to 11 3 children ages 0 to 11 4 children ages 0 to 11Total living with children ages 0 to 11 16 2 18 522 23 411 29 511 30 821 41 2.960.373.33 96.67 4.260.740.190.195.38 94.63 5.561.480.370.197.59 311344 560 4027 31 71 533 5131 92 93 5.132.157.28 92.72 6.624.470.500.17 11.7588.25 8.445.131.490.33 15.40 471562 1082 6331 42 100 1044 813911 3 134 4.111.315.42 94.58 5.512.710.350.178.74 91.26 7.083.410.960.26 11.71 ________________________________________________________________________

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131 Table 35 (continued).________________________________________________________________________ Traditional __________ Web-based __________ Total _________ Participants living with children n % of 540 n % of 604 n % of 1,144 ________________________________________________________________________ Total with no children ages 0 to 11 1 child aged 0 to 18 2 children ages 0 to 18 3 children ages 0 to 18 4 children ages 0 to 18 5 children ages 0 to 18 7 children ages 0 to 18 18 children ages 0 to 18 75 children ages 0 to 18Total living with children ages 0 to 18Total living with no children ages 0 18 499 571813 20101 92 448 92.4110.56 3.332.410.370.000.190.000.19 17.0382.96 511 874114 92110 155449 84.6014.40 6.792.321.490.330.170.170.00 25.66 74.34 1010 144 592711 2211 247897 88.2912.59 5.162.360.960.170.170.090.09 21.5978.41 ________________________________________________________________________ As shown in Table 35, the majority of all students (88.16%) lived with no children under the age of 12. In fact, most participants (72.99%) stated they were neve r responsible for the care of another person. Table 36 reveals that 10.76% of web-basedparticipants and 7.22% of traditional students stated that they were either usually or always responsible for some other person, such as a child or senior citizen.

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132 Table 36Responsibility for Child or Other Person Who Needs Assistance________________________________________________________________________ Traditional __________ Web-based ___________ Total _________ Responsible for care of other person n % of 540 n % of 604 n % of 1,144 ________________________________________________________________________NeverRarelySometimesUsuallyAlwaysNo response 408 51421425 0 75.56 9.447.782.594.630.00 427 50611847 1 70.70 8.28 10.10 2.987.780.17 835101103 3272 1 72.99 8.839.002.806.290.09 ________________________________________________________________________ However, a breakdown of these data shows there were significant differences between traditional participants and web-based participants, and also significa nt differences between Course Nine web-based respondents and all other web-basedrespondents. Responses of students regarding responsibility for another person wer e converted to numbers: Never = 0, Rarely = 1, Sometimes = 2, Usually = 3, andAlways = 4. Proc Univariate (SAS) yielded descriptive statistics and a n analysis of variance using the general linear model compared the groups for significant differences. It is believed that the individuals with 18 and 75 in their households were referring toliving in a dorm or Greek housing in which there may be many students under aged 19.

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133 Table 37 shows the responsibility for others reported by traditional participants as compared to web-based participants, as well as differences between Course N ine webbased respondents and all other web-based respondents. Table 37Comparison of Reported Responsibility for Others___________________________________________________ ______________________ n M SD sk k ___________________________________________________ ______________________ Traditional5400.5131.0582.1483.683Web-Based6030.6871.2391.6961.586 F (1, 1,143) = 6.42 p = 0.0114 ___________________________________________________ ____________________ n M SD sk k ___________________________________________________ ______________________ Course Nine Web4770.5891.1351.9112.552All other web1261.0561.5201.073-0.446 F (1, 603) = 14.45 p = 0.0002 ___________________________________________________ ______________________ Appendix I displays the way traditional and web-based participants answered each of the Likert-response goal conflict questions. As Appendix I shows, 33.33% oftraditional students and 35.76% of web-based participants stated that they often did othe r things when they felt they should be studying. Stress due to circumstances tha t conflicted with their studies caused problems for 22.41% of traditional students and25.33% of web-based students. Many participants, 37.58% of web-based students and39.44% of traditional students, stated that they procrastinate often. Most participant s, 67.41% of traditional students and 67.92% of web-based students, stated that thetechnology needed for their course did not cause them problems.

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134 Is there a difference in the course completion rates of post-secondary distance learners and traditional learners? As shown in Table 38, over 97% of web-based participants passed the courses in which they were enrolled, while 88.7% of traditi onal students passed their courses. This was a significant difference, 2 (1, 1,144) = 30.6709, p = <.0001. The correlation between passing and being a web-based student was not large (Phi coefficient = 0.1637). There was also a significant difference in the failure rate of participants, 2 (1, 1,144) = 33.1679, p = <.0001. in that a higher percentage of traditional participants failed than did web-based participants. Here again, thecorrelation between failing the course and being a traditional student was not l arge (Phi Coefficient = -0.1703). While 9.26% of traditional students failed their course, 1.26% ofweb-based students failed the course they were enrolled in. There was no signifi cant difference in the rates of withdrawal or receiving an incomplete in the cour ses.

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135 Table 38Course Completion of Traditional Students Compared to Web Students ___________________________________________________ _____________________________________ Format _____________________________________ Traditional __________ Web-based _________ Total __________ Correlation _______________________ Course completion n % of 540 n % of 604 n % of 1,1442 P Phi ___________________________________________________ _____________________________________ Pass47988.7058697.021,06593.0930.6709<.00010.1637Fail509.26101.66605.2433.1679<.0001-0.1703Withdraw101.8571.16171.490.93510.3336-0.0286Incomplete10.1910.1720.170.00630.9368-0.0023 Total5406041,144 ___________________________________________________ _____________________________________ What is the relationship between goal conflicts and course completion of postsecondary learners? Several variables included in this study can be considered goal conflicts. The predictor variable labeled conflict was calculated by adding the scores of the participant responses to Likert-scored questions regarding feelings of conflict. Other indicators that could act as goal conflicts, such as number of children, number ofemployed hours, number of credit hours, were also entered in the logistic regress ion model as having possible impact on course completion. Since it was not known whethergoal conflicts are predictors of course completion and since there are other vari ables that contribute to course completion, logistic regression was employed, holding otherpredicting variables constant. Figure 3, shown in Chapter Three, displays the model forthis analysis.

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136 Table 39 displays the results of the SAS logistic regression procedures that w ere run to identify possible predictors of passing the included course. The model for thisprocedure corresponds to the model displayed in Figure 3. Tables 40 and 41, found inAppendix L, display the results of logistic regression procedures following t he same model as Figure 3 showing the possible predictors for failing or withdrawal from t he included course. Self-efficacy was found to be the greatest predictor of passin g, 2 (1, 1,119) = 64.1669, p = <.0001, or not failing 2 (1, 1,119) = 37.7352, p = <.0001, in included courses. The self-efficacy odds ratio for passing was 1.339, indicating t hat an individual who scored one point higher on the Likert scale for the self-efficacy questi ons (for example a 3 instead of a 2) had odds of passing that were 1.339 times the odds ofpassing than he had at the lower score. The self-efficacy odds ratio for failur e was 0.792, with a negative maximum likelihood estimate, indicating that an individual who scoredone pointer higher on the Likert scale for the self-efficacy questions (for ex ample a 3 instead of a 2) had odds of failing that were 0.792 times the odds of failing than he hadat the lower score.

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137 Table 39Logistic Regression for Possible Predictors of Pass ing Course ___________________________________________________ __________________________________________________ n = 1,143 Pa ss = 1041 Not pass = 78 24 observations not us ed due to missing values Results of logistic regression ___________________________________________________ ____________________________ Analysis of maximum likelihood estimates Type III analysis of effects Odds ratio estimates Predictor Variable Maximum likelihood estimate Standard error2 p Odds 95% Wald confidence ratio estimates ___________________________________________________ _________________________________________________ Intercept:-5.14751.88837.43070.0064 Course format (Traditional) -1.04460.34988.92080.00280.3520.1770.698 Number children-0.18550.27970.43990.50720.8310.4801 .437 Number hrs worked*-0.03420.010410.72770.00110.9660. 9470.986 Credit hours0.01070.05670.03570.85021.0110.9041.130Conflicts (feelings of) -0.00850.03640.05480.81480.9920.9231.065 Arrange technology-0.09360.14310.42800.51300.9110.6 881.205 Analyze assignments0.60960.24326.28290.01221.8401.1 422.963 Estimate time -0.1423022670.39380.53030.8670.5561.353 Make schedules0.06100.21490.08060.77641.0630.6981.6 20 Set assignment goals0.07850.21070.13880.70951.0820. 7161.635 Block distractions -0.12370.21050.34490.55700.8840.5851.335 Categorize info0.05570.20850.07140.78931.0570.7031. 591 Practice important facts-0.00070.19310.00000.99720. 9990.6841.459 Relate information -0.09670.23450.17020.67990.9080.5731.437 Underline/outline info-0.20300.20330.99700.31800.81 60.5481.216 Use flashcards-0.07840.15550.25400.61420.9250.6821. 254 ___________________________________________________ _________________________________________________

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138 Table 39 (continued).___________________________________________________ _________________________________________________ n = 1,143 P ass = 1041 Not pass = 78 24 observations not used due to missing values Result s of logistic regression _______________________________________________ __________________________ Analysis of Maximum Likelihood Estimates ____________________ Type III Analysis of Effects ___________________ Odds Ratio Estimates __________________________ Predictor Variable Maximum likelihood estimate Standard error2 p Odds 95% Wald confidence ratio estimates ___________________________________________________ __________________________________________________ Reread/study notes0.32900.21262.39510.12171.3900.91 62.108 Do practice quizzes0.02650.17160.02380.87741.0270.7 341.437 Do assignments first0.15720.22810.47470.49081.1700. 7481.830 Complete work early-0.29530.20612.05360.15180.7440. 4971.115 Join study team-0.22370.19611.30080.25410.8000.5441 .174 Contact instructor0.53090.18168.54810.00351.7001.19 12.427 Get info another way-0.44850.20944.58560.03220.6390 .4240.963 Student disability (not having disability ) 0.62500.44711.95420.16211.8680.7784.488 Disability of relative (not present) -0.55580.40321.90060.16800.5740.2601.264 Self-Efficacy0.29200.036564.1669<.00011.3391.2471.4 38 Prior academic achv0.14961.10532.01700.15551.1610.9 451.428 ___________________________________________________ __________________________________________________ Effect of outlier removed (participant reporte d 440 hours worked). Course format was also an important predictor in passing, 2 (1, 1,143) = 8.9208, p = 0.0028, or failure 2 (1, 1,119) = 12.9946, p = 0.0003. The odds ratio for passing the course was 0.352 with a negative maximum likelihood estimate, indicatingthat a traditional participant was 0.352 times more likely to pass the course as a web-

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139 based participant. As pointed out in answering research question 3, over 97 % of web-based participants passed the courses in which they were enrolled, while 88.7 % oftraditional students passed their courses. The odds ratio for failing the course was 4.457 with a positive maximum likelihood estimate, indicating that a traditional stude nt was 4.457 times as likely to fail the course as a web-based participant. Feelings of conflict were calculated by summing the scores of the Liker t response goal conflict questions. As shown in Tables 39 41, conflict feelings report ed by participants did not contribute to a significant degree to participant course com pletion consisting of pass, fail, withdraw, or incomplete. The number of hours of employmentacted as predictor of passing the course 2 (1, 1,119) = 10.7277, p = 0.0011. However, the maximum likelihood estimate was –0.0342, indicating that the relationship wasinverse: those who passed worked fewer hours than those who did not pass. The oddsratio for hours worked was 0.966, indicating that for every hour more a participantworked his odds of passing the course was 0.966 times his odds of passing if he workedthe original number of hours. For example, a participant who worked 41 hours per week,rather than 40 hours per week, had odds of 0.947 times the odds of passing if theparticipant worked 40 hours per week. Proc GLM (SAS, 2004) was performed tocompare the hours worked of those who passed their courses to the hours worked of allthose who failed, withdrew, or took an incomplete. This procedure revealed that thosewho passed worked fewer hours ( M = 18.045 hours, SD =14.568) than those who did not pass ( M = 19.577 hours, SD =16.437). Participants who worked more hours were somewhat more likely to fail, 2 (1, 1,119) = 5.9581, p = 0.0146, or withdraw,

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1402 (1, 1,119) = 4.4994, p = 0.0339, from their included course. The odds ratio for failure was 1.028 for every hour more worked, and the odds ratio for withdrawal was 1.051 forevery hour more worked. Logistic regression revealed that participants who stated that they had a disability or illness were somewhat more likely than those who did not have a disability to fail t heir included course in this study, 2 (1, 1,119) = 4.8174, p = 0.0282. This was a yes or no question for which the odds ratio = 0.354 with a negative maximum likelihood estimate,indicating that those who did not have a disability or illness had 0.354 times the odds offailing their course than those who did have a disability or illness. Frequency studies revealed that 13.99% of those who failed stated they had a disability or illness, while7.10% of those who did not fail (passed, withdrew, or received an incomplete) had adisability or illness. The disability of a close relative or friend did not sig nificantly impact the completion rate of participants. Neither the number of credit hours carried nor the number of children had a significant effect on passing, failure, or withdrawal. The number of credit hour s carried may have impacted participants who received an incomplete. However, there wer e only two participants who received an incomplete and there therefore this analysi s could not be carried out.

