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Self-directed learning characteristics of first-generation, first-year college students participating in a summer bridge program
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English
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Hall, Jeffrey Drummond
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University of South Florida
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Subjects / Keywords:
Academic Success
At-risk
Freshmen
Persistence
Retention
Dissertations, Academic -- Higher Education Administration Adult Education -- Doctoral -- USF   ( lcsh )
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bibliography   ( marcgt )
non-fiction   ( marcgt )

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Summary:
ABSTRACT: The purpose of this study was to advance understanding of self-directed learning characteristics of first-year, first-generation college students participating in a summer bridge program. Understanding the experience of these students in higher education can lead to the development of programmatic and pedagogical strategies to better meet the needs of this at-risk student population. This study was conducted at the University of South Florida (USF), a large, public research university in Tampa. Participants were recruited from the Freshman Summer Institute (FSI), a summer bridge program for first-generation students at USF. Theoretical frameworks from higher education and adult education literature merged to provide an understanding of self-direction for the context of this study. Student retention and social integration theories from Tinto and Astin were studied, as they have been widely used to assist higher education professionals in understanding the reasons students leave college and to assist administrators in the development of strategies and programs to aid in the retention of at-risk students. An example of a retention strategy is the summer bridge program, used by a variety of colleges and universities to increase persistence of at-risk student populations. The adult education theory of self-directed learning complemented Tinto and Astin's theories. The Personal Responsibility Orientation (PRO) Model (Brockett & Hiemstra, 1991) served as a theoretical framework for understanding self-direction among the participants in the study. The PRO Model posits that learners utilize personal responsibility through the characteristics of the teaching-learning transaction along with their own personal learning characteristics to achieve self-directed learning within a broader social context. The Personal Responsibility Orientation to Self-Direction in Learning Scale (PRO- SDLS), based on a conceptualization of the PRO Model, was used to quantitatively measure self-directed learning among participation in the FSI Program. A series of correlations, dependent means t-tests, and factorial ANOVA's were conducted to examine the relationship between scores on both pre-test and post-test administrations of the PRO-SDLS. In addition to an investigation of the change in self-direction, relationships between academic achievement, gender, and ethnicity was also examined in the study. Measured increases in overall self-directedness as measured by the pre-test and post-test administrations of the PRO-SDLS were not considered statistically significant, however, significant correlational relationships (p<.01) were found between academic achievement and total PRO-SDLS scores. Subcomponent measurements of learner control and self-efficacy were also highly correlated to both admissions GPA and university GPA. No significant relationships were found between ethnicity, gender and scores on the PRO-SDLS. An implication for practice indicates that a shift in teaching pedagogy may be an integral component to increasing the academic success of first-year college students. Higher education faculty should be challenged to design curriculum that relies less on rote memorization and "spoon feeding" information to students. Instead, a learner-centered curriculum which gives control of the learning process to students is vital to instilling the habits of highly self-directed learners. In addition to revamped pedagogical strategies, this study calls for the development of national benchmarks and guidelines to more effectively evaluate the quality and impact of summer bridge programs.
Thesis:
Disseration (Ed.D.)--University of South Florida, 2011.
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Includes bibliographical references.
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by Jeffrey Drummond Hall.
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Title from PDF of title page.
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Document formatted into pages; contains 172 pages.
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Includes vita.

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Self Dir ected Learning Characteristics o f First Generation First Year College Students Participating in a Su mmer Bridge Program b y Jeffrey D. Hall A d issertation submitted in partial fulfillment of the requirements for the degree of Doctor of Education Department of Adult, Career and Higher Education College of Education University of South Florida Major Professor: Thomas E. Miller Ed.D. Donald A. Dellow, Ed.D. Patricia A. Maher, Ph .D. W. Robert Sullins Ed.D. Date of Approval: March 22 2011 Keywords: Retention, Persistence, At risk, Freshmen Academic Success Copyright 2011, Jeffrey D. Hall

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DEDICATION This is for those who dare to be the first in their family to earn a college degree; the ones who people say are not ready for college, or wh o may not have enough money, support, or a mult itude of other reasons why others believe they cannot do it. This is for those who discover their talents, persevere despite the odds, and remind us that America is still a land of opportunity for those willing to work hard and overcome obstacles.

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ACKNOWLEDGMENT S First, like to thank my parents, Carole and Franklin Hall for allowing me to pursue my own path and for being so supportive while I pursued my interests Their love, support, and understanding have made all the difference. I love you very much! There are so many people at the University of South Florida that have helped me reach this point in my journey. First, Dr. Karolyn Snyder gave me a start at USF and literally! Fr om the beginning of my career to the present day, she has been there and has played a vital role in the completion of this dissertation. One of the most important connections I made in my early years at USF was with Dr. Patricia Maher who has mentored me and supported my professional d evelopment Pat helped instill my passion for student success and gave me the tools necessary to be a successful practitioner. Thank you for introducing me to the Let Me Learn Process! Another person who has been vital to the completion of this work is Dr. Thomas Miller my Major Professor Thank you for affording me the opportunity to serve as your Graduate Teaching Assistant and making the completion of this project feasible. I have learned so much from you! Also, Troy Miller played a vital role in the collection and processing of data for this study. Lastly, n one of this would have been possible without the cooperation of the students in the summer 2009 Freshman Summer Institute. Finally for his support through the dissertation process and agreeing to move back to Florida so that I cou ld finish my work.

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i TABLE OF CONTENTS LIST OF TABLES ------------------------------------------------------------------------------i v LIST OF FIGURES ------------------------------------------------------------------------------vi ABSTRACT -------------------------------------------------------------------------------------vii CHAPTER ONE : INTRODUCTION TO THE STUDY --------------------------------------1 Problem Statement -----------------------------------------------------------------------4 Purpose -----------------------------------------------------------------------------------5 Research Questions --------------------------------------------------------------------7 Theoretical Framework ------------------------------------------------------------------8 Significance of the Study -----------------------------------------------------------------12 Research Design --------------------------------------------------------------------------1 3 Limitations --------------------------------------------------------------------------14 Definition of Terms ----------------------------------------------------------------------16 Organization of Dissertation -----------------------------------------------------------17 CHAPTER TWO : REVIEW OF THE RELATE D LITERATURE ------------------------19 First Generation College Students ----------------------------------------------------19 Retention and Involvement Theories ------------------------------------------------24 Departure -----------------------------------25 ---------------------------------------31 Environment Outcomes (I E O) Mode l ---------------------34 Summer Bridge Programs ------------------------------------------------------------36 Freshman Summer Institute at the University of South Florida ----------42 Other Summer Bridge Programs in the United Sta tes --------------------47 Self Directed Learning -------------------------------------------------------------------50 Overview of Self Directed Learning -------------------------------------------50 y Orientation Model -------52 Other Self Dir ected Learning Models --------------------------------------57 -----------------------------------57 Directed Learning Model --------------------59 Directed Reaming Mo del ---------------------------61 Instrumentation to Measure Self Directed Learning --------------------------------63 Self Directed Learning Readiness Scale (SDLRS) -----------------------64

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ii Personal Responsibility Orientation to Self Direction in Learning Scale (PRO SDLS) ----------------------------------------------------------67 Summary ---------------------------------------------------------------------------------75 CHAPTER THREE : METHOD S ---------------------------------------------------------77 Research Design -----------------------------------------------------------------------78 Population and Sample ------------------------------------------------------------------79 Variable s ------------------------------------------------------------------------------83 Instrumentation ----------------------------------------------------------------------84 Data Collection Procedures ------------------------------------------------------------86 Data Analysis ----------------------------------------------------------------------------8 7 Summary ----------------------------------------------------------------------------------88 CHAPTER FOUR: ANALYSI S OF DATA ------------------------------------------------89 Sample Population and Demographic Profile of the Respondents ----------------89 Descriptive Survey Data ------------------------------------------------------------------91 PRO SDLS Response Total and Comparison to Previous Studies --------91 Data Comparison between Pre test only Group ----------------------------93 Reliability of PRO SDLS Scores -----------------------------------------------94 Analysis of Resea rch Questions -------------------------------------------------------95 Summary ---------------------------------------------------------------------------------102 CHAPTER FIVE: FINDINGS, IMPLICATIO NS AND RECOMMENDATIO NS -----10 5 Introduction ----------------------------------------------------------------------------105 Summary of the Study -----------------------------------------------------------------106 Problem Statement -----------------------------------------------------------107 Research Setting -------------------------------------------------------------108 Methods ----------------------------------------------------------------------109 Principle Findings ----------------------------------------------------------------------109 Findings for Research Question One ---------------------------------------109 Findings for Research Question Two ---------------------------------------111 Findings for Research Question Three -------------------------------------111 Findings for Research Question Four ---------------------------------------112 Findings for Research Question Five ---------------------------------------113 Implications and Discussion of the Results ---------------------------------------114 Learner Control ----------------------------------------------------------------115 Self Efficacy ------------------------------------------------------------------118 Reliability of the PRO SDLS ------------------------------------------------119 Ethnicity and Gender ---------------------------------------------------------120 Summer Bridge Programs ---------------------------------------------------121 Recommendations for Future Research ---------------------------------------------12 3 Concluding Remarks -----------------------------------------------------------------127 LIST OF REFERENCES -----------------------------------------------------------------130

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iii APPENDIX A: TITUTIONAL DEPARTURE ------149 APPENDIX B: IRB INITIAL CONSENT FROM SECONDARY DATA SOURCE 150 APPENDIX C: INFORMED CONSENT FRO M SECONDARY DATA SOU RCE --152 APPENDIX D : IRB MODIFICATION APP ROVAL -----------------------------------155 APPENDIX E: PERMISSION TO USE PR O SDLS -------------------------------------157 APPENDIX F: PRO SDLS -------------------------------------------------------------------158 ABOUT THE AUT H OR -----------------------------------------------------------------END PAGE

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iv LIST OF TABLES Table 1 PRO SDLS Pre test D emographics -----------------------------------------82 Table 2 PRO SDLS P ost test D emographics ----------------------------------------83 Table 3 Descriptive Statistics for Academic Performance Measures --------------91 Table 4 Descriptive Data for Pre test Administration of PRO SDLS -------------92 Table 5 Descriptive Data for Post test Administration of PRO SDLS ------------92 Table 6 Comparison of Descriptive Statistics for PRO SDLS: Previous and Current Study ----------------------------------------------------------------93 Table 7 Comparison of Pre test only Group to Sample Population ----------------93 Table 8 Reliability Data for the PRO SDLS Scores --------------------------------95 Table 9 Correlations between Admissions GPA and PRO SDLS Pre test Scores -96 Table 10 Pre test and Post test Mean PRO SDLS Scores --------------------------97 Table 11 t test Results for Differences in Pre test and Post test Mean PRO SDLS Scores -------------------------------------------------------------------------97 Table 12 Correlations between University GPA and PRO SDLS Post test Scores 98 Table 13 PRO SDLS Post test Means and the Relationship of Gender & Ethnicity ---------------------------------------------------------------------------99 Table 14 Factorial ANOVA of PRO SDLS Post test Scores with Gender & Ethnicity ---------------------------------------------------------------------100 Table 15 PRO SDLS Change Score Means and the Relationship of Gender & Ethnicity --------------------------------------------------------------------------101

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v Table 16 Factorial ANOVA of PRO SDLS Change Score with Gender & Ethnicity -------------------------------------------------------------------------102

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vi LIST OF FIGURES Figure 1 Input Environment Outcome s (I E O) Model ----------------------10 Figure 2 ty Orientation Model ------11 Figure 3 on Process Context Model ---------------------56

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vii ABSTRACT The purpose of this study was to advance understanding of self directed learning characteristics of first year, first generation coll ege students participating in a summer bridge program. Understanding the experience of these students in higher education can lea d to the development of programmatic and pedagogical strategies to better meet the needs of this at risk student population. This study was conducted at the University of South Florida (USF), a large, public research university in Tampa. Participants were recruited from the Freshman Summer Institute (FSI), a summer bridge program for first generation students at USF. Theoretical frameworks from higher education and adult education literature merged to provide an understanding of self direction for the context of this study. Student retention and social integration theories from Tinto and Astin were studied, as they ha ve been widely used to assist higher education professionals in understanding the reasons students leave college and to assist administrators in the develop ment of strategies and programs to aid in the retention of at risk students. An example of a retent ion strategy is the summer bridge program, used by a variety of colleges and universities to increase persistence of at risk student populations. The adult education theory of self directed learning complemented Tinto and The Personal R esponsibility Orientation (PRO) Model (Brockett & Hiemstra, 1991) served as a theoretical framework for understanding self direction

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viii among the participants in the study. The PRO Model posits that learners utilize personal responsibility through the charac teristics of the teaching learning transaction along with their own personal learning characteristics to achieve self directed learning within a broader social context. The Personal Responsibility Orientation to Self Direction in Learning Scale (PRO SDL S) based on a conceptualization of the PRO Model, was used to quantitatively measure self directed learning among participation in the FSI Program. A series of correlations dependent means t tests, and conducted to examine the rel ationship between scores on both pre test and post test administrations of the PRO SDLS In addition to an investigation of the change in self direction, r elationships between academic achievement, gender, and ethnicity was also examined in the study. Me asured increases in overall self directedness as measured by the pre test and post test administrations of the PRO SDLS were not consi dered statistically significant, however, s ignificant correlational relationships (p<.01 ) were found between academic achi evement and total PRO SDLS scores. Subcomponent measurements of learner control and self efficacy were also highly correlated to both admissions GPA and university GPA. No significant relationships were found between ethnicity gender and scores on the PR O SDLS. An i m plication for practice indicate s that a shift in teaching pedagogy may be an integral component to increasing the academic success of first year college students. Higher education faculty should be challenged to design curriculum that relies less on

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ix centered curriculum which gives control of the learning process to student s is vital to instilling the habits of highly self directed learners. In addition to revamped pedagogical strategies, this study calls for the development of national benchmarks and guidelines to more effectively evaluate the quality and impact of summer bridge programs.

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1 CHAPTER ONE INTRODUCTION TO THE STUDY Increased access to higher education over the past forty years has resulted in an influx of new populations seeking postsecondary education. Legislation such as the G I Bill of 1944 and Higher Education Act of 1965 opened doors to a more diverse student body than ever before (Robert & Thompson, 1994). As a result, the number of high school students with aspirations of attending college has been on the rise. Between 1972 and 1998, the percentage of 16 to 24 year old high school graduates immediately enter ing college increased from 49% to 66% (U.S. Department of Education 2000). in part to the success of parents, teachers, and educational leaders in communicating the im portance of college. One group of high school students with increasing collegiate aspirations is those who are first in their immediate family to attend college. Referred to half of all college attendees (Berkner & Choy, 2008 ; Horn & Nunez, 2000; Pascarella, Pierson, Wolniak, & Tere nzini, 2004 ). First generation college students face challenges associated with access to higher education and experience disadvantages and possi ble deficits compared to those students whose parents are college educated (Choy, 2001; Coles, 2002; Lohfink & Paulsen, 2005; Swail, Cabrera, and Lee, 2005; Terenzini Springer, Yeager, Pascarella, & Nora, 1996).

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2 Compared to their counterparts, first gene ration students tend to be minority, come from lower income families, and have lower educational aspirations in high school (Choy, 2001; Kuh, Kinzie, Buckley, Bridges, & Hayek, 2007; Swail et al., 2005; Terenzini et al., 1996). Ishitani (2003, 2006) foun d that regardless of demographic and personal differences, first generation status remained a statistically significant indicator of difficulty in adjusting to and succeeding in college When controlling for characteristics that distinguish first generatio n students from their peers, first generation status is also negatively related to persistence and degree attainment in college (Ishitani, 2003, 2006; Nunez & Cuccaro Alamin, 1998; Pascarella et al., 2004; Warburton, Bugarin, & Nunez 2001 ). Additional r esearch concluded that the absence of a college degree with in the immediate family results in inadequate information regarding the college experience (Harrell & Forney, 2003; Pascarella et al., 2004; Somers, Woodhouse, & Cofer, 2004; Terenzini et al., 1996 ; Thayer, 2000). First generation students receive less assistanc e in preparing for college, feel less supported for attending college and lack a sense of belonging to the institution they attended (Choy, 2001; Terenzini et al., 1996). Increased accounta bility driven by politicians and legislators, has motivated educational institutions to take a serious look at how services are being provided to assist with the transition of at risk populations in higher education. Language written into the No Child Lef t Behind Act of 2001 and reauthorization s of the Higher Education Act of 1965 have forced institutions both at the K 12 and postsecondary levels to consider

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3 retention issues and how students persist through graduation at an acceptable rate. The move toward accountability has fallen squarely on the shoulders of educational institutions to demonstrate progress and measure results toward closing identified achievement gaps (Colyar, 2011; Kezar, 2000) I n response to calls for accountability tied to fundi ng decreasing graduation rates, greater diversity of incoming students, and expanded access to higher education retention programs at higher education institutions have grown exponentially One such program is the summer bridge program, designed to expo se and help newly admitted students to make the transition to college level coursework and campus resources in the summer before they start their college career s (Kezar, 2000) I nspired by decades of research on student retention and persistence summer br idge programs have been developed to help improve the overall retention rates of first generation and at risk college students (Gandara, 2001; Myers & Schirm, 1999; Nelson, Dunn, Griggs, Primavera, Fitzpatrick, Bacilious, & Miller, 1993; Teren zini, & Wrigh t, 1993). Although there is wide variation in the specifics of summer bridge programs they have demonstrated the ability to address academic preparation and social adjustment issues experienced by many incoming first year college students (Kezar, 2000; Pantano, 1994; Santa Rita & Bacote, 1996). These programs h ave existed for some time, but in the recent past a large r number of institutions bega n to realize their powerful potential for enhancing academic preparation and educational motivation (Kezar, 2000). The adult education theory of self directed learning (SDL) is an additional component germane to first generation college student success While there is no universally

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4 accepted definition of SDL, Malcolm Knowles definition is the most widely cited in the initiative, with or without the help of others, in dia gnosing their learning needs, formulating learning goals, identifying human and material resources for learning, choosing and implementing appropriate learning strategies, and evaluating learning Assisting first year college students in the transition from spoon fed high school students to autonomous, self directed learners who take responsibility for their learning is a major goal of academic support units in higher education ( Kreber, 1998; Maher, 2005). Brockett and Hiemstra (1991) pro posed that self retention, greater interest in continued learning, greater interest in the subject, more positive attitudes toward the instructor and enhanced self This study examine d potential rel ationships between SDL and first generation college student success. Problem Statement Investigations of the relationship between SDL readiness and first generation college student success are notably missing in the literature. Institutional efforts to fo ster the development of personal responsibility for learning may have an impact on academic success and persistence of first generation college students but have yet to be studied Compounding the problem is l imited research concerning the implementation of summer bridge programs as a tool to augment academic success and retention of first generation students.

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5 Purpose The purpose of this study wa s to investigate the change in self direction among first generation first year college students participating in the Freshmen Summer Institute (FSI), a summer bridge program at the University of South Florida (USF) Students chosen to participate in the FSI program reported on their admissions application s that neither parent had gr aduated from college. Additionally, expected family contribution figures from the Free Application for Federal Student Aid (FAFSA) During the summer 2009 semester, a one cr edit course called Strategic Learning was required of all FSI students and was completed during an intensive, six week period. Strategic Learning is a seminar style course based on a model of developing autonomous learners through their understanding of c oncepts related to motivation, attitude, goal planning, and the process of learning. The attributes of a self directed learner are discussed throughout the course curriculum with the course based on a belief that learning is a personal, individual, and in teractive process. Through the process of reflective practice, students had the opportunity to develop a deep u nderstanding of themselves as learner s and then intentionally apply that understanding to the development of the most effective strategies for success in both college learning and beyond. Typically, Strategic Learning is not part of the FSI summer curriculum and the inclusion of this course provided an opportunity to research first generation college direction in learning. In addition to Strategic Learning FSI participants also completed eight additional credit hours of coursewo rk in English

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6 composition and social science as well as a University Experience course designed to orient students to the social and academic cul ture of USF. The catalyst for the proposed research study stems from ( 2005 ) qualitative study with a similar group of FSI students at USF A report of reflective feedback from students in a Learning Strategies course was analyzed and yielded promising insights. Study participants described multiple examples of their growing ability to meet their academic challenges through a new understanding of themselves as learners and their ability to analyze tasks and use an informe d approach in the selection of the most appropriate strategies for success (Maher, 2005). because students appear ed to grow in their ability to use a process to analyze their immediate academic demands, the broader go al of increasing self direction and responsibility for learning was not measured. Maher (2005a) stated students increase their overall sense of responsibility and self direction in le 6). A pre and post test measurement of self direction was not conducted, resulting in an absence of evidence that stu dents became more self directed. Maher (2005a) efficacy for academic success in college and utilize it as a pre test and post Additionally, variables such as previous academic performance ( high school GPA) gender, ethnicity, and university GPA were not reported. In order to addre research investigate d the change in self direction among FSI students utilizing pre and post test