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141 Is there a difference in the instructional self-regulation of post-secondary distance learners and traditional learners? Using a four-point Likert scale, web-based participants reported greater self-regulation than did traditional partic ipants in response to all self-regulation questions. Table 42 displays the comparison of descriptivestatistics of the Likert-scored (scale = 1-4) self-regulation response s of traditional and web-based participants. Next an analysis of variance was carried out using a general linear model for each self-regulation question. There was a significant diff erence in the responses of web-based participants compared to traditional students in several c ases. As Table 42 reveals, there were significant differences ( p = <.0001) in response to the following statements: “I arrange to have the technology needed for my coursew ork,” “I analyze assignments to determine what I need to do,” “I try to relate new i nformation to what I already know,” “I e-mail or see my instructor for help when I don't unders tand,” and “If I don't understand one source, I get the information another way.” There we re also modestly significant differences ( p = <0.05) in response to these statements: “I make schedules for doing my assignments,” “I set daily or weekly goals for myse lf as I work on assignments,” “I reread or study my notes prior to a quiz or test,” and “I dopractice quizzes before taking a test.”

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Table 42Self-Regulation of Web-based Compared to Traditiona l Students ___________________________________________________ ___________________________________________________ ______________________________________________ Traditional students __________________ Web-based students ______________________ F Ratio _______________ Survey Statement n M SD n M SD F p ___________________________________________________ ___________________________________________________ ______________________________________________ I arrange to have the technology needed for my cour sework.5403.0331.0626033.3860.92336.17<.0001 I analyze assignments to determine what I need to d o.5403.2560.6966033.4510.64124.05<.0001 I try to estimate the amount of time needed for eac h assignment.5383.0590.7866043.1510.7973.770.0524 I make schedules for doing my assignments. 5402.7030.9306042.8160.9294.180.0412 I set daily or weekly goals for myself as I work on assignments.5402.7220.9576042.8680.9466.640.0101 I deliberately block out distractions when I study. 5382.5330.8036042.6130.7992.780.0959 When I study I intentionally categorize and classif y things in my mind.5382.7550.8186042.8310.8572.360 .1244 I practice saying important facts over and over to myself.5402.8510.8466042.9420.8633.170.0752 I try to relate new information to what I already k now.5403.0760.7286003.2430.69915.67<.0001 I underline, take notes, or outline new information as I read.5403.1110.8326043.1720.9091.390.2380 I use flashcards to study course material. 5392.2710.9896042.3661.0782.390.1222 I reread or study my notes prior to a quiz or test. 5403.5280.7036043.6110.6364.410.0359 I do practice quizzes before taking a test. 5382.6040.9106022.7990.94012.590.0004 ___________________________________________________ ___________________________________________________ ______________________________________________142

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Table 42 (continued).___________________________________________________ ___________________________________________________ ______________________________________________ Survey Statement _________________________________ Traditional students __________________ Web-based students ______________________ F Ratio _______________ n M SD n M SD F p ___________________________________________________ ___________________________________________________ ______________________________________________ I do my course assignments first, before I do other things.5402.6800.7896042.7480.7662.230.1356 I complete my assignments days or weeks before they are due.5402.2440.8506042.2530.8390.030.8593 I join study teams or virtual study teams via lists erv, chat or e-mail, etc.5401.7220.8296041.8100.846 3.100.0785 I e-mail or see my instructor for help when I don't understand.5402.4540.9516042.7220.87824.56<.0001 If I don't understand one source, I get the informa tion another way.5402.8570.7826033.0400.73316.56<.0 001 ___________________________________________________ ___________________________________________________ ______________________________________________ Numerator degrees of freedom = 1 for all F-tests.143

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144 What is the relationship between the instructional self-regulation and course completion of post-secondary learners? There were many facets of the construct selfregulation. Each of eighteen self-regulatory practices shown in Appendix C was placed into the model leading to course completion. Since it was not known whether self-regulation is a predictor of course completion and since there are other variables that contribute to course completion, logistic regression was employed, holding otherpredicting variables constant. The dependent variable, course completion, was vie wed as a dichotomous variable for each case: pass, fail, withdraw, or incomplete. As shown in Table 39, participants were somewhat more likely to pass their included course if they answered positively to the following statement: "I anal yze assignments to determine what I need to do." Logistic regression for passi ng the course revealed that for this question 2 (1, 1,119) = 6.2829, p = 0.0122. The odds ratio for the data from responses to this question was 1.84, indicating that an individual who scoredone point higher on the Likert scale for the statement (for example a 3 instead of a 2) had odds of passing that were 1.84 times the odds of passing than the student who scored 2for this question. The likelihood that the participant would pass their included coursewas also increased when participants answered positively to the statement "I email or see my instructor for help when I don't understand." For this question 2 (1, 1,119) = 8.5481, p = 0.0035, and the odds ratio = 1.70. If participants answered positively to the statement "If I don't understand one source, I get the information another way," the ir odds of passing were decreased, as indicated by the negative value of the maximumlikelihood estimate. In this case 2 (1, 1,119) = 4.5856, p = 0.0322 and the odds ratio = 0.639.

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145 Participants were also slightly more likely than not to fail their included c ourse if they answered the Likert-scaled statement that they did not or rarely did analyze assignments to determine what I need to do," 2 (1, 1,119) = 6.1787, p = 0.0129, odds ratio = 0.514. The maximum likelihood estimates for both of these questions werenegative values indicating an inverse relationship, a fact that corresponds to the sm all odds ratio in each case. Participants were slightly more likely to withdraw from their included cours e if they answered that they did not or rarely did "email or see my instructor for hel p when I don't understand," 2 (1, 1,119) = 7.4234, p = 0.0064, odds ratio = 0.346. Next, a predictor variable, self-regulation, was calculated by summing the sc ores of the individual self-regulation practices of each participant. Logistic re gression was run again for each case of completion, pass, fail, withdraw, or incomplete. This tim e the self-regulation construct replaced the individual self-regulation practices shown in Figure 3. As shown in Table 43, self-regulation as a summed construct was not found tobe a predictor of passing, failing, or withdrawal from the courses included in this s tudy. Only two participants received an incomplete and it was not possible to create a va lid model for-self regulation as a predictor of receiving an incomplete from thes e courses using logistic regression.

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146 Table 43The Construct Self-Regulation as Predictor of Cours e Completion ___________________________________________________ _______________________________________________ n = 1,143 6 observations not used due to miss ing values Results of Logistic Regression ___________________________________________________ ______________ Analysis of Maximum Likelihood Estimates Type III Analysis of Effects Odds Ratio Estimates Maximum Likelihood estimate Standard error2 p Odds ratio 95% Wald confidence estimates ___________________________________________________ _______________________________________________ Pass Pass = 1050 Not pass = 79 0.00830.01810.21100.64601.0080.9731.045 Fail Fail = 60 Not Fail = 1078 -0.03350.02032.73020.09850.9670.9291.006 Withdraw Withdraw = 17 Not WD = 1,121 0.09390.03786.17430.01301.0981.0201.183 Incomplete* Incomplete = 2 Not Incom = 1136 ___________________________________________________ _______________________________________________ There were only two participants who received Inc omplete therefore it was not possible to run this analysis.

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147 Chapter 5 Discussion Major Findings in this Study The purpose of this study was to identify the goal conflicts and self-regulation habits of post-secondary learners, to determine the effect of these factors on c ourse completion, and to compare these issues in distance and traditional learners. To beg in this discussion let us examine the major findings for each research question. What goal conflicts commonly arise for post-secondary learners? Procrastination, socializing, and employment appeared as goal conflicts for more participants than did other conflicts in this study. Over 93% of participants experi enced procrastination as a goal conflict at least part of the time and nearly 75% of respondents found socializing to be in conflict with their studies at least some of the time.Employment became a conflict for learning at some point in their academic l ife for more than 72% of participants. Gender related differences appeared in some goal conflicts. More men (84.59%) than women (71.07%) stated that their social life affected their studies at leas t sometimes. On the other hand, nurturing and childcare appeared to be a responsibilityfor women more often than for men. More women (20.70%) than men (11.32%)reported responsibility for the care of another individual and a larger percentage offemales (12.71%) compared to males (9.12%) stated that they have children 11 or under.

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148 The study revealed that in addition to actual, physical conditions such as employment, having children, or caring for other individuals, feelings and menta l stimuli can also interfere with learning. Over 90% of all students responded positively to s ome degree to the following Likert-scored statements (shown in parentheses fo r each are the percentage of total participants who responded any way except “not true,” and the m ean and standard deviation of their Likert response on a scale of 14): I do other things when I should be studying (97.90%, 3.18, 0.73) It is difficult to study because I have other things on my mind (96.33%, 3.07,0.79) I am under stress due to circumstances that conflict with my studies (90.47%,2.78, 0.92) One or more distracting factors interfere with my learning (90.47%, 2.66,0.86) I procrastinate (93.79%, 3.10, 0.89) Thus, the study confirmed that mental processes were important conflicts in learning situations. Are there differences between post-secondary distance learners and traditional learners in the number and perceived intensity of goal conflicts? This study found that there were significant and interesting goal conflict differences betw een traditional and web-based participants. Significantly more web-based students than traditional students were employed and were employed more average hours than traditional students Webbased participants also had more children under 12 than did traditional participants.However, Course Nine participants heavily influenced the differences between web

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149 based students and traditional students. Course Nine students comprised a great maj ority (78.97%) of web-based learners so differences between Course Nine participants a nd all other web-based participants were important. There was a significant difference, F (1, 1,142) = 80.99, p = <.0001, in the number of hours worked. Web-based students worked an average of 21.745 hours perweek, ( SD = 15.060) while traditional students worked an average of 14.195 hours per week, ( SD = 13.191). Course Nine web-based participants worked fewer hours ( M = 20.723, SD = 14.648) than did all other web-based participants ( M = 25.575, SD = 16.003). This difference was also significant, F (1, 603) = 10.57, p = 0.0012. Web-based students also had more children under the age of 12 ( M = 0.192, SD = 0.578) than did traditional students ( M = 0.061, SD = 0.302). Here again, Course Nine web participants had fewer children ( M = 0.134, SD = 0.462) under the age of 12 than did all other web-based participants ( M = 0.409, SD = 0.858), thus shifting the overall data of web-based respondents. There were also several significant differences between traditional a nd webbased students in Likert-scored feelings of conflict. Web-based students re ported modestly significantly higher Likert-scored feelings, F (1, 1,141) = 5.73, p = 0.0169, than traditional students when responding to the statement “I am under stress due tocircumstances that conflict with my studies.” Web-based students felt some what more intensely than did traditional students the disapproval of someone close to themregarding their taking classes, F (1, 1,142) = 5.70, p = 0.0171. Traditional students, on the other hand, indicated that their social life affected their study time to a somewhat greater degree than did web-based students F (1, 1,140) = 4.69, p = 0.0306. Both

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150 traditional and web-based students stated that they procrastinated. While both groupshad mean scores over 3.0, based on a Likert scale of 1 – 4, concerning feelings about thestatements “I do other things when I should be studying,” and “I procrastinat e,” there was not a significant difference between the two groups. Course Nine web-based participants scored significantly higher than all ot her web-based participants in their responses to the statements “I do other thing s when I should be studying,” and “My social life affects my study time.” These dif ferences could be due to the influence of their younger age and being close to campus. Is there a difference in the course completion rates of post-secondary distance learners and traditional learners? Significantly more web-based participants passed their included courses than did traditional participants. Further, more traditionalparticipants failed their included courses than did web-based participants. Thi s could be due to the youth of traditional students and distractions in the lives of the younger,campus-based participants. What is the relationship between goal conflicts and course completion of postsecondary learners? The number of hours participants worked per week influenced their course completion: the more hours a participant worked, the more likely he w as to not pass the course, either by failing or withdrawal. Students who reported a disabili ty or illness were somewhat more likely to fail than those who did not report a disabilit y or illness. Neither the number of children under 12 nor the credit hours appeared to affectthe course completion of participants. Is there a difference in the instructional self-regulation of post-secondary distance learners and traditional learners? Respondents used a four-point Likert scale

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151 to score their responses to a series of self-regulation statements. Web-base d participants reported greater self-regulation than did traditional participants in response to all selfregulation questions. In responding to several statements, web-based participant s reported significantly greater self-regulation than did traditional lear ners. There were significant differences ( p = <.0001) in response to the following statements: “I arrange to have the technology needed for my coursework,” “I analyze assignments to deter mine what I need to do,” “I try to relate new information to what I already know,” “I e -mail or see my instructor for help when I don't understand,” and “If I don't understand onesource, I get the information another way.” There were also modestly signifi cant differences ( p = <0.05) in response to these statements: “I make schedules for doing my assignments,” “I set daily or weekly goals for myself as I work on ass ignments,” “I reread or study my notes prior to a quiz or test,” and “I do practice quizzes before t aking a test.” Based on the findings of this study there is no indication as to whether web-based students are naturally better self-regulators or whether the nature of th e course format required web-based students to be better at self-regulation in order to participat e in the course. A third possibility is that web-based students are simply more likely th an are traditional students to report themselves as better self-regulators. What is the relationship between the instructional self-regulation and course completion of post-secondary learners? Participants were more likely to pass their included course if they answered positively to several self-regulation statem ents: "I analyze assignments to determine what I need to do," or "I email or see my i nstructor for help when I don't understand." If participants answered positively to the statem ent "If I

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152 don't understand one source, I get the information another way," their odds of passingwere decreased, as indicated by the negative value of the maximum likelihood e stimate. Participants were slightly more likely to withdraw from their included cours e if they answered that they did not or rarely did "email or see my instructor for hel p when I don't understand." Self-regulation as a summed construct was not found to be apredictor of passing, failing, or withdrawal from the courses included in this st udy. Web-based participants scored themselves higher than traditional students did on all facets of self-regulation and a higher percentage of web-based participa nts than traditional participants did pass their included course.Implications of Study Findings Significantly more web-based participants passed their included courses than di d traditional participants. This result agrees with the findings of Searcy (1993 ), and Hogan (1997) but is in conflict with the results of the study by Sinclair Community College(1999). This is possibly due to the self-reported greater amount of self-regulati on of distance learners than traditional learners. Since one of the predictors of pass ing the course was responding positively to the statement "I analyze assignments t o determine what I need to do," instructors might build in a course assignment analysis and requir e that students complete the analysis and fill in self-designated goal assig nment completion dates. The survey responses of study participants identified their major learning conflicts. Subsequently the conflicts of web-based participants were compared t o those of traditional students. Important findings were that web-based students do have m ore

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153 conflicts than traditional students. They have more children under twelve, more of themare employed, and they work more hours than do traditional students. Web-based students reported modestly significantly higher Likert-scored f eelings than traditional students when responding to the statement “I am under stress due t o circumstances that conflict with my studies.” Web-based students also felt somewhat more intensely than did traditional students the disapproval of someone close to themregarding their taking classes. Due to the nature of their learning environme nt, usually home or work, web-based learners' perceptions of goal conflict magnitude may begreater than are those of traditional learners. Web-based learners’ fam ily or job commitments may greatly impact their learning experience, because they are learning in the home or workplace. However, web-based learners may enroll in distance course s because of additional goal conflicts that preclude taking traditional courses Another important finding was that these conflicts do not appear to affect course completion since web-based participants were more likely to pass their include d course than were traditional students. This may be related to the finding that web-basedparticipants reported greater scores in all self-regulation practice s listed in the survey than did traditional learners. As previously discussed, this may be because web-bas ed learners are required to perform more self-regulatory habits than are tradit ional learners in order to perform the tasks required by their distance coursework. A sec ond possibility is that, by nature, the web-based learner may be more inclined to per form self-regulatory tasks than is the traditional learner. Again, the third possibi lity is that web-based students may be more likely to report higher self-regulation scores tha n are traditional students even though possibly the actual self-regulatory practices m ay be

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154 approximately equal to those of traditional students. It seems most likely that t he distance coursework may require web-based learners to exhibit more self-re gulation than does traditional coursework. Web-based learners must perform the self-regulati on behaviors of traditional learners and also must: Interact with their instructor and obtain course instructions via the Internet Resolve coursework questions via Internet or another source Acquire and use the technology required to access their coursework Make schedules for learning at home or work Put aside people and activities at home or work during learning time Ask for help or check on grade standing via e-mail, or form virtual study teams via listserv, chat, email Students were more likely to pass their course if they responded positively to t he statement "I email or see my instructor for help when I don't understand." In order to support learners, instructors might find it helpful to require students to contact theinstructor periodically throughout the course. This could be done via required emailfeedback regarding course progress or chat room participation. Since procrastination was a conflict experienced by most participants, instr uctors may find it helpful to require that assignments be handed in according to periodicdeadlines during the course, rather than allowing assignments to be handed in at the end of the course. This coordinates with the findings of Majchrzak, (2001), who found thatstudents scored higher on post-tests when they had periodic contingency deadlines rat her than being allowed to hand in all assignments all at once. It also complements the

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155 findings of Ferrari, Johnson, and Williams (1995), who reported that student completionrate suffered when students were allowed to set their own assignment time s chedule. A major goal of the study was to identify factors that prevented students from learning which, in this case, translated to failure or withdrawal from their inc luded course. The study revealed that those who passed worked fewer hours than those whodid not pass. The more hours participants worked, the more likely they were to fail orwithdraw from their included course. While there is a correlation between hours ofemployment and the rate of passing the course, this study did not identify employ ment as a cause of not passing. Also, participants who stated that they had a disability or illness were somewhat more likely to fail their included course than those who did not have adisability or illness. The study revealed that 13.99% of those who failed reported ha ving a disability or illness, while 7.10% of those who did not fail (passed, withdrew, orreceived an incomplete) reported a disability or illness. Here again, corr elation does not imply causality. The disability or illness of a close relative or friend did not significantly impact the completion rate of participants. Neither the number of credit hours carr ied nor the number of children had a significant effect on passing, failure, or withdraw al. The study repeated many past findings that self-efficacy is a major pre dictor of academic success, which includes course completion. Zimmerman & Pons, (1990) hadpreviously established that self-efficacy may represent a major predi ctor of using selfregulatory learning strategies. Hagen and Weinstein (1995) demonstrated thatinstructions to students are vital: those who believe that a task is do-able with effor t maintained high efficacy, set challenging goals, and employed appropria te learning strategies. In order to establish high student self-efficacy regarding their coursework,

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156 instructors in both traditional and web formats would do well to give explicit instruct ions to students regarding course assignments, and, further, to break down coursework intosmall tasks that students perceive as do-able. While the study revealed some differences between web-based and traditiona l students, it also revealed that lines of distinction between the two groups are bec oming less clear. For example, some traditional courses have begun to offer web compl etion as an option and many students who live on or near campus and who are otherwisetraditional students now include web-based courses in their schedule. The manycampus-based students who take web-based courses soften the differences between thetraditional students and web-based students. This study found there is a higher completion rate for web-based students and that the number of hours of employment affect the completion rate. Therefore, it issuggested that universities encourage students who are employed to seek cours es that are offered in web format or offer web participation as an option for traditional courses The web format would allow the student to arrange his hours for study around his workinghours.Conclusions It is important to recognize the blending of course formats as instructors att empt to meet the needs of learners. The goal conflicts and self-regulation habits note d in this study may be helpful in planning future instruction. Information concerning therelationships of those goal conflicts and self-regulation to course format and to c ourse completion may also be helpful in designing instruction for both classroom and web-based formats

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157 Caution should reign in making any assumptions or generalities concerning the demographics of web-based and traditional students. The study originated in the col lege of education in a major urban university, thus many of the courses were educationcourses and many participants were education majors. Course Nine fulfilled Gor don Rule requirements for those wanting to graduate, and so it attracted students f rom other majors, business in particular. Due to rapid changes in learner populations and in instructional formats, findings from this study should not be generalized beyond the study itself. As seen in ChapterFour, it is increasingly more difficult to classify learners as tradit ional or web-based students. Students who were once strictly traditional learners often now take one or more web-based course during the same semester that they are in traditional c lassroom settings for other classes. Further, courses that once were strictly t raditional in presentation format now often contain elements of distance learning. In some cours es students form on-line study groups or have options of submitting their work via email.The blending of formats will most probably increase as instructors and learninginstitutions seek to meet learner needs.Suggestions for Further Studies Further studies concerning learner goal conflicts may yield informati on that will help students overcome instructional conflicts. This study revealed that the more hours students worked, the less likely they were to complete their course with a passing grade. Future studies should examine this phenomenon in greater detail. Studies into thenumber of hours worked, the days and time of employment, the type of employment,cooperation levels of employers, the incentives for further education offered by

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158 employers, and the location of employment relative to student’s home and school wouldbe appropriate. This study found that students who had a disability or illness were more likely to not pass than those who did not have a disability. Studies into details of thisphenomenon would be helpful. For example further studies into the nature of studentdisabilities and illnesses, adaptations made by the student with disabilities a nd adaptations offered by the educational institution and individual instructors aresuggested. Further examination of self-regulation is appropriate. In this study web-bas ed participants reported greater self-regulation than did traditional partic ipants. As previously mentioned, this could be because web-based students are more likely thantraditional students to participate in self-regulatory practices. It is a lso possible that people with greater self-regulation are more likely to take web-based cours es. Further, it is possible that participation in web-based courses force students to become bette r selfregulators. Investigation into this phenomenon might yield insight into learnercharacteristics and give information on better meeting learner needs. This study revealed that participants were more likely to pass their include d course if they answered positively to the following self-regulation stateme nts: "I analyze assignments to determine what I need to do," or "I email or see my instructor for help when I don't understand.” Therefore future research should include studies into themethods of analysis of assignments, analysis of planning and scheduling, andcomparison of actual study time to planned study time. In addition, since theeffectiveness of instructor-student communication is vital to the outcome of student

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159 course completion, studies that compare differences in instructor-student intera ction between sections of courses and between traditional and web-based formats of the sa me course are appropriate. Inquiries into particular study habits could advance the pursuit of satisfactory course completion. Suggested research includes studies concerning where and whenweb-based and traditional students do their course assignments, the nature of studyactivities, and the length of time spent in study sessions. Also helpful would beexploration into activities performed in preparation for quizzes and examinations.Limitations to This Study Generalizability of the study. This study took place at one research-based urban university located in the southeast during one fall semester. The results of the s tudy should not be generalized to other universities or other localities. Similar studie s in other localities or other universities in different settings might obtain diffe rent results. Even the semester of the study or time of year might make a difference in outcom e. Changes in technology and its use are occurring rapidly, therefore results woul d likely differ should the study be repeated at a different point in time. Technology it self is advancing exponentially, as is student ability to use technology. Only a few years ago adults struggled to grasp computer skills in order to perform necessary tasks. In s ome cases this is still occurring. Most students today learn to use computers while the y are in elementary and high school and arrive at college comfortable with the use of tec hnology. Instructional delivery methods are changing rapidly and often include a vari ety of technology-based options. Thus, should the study be repeated at a different time, resultsmay be different from the results obtained in this particular study.

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160 Elevated type1 error rate. There exists a strong possibility that the type 1 error rate may be elevated since many different tests were run on the same data for this study and since .05 was used for a test of significance for each test. For this reason t erms such as “somewhat more” and “modestly significant” have been used to describe those re sults that have a probability greater than .01 and less than .05. Possible bias caused by Course Nine web-based participants. Several of the research questions in this study deal with differences between web-based stude nts and traditional students. Some important demographic differences became apparent in t his study. Most web-based students are older and further along in their college caree r than traditional students; they are also more likely to have chosen a living or marita l mate. However, 477 (78.97%) of the web-based students in this study were enrolled in thesame course, Course Nine, and were taught by the same instructor. Many compar isons between Course Nine participants, all other webbased participants, and traditionalparticipants were pointed out in Chapter Four. The data representing the demographi cs of Course Nine participants were often between those of traditional students and al l other web-based students. Thus, a bias existed in this study. Had the study not includedCourse Nine web-based participants, the values of web-based participant dem ographics may have revealed even older students who lived further from campus, worked morehours, had more children, etc. On the other hand, since there were more Blacks andAsians in Course Nine than in all other web-based courses, the number of Blacks andAsians in all web-based courses may have been exaggerated. Similarly, since Course Nine students carried more credit hours than did all other web-based students, all we bbased students may actually carry fewer credit hours.

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161 However, it is also true that many web-based courses are now offered and are taken by students who are campus-based and who are younger than the average off-campus web-based student. Thus students who were formerly traditional students arenow also web-based students. Assumption of equality of instruction. This study proceeded with the assumption that when different sections of one course were taught by more than one instructor,instructional effectiveness and grading protocol for all sections were approx imately equal. Syllabi for the courses included in this study were collected and examine d. Requirements for course completion were approximately the same for instruct ors of different sections of the same course.There may be differences in the effectiveness of instructor-student communic ation. If these differences were great the outcome of this study could be effected. Mechanical problem with web version of the survey. It became apparent that there was a problem when it was noted that 63 web-based participants indicated that t hey attended their included course weekly. This was not possible for these particularstudents since they were enrolled in the classes of Instructor U, who taught onl y webbased courses that semester. Examination of the web survey revealed that there was a mechanical problem for the web-based responses to the following questions: How often do you physically attend class in a traditional classroom for thiscourse? For this course, what face-to-face, real-time contact have you had with theinstructor or course assistance in scheduled class meetings?