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7 data In addition to measuring self direction through administration of a quantitative instrument this resea rch further built including variables such as previous academic achievement (high school GPA), gender, ethnicity and university GPA. Research Questions This study wa s designed to answer the following research questions: 1. What is the relationship between pre test scores of the Personal Responsibility Orientation to Self Direction in Learning Scale and previous academic achievement as measured by university admissions grade point average? 2. What differences in scores were meas ured between pre test (given July, 2009) and post test (given January, 2010) administration of the Personal Responsibility Orientation to Self Direction in Learning Scale ? 3. What is the relationship between post test scores of the Personal Responsibility Orientation to Self Direction in Learning Scale and academic achievement as measured by university grade point average at the end of the third full semester ? 4. How are participants' levels of self direction following involvement in a summer bridge program, a s indicated by post test scores of the Personal Responsibility Orientation to Self Direction in Learning Scale, different for participants' based on gender and ethnicity? 5. How is the impact of a summer bridge program, as indicated by a change in self direc tion scores on the Personal Responsibility

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8 Orientation to Self Direction in Learning Scale, different for participants' based on gender and ethnicity? Theoretical Framework Theoretical frameworks from higher education and adult education guide this study Higher education t Environment Outcomes (I E O) Model. Among the most cited theories in the literature, each theoretical model is useful in the discussion of first generation student integration, retention and academic success From adult education literature, Orientation Model provided a framework for the study of self directed learning. Tint eparture (see Appendix A ) describ es a cademic and soc ial integration is essential to student retention. Tinto (1993) argued that institutions attempting to increase student retention should focus on the following six entry attributes, goals/commitments, institutional experiences, i ntegration, re evaluation of goals/commitments and outcomes. in understanding the interaction between academic and social elements that often cause students to voluntarily w ithdraw from the institution. degree of social and intellectual integration and therefore membership in academic and

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9 stressed that stud ents were less likely to drop out when they were integrated academically and socially. Academic integration includes intellectual needs while social integration is concerned with meaningful relationships with faculty and other students (Tinto, 1993). Sum mer bridge programs are one example of how an institution can help promote study. of promoting full integration, Astin (1975, 1984) emphasized student involvement and asserted that student development occurs through engagement in college activities and t in a longitudinal study of college student persistence from which Astin (1975) concluded that factors contributing to persistence were associated with student involv ement in college life. Conversely, factors contributing to departure from college were associated psychologically involved themselves in the academic and social opportunities in the college environment were more likely to persist (Astin, 1975). generation which institut ions of higher education have developed student retention interventions g eneration college students as these t erms are often associated with habits of self directed learn ers.

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10 Astin built upon research in student involvement and persistence and developed the Inputs Environment Outcomes (I E O) Model as a framework for assessment in higher education (Astin, 1993; Thurmond & Popkess Vawter, 2003). The premise of the I E O Model (Figure 1) is that educational outcomes are evaluated in terms of the characteristics of students (inputs) in the broad context of the college or university setting (environment). This mod el suggests that student s are not actively developed by faculty and university programs but passively through interactions with the institutional environment (Hutley, 2008). Figure 1. Inputs Environment Outcomes (I E O) Model ( Astin, 1985). interest. The single most important environmental factor, according to Astin, is student tudent community has stronger direct effects on student satisfaction with overall college experience than any other In order to foster a sense of community for first generation college students, institutions h ave turned to residential summer bridge programs (Kezar, 2000) According to Hicks (2003), a significant component of student Inputs Environment Outcomes

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11 success is how well first generation students connect with the institution and its student body, making the environmental compone the current st udy of first generation student success. colleges and universities to launch recruitment and retention programs geare d toward improving the success rates of first generation and other at risk groups (Swail, Redd, & Perna, 2003). S ummer bridge programs in particular, have gained popularity as institutions respond to calls for accountability in meeting the needs of incre asingly diverse student populations (Kezar, 2000). The adult education concept of self directed learning provides the final theoretical Personal Responsibility Orientation (PRO) Model ( F igure 2) creates clear delineations betw een SDL as a teacher driven instructional process and as a characteristic of the learner Figure 2. Personal Responsibility Orientation Model ( Brockett & Hiemstra, 1991).

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12 The PRO Model views knowle dge, skills, and exper iences as transferable to other situations and that learning may or may not occur in isolation (Hiemstra, 1994). teaching learning transaction in which the student assumes the primary responsibility for planning, implementing, and evaluating the learning experience with the teacher to the characteristics of individuals that contribute toward th eir taking personal responsibility for their own learning. The combination of the teaching learning transaction (self directed learning) and personality characteristics of the learner (learner self direction) direction within the broader social context (Brockett & Hiemstra, 1991). The PRO model is a viable and relevant conceptual framework for which to understand SDL In the context of first generation college students, the PRO Model is an especially good choice as a theoretical framework given the possible relationship to student retention and develo Environment Ou tcomes (I E O) model (1993) is of particular interest in regards to possible relationships between model. Currently, research has not been conducted utilizing these two theories collectively to investigate the relationship of SDL and first generation student success. Significance of the Study Despite a growing body of literature pertaining to first generation and low income college students, no research has been found that examines the relationship between self

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13 directed learning and academic success among this group of students. This stu dy provide d quantitative data ident ifying possible relationships between participation in a summer bridge program and self direction in learning among first generation college students participating in the Freshman Summer Institute at the University of South Florida. In addition to measuring change in self direction, this study examine d the relationshi p between gender, ethnicity, academic achievement and self direction. Furthermore, gaps in the literature reveal a possible relationship between theoretical frameworks in the fields of adult and higher education. Additional research is needed and may inform university adm inistrators in developing strategies to retain and promote academic success among at risk student populations. This study is among the first to investigate the relationship between SDL readiness and academic success of first generation first year college students Research Design This study examined secondary data obtained by the Tutoring and Learning Services (TLS) Department at USF During the summer 2009 semester, the Director of TLS partnered with the Director of FSI to offer all incoming FSI students a one credit hour course called Strategic Learning With consent from the USF Division of Research Integrity & Compliance (see App endices B, C, & D), an instrument designed to measure SDL was given to all participants in the FSI program The instrument chosen was the Personal Responsibility Orientation to Self Direction in Learning Scale ( PRO SDLS). The PRO SDLS (see Appendix F) was developed by Stockdale (2003) and was the product of an attempt to develop a reliable and valid instrument to measure self

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14 directedness in learning among college students based on an operationalization of the PRO Model of self c kdale & Brockett, 2010, p. 1). Completed PRO SDLS instruments were entrusted advisor in the FSI program. The advisor scored and coded each instrument so that the researchers could not identify students. In addition to PRO SDLS scores, the advisor entered additional non identifying student information including variables such as gender, ethnicity, high school GPA, and admissions GP A into the database. In order to answer the research questions proposed in this study, a quantitative correlational research design was used to determine if statistically significant differences exist in variables measured. Descriptive statistics i nclud ing measures of central tendency, variability, standard deviation, minimum/maximum values, skewness, and kurtosis were reported for all variables in this study. In addition, a series of Pearson Product Moment Correlation Coefficients, a dependent means t t est, and a factorial ANOVA was lpha was conducted to confirm rel iability of the PRO SDLS scores. Limitations The primary limitation to this research is that the data gathered is self repor ted data from the survey participants. Participants may have answered the PRO SDLS survey based upon what they believed to be the most socially acceptable answer or the answer that they believed the surveyor wanted the participant to report. An additiona l concern is that the data be ing analyzed is secondary data. Secondary data analysis is the process of statistically examining data collected by some other organization, group, or

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15 individual at some prior time Secondary data analysis is often chosen by researchers because of the data quality and increased sample size (McMillan & Schumacher, 2010). A drawback of utilizing secondary data is the lack of control over the data collection process, however, this concern is miti gated in the current study due to status as co investigato r during the initial data collection. This study was conducted at the University of South Florida, a large, metropolitan public, multi campus research university. Results of this study can only be generalized to one group of first generation students participating in a summer bridge program It is not assumed that results of this research can be generalized to subsequent groups of students at the same university or to those attending other institutions of higher education. Though problems of generalizability exist, researchers have suggested that single institution studies may contribute to a better understanding of the issues of student retention and degree attainment ( Nora, Barlow, and Crisp 2005 ) In addition to generalizability, changes in level of self direction as measured by the PRO SDLS may be att ributable to factors outside participation in the FSI program Some of these factors include: 1. Natural growth and maturity of first year college students over th e span of data collection leading to higher scores on the PRO SDLS. 2. The addition of the Strategic Learning course to the summer 2009 curriculum may have had an effect on changes in self direction. Historically, this course has not been included in the cu rriculum. 3. C oursework undertaken during the second semester of college.

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16 4. I n class experiences not shared by all participants, leading to a change in self direction. 5. O ut of class experiences not shared by all participants in the FSI program leading to a ch ange in self direction. Definition of Terms The following definition of terms offers the reader a context for understanding the terminology in relationship to the current research. First Generation College Student N either parent possesses more than a high school education Freshman Summer Institute (FSI) A summer bridge program for first generation college students at the University of South Florida. Grade Point Average (GPA) C umulative grade point average earned in academic courses com pleted by the student. For the purpose of this study, High School GPA refers to the admissions GPA in core subject areas computed by the Office of Admissions at the University of South Florida Admissions GPA does not include bonus points given for Advance d Placement (AP), honors, or dual enrollment coursework. University GPA refers to course grades earned by the student while enrolled at USF. Personal Responsibility Orientation (PRO) Model of Self Direction in Learning Brockett and Hiemstra's (1991) conceptual model that recognizes differences and similarities between self direction as a teaching and learning transaction and as a personal orientation internal to the individual. In this model the "personal responsibility of the learner in both

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17 actions and thoughts is paramount in determining their level of self directedness" (Brockett & Hiemstra, 1991, p. 27). Personal Responsibility Orientation to Self Direction in Learning Scale (PRO SDLS) instrument utilized in this investigation based on Brockett and Hiemstra's (1991) PRO Model of Self Direction in Learning. Self Directed Learning (SDL) process in which individuals take the initiative, with or without the help of others, in diagnosing their learn ing needs, formulating learning goals, identifying human and material resources for learning, choosing and implementing Knowles, 1975, p. 18). Summer Bridge Program P rograms that provide comprehensive support to assist first year college students in preparation for the rigors of university work. Organization of Dissertation Chapter One as written above, contains an introduction to the study, statement of purpose, research questions, the oretical framework s significance of the study, research design, limitations, and definition of terms. In the remainin g body of this study, Chapter Two provides a comprehensive review of the literature and integrates the literature to form a foundation f or new research. Chapter Three describes the general methodological approach, research setting, population and sample, instrumentation and data gathering strategies, and analytical procedures to be used. Chapter Four provides the resul ts of the statistical analyses conducted to answer the research questions. Finally, Chapter Five summarizes the study

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18 and reports the findings for each research question. The second part of the chapter discusses implications for practice and future research.