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162 If the web-based respondent used a scroll roller on the mouse to go to the next question while the response to either of these questions was highlighted, the highlight ed response was changed according to the length of the scroll. Further examination of t he web-based survey indicated that this phenomenon did not occur elsewhere in the survey.One student of Instructor A and one of Instructor I signed up for web courses butindicated weekly attendance. This was entirely possible since these two inst ructors taught both traditional and web-based sections of their courses. These participants were changed from web to classroom, however these students also could have used the mousescroll roller and the "weekly" responses could have been an error that resulted from the mechanical problem described. Classifying participants as web-based or traditional. There may have been errors in other cases of changing or not changing participant format from the reported format. In addition to the two participants mentioned who were changed from web totraditional format, two other participants, one each for the same two instructors, w ere changed from traditional to web-based classification. They were enrolled in t raditional sections but indicated rare attendance. These two participants were chang ed to webbased classification since they could complete the course via the web. Eight students were enrolled in traditional classes but indicated they rarely attended. Their instructors indicated that they did not offer students the option of turningin work via the web. These participants were retained in the traditional classroomcategory, even though they were not attending regularly. Close attention was paid to the format of respondents and the study proceeded with the assumption that participant format was correctly identified. Since ove r 1,100

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163 students participated in this study it seems likely that should there be error in t he format of a very few respondents, there would be very little distortion in the resulting dat a. Unequal incentives for study participation. One instructor in this study offered extra credit to his students who took the study survey. Several instructors introduce d me and strongly encouraged their students to participate. Still other instructors show ed little enthusiasm regarding the survey but allowed their students to participate. Stude nts taking Course Two did not hear of the survey until a week after it was made availabl e to other students. Thus there existed possible variation in student motivation to take thetime to fill out a survey thoughtfully, completely, and honestly. Therefore allparticipants were offered an opportunity to enter a drawing for $25.00, $50.00, and$100.00. It was hoped that that this enticement would equalize to some degree theappeal to complete the survey. It is not known whether this procedure made little orgreat difference in the number of participants, but it did serve to make one identicalinducement that could be offered to all potential participants. Calculating the number of children of participants. Many students who were under the age of 24 stated that they were living with one or several children aged 18 andunder, yet stated that they never had responsibility for the care of a child or othe r person. Fourteen participants stated that they were living with 18 to 75 children under the ag e of 19. It is believed that these participants were living in a dorm or other student housin g and counted fellow students as children under 19 years of age. Since it was not probablethat a participant under the age of 28 was parent to 12-18 year old, children aged 12 to18 reported by participants who reported their age as 28 or less were not counted. Also,participants who stated they were never responsible for the care of a child or othe r

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164 individual were omitted in calculation of the number of children. This problem mayhave been avoided if participants had been asked if they lived in a dorm or had thequestion limited answers to include only children of participants. Missing completion data. While 1,225 students completed the survey, there were 81 participants for whom no completion data was available. These participants enter ed the wrong ID number or entered no ID number at all and their data is not included. Thusthe data analyzed includes the responses of only the 1,144 participants for whomcompletion data is available. Speculation as to the reasons that no ID was entered or a n incorrect ID was entered led to the possibility that students were wary of ent ering even part of their social security number, which was being used as students’ ID numbe r at the time of this study. Another possibility is that participants did not realize that e ntering the correct number was crucial to the study. Yet another possibility is that they may have completed more than one survey, using different ID numbers in each case, in hopesof winning the drawing. In any event, the effect on the study was that 81 fewer tot al participants could be included in the study, however the number of included participantswas sufficient for answering each study question. Low participation by Course Two students. Instructors B, C, D, E, and F all taught Course Two, as shown in Table 4. Students in all sections of those classes did nothear of the survey until six days later than the other included courses due to a problem in their on-line course communications. These students were not offered credit or extracredit for participation in the survey, however the notice sent to them did include mymessage and the information about entering the drawing. As it turned out, fourteen ofthe 121 enrolled (11.57%) in the classroom-based sections of Course Two participated in

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165 the study, compared to 54.39% average participation by all traditional sections. Fi fteen of the 74 (20.27%) enrolled in the web-based sections of Course Two participated,compared to 49.43% average participation by all included web-based sections. Differences between paper and web versions of the survey. There were 1,144 participants in this study for whom completion data was available. Of these, 747participants took the survey on-line, while 397 filled out a paper version of the samesurvey. All 604 web-based participants took the survey on line. In addition, 143 of the540 traditional classroom-based participants took the on-line survey instead of the pap er version. The paper survey differed from the on-line version in several minor ways. In the on-line survey participants selected their instructor, course, and section from a list, while the paper survey asked participants to write their instructor, course and sect ion in the space provided. There was a question on the on-line survey asking participants howmany on-line surveys they had completed in the past. This question was not on thepaper survey. Of the 540 traditional students who completed the survey and for whomcompletion data was available, 143 took the survey on-line and 139 answered thisquestion. There were only 735 total responses to this question. Those who did notrespond to the question were those who were not asked the question (those who took thepaper survey) or who did take the on-line survey but did not answer the question. Itcannot be assumed that those who did not respond had never filled out an on-line surveypreviously, thus zero was not entered for these participants. Instead, the average was the average of the 735 who did respond to the question. Repetition of the study shouldinclude the question of all participants, including those who take a paper survey.

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166 Assumption of truthfulness in participants. This study proceeded with the assumption that participants answered completely and truthfully. However, there i s a possibility that some students may not have had time or the inclination to be thorough orhonest in their answers. When the paper survey was administered to traditional cla sses it appeared that most students were taking their time and were being sincere wit h their answers. It is believed that a great majority of the over-1000 participants did answer as honestly and completely as they could.

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179 Searcy, R.D., (1993). Grade distribution study: telecourses vs. traditional courses. Prepared for the Calhoun Telecourse Steering Committee. Decatur: Calhoun Community College. Seifert, T. (1995). Academic goals and emotions: a test of two models. The Journal of Psychology, Sept 1995, p543(10). Senecal, C. (1995). Self-regulation and academic procrastination. The Journal of Social Psychology,135 (5) 607620. Sheldon, K. (1998). Not all personal goals are personal: comparing autonomous and controlled reasons for goals as predictors of effort and attainment. Personal ity & Social Psychology Bulletin, 24 (5), 546-558. Shih, C. C. (1997). Student learning styles, motivation, learning strategies, and achievement in web-based courses. [Online]. 16 June 2000. Available:http://iccel.wfu.edu/publications/journals/jcel/jcel990305/ccshih.htm Shih, C.C. (1998). Relationships among student attitudes, motivation, learning styles, learning strategies, patterns of learning, and achievement: A form ative evaluation of distance education via Web-based courses. Unpublished dissertation. Iowa: IowaState University. Schunk, D. (1996). Learning theories: An educational perspective, 2 nd Edition. Englewood Cliffs: Prentice-Hall. Schunk, D. (1998). Teaching elementary students to self-regulate practice of mathematical skills with modeling. In Schunk, D. & Zimmerman, B. (1998). Self-regulated learning: From teaching to self-reflective practice. New York: Guilford.

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180 Strage, A. (1998). Family context variables and the development of selfregulation in college students. Adolescence,33 (129) 17-32. Trochim.W. (1999). Research methods knowledge base, 2nd edition. [Online]. 16 June 2000. Available: http://trochim.human.cornell.edu/KB/contents.htm Vancouver, J. (2000). Self-regulation in organizational settings: A tale of two paradigms. In Boekaerts, M., Pintrich, P., & Zeidner, M. (Eds.), Handbook of SelfRegulation (pp. 303-341). San Diego: Academic. Visser, L., Plomp, T., Amirault, R., & Kuiper, W. (2002). Motivating students at a distance: The case of an international audience. Educational Technology Rese arch & Development 2002, 50(2), 94-110. Williams, M. (1996). Learner-control and instructional technologies. In Jonassen, D. H. (Ed.). (1996). Handbook of research for educational communications and technology. New York: Macmillan Library Reference USA. Wilson, B., & Myers, K. (1999). Situated cognition in theoretical and practical context. In D. Jonassen & S. Land (Eds.), Theoretical Foundations of LearningEnvironments. Mahwah: Erlbaum. Winne, P, & Perry, N. (2000). Measuring self-regulated learning. In Boekaerts M., Pintrich, P., & Zeidner, M. (Eds.), Handbook of Self-Regulation (pp. 303-341). San Diego: Academic. Zhang, J., & Li, F., & Duan, C., & Wu, G. (2001). Research on self-efficacy in distance learning and its influence on learners' attainments. Paper presente d at ICCE International Conference on Computers in Education, 2001, Soule, Korea.

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181 Zimmerman, B. L., & Martinex-Pons, M. (1990). Student differences in selfregulated learning: relating grade, sex, and giftedness to self-effi cacy and strategy use. Journal of Educational Psychology, 82(1), 51-59.

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182 Appendices

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183 Appendix A Pilot Study In the fall of 2001, a pilot study was introduced at a major urban research university in the Southeast. The participants included 171 traditional learners and 126distance learners in the College of Education. Both undergraduate and graduate stude nts participated in the study. A self-report survey addressing perceived goal conflicts and self-regulation was conducted. Participants selected either the survey on-li ne or an identical paper and pencil survey. The questions included on the pilot survey are liste d at the end of Appendix A. The pilot survey was administered during weeks 10 12 of the 2001 fall semester. It was planned that course achievement data would be collected at t he end of the semester. However, variations in grading protocol and slight differences in grades received rendered this information unusable. Student achievement or course complet ion data was not collected for the pilot. The participants ranged in age from 18 to 51, having a mean age of 27 years. In that study, 297 participants completed the survey. Of that number, 126 were distancelearners and 171 were traditional learners. Participants included 145 undergraduatestudents and 152 graduate students. The pilot study presented design problems in the participant number in each course format (distance learning or traditional classroom) and also in the num ber of participants taught by each instructor. Of the 126 distance learners, 117 were enr olled in four different educational psychology courses taught by one instructor. Of the 171

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184 Appendix A (continued). traditional learners, 73 participants enrolled in an undergraduate education courseincluding the same content; however, four different instructors headed the class es. Sixty-six were in two graduate education measurement courses with differentinstructors; sixteen were in a graduate web programming course, and 16 were i n a graduate research course. Table 44 is a frequency table that illustrates t he course format, instructor, and graduate/undergraduate status and the number of participants in eachcourse.Analysis of Pilot Data Descriptive statistics, including the number of observations, the mean, the standard deviation, the variance, skewness, kurtosis, range, and plots showing thedistribution of each indicator, were obtained using the SAS system. Likert response s to several questions were not normally distributed. High school GPA and college GPAchoices on this pilot survey were listed as number ranges. For example, 3.0 to 3.49 wasone choice. Choices for hours worked per week were also listed in ranges, such as 5 to 9hours per week. These number ranges presented analysis problems. Therefore, thecurrent study questionnaire will itemize the choices in individual numbers. Internal survey structure was examined by an exploratory factor analysi s on the Likert response questions, unrotated, using the SAS system. Only Likert scale qu estions were included in this factor analysis because other potential goal conflict s, such as number of children or hours worked, employed varied scales.

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185 Appendix A (continued) Table 44Pilot Study: Course Level, Number of Participants, and Instructor in Each Cla ss _____________________________________________________________________ Traditional learners Web-based learners Undergraduate ________________ Graduate _______________ Undergraduate ______________ Graduate ____________ CseInstrnCseInstrnCseInstrnCseInstrn _____________________________________________________________________ T1A,B,C,D73T2E38 T3F16T4G28T5H16D1H2D2H7 D3I38D4I17D5I34D6I28 _____________________________________________________________________ The factor analysis was run with the number of factors were forced to three, and an orthogonal rotational procedure, Varimax, was used. This resulted in a pattern ofthree distinct factors, self-regulation, goal conflicts, and goal-orient ation. The goal conflict questions had standardized regression coefficients ranging from .378 to .675,with the exception of question 31: “My spouse/friends/family approve of my takingclasses,” which had a coefficient of .133. This question was replaced in the studysurvey by the following question: “Someone close to me disapproves of my takingclasses.” The self-regulation questions, Questions 41 through 56, showed coefficientvalues ranging from 0.22 to 0.63.

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186 Appendix A (continued). Cronbach's (1951) reliability coefficient, alpha, was run on the Likert response questions pertaining to goal conflicts (questions 29 through 35 in the pilot study) toestablish general score reliability for the construct goal conflict s. Cronbach's Alpha for this cluster of questions was 0.70. This is not an extremely high value, possibly becausethere are few items in this cluster of questions. Cronbach's (1951) reliability coefficient, alpha, was run on the self-regulation questions in the pilot study (Questions 41 through 56 on the survey) in order to establishgeneral reliability of the scores for the construct goal conflicts. Cronbach's Coefficient Alpha for this cluster of questions was 0.79. Using the SAS system, an analysis of variance using a general linear model for unequal cells was performed comparing the goal conflicts and self-regulat ion of distance learners and traditional learners. Several significant variations exist ed between the two groups as a result of this self-report questionnaire using a five point Likert scale. The most significant findings are listed in Table 45. In open-ended questions, participants in the pilot study ( n = 297) were questioned as to the logical content of questions, whether they were confused by any of thequestions, and whether they perceived omissions in the questions. Appendix E containstwo of eleven total pages of pilot study participant input in response to the question"Briefly list sources of stress or other factors not mentioned previously that c ould impact your time, emotions, or attitude while taking this course." Several questions wer e added to the current study survey and several questions were reworded as result of part icipant response to this question.

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187 Appendix A (continued). Table 45Summary of Significant Findings in Pilot Study_____________________________________________________________________ TraditionalWeb-based Construct nFMSDMSD ___________________________________________________________________________ Goal Conflicts Course-related stress29616.85***2.871.293.501.35Worried about demands of course296 8.68***2.681.273.101.71Impact on coursework when illness ordisability of friend/family memberexisted 295 7.45**1.941.292.351.26 Self-Regulated Learning Related new information to old whenLearning 295 5.81*4.210.764.000.77 Re-read or studied notes prior to quiz or test296 7.50**4.560.774.310.80Joined study teams or virtual study teams vialistserv or e-mail 29612.23***2.261.231.791.02 Set daily or weekly goals as they worked onassignments 29610.86**3.321.193.750.99 ___________________________________________________________________________* p < .05 ** p < .01 *** p < .001

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188 Appendix A (continued). Survey Changes as a Result of the Pilot Study Several goal conflicts were added at the suggestion of pilot study participa nts. Also several questions were reworded to clarify meaning. Some questions wer e omitted as they appeared to be either redundant or non-relevant when an exploratory factoranalysis was performed by SAS. Question 53 was “I make schedules for working on and completing myassignments.” This has been eliminated and replaced with a new survey question: “ I make schedules for doing my assignments.” Numbers on the new survey do notmatch those on the pilot survey. Question 49 was “I schedule my work so that assignments are done on time.” Thishas been reworded as: “I make schedules for doing my assignments.” Question 53 was “I make schedules for working on and completing myassignments.” This was eliminated and replaced with a new survey question: “Imake schedules for doing my assignments.” Question 31 was “My spouse/friends/family approve of my taking classes.” Thisnow reads: “Someone close to me disapproves of my taking classes.” Question 32 was “Illness or disability (my own) affects my schoolwork.”Reworded, it now reads: “I have an illness or disability that affects my schoolw ork.” Question 33 was “The illness or disability of a family member or friend aff ects my schoolwork.” It now reads: “Someone close to me has an illness or disability tha t affects my time.”

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189 Appendix A (continued). Question 35 was “My spouse/partner/friends either subtly or overtly, sabotage mystudies.” This has been reworded as: “Intentionally or not, someone close to mesabotages my studies.” Participants suggested the following potential goal conflicts during the pilot study and will be included in the survey in the proposed study: I procrastinate. My social life affects my study time. World affairs or thoughts of war affect my current schoolwork. The pilot survey contained questions designed to identify the goal orientation ofparticipants. These questions were omitted from the new research survey.Survey Questions in the Pilot Survey1. ID: Please enter the last five numbers of your I.D2. Instructor's last name3. Please select your course number.4. Course assistant's name: Please enter the name of your course assista nt. 5. I enjoy learning new things. 6. It's important to become an acknowledged expert in my field. 7. Understanding the content is important to me.8. I care what others think of my grades. 9. I like to learn material in my major area of interest. 10. It's important to get a better grade than most of my classmates.11. My grades are important to my friends/family.

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190 Appendix A (continued). 12. Knowing the material is more important to me than a grade.13. It's important to appear smart and capable.14. I do my assignments because I want to learn the material.15. I believe that I will learn a lot in this course.16. I have heard that this is a difficult course.17. I believe that I will get a good grade in this course.18. This course is being presented in a format that I expected.19. I am comfortable with the format of this course.20. I feel confident I can do the assignments required for this course.21. This course is stressful for me.22. Which of the following will be the most important reward for completing this course? Maintaining a high GPA Approval of family and friends Knowing a lot about the subject Understanding things I didn't know before 23. Which of the following will be the most important reward for getting your degre e? Increase in income Approval of family and friends Knowing a lot about my major area of interest Gaining recognition as being knowledgeable. Being able to use what I have learned.

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191 Appendix A (continued). Becoming expert in my field. 24. I am comfortable with the technology needed for this class.25. Even when new material is difficult, I believe that I can learn it.26. I am worried about the demands of this course.27. Before taking a test I am anxious.28. I am comfortable while taking exams.29. I often have to do other things when I should be studying.30. At times it is difficult to study because I have other things on my mind.31. My spouse/friends/family approve of my taking classes.32. Illness or disability (my own) affects my schoolwork.33. The illness or disability of a family member or friend affects my schoolwor k 34. I am under stress due to circumstances that conflict with my studies.35. My spouse/life partner/friends either subtly or overtly, sabotage my studies.36. How many children under 18 live in your household?37. How many hours per week are you employed?38. How many credit hours are you taking?39. (Open ended fill in text) Briefly list any other sources of stress or other factors not previously mentioned that could negatively impact your time, emotions, or attitude w hile taking this course.40. The factor(s) mentioned in the last question interfere with my learning: (all of the time) (most of the time) (sometimes) (rarely) (never) 41. When I study, I intentionally categorize and classify things in my mind.

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192 Appendix A (continued). 42. I deliberately block out distractions when I study.43. I practice saying important facts over and over to myself.44. I try to relate new information to what I already know.45. I underline, take notes, or outline new information as I read.46. I reread or study my notes prior to a quiz or test.47. I do practice quizzes before taking a test.48. I e-mail or see my instructor for help when I don't understand the material.49. I schedule my work so that assignments are done on time.50. I use flashcards to study course material.51. I analyze assignments to determine what I need to do.52. I try to estimate the amount of time needed for each assignment.53. I make schedules for working on and completing my assignments.54. I set daily or weekly goals for myself as I work on assignments.55. I join study teams or virtual study teams via listserv, chat, email, etc.56. If I don't understand one source, I try to get the information another way.57. Major?58. Gender?59. Age?60. College GPA (on a 4.0 scale)?61. High school GPA (on a 4.0 scale)?62. Race/Ethnicity:63. Current year in school or status.

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193 Appendix A (continued). Feedback(Open ended fill in text)64. Please describe questions that were confusing for you to answer.65. Please add any information that you feel should be added.

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194 Appendix B Learning Factors Survey This study was designed to gain a better understanding of the demographics, lifes tyles, and learning factors of traditional and distance students. It is hoped that the resul ts of this survey will help instructors better meet the needs of learners.Your responses to questions in this survey will be kept strictly confidential andindividual responses will not be identified or reported. Your participation is appreciated. Please enter the last five numbers of your Soc. Sec #: Instructor: __________________Course name/number: _______________________ Section (if known) _________I. Demographics: We are studying some of the ways that distance andtraditional students differ. For this reason we ask that you share thefollowing information: A. Please indicate your current year in school or status: Freshman [] Sophomore [] Junior [] Senior [] Graduate Student [] Other []B. Gender: [] Male [] Female C. Age? __________D. What was your high school GPA (on a 4.0 scale)? ______________E. What is/was your undergraduate college GPA (on a 4.0 scale)? __________ F. Race/Ethnicity: Black [] Caucasian [] Hispanic [] Asian [] Middle Eastern [] American Indian [] Other []

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195 Appendix B (continued). II Please complete the following statements by selecting the appropr iate answer: A. How often do you physically attend class in a traditional classroom for thiscourse? [] Not at all. The entire course is online. [] I attend class only once or twice per semester (orientation and final ex am). [] The class meets weekly, but I can do most of the work without attending classes in person, so I rarely attend class. [] I attend class only for proctored exams. [] I attend class one or more times a month. [] I attend class one or more times a week.B. For this course, what face-to-face, real-time contact have you hadwith the instructor or course assistant in scheduled class meetings ? [] Once or twice at most. [] More than twice but less than weekly. [] At least weekly. C. Counting this course, what is the total number of web-based courses you arecurrently taking? None [] 1 [] 2 [] 3 [] 4 [] 5 [] More than 5 [] D. Prior to and not counting this semester how many web-based courses have youtaken in the past two years? None [] 1 [] 2 [] 3 [] 4 [] 5 [] 6 [] 7 [] 8 [] More than 8 [] E. How far do you live from campus? On campus [] 1-5 miles [] 6-20 miles [] 21-50 miles [] Greater than 50 miles []F. What contact have you had with the instructor outside of the classroom? (Check all that apply.) E-mail [] Phone [] Chat Room [] Instructor’s office []Other (describe) D. How many times have you met with the instructor in person outside ofscheduled class time: None [] 1 [] 2 [] 3 [] 4 [] 5 [] 6 [] More than 6 []

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196 Appendix B (continued). E. What is the single most important reason that you registered for this partic ular format/section for this course? [] It was available and no other section was available. [] It fits my class schedule [] It fits my work schedule [] Convenience for my family obligations [] Religious or cultural concerns. [] To accommodate a physical disability that I have. [] To accommodate a learning disability that I have. [] To avoid driving hassles [] To avoid parking hassles [] Convenient location (or non-location if web-based). [] Preference for this instructor [] Preference for traditional classroom courses. [] Preference for web-based courses [] Other (describe): __________________________________ F. What is the format of other courses you are taking this semester? [] Primarily Web-based (meet face to face only once or twice per semester).[] Primarily Traditional (usually meet one or more times a week).[] Primarily Traditional (usually meet one or more times a month).[] Meet at least once a week but I can do most of the work without attending classes in person.[] Mixed some are web-based or don’t require attendance, and some are traditional. [] Not applicable – this is the only course I am taking this semester.III. Instructional Goal Conflicts: For all of us, there are several areas in our li ves that compete with our studies. To help us understand your busy sched ule and home situation, please answer the following questions:A. Including yourself, how many people live in your household or dorm room?_____________ B. How many children live with you? Age 0 – 3 __ ; Age 4 – 7 __; Age 8 – 11 __; Age 12 – 18 ___.C. Are you responsible for a senior citizen, child, or other person who needs assistance? (Please select the appropriate response.) Never [] Rarely [] Sometimes [] Usually [] Always []

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197 Appendix B (continued). D. How many hours per week are you employed? _______________ E. How many credit hours are enrolled in this semester? _________________ F. Are you married? Yes [] No [] Living with a significant other? Yes [] No [] G. Do you have an illness or disability? Yes [] No [] If so, does it affect your studies? Very little [] Somewhat [] A lot [] H. Does someone close to you have illness or disability? Yes [] No [] If so, does it affect your studies? Very little [] Somewhat [] A lot [] Please add any other situations that affect your study time for this course: _________________________________________________________________ For these items please select the one response that best reflects whether you do these things and, if so, how often. Try to quantify with numbers ranging from 1 (Not true) to 4 (often). (1) (2) (3) (4) Not Rarely Sometimes Often True a. I do other things when I should be studying. 1 [] 2 [] 3 [] 4 [] b. It is difficult to study because I have other things on my mind. 1 [] 2 [] 3 [] 4 [] c. I am under stress due to circumstances that conflict with my studies. 1 [] 2 [] 3 [] 4 [] d. One or more distracting factors interfere with my learning. 1 [] 2 [] 3 [] 4 [] e. Someone close to me disapproves of my taking classes. 1 [] 2 [] 3 [] 4 [] f. My social life affects my study time. 1 [] 2 [] 3 [] 4 [] g. World affairs or thoughts of war affect my current schoolwork. 1 [] 2 [] 3 [] 4 [] h. Intentionally or not, someone close to me sabotages my studies. 1 [] 2 [] 3 [] 4 [] i. I procrastinate. 1 [] 2 [] 3 [] 4 [] j. The technology needed for this course causes problems for me. 1 [] 2 [] 3 [] 4 []