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19 CHAPTER TWO REVIEW OF THE RELATE D LITERATURE The relevant literature related to this research is divided into several components First, research on first generation college students is discussed and is highlighted by pertinent retention and student involvement theories that have achieved significant attention in the literature over the last thirty years. The second phase of the literature review describes summer bridge programs for first generation students and presents specific programmatic examples The final compone nt of the literature review provide s an overview of self directed learning and discuss es specific theoretical conceptualizations. The chapter concludes with an overview of instrumentation designed to measure self directedness First Generation Col lege Students A strong rela education level and the likelihood that his or her children will enroll in college (Choy, 2001) Among high school students w ith at least one parent earning a college. This number decreased to 75% for high school graduates whose parents had some college experience. For those who had neither parent attend a college of university only 59% had enrolled in some form of higher education. This p opulation is refer four year colleges and universities (Choy, 2001).

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20 The U.S. Department of Education Center of Education Statistics (2001) defined first generation students a (p. 153) and classified first generation as a subgroup of the at risk student population Currently, f irst generation students represent between one quart er and one half of college attendees (Berk ner & Choy, 2008 ; Horn & Nunez, 2000 ; Pascarella, Pierson, Wolniak & Tere nzini, 2004 ) These students face challenges associated with access to higher education and experience disadvantages and possible deficits compared to those students whos e parents are college educated (Choy, 2001; Coles, 2002; Lohfink & Paulsen, 2005; Swail, Cabrera, and Lee, 2005; Terenzini Springer, Ye ager, Pascarella, & Nora, 1996) Compared to their counterparts, first generation students tend to be minority, come from lower income families, and have lower educational aspirations in high school (Choy, 2001; Kuh, Kinzie, Buckley, Bridges, & Hayek, 2007; Swail et al., 2005; Terenzini et al., 1996). According to Horwedel (2008), the rapidly growing Hispanic population acros s the nation has increased the first generation population in higher education. First generation students are more likely than their non first generation peers to be Hispanic (18% versus 7%) and African American (14% versus 8%). These facts are a concern b ecause Hispanic and African American students earn colle ge degrees at lower rates than Caucasian and Asian students ( Hochlander, Sikora, Horn, & Carroll, 2003; Sengupta & Jepsen, 2006). Carey (2004) noted that 63% of all students enrolled in college gradu ate d in six years, however, only 47% of Hispanics and 46% of African Americans complete 4 year degrees within the same timeframe.

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21 In addition to minority status, Chenoweth and Galliher (2004) assert ed that lower family income makes the college going process particularly challenging for students whose parents did not attend college. Statistics indicate 29% of first generation students come from low income families compared to 9% of their peers (Warburton, Bugarin, & Nunez, 2001). First generation students are more likely to start their collegiate career s at a two year rather than a four year school and in a public rather than private institution (Tinto, 2004). Striplin (1999) contended that first generation students are often counseled and placed in vocational, technical and/or remedial programs. Higher income students are also more likely to earn degrees and lower income students are more likely to earn certificates (Adelman, 2005; Carroll, 1989; Hochlander et al., 2003; Kuh et al., 200 7) F irst generation student s who enroll at traditional four year universities are less likely to succeed academically and per sist to graduat ion than their non first generation counterparts Even when controlling for chara cteristics that distinguish these students from their peers, first generation status is negatively related to persistence and degree attainment in college (Ishitani, 2003 2006 ; Nunez & Cuccaro Alamin, 1998; Pascarella, Pierson, Wolniak, & Terenzini 2004; Warburton, et al., 2001). In addition to socioeconomic status, researchers have argued that students with college educated parents have other distinct advantages over their first generation peers. Incorporating the theory of cultural and social capital, r esearchers have demonstrat ed that a better understanding of higher education culture leads to increased access to essential knowledge and information ( Pascarella et al., 2004; Thayer, 2000 ). Several studies have

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22 concluded that the absence of a college degree wit hin the immediate f amily resulted in inadequate information regarding the college experience (Harrell & Forney 2003; Pascarella et al., 2004; Somers, Woodhouse, & Cofer, 2004; Terenzini et al., 1996 ; Thayer, 2000 ). First generation students report ed less assistan ce in prep aring for college, fe l t less supported for attending college and lack ed a sense of belonging to the institution they attend ed (Choy, 2001; Terenzini et al., 1996). Ting (2003) contended that first generation students and their families were typically unfa miliar with the college admission and financial aid processes. Because of a limited understanding of what higher education entails, first generation students are disadvantaged when it come s to level of family support, degree expectations, planning, and college preparation in high school ( Nunez & Cuccaro Alamin, 1998; Pascarella et al, 2004). R egardless of demographic socioeconomic, and personal differences, first generation status remained a statistically significant indicator of difficulty in adjustin g to and succeeding in college ( Ishitani 2003, 2006). Culturally, first generation students find themselves in a process of identity renegotiation as they gain familiarity with a world that was previously unknown in their culture (Chickering, 1969; Londo n, 1992). Chickering (1969) described multifaceted obstacles and barrie rs to success confronted by college students and d eveloped seven vectors to address the emotional, interpersonal, ethical, and intellectual aspects of en vectors, Lemons and Richmond (1987) identified four that were of particular concern to first generation college students: achieving competence, desiring autonomy, establishing identity, and developing purpose.

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23 efficacy Bandura (1977) described self efficacy what they believe exceeds their coping skills 193). Researchers have found that first generation students tend to have lower self efficacy, causing them to discredit their own abilities and potential as inferior (Choi, 2005; Hellman, 1996; Pajares & Schunk, 2001; Ramos Sanchez & Nichols, 2007). Further e mpirical data indicate a correlation between academic self efficacy and perceived college stress for first generation stud ents (Solberg & Villarreal, 1997). First generation students tend to enter the classroom with lower self efficacy than other students and are more likely to succeed in college if they begin to develop their own professional identity early in the undergradu ate experience (Speirs Neumeister & Rinker, 2006). Research has linked the absence of information about the college experience and lower self efficacy to decrease d academic performance of first generation students These students will earn lower grades a nd are more likely to drop out of college altogether before the end of the first semester when compared to other first year students (Riehl, 1994; Hoffman, 2003; Nunez & Cuccaro Alamin, 1998; Strayhorn, 2006; Ting, 2003) Further research by Ishitani (200 6) demonstrated that first generation students we re also more likely to drop out during the sophomore year of college, indicating that attrition of first generation students is an important concern beyond the first year of college

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24 Researchers have cited the need to understand first generation student attrition and have call ed for higher education professionals to be vigilant in meeting the needs of first generation stu dents (McMurray & Sorrells, 2008 ). A review of the literature indicates that first ge neration students enter college at a disadvantage in comparison to their peers. After admission and enrollment in classes, first generation students have to negotiate the difficult transition into academia and ma y experience difficulty remaining enrolled a nd attaining a degree (Horn & Nunez, 2000). A review of student retention and involvement theory is an important next step in the discussion of first generation college students in higher education. Retention and Involvement Theories The study of retent ion and student involvement is vital to the study of first generation student persistence Nearly 50% of all attrition takes place during the firs t year of college and more than 40% of first year students never obtain a degree (Tinto, 1993, 1998). The situation is particularly dire for first generation students who have greater difficulty transitioning into higher education and experience higher departure rates (Choy, 2001; Dennis, Phinney & Chuateco, 2005; Tym, McMillon, Barone & Webster, 2004). F irst generation college students tend ed to complete fewer credit hours, take fewer humanities and fine arts courses, and study fewer hours while also working more hours pe r week ( Terenzini Springer, Yae ger, Pascarella, & Nora, 1996). In addition, f irst g eneration students also had less knowledge about educational processes, receive less family support, and are more lik ely to take remedial courses (Be rkner & Chavez, 1997). Researchers found that student engagement involvement, and peer

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25 support systems bot h inside and outside of the classroom have help ed retain stud ents at the university (Dennis et al. 2005; Kuh, Kinzie, Buckley, Bridges, & Hayek, 2006; McCarthy & Kuh, 2006; Tinto, 1993). This section of the literature review describes pertinent theories related to student retention and involvement. T heories reviewed include M odel of Institutional Departure, Theory of Student Involvement Inputs Environment Outcomes (I E O) Model Among the most cited theories in the literature, each theoretical model is useful in the discussion of fir st generation student retention eparture (see Appe ndix A ) describ es a cademic and social integration is essential to student retention. ry was inspired the v an Genn rites of passage and theories v an Genn rites of passage theory describes the process involved in establishing membership in traditional societies Tinto (1987) suggested that, a ceremonies an d traditions as illustrated in v unofficial ceremonies that must take place for a student to establish his or her membership into the n ew collegiate community. I ntegration, according to Tinto (1993), decision to leave or depart from an institution. Tinto (1998) contended that students achieve integration after successfully navigating the states of

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26 separation, transition, and incorporation. S eparation is described as the ability of students to remove themselves from the norms of past community, famili es, friends, and other associations Transition occurs next as the student experiences academic and social cultures but has yet to take on the norms of the new collegiate environment. The last step is incorporation, which takes place when a student has become fully involved in the academic and social communities of the new institution (Tinto, 1998) T is (1897) theory of suicide. Durkheim (1897) found that suicidal tendencies were pronounced in those who were not socially integrated into the existing social system. In incorporati theory, Tinto did not sugges t that departing students literally committed suicide, but instead used it as an analogy in that individuals committing suicide are voluntarily withdrawing from the community in the same way students voluntarily wi thdraw from an institution. Tinto (1993) looked at both formal and informal academic and social experiences, including contact with professors, membership in student groups, interpersonal relationships with other students and academic performance. Tinto in understanding the interaction between academic and social elements that often cause students to voluntarily withdraw from the institution. degr ee of social and intellectual integration and therefore membership in academic and stressed that students were less likely to drop out when they were integrated academi cally

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27 and socially. Academic integration is concerned with intellectual needs while social integration is concerned with meaningful relationships with faculty and other students (Tinto, 1993). In explaining the academic integration element s of his theory, Tin to stated that the se elements often have little or nothing to do with academic success Tinto (1993) stated that commitments both to those goals and to the institut ion within which they may be 116). Conversely, Tinto argued that and intellectual integration into the academic and social communities of the college, the greater the likelihood of dep Tinto (1993) further enhanced the argument that academic success may have something to do with r etention but that personality characteristics and cultural attributes may have more significant influence on student retention Tinto argued that institutions attempting to increase student retention should focus up on the following entry attributes, goals/commitments, institutional experiences, integration, re evaluation of goals/commitments and outcomes. These components are descr ibed in detail below. s model is concerned with pre entry attributes of the student These attributes include family characteristics, academic preparation, financial disposition, first generation status, and cult ural backgro und (Tinto, 1993). Additional r esearch h as indicated that these attributes strongly influence whether a student fits within