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198 Appendix B (continued). IV. Learning Strategies: We all have personal strategies for learning new material. Which of the following do you use? Directions: For these items please select the one response that best reflects whether you do these things and, if so, how often. Try to quantify with numbers ranging from 1 (Not true), to 4(Almost Always) (1) (2) (3) (4) Not Rarely Often Almost true Alw ays A. I arrange to have the technology needed for my coursework. 1 [] 2 [] 3 [] 4 [] B. I analyze assignments to determine what I need to do. 1 [] 2 [] 3 [] 4 [] C. I try to estimate the amount of time needed for each assignment. 1 [] 2 [] 3 [] 4 [] D. I make schedules for doing my assignments. 1 [] 2 [] 3 [] 4 [] E. I set daily or weekly goals for myself as I work on assignments. 1 [] 2 [] 3 [] 4 [] F. I deliberately block out distractions when I study. 1 [] 2 [] 3 [] 4 [] G. When I study I intentionally categorize and classify things in my mind. 1 [] 2 [] 3 [] 4 [] H. I practice saying important facts over and over to myself. 1 [] 2 [] 3 [] 4 [] I. I try to relate new information to what I already know. 1 [] 2 [] 3 [] 4 [] J. I underline, take notes, or outline new information as I read. 1 [] 2 [] 3 [] 4 [] K. I use flashcards to study course material. 1 [] 2 [] 3 [] 4 [] L. I reread or study my notes prior to a quiz or test. 1 [] 2 [] 3 [] 4 [] M. I do practice quizzes before taking a test. 1 [] 2 [] 3 [] 4 [] N. I do my course assignments first, before I do other things. 1 [] 2 [] 3 [] 4 [] O. I complete my assignments days or weeks before they are due. 1 [] 2 [] 3 [] 4 [] P. I join study teams or virtual study teams via listserv, chat or e-mail, etc. 1 [] 2 [] 3 [] 4 [] Q. I e-mail or see my instructor for help when I don't understand. 1 [] 2 [] 3 [] 4 [] R. If I don't understand one source, I get the information another way. 1 [] 2 [] 3 [] 4 []

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199 Appendix B (continued). V. Do you have other strategies for learning new things? If so, please describethem:______________________________________________________________ I. Other Learning Factors: These items have to do with your confidence regarding technology, doing the assignme nts, and completing the course. Directions: For these items please select the one response that best reflects the extent to which you believe you can perform each task. Try to quantify with numbers ranging from 1 (Probably not), to 4 (Definitely I can). (1) (2) (3) (4) Probably Maybe Probably Definitely not I can I can I can 1. I can acquire and use the technology needed for this course. 1 [] 2 [] 3 [] 4 [] 2. I can master the technology necessary to complete this course. 1 [] 2 [] 3 [] 4 [] 3. I can perform the tasks that are necessary to pass this course. 1 [] 2 [] 3 [] 4 [] 4. I can do the assignments required to complete this course. 1 [] 2 [] 3 [] 4 [] 5. I can complete this course. 1 [] 2 [] 3 [] 4 [] 6. I believe I can pass this course this semester/term. 1 [] 2 [] 3 [] 4 [] G. I can complete this course with a satisfactory grade. 1 [] 2 [] 3 [] 4 [] Thank you for your participation.

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200 Appendix C Survey Questions Sorted by Demographics and ConstructsDemographics1. Gender? 2. Age? 3. High school GPA (on a 4.0 scale)? 4. College (Undergraduate) GPA (on a 4.0 scale)? 5. Race/Ethnicity: 6. Current year in school or status. Identification of Web-Based and Traditional Classroom Students 1. How often do you physically attend class in a traditional classroom for this cours e? [] Not at all. The entire course is online. [] I attend class only once or twice per semester (orientation and final ex am). [] The class meets weekly, but I can do most of the work without attending classes in person, so I rarely attend class. [] I attend class only for proctored exams. [] I attend class one or more times a month. [] I attend class one or more times a week.2. For this course, what face-to-face, real-time contact have you hadwith the instructor or course assistant in scheduled class meetings? [] Once or twice at most. [] More than twice but less than weekly. [] At least weekly.

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201 Appendix C (continued). 2. Counting this course, what is the total number of web-based courses you arecurrently taking? None [] 1 [] 2 [] 3 [] 4 [] 5 [] 3. Prior to and not counting this semester how many web-based courses have you takenin the past two years? None [] 1 [] 2 [] 3 [] 4 [] 5 [] 6 [] 7 [] 8 [] 4. How far do you live from campus? On campus [] 1-5 miles [] 6-20 miles [] 21-50 miles [] Greater than 50 miles []5. What contact have you had with the instructor outside of the classroom? (Check all that apply.) E-mail [] Phone [] Chat Room [] Instructor’s office []Other (describe) 6. How many times have you met with the instructor in person outside of scheduled class time? None [] 1 [] 2 [] 3 [] 4 [] 5 [] 6 [] 7. What is the single most important reason that you registered for this particul ar format/section for this course? [] It was available and no other section was available.[] It fits my class schedule.[] It fits my work schedule.[] Convenience for my family obligations.[] Religious or cultural concerns.[] To accommodate a physical disability that I have.

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202 Appendix C (continued). [] To accommodate a learning disability that I have.[] To avoid driving hassles.[] To avoid parking hassles. [] Convenient location (or non-location if web-based). [] Preference for this instructor.[] Preference for traditional classroom courses.[] Preference for web-based courses.[] Other (describe): __________________________________ 8. What is the format of other courses you are taking this semester? [] Primarily Web-based (meet face to face only once or twice per semester).[] Primarily Traditional (usually meet one or more times a week).[] Primarily Traditional (usually meet one or more times a month). [] Meet at least once a week but I can do most of the work without attending classes in person.[] Mixed some are web-based or don’t require attendance, and some are traditional. [] Not applicable – this is the only course I am taking this semester.

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203 Appendix C (continued). Table 46Goal Conflict Questions and Their Variable Names___________________________________________________________________Item # Likert response goal conflict question Variable name ___________________________________________________________________30. I do other things when I should be studying.DOOTHER31. It is difficult to study because I have other things on my mind.ONMIND32. I am under stress due to circumstances that conflict with my studies.ST RESS 33. One or more distracting factors interfere with my learning.DISTRAC T 34. Someone close to me disapproves of my taking classes.DISAPPRO35. My social life affects my study time.SOCIAL36. World affairs or thoughts of war affect my current schoolwork.WORLDAF F 37. Intentionally or not, someone close to me sabotages my studies.SABOTAGE38. I procrastinate. PROCRAST 39. The technology needed for this course causes problems for me.TECHPROB_________________________________________________________________________A. Including yourself, how many people live in your household or dorm room? B. How many children live with you? Age 0 – 3 __ ; Age 4 – 7 __; Age 8 – 11 __; Age 12 – 18 ___.C. Are you responsible for a senior citizen, child, or other person who needs assistance? (Please select the appropriate response.) Never [] Rarely [] Sometimes [] Usually [] Always []D. How many hours per week are you employed? E. How many credit hours are enrolled in this semester?

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204 Appendix C (continued). F. Are you married? Yes [] No [] Living with a significant other? Yes [] No [] G. Do you have an illness or disability? Yes [] No [] If so, does it affect your studies? Very little [] Somewhat [] A lot []H. Does someone close to you have illness or disability? Yes [] No [] If so, does it affect your studies? Very little [] Somewhat [] A lot []I. Please add any other situations that affect your study time for this course.

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205 Appendix C (continued). Table 47Self-Regulation Questions and Their Variable Names___________________________________________________ ______________________ Survey Statement Variable name ___________________________________________________ ______________________ I arrange to have the technology needed for my cour sework.ARRTECH I analyze assignments to determine what I need to d o.ANALYZE I try to estimate the amount of time needed for eac h assignment.ESTITIME I make schedules for doing my assignments.SCHEDULEI set daily or weekly goals for myself as I work on assignments. SETGOALS I deliberately block out distractions when I study. BLOCKDIS When I study I intentionally categorize and classif y things in my mind.CATGORIZ I practice saying important facts over and over to myself. PRACTICE I try to relate new information to what I already k now.RELATE I underline, take notes, or outline new information as I read.UNDERLIN I use flashcards to study course material. FLASHCAR I reread or study my notes prior to a quiz or test. REREAD I do practice quizzes before taking a test.PRCTQUIZI do my course assignments first, before I do other things. FIRST I complete my assignments days or weeks before they are due.EARLY I join study teams or virtual study teams via lists erv, chat or e-mail, etc.STUDYTEM I e-mail or see my instructor for help when I don't understand.CONTACT If I don't understand one source, I get the informa tion another way.ANOTHER ___________________________________________________ ______________________

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206 Appendix C (continued). Table 48Self-Efficacy Questions and Their Variable Names__________________________________________________________________ Self-Efficacy questionsVariable name__________________________________________________________________I can acquire and use the technology needed for this course.USETECHI can master the technology necessary to complete this course.MASTTECHI can perform the tasks that are necessary to pass this course.PERFORMI can do the assignments required to complete this course.DOASSIGNI can complete this course.CANCOMPLI believe I can pass this course this semester/term.CANPASSI can complete this course this term with a satisfactory grade.SATISG RADE __________________________________________________________________

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207 Appendix D Traditional students received the following verbal announcement concerning thesurvey and the drawing: My name is Barbara Moore. I am a doctoral candidate in the College ofEducation and I am conducting the research for my dissertation.I am studying instructional goal conflicts (things that stop you fromstudying), instructional self-regulation (the way you study), and theirrelationship to course completion (pass, fail, withdraw, incomplete).I will also compare these items in web-based courses to the same inclassroom-based courses. This course has been chosen because it isoffered in both formats.I have created a survey to gather the information and I need your input!Your instructor may or may not be offering you credit or extra credit forparticipation in this study, therefore I am offering, as a thankyou/incentive for your participation, one entry per participant in adrawing for the following: one $100 gift, one $50 gift and two $25 gifts.Please take a few minutes to complete the survey and the drawing entryform, then place them in the two separate boxes near the door. Thankyou.

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208 Appendix D (continued) The following email announcement was sent to web-based instructors Friday, Oct ober 3, 2003:My survey is available online and will be available to your students through Sunday,October 19. If you would like to check out the survey yourself, please feel free to do so.If you try out the survey or drawing entry, please let me know what student number youenter and course you select (or name used on the drawing entry) so that I can re move that data before analysis.Please contact your students either via email or a posting on your course web s ite, using the announcement below.Thanks,Barb***************** Message to Students Follows *************The following message has been received from Barbara Moore, doctoral candidate in the College of Education, regarding her dissertation research. Please note the infor mation about the drawing $100, $50, $25!You are invited to please participate in a survey! It will be available for t wo weeks only. I am studying instructional goal conflicts (things that stop you from study ing), instructional self-regulation (the way you study), and their relationship to cour se completion (pass, fail, withdraw, incomplete).I will also compare these items in web-based courses to the same in classr oom-based courses. This course has been chosen because it is offered in both formats.Your input is very much needed!Your instructor may or may not be offering you credit or extra credit for partic ipation in this study, therefore I am offering, as a thank you/incentive for your parti cipation, one entry per participant in a drawing:One $100. gift to be given away.One $50. gift to be given away.Two $25. gifts to be given away.When you submit your responses to the survey, you will be directed to an on-line formto fill out. At that point your survey information will be separated from the entry f orm. The entry form requires contact info from you so that I can contact you if your ent ry

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209 Appendix D (continued) form is drawn. Only one entry per participant will be considered. Winner’s enrollmentin a participating course must be verified before award is made. The drawing wil l be held in the Secondary Ed office, 4 th floor EDU, at 4 p.m. on Friday, October 31, 2003. You need not be present to win. Information on the entry form will be discardedfollowing the drawing and will not be used in any other way.To take survey go to: http://www.math.usf.edu/~tmajchrz/barb/intro.html Thank you!Barb Moorebmoore@tempest.coedu.usf.edu

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210 Appendix D (continued) Traditional classroom participants received the entry form below and were requested to fill it out and return it when they turned in their survey. An on-line versionof the entry form was offered to participants upon submission of the survey. As incentive and thank you for completing this form, I am offering a chance in adrawing that will be held in the Secondary Ed office (3rd floor, EDU) on Friday,October 31, 2003, at 4 pm. You need not be present to win but I need contactinformation if you want to be included in the drawing. The information will notbe connected in any way to the survey or your responses to the survey. After thedrawing, the information will be discarded and not used in any other way.The following reward/incentives will be offered: Two gifts of $25. each, one gi ft of $50. and one gift of $100. Winners will be drawn in the order listed above.If you wish to be included in the drawing, please fill in enough information sothat I can contact you if your name is drawn. Name: ___________________________________ Address:__________________________________ Phone: ___________________________________Thank you for your participation in the survey. Barb MooreOnly one drawing entry per student will be considered. Your chances of winningdepend on the total number of valid entries received.

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211 Appendix E Pilot Participant Responses Regarding Other Goal Conflicts Pilot study ( n = 297) participants' responses to Question 39 on the pilot survey, "Briefly list sources of stress or other factors not mentioned previously that c ould impact your time, emotions, or attitude while taking this course." (This is only a parti al listing of typical responses 11 pages total are available.) 7152 I am currently mentoring several candidates for National Board (NBPTS ) certification. I'm also mentoring a beginning teacher and helping to faci litate my county's new teacher induction program. 9247 I have so much school work, trying to graduate in three years, maintaining a job, and a boyfriend that is an hour away. I am close with my family so I gohome a lot as well. I am in a sorority so that takes up too much time as well. 01127 sorority life, boyfriend problems01337 Class, boyfriend, work, homework, not understanding classwork.01389 work demands01604 I am a full-time teacher. I have returned to teaching after 15 years in business. The education course demands that the state has placed on me and the short amount of time that I have to complete these courses in heightens my stress level. Furthermore I have three preparations thissemester, two of which are courses that I have never taught before. In order todo a good job, it takes time to prepare. And as you can see from my course load,I am taking two other distance courses besides Dr. **********'s. Very littletime is left for anything else.

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212 Appendix E (continued). 01675 other course requirements. practicum requirement. over 40hour work week. Still maintaining a household and raising a teenager. 01688 Middle school children and their activities02941 health issues, boyfriend, and other classes.03765 I am 9 months pregnant with a high risk pregnancy03916 my wedding was 10/27/01. i am a full-time kindergarten teacher (2nd yr) and this requires too much of my time, i have taken four courses at a timeand spent less time on homework. A lot of the questions have faults in them -that makes it hard for me to absorb information. 03926 Slow internet connections, bills, lesson plans, other course that requires reading, pets, projects at work & learning new materials & learning how to us e new resources at work, traffic 04636 Terrorist attacks and keeping up w/news05323 illness, family problems, living problems, friend problems05467 stress from work, money, family status meaning their health specifically my grandmother, the person I live with is noisy, other class requirements and deadlines05666 An on-line Measurement class!!!! A second job!!! And EVERY EXTRA HOUR UNTIL 11:30 P.M. DOING COURSEWORK EVERY DAY!!!....NO LIFE. All I do is learn, learn, learn! I should have taken the class version of these courses.05760 The amount of time and work that is needed to complete this course, work, my reading disability, illness of my father and a good friend.

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213 Appendix F Informed Consent Information about this research: Title of Study:Learning FactorsPrincipal Investigator:Barbara Moore Study Location:University of South Florida You are being asked to participate because I am collecting information abou t the goal conflicts, self-regulation, and course completion of students taking this course.General Information about the Research Study The purpose of this research study is to examine the goal conflicts of post-se condary learnersand the effect of goal conflicts on self-regulation and course completion. Dat a collection, concerning distance learners as well as students in traditional clas srooms, will utilize a selfreport questionnaire. Plan of Study You are requested to please fill out the following survey. It should take 10 15 minutes. The data you submit will be added to a database of responses. In addition to the information you submit, your course completion data (pass, fail, withdraw or incomplete) will be used in the study. Your instructor will ma ke your completion data available to me attached to only the last 5 numbers of your soc ial security number, so that I will not have information as to your name or any other identifying information. There is no financial payment for your participation in the study.

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214 Appendix F (continued). Benefits of Being a Part of this Research Study You will not directly benefit from participating in this survey, unless your instructor has agreed to provide extra credit for its completion. It is expected that you will utilize the survey as a simple and quick proces s to give input/feedback regarding the course and the learning process in this lea rning environment. It is further anticipated that the results of this study will ass ist in the design of effective instruction in both distance learning courses and traditional classroom se ttings. Risks of Being a Part of this Research Study There are no known risks as a result of participating in this study. Confidentiality of Your Records Your privacy and research records will be kept confidential to the extent o f the law. Authorized research personnel, employees of the Department of Health and Hum an Services and the USF Institutional Review Board may inspect the reco rds from this research project. The results of this study may be published. However, the data obtained from you will be combined with data from other people in the publication. The published results will not include your name or any other information that would in any wa y personally identify you. As primary investigator in this research, I will have knowledge of only t he last five digits of your social security number. This will be used to verify with your instructor that you are enrolled in this course. I will not have knowledge of y our name or any other identifying information. Your instructor will not have access to your individual response to the survey, but will have only the information t hat you

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215 Appendix F (continued). did complete the survey. The data obtained in this study will be stored in a file on a server at t he University of South Florida, and, although a password is required to access this file, there is apossibility that others may see the data. Volunteering to Be Part of this Research Study Your decision to participate in this research study is completely volunta ry. You are free to participate in this research study or to withdraw at any time. If you choose not to participate, or if you withdraw, there will be no penalty. Your decision will not adversely af fect your course grade. Your Consent: By completing the survey I agree that: I have fully read, or have had read and explained to me in my native language, this informed consent form describing a research project. I have had the opportunity to question one of the persons in charge of this resea rch and have received satisfactory answers. I understand that I am being asked to participate in research. I understand the risks and benefits, and I freely give my consent to participate in the research pro ject outlined in this form, under the conditions indicated in it. I have been given a copy of this informed consent form, which is mine to keep. Questions and Contacts If you have any questions about this research study, contact Barbara Moore, (813) 784-5525.

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216 Appendix F (continued). If you have questions about your rights as a person who is taking part in a resea rch study, you may contact a member of the Division of Research Compliance of the Uni versity of South Florida at (813) 974-5638. Investigator Statement: I certify that participants have been provided with an informed consent form that has been approved by the University of South Florida's Institutional Review Boar d. That contains the nature, demands, risks and benefits involved in participating in this study. I furt her certify that a phone number has been provided in the event of additional questions. Institutional Approval of Study and Informed Consent This research project/study and informed consent form were reviewed a nd approved by the University of South Florida Institutional Review Board for the protecti on of human subjects. This approval is valid until 8/31/04. The board may be contacted at (813) 974-5638.

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217 Appendix G Table 49Rotated Factor Pattern (Standardized Regression Coefficients)_______________________________________________________________ VariableFactor1Factor2Factor3Factor4 _______________________________________________________________ DOOTHER-0.377960.030220.594430.17991ONMIND-0.22910 -0.037960.684520.02778STRESS 0.00060-0.052010.61956-0.06691DISTRACT-0.15054-0.062820.64775-0.05684DISAPPRO0.02290-0.036710.15223-0.16070SOCIAL-0.209270.002560.387140.03108WORLDAFF0.10250-0.022970.32704-0.05610SABOTAGE0.02505-0.016460.36013-0.05935PROCRAST-0.430830.074660.451980.17097TECHPROB0.02004-0.020490.29219-0.29599ARRTECH0.144730.057930.020740.30764USETECH0.091210.30891-0.047580.63797MASTTECH0.134740.38343-0.000470.59044 _______________________________________________________________

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218 Appendix G (continued). Table 49 (continued)._______________________________________________________________ VariableFactor1Factor2Factor3Factor4 _______________________________________________________________ ANALYZE0.518700.102130.020630.20314ESTITIME0.543860.048510.011650.03399SCHEDULE0.652260.03607-0.07162-0.14783SETGOALS0.664340.07333-0.07456-0.14103BLOCKDIS0.538520.06514-0.26231-0.00467CATGORIZ0.603170.06151-0.027110.00417PRACTICE0.499060.014310.034110.08461RELATE0.477650.061610.044450.18614UNDERLIN0.508200.03095-0.053820.12885FLASHCAR0.35490-0.01063-0.014460.03016REREAD0.327390.06810-0.043140.25826PRCTQUIZ0.429790.02057-0.072470.16108FIRST0.59341-0.00178-0.26434-0.04728EARLY0.535550.01268-0.30143-0.04422STUDYTEM0.39031-0.00384-0.08275-0.00520CONTACT0.409720.100620.011710.08584ANOTHER0.370200.05349-0.025010.19709_______________________________________________________________

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219 Appendix G (continued). Table 49 (continued)._______________________________________________________________ VariableFactor1Factor2Factor3Factor4_______________________________________________________________PERFORM0.084270.74123-0.034240.35542DOASSIGN0.114830.74999-0.062180.27538CANCOMPL0.052400.88780-0.024980.10463CANPASS0.069990.90995-0.062100.03004SATISGRADE0.089530.84251-0.059370.05523_______________________________________________________________

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220 Appendix H Table 50Number of Participants Living with Children________________________________________________________________________ Traditional __________ Web-based _________ Total ________ Number of children n % of 540 n % of 604 n % of 1,144 ________________________________________________________________________ 1 child 0 to 3 2 children 0 to 3Total living with children ages 0 to 3Total living with no children ages 0 to 3 1 child aged 0 to 7 2 children ages 0 to 7 3 children ages 0 to 7 4 children ages 0 to 7Total living with children ages 0 to 7Total living with no children ages 0 to 7 1 child aged 0 to 11 2 children ages 0 to 11 3 children ages 0 to 11 4 children ages 0 to 11Total living with children ages 0 to 11Total with no children ages 0 to 11 16 2 18 522 23 411 29 511 30 821 41 499 2.960.373.33 96.67 4.260.740.190.195.38 94.63 5.561.480.370.197.59 92.41 311344 560 4027 31 71 533 5131 92 93 511 5.132.157.28 92.72 6.624.470.500.17 11.7588.25 8.445.131.490.33 15.4084.60 471562 1082 6331 42 100 1044 813911 3 134 1010 4.111.315.42 94.58 5.512.710.350.178.74 91.26 7.083.410.960.26 11.7188.29 ________________________________________________________________________

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221 Appendix H (continued). Table 50 (continued)._______________________________________________________________________ Traditional __________ Web-based _________ Total ________ Number of children n % of 540 n % of 604 n % of 1,144 _______________________________________________________________________ 1 child aged 0 to 18 2 children ages 0 to 18 3 children ages 0 to 18 4 children ages 0 to 18 5 children ages 0 to 18 7 children ages 0 to 18 18 children ages 0 to 18 75 children ages 0 to 18Total living with children ages 0 to 18Total living with no children ages 0 18 571813 20101 92 448 10.56 3.332.410.370.000.190.000.19 17.0382.96 874114 92110 155449 14.40 6.792.321.490.330.170.170.00 25.66 74.34 144 592711 2211 247897 12.59 5.162.360.960.170.170.090.09 21.5978.41 _______________________________________________________________________

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222 Appendix I Table 51Comparing Goal Conflict Likert Responses by Format___________________________________________________ ________________________ QuestionResponse Traditional Web-Based ____________ ___________ % of % of n 540 n 604 ___________________________________________________ ________________________________ I do other things when I should bestudying. Not true91.67142.32 Rarely7213.337612.58Sometimes27951.6729749.17Often18033.3321635.76 It is difficult to study because I haveother things on my mind. Not true173.15233.81 Rarely9417.4110517.38Sometimes25947.9628647.35Often16931.3018931.29 I am under stress due to circumstancesthat conflict with my studies. Not true Not true519.44569 .27 RarelyRarely17432.2213622.5 2 SometimesSometimes19335.742 5842.72 OftenOften12122.4115325.33 No Response No Response10. 1910.17 ___________________________________________________ ________________________________

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223 Appendix I (continued). Table 51 (continued).___________________________________________________ _________________________________ QuestionResponse Traditional Web-B ased ____________ ___________ % of % of n 540 n 604 ___________________________________________________ ________________________________ One or more distracting factorsinterfere with my learning. Not true 5510.19528.61 Rarely17432.2218530.63Sometimes 23543.5225842.72 Often 7614.0710717.72 No Response00.020.33 Someone close to me disapproveswith my taking classes. Not true51194.6355692.05 Rarely203.70203.31 Sometimes71.30193.15Often20.3781.32 No Response00.0010.17 My social life affects my study time. Not true12322.7816226.82 Rarely18634.4421134.93Sometimes15929.4416927.98Often7213.33599.77 No Response00.0030.50 ___________________________________________________ ________________________________

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224 Appendix I (continued). Table 51 (continued).___________________________________________________ _________________________________ QuestionResponse Traditional Web-B ased ____________ ________ % of % of n 540 n 604 ___________________________________________________ ________________________________ World affairs or thoughts of waraffect my current schoolwork. Not true36166.8539264.90 Rarely14827.4115725.99Sometimes275.00477.78Often40.7460.99 No Response00.0020.33 Intentionally or not, someone closeto me sabotages my studies. Not true36868.1540667.22 Rarely9818.1510517.38Sometimes6111.307011.59Often122.22223.64 No Response10.1910.17 I procrastinate. Not true275.00426.95 Rarely9717.969515.73Sometimes20237.4123939.57Often21339.4422737.58 No Response10.1910.17 ___________________________________________________ ________________________________

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225 Appendix I (continued) Table 51 (continued). ___________________________________________________ ________________________________ QuestionResponse Traditional Web-B ased ____________ ________ % of % of n 540 n 604 ___________________________________________________ ________________________________ The technology needed for thiscourse causes problems for me. Not true36467.4140967.92 Rarely10118.7012520.70Sometimes6111.30579.44Often142.59121.99 No Response00.0010.17 ___________________________________________________ ________________________________