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28 the institution and relates to student interaction with the other components of model (Dennis, Phinne y & Chuateco, 2005; Raley, 2007 ). The second comp goals the student has about his or her academic major and career choices and how committed he or she is to reaching those goals and remaining at the institution (Tinto, 1993). Tinto states that external commitments such as family, financial, and other obligations m ay interfere with the remaining argument, Dennis, et al. (2005) studied 100 first generation college students and found that these students often had additional responsibilities and obligations to their families that conflict ed with their commitment to obtaining a degree. mode l is concerned with academic and social interactions within the ins titution (Tinto, 1993). These formal and informal experiences along with the research of others in the field (Gloria, Kuprius, Hamilton & Wilson, 1999; Kuh, Kinzie, Buckley, Bridges, & Hayek, 2006 ) indicate d that a balance of positive interactions in both academic and social settings within the university is vital for persistence. Positive, formal interactions wi th faculty members in classroom/laboratory confidence outside the classroom (Tinto, 1993). Examples of informal interactions include participation in intramural sports and club activities on campus. Additional research ha s found that campus involvement and a feeling of belonging are essential for student transition in this stage of the model (Kuh, 2007; Kuh et al., 2006; Perez, 2006)

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29 The fourth component, integration, is the most crucial component for student success. Inte gration occurs when the student begins negotiating a fit with the institution. in the academic and social systems. If a student does not have positive experiences in both s ystems, the student may choose to depart from the institution (Ti nto, 1993). Kuh (2007) suggested that it is the responsibility of the institution to create opportunities for student academic and social pursuits. evaluation of g oals/commitments is important because students often change their original goals based on academic and social interactions experience d during college If conflict exists during the re examination of goals from within or outside the institution, there is a risk that commitment to completion of goals may lessen and lead to departure (Tinto, 1993). During this stage, a student finaliz es the decision regarding degree completion. This decision is base d on the c umulative effects of academic and social interactions within the institution. D uring this final phase, student weigh their personal and professional goals against their external commitments and the level of support they have received from both academic and social communities in which they participate. This final juncture is where a student make s a f inal decision about departure from an institution (Tinto, 1993). ion, there have been criticisms of hi s theory. Tierney (1999) believed

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30 missed the mark for mi minority students must assimilate into the cultural mainstream and abandon their ethnic id entities in order to succeed on predominately white campuses. Tierney (1992) also faulted discr imination in the United States and asserted anthropolo gical notions of ritual, and in doing so has created a theoretical construct with practical implications that hold potentially harmful consequences for racial and ethnic is devoid of any emphasis on the institutional contribution and responsibility to the withdrawal of the student (Yorke, to student retention, York e believed that if an institution does not provide the necessary attributes for academic integration, as in the case of not providing an environment that encourages learning, the accountability of the institution is absent. Yorke (1999) contended that c entered terms then there is some risk of blaming the victims of circumstance which are not their own doing and of institutions failing to submit themselves to a level of self scrutiny appropriate to the quality of assurance activity that is expected of the A final critique the predictive accuracy of the model within the context of commuter versus residential campuses (Braxton, Sullivan, & Johnson, 1997; Weissberg, Owen, Jenkins, & Ernest, 2003). In a review of research

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31 integration on persistence at residential campuses while finding only moderate affirmation at predominately commuter campuses. Tinto, however, ackn owledged student departure and is vital to the current study. research has inspired colleges and universities to launch retention programs geared toward improving the success rates of first generation and other at risk grou ps. Student retention, however, is one piece of the puzzle. In order to increase persistence, higher education professionals must determine how to integrate at risk student groups into the culture of the institution (Swail, Theory of Social Integration, discussed below. Theory of Social Integration Theory of Social Integration retention model. Instead of promoting full integration, Astin ( 1975, 1984) emphasized stude nt involvement and asserted that student develop ment occurs through engagement in college activities and that full integration is not required for persistence Astin (1975) concluded that factors contributing to persistence were associated with student involvement in college life. non involvement. Astin believed that students who physically and psychologically involved themselves in the academic and social opportun ities in the college environment were more likel y to persist (Astin, 1975).

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32 Astin clearly intended involvem ent to be behavioral in nature (Berger & Mi lem, 1999). Astin (1984) asserted that does, how he or she behaves, that defines and identifies involvement (p. 298). According to A stin, factors contributing to persistence are associated with involvement in college life with an absence of involvement leading to departure from the institution (Astin, 1984). gener ation which institutions of higher education have developed student retention interventions ance or time g eneration college students as these terms are often associated with habits of self directed learn ers. Astin (1993 ) discussed the need for a point of identification for the individual wit hin the institution and believed that a student can be alienated from certain campus arenas but still persist due to relationships in other areas such as academics, Greek life and athletics. These points of identification provide sufficient involveme nt to maintain a positive connection with the institution. Astin (1984) argued that student involvement is a behavioral manifestation of the psychological construct of motivation and offers five basic postulates :

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33 1. Involvement refers to the investment of physical and psychological energy in various objects. These objects may be highly generalized (student experience) or highly specific ( biology exam). 2. Regardless of its object, involvement occurs along a continuum. Different students exert different degre es of involvement in a given object and the same student manifests different degrees of involvement in different objects at the same time. 3. Involvement has both quantitative and qualitative features. 4. The amount of student learning and personal development a ssociated with any educational program is proportional to the quality and quantity of student involvement in that endeavor. 5. The effectiveness of any educational policy or practice is directly related to the capacity of that policy or practice to increase s tudent involvement. Astin (1984) found that almost every factor that promoted persistence was one egative effect on persistence. The single most important factor in persistence concerned th place of residence. Students living on campus were more likely to persist than other students. The impact of living on campus is a positive predictor of persistence for all types of students, regardless of characteristics such as ethnicity, gender, socioeconomic status, and ability (Astin, 1984) This finding has likely inspired the residential component found in most summer bridge programs.

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34 nputs Environment Outcomes (I E O) Model Astin built upon his research in student involvement and persistence and developed the Inputs Environment Outcomes (I E O) Model (see Figure 1 ) as a framework for assessments in higher education ( Astin, 1993; Thurmond & Popkess Vawter, 2003). The premise of the I E O Model is that educational outcomes are evaluated in terms of the characteristics of students (inputs) in the broad context of the college or university setting (en vironment). This model suggests that student s are not actively developed by faculty and university programs but p assively through interactions with the institutional environment (Hutley, 2008). Described as a psychological developmental approach, Asti E O Model described the inputs as having a double im pact on the outcomes both directly and indirectly via the environment. Inputs refer to personal characteristics the student developed t alent. Examples of inputs include demographic information, educational background, financial status, behavior pattern, degree aspiration, career choice, life goals, political orientation, reasons for attending the selected college, and academic major (Asti n, 1993). The e nvironment component educational programs. The environment includes everything and anything that happens during the collegiate experience that might impact the student. Items in the environmen t can include things such as educational experiences in and out of the classroom, interventions, programs, faculty, staff, curricula, facilities, institutional climate, teaching

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35 style, friends, roommates, extra curricular activities and affiliations with s tudent organizations The single most important environmental factor, according to Astin, is student commun ity (Astin, 1993). Astin stated stronger direct effects on student satisfaction with overall college experience t han any Astin, 1993, p. 352). According to Hicks (2003), a significant component of student success is how well first generation students connect with the institution and its student body, making the environmental component o model particularly important to the current st udy of first generation student success. Outcomes E O Model. Astin (1993) referred to outcomes as the talents an institution is trying to develo p in its educati onal programs. Outcomes are outcome variables which may include grade point average, exam scores, post tests, course grades, degree completion, curricula, classroom experience, and overall course satisfaction. In applying his involvement theory and I E O model to the issue of student retention, Astin (1993) conducted an empirical study of his models through the Higher Education Research Institute (HERI) at the University of California, Los Angeles. In hat the three most important forms of student involvement were academic involvement, involvement with faculty, and involvement with student peer groups. Of the three, student peer group was found to be the most potent source of influence on growth and de velopment during the undergraduate years that implications for practice should be overarching and that institutions can solve the persistence issue by looking inward and

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36 using existing institutional resources. Believing tha t the ongoing commitment of faculty and staff of an institution is paramount t o student retention, Astin called for institutional change and new ways to actively involve students and faculty in their intellectual life. According to Astin, hange r equires a deep er understanding of the impor t ance Astin, 1993, p. 212). colleges and universities to launch r ecruitment and retention programs geared toward improving the success rates of first generation and other at risk groups (Swail, Redd, & Perna, 2003). S ummer bridge programs in particular, have gained popularity as institutions respond to calls for accou ntability in meeting the needs of increasingly diverse student populations (Kezar, 2000). Summer bridge programs are described in detail in the next section of the literature review. Summer Bridge Programs Nurturing the academic and social development of first year college students is the most meanin gful intervention a college or university can make to increase retention (Levitz & Noel, 1989). Research has repeatedly suggested that the first year of college is the decisive connection point between the student and the institution and that assisting f irst year students with their academic, personal, and social adjustment to college is crucial in i mproving their persistence and graduation rates ( Astin, 1993; Noel, Levitz, & Saluri, 198 5; Pascarella & Terenzini 1998; Terenzini Rendon, & Upcraft, 1994; Tinto, 1996 1997 ; Upcraft & Gardner, 1989). Cabrera, Nora, & Casta neda (1993) asserted that

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37 in a ddition to academic/social integration and goal commitment by the student, institutional commitment is the most important factor in student persistence. Tinto (1996) also called for institutional commitment and argued that a n institution can make to increase student retention to graduation is to ensure that students receive the guidance they need at the beginning of the journey through college to ). In order to promote early integration into the university e nvironment institutions have implemented summer bridge programs to assist first generation students in the transition to college (Kezar, 2000). A review of the literature pertaining to these programs and specific programmatic examples are included in this section of the literature review Inspired by decades of research on student retention and persistence, summer bridge programs have been developed to improve overall retention rates of first generation and at risk college students ( Gand ara, 2001; Myers & S c hirm, 1999; Nelson, Dunn, Griggs, Primavera, Fitzpatrick, Bacilious, & Miller, 1993; Teren zin i & Wright, 1993). According to Kezar (2000), the purpose of these programs is to retain at risk student populations at the institution and provide them an equal footing with their peers. Colyar (2011) elaborated on the purpose and stated intended to address important preparation and achievement gaps t hat are evident in the research (p. 123). Although extreme programmatic variation exists summer bridge programs have demonstrated the ability to address academic preparation and social adjustment issues experienced by many incoming first year college students (Ke zar, 2000; Pantano, 1994; Santa Rita & Bacote, 1996 ).

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38 Summer b ridge programs h ave existed for some time, but in the recent past a large r number of institutions bega n to realize their powerful potential for enhancing academic preparation and educati onal motivation (Kezar, 2000). Increased pressure and calls for accoun tability tied to funding are cited as a major influence for increased interest in student retention p rograms in the past decade (Levitz, Noel, & Richter, 1999). Additional pressure has come from r ecent reauthorization s of the Higher Education Act of 1965 w hich has included language requiring colleges and universities to report degree completion rates (Kuh, Kinzie, Schuh, Whitt, & Associates, 2005). According to the U. S. Department of Education (2009 ), funding has increased substantially for programs aimed a t attracting first generation and low income college students to attend and complete college degrees. E xamples of state level incentives have come in the form of accountability systems and incentive grants that tie institutional budgets to performance and increases in student retention (U. S. Department of Education, 2009 ). Tinto (2003) reported that state and federal funds aimed at increasing student retention have been u tilized to encourage development of innovative programs which meet the needs of disadv willing to grant universities and colleges a great deal of autonomy, at least in regards to student retention and graduatio n This autonomy has decreased in an era of accountability in higher education. Summer bridge programs typically take place in the summer between the high school and freshman year of college. Programming varies widely in format, po pulations served, and curricu la but generally include s academic

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39 courses, advising/counseling services, and programming design ed to better integrate students ( Kezar, 2000; Pantano, 1994, Terenzini Allison, Gregg, Jalomo, Millar, Rendon, & Upcraft, 1993; Villapando & Solorzano, 2005; W erner Smith & Smolin, 1995 ). Programs are t ypically from three to six weeks in length and include a required residential component aimed at promoting academic and social integration with faculty and other students (Astin, 1993; Colyar, 2011; Pascarella, 2004; Woosley, 2003). The placement of summer bridge programs at the beginning of the college experience supports research citing the first two to six weeks of college as being the most critical transition period ( Astin, 1993; Woosley, 2003). Little empir ical research on summer bridge program exists despite the fact institutions are investing enormous funds and human resources to ensure high participation and success ( Kezar, 2000; Santa Rita & Bacote, 1996 ). In addition, the extreme variation of programs h ad resulted in a dearth of research on program effectiveness (Kezar, 2000; Maples, 2002). Of the studies that do exist, few are applicable to the field as a whole York & Tross (1994) disclosed that studies on summer bridge programs have based their assessment on survey questions asked of student s with no data regarding GPA and persistence rates. The result of this approach is little more than a program evaluation providing little application to th e field (York & Tross, 1994). Despite this criticism, program evaluation and continuous improvement is vital. Levin & Levin (1991) stressed the importance of program evaluation and noted that associated claims o f success) to thorough scrutiny. W ithout a systematic, component by component analysis

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40 of multiple component retention programs, no one will know what is (or, more usually, T he enormous diversity of summer bridge programs limits the ability to generalize across institutions and has resulted in minimal research on the topic in terms of academic success and retention of summer bridge participants (Cabrera, Castaneda, Nora, & Hengst ler, 1992). Of that which is published, it was found that students performed better academically and were retained at a higher rate ( Ackermann, 1990; Garcia, 1991; Santa Rita & Ba cote, 1996 ; Walpole, Simmerman, Mack, Mills, Scales, & Albano, 2008 ). While encouraging, these studies are inconclusive because they lacked control group s and did not allow a true comparison between students with similar characteristics that did not participa te (Kezar, 2000; Maples, 2002) Maples (2002) argued that summer bridge Complicating analysis further, Myers & S c hirm (1999) argued that summer bridge program outcomes are more social than academic. A second issue regarding retention and summer bridge programs is the absence of consensus regarding the point at which retention should be measured Garcia (1991) studie d 19 summer bridge programs in California and found mixed results regarding retention in successive years. Garcia found that students in the summer bridge program had higher retention rates the first year but lower rates the second when compared to the institutional averages of all students (Garcia, 1991). Moreover, r esearch has focused o n first to second semester retention while further research has focus ed on first to second, third, and fourth year retention. Without consistent measures of program success and

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41 standardization of the evaluation process, the limited studies available provi de little generalization to other institutions (Garcia, 1991). Few scholars disregard the importance of summer bridge programs, however, some have suggested that these programs do little to empower students and may disenfranchise them by operating o n a deficit model and mark ing participants as different from their peers (Christensen, 2004; Colyar 2011; Oseguera, Locks, & Vega 2009; Walpole, 2011) students do not have the necessary skills and abilities to succeed in college and therefore must be llpot, Hope, Johnston, Mery, Serban, et al., 2007). Christensen (2004) believed that being treated from a deficit perspective takes a heavy toll and warned that students do not want here the system is based upon finding what I cannot do and having al. (2009) argued that the deficit model is partic ularly evident when discussing minority students and cultural backgrounds that prevent them from achieving success. In contrast to the deficit model, Christensen (2004) advoc recognizing that students have both strengths and weaknesses with varying learning styles and cultural backgrounds. C olyar (2011) supports this model and suggested new program structures which recognize the assets students bring to the insti tution. Suggestions inclu support network as well as service learning projects in the local community (Colyar 2011) According to Colyar (2011) transitional programs should

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42 communities so tha (p. 135). Despite the absence of rich empirical research and other criticism s described above t he literat ure on summer bridge programs suggests they are an effective method of in troducing students to university life, providing social and academic support, improving basic study skills and ultimately retaining the student at the institution The next section of the literature review describes the summer bridge program studied in t his research as well as brief examples of similar programs around the country. Freshman Summer Institute at the University of South Florida alternative admissions program which supp orts first generation, low income students throughout their first year of college. The FSI program is one of four programs supervised by the director of First Generation Access and Pre Collegiate Programs (FGAPP). FGAPP is housed within Undergraduate Stu dies, which supervises other st udent support areas within the U niversity. In addition to FSI, other programs that FGAPP facilitates include th e federally funded TRIO programs: Student Support Services (SSS ) and Upward Bound, a program supporting low income hi gh school students and families. A grant funded program (ENLACE) delivers programs, initiatives and events that pr omote the success of Hispanic and first generati on students. Students are selected for the FSI program through their fall admis sion appli cation to the university. The U niversity uses academic success predictors (high school grade point average, SAT/ACT test score results) to make admissions decisions for its

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43 applicants. If a student falls below the U l applicants, the admissions office reports students who have identified themselves as bei ng first as neither parent having c ompleted a baccalaureate degree Next, the flagged first gener ation applicant is notified that although he or she has been denied admission to the U ni versity for the fall semester, he or sh e has been accepted for admission in the summer term with the condition that he or she successfully complete s the Free Applicatio n for Federal Student Aid (FAFSA). Students with the lowest expected family contributions (EFC) scores, determined by the FAFSA, are referred to the FSI and SSS programs. Other stud ents, who are first generation but not low income, are offered summer adm ission to USF but without the formal support of SSS or FSI. Depen ding on the year and resources, 150 to 250 students enter the U niversity through the FSI program. FSI p articipants live with other program participants and peer counselors in a residen ce hall reserved for the program Peer counselors are paid former FSI students who are respon sible for monitoring and providing opportunities for social interactions. During move in week, the new students receive comprehensive orientation sessions from b oth the Office of Orientation and the FSI staff. It is also during this time that students meet their counselor s/advisors for the first time. The counselor/advisor relationship is the most c ritical element of the FSI program FSI Counselor s /advisors are trained to take care of the specific needs of low income, first generation college students and are expected to go beyond the training of normal academic advising duties, hence the designation, counselor/advisor. The advising

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44 model is descri s /advisors are trained to seek students. According to Albecker (2005), i involving and moti vating students to seek help when needed (p. 1). He i sserer & Parette (2002) contend that intrusive advising results in improved retention rates, increased number of credit hours completed, increased GPA, and an improvement in the use of study skills, tim e management strategies, and classroom attendance. Counselor s /advisors are trained in a variety of USF policies and procedures, including, but not limited to, financial aid, housing, dining, camp us resources, and academic programs The typical student l oad per counselor/advisor is 45:1, which allows each counselor/advisor the kind of flexible schedule necessary to take care of students needs immediately. Students and their counselor/advisor are required to meet a minimu m of three times each semester dur ing their freshman year. As a result of these sessions, counselor s /advisors get to know their students very well an d students quickly learn where to go for assistance. first semester is an intensive six week summer term. They complete nine semester hours (three courses) that typically include their first college composition course a long with two other general education requirements. Unlike other summer bridge programs described in the literature remedial education is not a component of th e FSI curriculum. Achievement is important during this term, for any (2.0) result s in dismissal from the

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45 U niversity. Successful completion of their summer coursework allows students to continue their education into the fall semester. During the subsequent fall and spring semesters, students continue their one on one sessions with their counselor/advisors but also begin attending on campus workshops. Workshops are intended to be educational in nature and pro vide opportunities for students to get involved with departments that offer services and support to students. Guest lectures, wellness demonstrations, debates, study skill seminars and personal development workshops are some examples of the opportunities students find to fulfill their workshop requirements. Students who do not complete one on one sessions or workshop requirements are n and that results in an administrative intervention to evaluate the The FSI office is located cent rally on campus in the Student S ervices building. It features a large lobby area which a ccommodates the high volume of s tudent traffic the office receives. There is also a computer lab with over twenty c omputers that allow FSI students to print course documents free of charge. The office is home to the FSI staff, which includes a director, a coordinator, three counselor s /advisors, two graduate assistants, several student employees and an office manager. Evaluation of the FSI program is based primarily on the fall to fall freshma n year retention rate into the second year. Beyond that, programmatic evaluation includes evalua tion of the program that includes their satisfaction level with their counselor/advisor, availability of resources, residential and social experiences and their

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46 recommendations for how to better provide for future FSI students. Although participation in t he FSI program is not required for continuing (sophomore) students, the director tracks retention past the first year, along with graduation rates to assist the university in identifying areas of improvement within the general population of students. Durin g the summer 2009 semester, a one credit course called Strategic Learning was required of all FSI students and was completed during an intensive, six week period. Strategic Learning is a seminar style course based on a model of developing autonomous learn ers through understanding concepts related to motivation, attitude, goal planning, and the process of learning. The attributes of a self directed learner are discussed throughout the course curriculum with the course based on a belief that learning is a p ersonal, individual, and interactive process. Through the process of reflective practice, students had the opportunity to develop a deep understanding of themselves as a learner, and then intentionally apply that understanding to the development of the mo st effective strategies for success in both college learning and beyond. The following learning outcomes are intended as a result of participation in the Strategic Learning course: 1. Describe their individual learning characteristics by utilizing the resul ts of various self assessments 2. Assess the effectiveness of both past and current approaches to academic learning 3. Develop a systematic approach to the analysis of academic task expectations based on a metacognitive model 4. Explore multiple models of proven le arning tactics and resources and select strategies appropriate to each assigned task