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226 Appendix J Table 52Frequency of Participants' College Majors Sorted by Format________________________________________________________________________ Traditional _________ Web-based __________ Total _________ Major n % of 540 n % of 604 n % of 1,144 ___________________________________________________ ___________________________________________________ ______ Education22241.119315.4031527.53Business448.1516727.6521118.44Bio Sciences, Pre-Med, Pre-dental7012.966911.4213912.15Communications, MIS, LIS173.156310.43806.99Undecided529.63101.66625.42Psychology112.04467.62574.98Nursing356.48172.81524.55Criminology91.67376.13464.02Engineering152.7871.16221.92Wellness, wellness educ, sports med142.5920.33161.40Political Science, law, pre-law40.7491.49131.14Chemistry, Physics, Math61.1171.16131.14Finance/ economics30.56101.66131.06Marketing, advertising40.7481.32121.05International Studies, Internat'l Business30.5691.49121.05 ________________________________________________________________________

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227 Appendix J (continued). Table 52 (continued).________________________________________________________________________ Traditional _________ Web-based __________ Total _________ Major n % of 540 n % of 604 n % of 1,144 ________________________________________________________________________ Accounting40.7471.16110.96Fine Arts: dance, art, theater, music40.7471.16110.96Architecture40.7420.3360.52Sociology40.7430.5070.61Computer Sci, computer engineering40.7420.3360.52Anthropology, history10.1960.9970.61Social Work10.1950.8360.52English 10.1920.3330.26 Other Languages00.020.3320.17Journalism10.1900.0010.09Philosophy00.010.1710.09Humanities00.010.1710.09Liberal Arts10.1900.0010.09No Response 181.57 Total 1144100. ________________________________________________________________________

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228 Appendix K The Likert-scored Conflict Measures of Course Nine Students Compared to all other Web-Based Students. Table 53Likert-Scored Conflicts of Course Nine Web Students Compared to All Other Web-Based Students ___________________________________________________ ________________________________________________ Course Nine students _____________________________________ All other web-based students ____________________________________ Variable n M SD Sk K n M SD Sk K ___________________________________________________ ________________________________________________ DOOTHER4753.2400.707-0.6660.2651272.9840.816 -0.504-0.192 ONMIND4753.0860.771-0.538-0.1071272.9760.895-0.5619.427 STRESS4752.8460.895-0.420-0.5511272.8270.969-0.4430.745 DISTRACT4752.7030.833-0.210-0.4981262.6830.960 -00148-0.938 DISAPPRO4751.1330.5214.20617.4701271.1500.4733.2019 .271 SOCIAL4742.3100.9480.137-0.9311261.8330.8560.796-0. 057 WORLDAF4741.4790.7011.3521.2111271.3310.592 1.6211.576 SABOTAGE4751.5370.8521.4571.0721271.4410.7831.5581. 156 PROCRAST4753.1240.874-0.759-0.1641272.9130.976-0.60 5-0.584 TECHPROB4751.4320.7121.5441.5341271.5510.8611.4691. 192 ___________________________________________________ ________________________________________________

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229 Appendix K (continued). Table 54F Ratios of Likert-Scored Conflict Measures Comparing Course Nine Students to All Other Web-Based Students___________________________________________________ _______________________________ F Ratio Survey statement n F p ___________________________________________________ _______________________________ I do other things when I should be studying. 60212.270.0005 It is difficult to study because I have other things on my mind. 6021.900.169 I am under stress due to circumstances that conflict with my studies. 6020.050.830 One or more distracting factors interfere with my learning. 6010.060.811 Someone close to me disapproves of my taking classes. 6020.110.740 My social life affects my study time. 60026.18<.0001 World affairs or thoughts of war affect my current schoolwork. 6014.770.029 Intentionally or not, someone close to me sabotages my studies. 6021.310.253 I procrastinate. 6025.550.019 The technology needed for this course causes problems for me. 6022.580.109 ___________________________________________________ _______________________________

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230 Appendix L Table 40 Logistic Regression for Possible Predictors of Fail ing Course ___________________________________________________ __________________________________________________ n = 1143 Fail = 59 Not fail = 1060 24 observations not used due to missing values Logistic regression results ___________________________________________________ _____________________________ Analysis of maximum likelihood estimates Type III analysis _____of effects_____ Odds ratio estimates Predictor variable Maximum Likelihood estimate Standarderror2 p Odds 95% Wald confidence ratio estimates ___________________________________________________ _________________________________________________ Intercept:3.42922.05752.77770.0956 Course format (Traditional) 1.49450.414612.99460.00034.4571.97810.044 Number children0.02280.35400.00410.94871.0230.5112. 047 Hrs worked*0.02780.01145.95810.01461.0281.0051.051Credit hours0.04470.06830.42900.51251.0460.9151.196Conflicts feelings0.01640.04060.16270.68671.0160.93 91.101 Arrange technol0.09570.15740.37000.54301.1000.8081. 498 Analyze assignments-0.66530.26776.17870.01290.5140. 3040.869 Estimate time0.13370.24580.29600.58641.1430.7061.85 1 Scheduleassignments 0.07690.23720.10500.74591.0800.6781.719 Set goals-0.18350.22780.64910.42040.8320.5331.301Block distractions0.12180.23580.26670.60561.1300.71 11.793 Categorize info-0.12620.22680.30960.57790.8810.5651 .375 Practice main facts-0.04300.21290.04090.83980.9580. 6311.454 Relate information0.00280.25400.00010.99121.0030.61 01.650 Underline/outlineinfo 0.30860.22741.84200.17471.3620.8722.126 Use flashcards0.08990.17510.26400.60741.0940.7761.5 42 ___________________________________________________ _________________________________________________

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231 Appendix L (continued). Table 40 (continued).___________________________________________________ _________________________________________________ n = 1143 Fail = 59 Not fail = 1060 24 observations not used due to missing values Logistic Regression Results ___________________________________________________ ______________ Analysis of maximum likelihood estimates Type III analysis _____of effects_____ Odds ratio estimates Predictor variable Maximum Likelihood estimate Standard error2 p Odds 95% Wald confidence ratio estimates ___________________________________________________ __________________________________________________ Reread/study notes -0.39100.22692.97130.08480.6760.4341.055 Practice quizzes -0.21540.19021.28310.25730.8060.5551.170 Do assignments first -0.25860.25441.03300.30950.7720.4691.271 Complete work early 0.24920.22981.17570.27821.2830.8182.013 Join study team 0.04660.22580.04270.83641.0480.6731.631 Contact instructor -0.28450.20002.02240.15500.7520.5081.114 Get info another way 0.40180.23262.98400.08411.4940.9472.358 Student disability (no disability) -1.03930.47354.81740.02820.3540.1400.895 Disability of relative (not present) 0.81680.46663.06410.08002.2630.9075.648 Self-Efficacy-0.23270.037937.7352<.00010.7920.7360. 853 Prior achievement -0.10400.11560.81050.36800.9010.7191.130 ___________________________________________________ __________________________________________________ Effect of outlier removed (participant reported 4 40 hours worked).

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232 Appendix L (continued). Table 41Logistic Regression for Possible Predictors of With drawing from Course ___________________________________________________ __________________________________________________ n = 1143 Withdraw = 17 Not Withdraw = 1102 24 observations not used due to missing values Logistic Regression Results ___________________________________________________ ___________ Analysis of maximum likelihood estimates Type III analysis _____of effects_____ Odds ratio estimates Predictor variable Maximum Likelihood estimate Standard error2 p Odds 95% Wald confidence ratio estimates ___________________________________________________ _________________________________________________ Intercept:-0.99144.20710.05550.8137 Course format (Traditional) -0.15060.74120.04130.83900.8600.2013.677 Number children0.20690.52850.15320.69551.2300.4363. 465 Hrs worked*0.04970.02344.49940.03391.0511.0041.100Credit hours -0.04940.11430.18690.66550.9520.7611.1 91 Conflicts feelings 0.05440.07800.48530.48601.0560.9061.230 Arrange technol0.01600.31380.00260.95941.0160.5491. 879 Analyze assignments -0.18450.56200.10770.74280.8320.2762.502 Estimate time-0.06780.52040.01700.89630.9340.3372.5 91 Schedule assignments-0.47460.42881.22500.26840.6220 .2681.442 Set goals-0.08110.49110.02720.86890.9220.3522.415Block distractions0.42930.48070.79790.37171.5360.59 93.941 Categorize info0.04600.50990.00810.92811.0470.3852. 845 Practice main facts0.28630.46950.37180.54201.3310.5 303.342 Relate information0.54710.59110.85680.35461.7280.54 35.505 Underline/outline info0.23800.50430.22280.63691.269 0.4723.409 Use flashcards0.01380.31280.00190.96491.0140.5491.8 72 ___________________________________________________ _________________________________________________

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233 Appendix L (continued). Table 41 (continued).___________________________________________________ __________________________________________________ n = 1143 Withdraw = 17 Not Withdraw = 1102 24 observations not used due to missing values Logistic Regression Results ___________________________________________________ __ Analysis of maximum likelihood estimates Type III analysis _____of effects_____ Odds ratio estimates Predictor variable Maximum likelihood estimate Standard error2 p Odds 95% Wald confidence ratio estimates ___________________________________________________ __________________________________________________ Reread/study notes -0.38800.58250.44380.50530.6780.2172.125 Practice quizzes0.51770.40711.61700.20351.6780.7563 .727 Do assignments first 0.46770.51320.83060.36211.5960.5844.364 Complete work early0.51790.43661.40730.23551.6780.7 133.949 Join study team 0.75690.38683.82900.05042.1320.9994.550 Contact instructor-1.06150.38967.42340.00640.3460.1 610.742 Get info another way 0.24370.45440.28760.59181.2760.5243.109 Student disability (no disability) 0.61821.15980.28420.59401.8560.19118.017 Disability ofrelative (not present) 0.34310.89970.14540.70301.4090.2428.220 Self-Efficacy-0.39670.070331.8440<.00010.6730.5860. 772 Prior achievement-0.14510.23390.38460.53520.8650.54 71.368 ___________________________________________________ __________________________________________________ Effect of outlier removed (participant reporte d 440 hours worked).

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About the Author Barbara Moore received her Bachelor of Science degree from Michigan Stat e University, her teaching credentials from Madonna University, and a Master ofEducation in Curriculum and Instruction from the University of South Florida. Shepresented Higher Level Thinking Skills and Individual Differences: Bridging Gaps with Technology at the SITE 2000 Conference in San Diego in February of 2000. She collaborated in the writing of a summary of many of the papers and presentationspresented at the same conference. Preservice Teacher Education (Moore, B., Burkett, R., White, J., & Feyten, C.), was published in Willis, D., Price, J., & Willis, J. (Eds.),SITE 2000: Society for Information Technology and Teacher Education 11thInternational Conference Proceedings. Ms. Moore was named Teacher of the Year a t East Bay High School for the 2003 –2004 school year. She is currently serving asChairman of the Science Department at Spoto High School in Hillsborough County,Florida.


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Moore, Barbara.
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Goal conflicts, self-regulation, and course completion :
b a comparison of web-based learners to traditional classroom learners
h [electronic resource] /
by Barbara Moore.
260
[Tampa, Fla] :
University of South Florida,
2006.
3 520
ABSTRACT: The purpose of this study was to examine the goal conflicts, self-regulation, and course completion of post-secondary learners and to compare these factors in distance and traditional learners. Participants completed a self-report survey given on-line to those who had Internet access and administered in paper format to students in traditional classrooms. Procrastination, socializing, and employment were the most common goal conflicts reported by participants. Significantly more web-based students than traditional students were employed and were employed more average hours. Web-based students also had more children under the age of 12 than did traditional students. A significantly greater percentage of web-based participants than traditional students passed the courses included in this study. Web-based participants reported a significantly greater amount of self-regulation than did traditional students. Contacting the instructor for help and analyzing assignments contributed significantly to passing courses included in this study. Distinctions between distance learners and traditional learners are becoming less clear since some traditional courses have begun to offer web completion as an option. Many students who live on or near campus and who are otherwise traditional students now include web-based courses in their schedule.
502
Dissertation (Ph.D.)--University of South Florida, 2006.
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Includes bibliographical references.
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Text (Electronic dissertation) in PDF format.
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System requirements: World Wide Web browser and PDF reader.
Mode of access: World Wide Web.
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Title from PDF of title page.
Document formatted into pages; contains 233 pages.
Includes vita.
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Adviser: James White, Ph.D.
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Education.
Learning
Instruction.
Distance learning.
Self-efficacy.
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Dissertations, Academic
z USF
x Secondary Education
Doctoral.
773
t USF Electronic Theses and Dissertations.
4 856
u http://digital.lib.usf.edu/?e14.1608