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47 5. Utilize the tools of reflection, self assessment, and self regulation for the purpose of improving current academic standing. Typically, Strategic Learning is not part of the FSI summer curriculum and the inclusion of this course provided an opportunity to research first generation college direction in learning. In addition to Strategic Learning FSI participants also completed eight additional credi t hours of coursework in English composition and social science as well as a University Experience course, designed to orient students to the social and academic culture of USF. Other Summer Bridge Programs in the United States As noted by Kezar (2000), the population served by summer bridge programs varies greatly. One of the more prominent programs is at the University of California, Berkley (UCB). UCB began the Summer Bridge Program in 1973 to assist students in successful academic, social, and p ers onal transition. Offering an academically rigorous residential program, UCB cultivates a diverse community of scholars by preparing them to meet the challenges of a large public research University. Unlike the FSI program, not all participants to Summer Bridge are conditional admits or first generation. Services offered to ensure successful transition and admi ssion to the U niversity includ e weekly seminars designed to facilitate a well balanced college lifestyle, workshops aimed at a variety of academic and social subjects, tutoring, and intensive advising (University of California, Berkley Summer Bridge Program, 2010). Arizona State University (ASU) offers the Summer Bridge Program to under represented groups including, but not limited to, first ge neration college students. Unlike

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48 the FSI program p artic rogram is voluntary and n ot a condition of admission During an intensive, five week program, Summer Bridge assists freshmen from under represented groups in making a successful transition from high school to college and offers special support programs and services to ensure student credit hours of college coursework prior to the fall semester. Touted benefits include interactions with faculty, tutors, peer mentors, residence services staff, and program staff. Participants receive housing, a partial meal plan, textbooks, dedicated tutoring services, and special events programming (Ari zona State University Student Success, 2010). While programs offered at USF, UCB, and ASU target students of all majors and ability, other summer bridge programs have been developed for students within part icular majors such as math and s cience (Kezar, 2000). An example of such a program is in the School o f Engineering at the University of New Mexico (UNM) The Freshman Summer Bridge Program (FSBP) assists under r epresented students pursuing degrees in Engineering or Computer Science FSBP provides beginning engineering students with a college specific orientation detailing the demands of college academics. Orientation is followed by a cost free, intensive four week residential program where students have the chance to earn UNM credit hours. Additional advertised benefits of participation include enhanced academic success, development of social and academic support networks, and special access to advisors (University of New Mexico Engineering Student Services, 2010).

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49 The University of Tennessee rogram is a cooperative effort between the National Science Foundation and six universities in the Tennessee. Offered through the College of Engineering, Summer Bridge focuses on enabling participating minority high school graduates an easier transition to college life. emphasizes academic success in math, chemistry, and physics. Success is achieved through supervised study sessi ons, study skills training, and building communication Bridge is also a residential program with students required to reside on campus during the three week session (Univ ersity of Tennessee Knoxville College of Engineering, 2010). A review of the literature pertaining to summer bridge programs indicates a wide variety of programs offered across the country. Despite a diversity of programs, each shares an emphasis on aca demic and social integration of students into the institution. These programs have been inspired upon the theoretical constructs of retention and involvement posited by Tinto (1975, 1987, 1993) and Astin (1984, 1993). Despite the link to student developm ent theory, empirical research remains weak regarding summer bridge programs (York & Tross, 1994). Published research is typically a program evaluation, making generalizations about impact difficult due to the variety of formats offered (Kezar, 200 0). In contrast, the current study explore s the relationship between first generation students, a summe r bridge program, and self directed learning. This unexplored area of research may yield more generalizable data that a program evaluation

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50 is unable to provid e The following section of the literature review discusses the theory of self directed learning and instruments that have been de signed to measure it. Self Directed Learning Promoting the capacity for self directed learning (SDL) among college students is an important goal of higher education (Kreber, 1998). Brockett and Hiemstra (1991) proposed that self continued learning, greater interest in the subject, more positive attitudes toward the instructor and enhanced self n the field of adult education, SDL is important in the discussion of first generation student success due to it s possible impact on retention and student success The next section of the literature review provide s an overview of SDL Following the overview, theoretical models of self direction are discussed as well as instruments designed to measure self directedn ess. Overview of Self Directed Learning Malcolm Knowles (1975), a pioneer in the field of adult education, identified the adult learner as self directing, intrinsically motivated, an independent learner, and one who brings life experience and knowledge to the learning environment. While there is no universally accepted definition of SDL, Malcolm Knowles definition is the most widely cited in the literature. Knowles (1975) d efined SDL take the initiative, with or witho ut the help of others, in diagnosing their learning needs, formulating learning goals, identifying human and material resources for learning, choosing and implementing appropriate learning strategies, and evaluating learning outcomes (p. 18). In an alt ernate definition of SDL Brockett & Hiemstra (1991)

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51 described a combination of process and personal el ements in which an individual T The Inquiring Mind (19 61) was the starting point for discussion of SDL Based on interviews with adult learning participants, Houle (1961) was the first to describe the motives for learning and resulting activities of a group of independent minded learners who wished to pursue their education outside of the traditional school setting. With information gleaned from interviews, Houle proposed three categories of learning orientations to explain why learners participate in continuing education activities. The first category consi sted of goal oriented learners who pursue d educational opportunities as a means to another goal. The seco nd category contained activity oriented learners who partake for the social opportunities afforded by participation. The final category was learning oriented learners who engage d in activities for the sake of learning in and of itself (Houle, 1961). Building on notion that learners engage in activity for the sake of learning Tough (1967, 1971) focused his attention on studyin g self directed learning projects. Tough provided a quantifiable framework through which to study SDL and found that 90% of adults initiated an average of at least eight SDL project s a year. Tough advanced the notion that SDL was widesp read and part of an contribution to the field, as stated by Brockett and Hiemstra (1991), w direction has long been assumed to be a major goal of adult education, it was not until

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52 As the conceptualiz ation of self directed learning evolved, o ne of the more contentious areas of debate centered on whether SDL i s a n instructional process or a personality c haracteristic (Brockett & Hiemstra, 1991). In an attempt to more clearly define self direction, scho lars reviewed and categorized decades of SDL literature A review of the literature revealed separate conceptualizations of self direction as a process of learning in which people take the primary responsibility or initiative in the learning experience, a nd self direction as a personal attribute (personality characteristic ) of the learner (Brockett & Hiemstra, 1991; Caffarella, 1993; Garrison, 1997; Long, 2000; Merriam, Caffarella, & Baumgartner, 2007 ). In developing models of SDL, researchers have cited the need to distinguish As part of the theoretical framework for this study, (1991) multi dimensional Personal Responsibility Orientation Model ( PRO ) makes a dis tinction that will be discussed in detail below. Following a discussion of the PRO Model, other SDL theories such as Dimensional Model (1991), Directed Learning Model (1991), Self Directed Reaming model (1997) are discussed The Personal Responsibility Orientation (PRO) Model ( see F igure 2) creates clear delineation s between self directed learning as an instructional process and as a characteri stic of the learner (Brockett & Hiemstra, 1991) The PRO Model permits a

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53 view of SDL as occurring on a continuum, where knowledge, skills, and experiences are transferable to other situations and that learning may or may not occur in isolation (Hiemstra, 1994). According to the model, learners utilize personal responsibility through the characteristics of the teaching learning transaction along with their own personal learning characteristics to achieve SDL within the broader social context (Brockett & Hi emstra, 1991). Each component of the model is discussed below. The self directed learning component of the PRO Model emphasizes the teaching learning transaction in which the student assumes the primary responsibility for planning, implementing, and eval uating the learning experience with the teacher facilitating the process. The l earner self direction component on the other hand, refers to the characteristics of individuals that contribute toward their taking personal responsibility for their own lea rning. The combination of the teaching learning transaction and personality characteristics of the learner contributes to the outcome of self direction in learning (Brockett & Hiemstra, 1991). The role of personal responsibil ity in self directed learning was cited repeatedly in the literature and is a major component of the PRO Mode (Brockett & Hiemstra, 1991; Candy, 1988; Garrison, 1997, Guglielmino, 1977; Houle, 1961; Knowles, 1970). The PRO Model posits that human beings are capable of assumin g personal responsibility for their own learning. In citing humanist scholars such as Abraham Maslow and Carl Rogers Brockett & Hiemstra (1991) refer to the capacity of humans to make significant personal choices given the constraints of heredity, persona l history, and environment Personal responsibility,

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54 individuals to take control of their own learning that determines their potential for self direction (Brockett & Hiemstra, 1991, p. 26). The authors do not imply that individuals have control over their personal life circumstances, rather, it refers to the control all humans have over the manner in which they will respond to a situation. Thus, the PRO pers onal responsibility to activate the learning process. The learner may choose various characteristics of the teaching learning transaction in conjunction with their own characteristics as a learner to arrive at self direction in learning (Brockett & Hiem stra, 1991). The aforementioned components are placed inside of a circle which represents in which learning occurs. The social context component in the PRO Model recognizes that learning occurs within a greater social context and addresses the role of institutions and policies in the development of SDL. Th is of understanding environmental circumstances in the learning process (Brockett & H iemstra, 1991) Social context includes both the teaching learning transaction and the characteristics of the learner. Personal responsibility, however, continues to reside within the individual. The social context includes both political and social elements and expands beyond the physical environment to include emotional aspects of th e learner (Hiemstra & Brockett, 1994). According to Hiemstra (1994), if the social context is restrictive, it can limit freedom and curtail learning. Despite these restrictions, it is assumed that individuals still possess degrees of personal responsibili ty and are at the very least able to control how they will respond to any given situation (Hiemstra, 1994).

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55 Criticisms of the PRO Model prima rily concern the social context. Flannery (1993) argued that Brockett and Hiemstra minimized the sociological and cultural issues by giving them only cursory examination. Flannery asserted that the PRO Model countries that might work against self direction in learning, and a method of learning (Flannery, 1993). Newell (1995) also argued for expansion of the Finally, Song & Hi ll (200 7) alluded to the growth of distance learning and felt the PRO Model was not representative of online learning environments Brockett & Hiems tra (2010) have acknowledged criticisms of the social context component of the PRO Model. During a recent presentation at the 2010 International Self Directed Learning Symposium the auth ors admitted that they did not have a good understanding of the social context of SDL. According to Brockett & Hiemstra (2010), e included [socia l context] in th e model but kind of left it for others to address. This has been done over the past two decades and we now have a better understanding of its importance. We now understand that context is an essential component of self directed learning and 2010, p. 4) A proposed revision of the PRO Model, the Person Process Context (PPC) Model (see Figure 3) places the social environment (context) on equal footing with the teaching l earning (process) and personal characteristics (person) components The authors disclosed that [now] think of context as a combination of the learning

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56 environment and sociopolitical factors that can impact opportunity to foster self directed learning (Brockett & Hiemstra, 2010, p. 7) Due to the provisional nature of the updated model, it was not be used as a theoretical framework in the current research study. Figure 3 Proposed Person Process Context (PPC) Model ( Brockett & Hiemstra, 2010 ). A dditional concern s with the PRO Model center on ambiguities related to the personal responsibility component Kohns (2006) indicated that despite presenting hing learning Further criticism comes from New e ll (1995) who suggested that personal responsibility is too restric tive in relation to the metacognitive dimensions and should be expanded into personal dimensions. A final c ritique of the PRO Model was offered by Garrison (1997) who advocated the need to take a more comprehensive look at the psychological dimension of SDL Garrison felt the study of SDL has over emphasized external control and management of learning tasks and de emphasized psychological aspects of SDL.

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57 Garrison asserted that the PRO Model is limited in that it seems to repr esent only a personality factor or disposition to be self directed (Garrison, 1997). In order to address shortcomings in the PRO and other models, Garrison (1997) developed the Self Directed Reaming Model, which will be discussed shortly Despite concerns with the PRO Model, it remains a viable and relevant conceptual framework for which to understand SDL In the context of first generation college students, the PRO Model is an especially good choice as a theoretical framework given the pos sible relationship to student retention and development theories. Input Environment Outcomes (I E O) Model (1993) complements the PRO Model in that each focuses on the role of the social context (environment) in the desired outcomes of both social integration and SDL. Currently, research has not been conducted u tilizing these two theories collectively to investigate the relationship of SDL and first generation student success. Other Self Directed Learning Models 91) Personal Responsibility Orientation (PRO) Model has been selected as a theoretical framework for the current study, other scholars have pr oposed theories of self direction in learning that are important in the discussion of the topic Among the theori es that will be discussed in this section of the literature review Directed Learning Model (1991), an Self Directed Reaming M odel (1997). Dimensional Model. jor contribu tion to the discussion of SDL i s the notion that adults utilize SDL differently in formal as opposed to

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58 non formal settings. In SDL Candy (1991) also em phasized that self direction is not only a goal but also a process. In addition, both Candy (1991) and Brockett & Hiemstra (1991) argued that SDL occurs on a continuum. U tilizing a constructivist philosophy, Candy sought to understand how adults utilize lifelong self direction and p osited two distinctions of SDL : outcome and method Candy further divided outcome and method and proposed a model of SDL encompassing four dimensions: personal autonomy, self management, learner control, and autodidaxy. The first dimension, personal auto nomy, varies from situation to situation. As a result, no assumption can be made that because one person was self directed in one situation that they will display the same attitude and behavior in another situation or in another area (format) of learning (Candy, 1991). management and learner control. Self management ref ers to the skills and competencie s of the self directed learner and their willingness and capacity to manage their own learning. Learner control on the other hand, directedness as well as the self directedness of the student. In distinguishing learner control from self management Candy described learner control as an approac h to learning and planning instruction in which students assume control over the learning proc ess while self management referred manage their own learning. Candy argued that learner control has several advantage s,

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59 including improved curiosity and critical thinking, better retention and understanding, and superior learning outcomes (Candy, 1991). The final component which is best described as the independent pursuit of learning and self education. According to Candy (1991) autodidaxy has become extremely widespread and has limitless possibilities. Candy arise from, and occur within the context o the framework. In criti quing formulated his work into a model or conceptual framework whi (p. 64). Directed Learning Model (1991) model for s tages of self directed learning provides useful perspective regarding growth through stages of self direction. In his framework, Grow stated that his intent was not to address SDL theory, but rather to focus on the teachi ng learning transaction which is also a main component of the PRO Model increasing self direction and that teachers c The four stages outlined by Grow are: dependent, interested, involved, and self directed (Grow, 1991). model, learners need an expert authority figure to explicitly direct lea rning. Moving to the second stage, learners become more interested and are willing to complete relevant assignments. Students at this stage are

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60 also confident, but lack a deep foundation of the subject matter. In the third stage, learners have both the skills and knowledge to actively participate in the ir own learning but still require guidance from the instructor According to Grow (1991) stage three concept, more confidence more sense of direction, and a grea In the final stage, learners take responsibility and set their own goal and achievement standards. Stage four indicates that the student possesses skills in time and project management, self eva luation and monitoring, and effective identification and use of resources (Grow, 1991). Like Candy (1991), Grow believed that readiness for self direction is situational and possibly task specific. In his view that self direction was a characteristic of the learner, Grow (1991) direction and facilitating them to advance to greater self direction in learning situations In each stage of his model, Grow described the role of the teacher and instructional techniques best suited to assist the student in becoming more self directed. In addition to acknowledging the teaching learning transaction in self direction, Grow als o discussed the importance of of motivation and control (Grow, 1991) The major criticism of Gr in the model is diagnosed (Tennant, 1992). Grow (1994) ing faith Grow conceded that teaching is an imprecise enterprise

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61 requiring a variety of techniques to integrate SDL models into the ins tructional process (Grow, 1994). Self Directed Reaming Model. Similar to Brockett & Hiemstra (1991) conceptualization Garrison (1997) saw SDL as both a personal characte ristic and a learning process In addition, Garrison also stated that personal responsibility should be included in any theoretical concept of SDL. Garrison placed a large emphasis on the actual learning process ; the cognitive plus motivational dimensions of learning. Garrison developed a model of SDL with three distinct, yet interconnected and overlapping dimensions : self management, self monitoring, and motivation (Garrison, 1997). The self related to external task control. These issues center upon the activation of learning goals and use of learning resources. Garrison indicated that SDL experiences may include the use of facilitators to provide support and direction, thereby creating a collaborative learning experience (Garrison, 1997). next dimension, self es come into play during self monitoring. Foremost is cognitive ability, which suggests that learners will not succeed and persist without cognitive abilities and strategies (Garrison, 1997). Garrison refers to self efficacy and the seminal work of Bandura (1977) and ot hers who suggested the importance of self observation, self judgment, and self reaction (Garrison, 1997)

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62 The last dimension, motivation, is seen as the most pivotal and pervasive to Motivation is broken down into two p arts: entering motivation and task motivation. Garrison referred to e ntering motivation as the decision to participate and believed that motivation is higher when learners perceive that learning goals meet the needs of students and a re achievable. Garriso n suggested entering motivation can be strengthened by offering students choices regarding educational objective s (Garrison, 1997). The second aspect of motivation, t ask motivation involves staying on task and persisting and is directly tied to task cont rol, self management, and the concept of volition. Volition is sustaining intentional effort or diligence and is viewed as an important aptitude for SDL directing and sustaining effort toward learn Brockett & Himestra, Grow, and Garrison (1997) each emphasize d and acknowledge d the importance of the teaching learning transaction, and discuss ed the Howe v er Garrison a more comprehensive look at the psychological dimension of SDL Garrison suggested a personality factor or disposition to be self rrison felt the study of SDL had over emphasized external control and management of learning tasks and de emphasized psychological aspects of SDL. In developing his model and addressing s hortcomings of

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63 the PRO Model, Garrison (1997) identified and integrated cognitive and metacognitive processes throughout his model. The literature indicates that theoretical frameworks of SDL can be useful to professionals in higher education. Constructs such as personal responsibility, self efficacy, motivation, learner control, and autonomy can assist in the development of programs targeted to the retention and academic success of first gen eration and other at risk student populations. In the final phase of the literature review, two quantitative instruments designed to measure self directed learning will be discussed Instrumentation to Measure Self Directed Learning The early work of Houle (1961) and Tough (1971) established both the existence and frequency of self ) supplemented the initial construct of self direction and proposed a linear process describing the activity Shortly thereafter, efforts began to quantify and measure self direction (Stockdale, 2003) Two scales developed to measure SDL are reviewed in this section. The first Directed Learning Readiness Scale (SDLRS), is by fa r the most widely used instrument to measure self directedness. According to Stockdale and Brockett (2000), approximately 70% of published articles involving the measurement of self Redding & Aagaard (1992) argued that the construct of self direction has been operationalized this scale.

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64 The second scale, The Personal Responsibility Orientation to Self Direction in Learning S cale (PRO SDLS) was developed by Stockdal e (2003) as part of her dissertation research and is based on the theoretical constructs of the Personal Responsibility Orientation (PRO) Model discussed previously The PRO SDLS (see Appendix F) rests on more than three decades of research and was develo ped as a way to measure SDL in college students and w as chosen for the current study due to its applicability in higher education Self Directed Learning Readiness Scale (SDLRS) In an attempt to understand the dynamics of SDL in various environments and operationalize SDL empirically, Guglielmino developed a framework to measure an direction in learning (McCune & Guglielmino, 1989) idual self perceptions was translated into a measurement scale called the SDLRS. According to Guglielmino consensus from a panel of experts on the most important personality characteristic of highly self directed learners and to direction in The SDLRS was developed in several stages with the participation of a panel of 14 experts in the adult education field, including well known scholars such as Houle, Knowles, and Tough. The panel of experts participated in a three round Delphi survey technique to identify the characteristics of the self directed learn er (Guglielmino, 1977). From this effort, 56 characteris tics of the self directed learner were identified with 33 of

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65 the items being rated as essential for self direction in learning (Guglielmino, 1989 a ). The 33 essential characteristics were used to develop a 41 item survey, which formed the initial instrumen t (Guglielmino, 1977). A factor analysis identified the following eight principal factors: 1. Openness to learning opportunities 2. Self perception as an effective learner 3. Initiative and independence in learning 4. ng 5. Love of learning 6. Creative spirit 7. Positive orientation to the future 8. Ability to use basic study and problem solving skills The instrument was administered to students in various educational classroom settings. A Cronbach alph a reliability coefficient of .87 was reported for the original 41 item instrument (Guglielmino, 1977). Further revision of the SDLRS removed nine of the original items and added 26 new items, yielding the current 58 item Likert scale instrument. The scale yields one total score ran ging from 176 to 290, which can then be interpreted against a norm (Guglielmino, 1977). Translated into 14 languages, the SDLRS has gained wide acceptance in the field of adult education (Caffarella & Caffarella, 1986; Herbeson, 1991). A significant number of studies have been conducted to a ffirm the validity of the SDLRS (Bonham, 1989; Brockett, 1982; Clark, 1991; Finestone, 1984; Long & Agyekum, 1983; Morris,

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66 1997; Murray, 1987; Savoie, 1980; Torran ce & Mourad, 1978; Wiley, 1981). Desp ite widespread popularity and faith in the SDLRS, it has come under some scrutiny. One of the most basic critiques concerns the age of the instrument. Stockdale (2003) observed that the SDLRS had not been revised since being developed in 1977. A livel y debate ensued after Field (1989) analyzed and criticized the validity and reliability of the SDLRS. Field also criticized the use of the Delphi technique to formulate items and questioned the clarity of some of the scale items and definitions. He also found 11 of the 58 items on the SDLRS instrument did not significantly correlate to the total score. This observation led Field to conclude that only a single construct, love and enthusiasm for learning, were representative of the SDLRS. Field argued that (Field, 1989, p. 138). Several lively retorts supported the SDLRS and criticized Field for a lack of integrity in his study (Guglielmino, 1989; Long, 1989; McCune, 1989). In her response, Guglielmino (1989 b i s so filled with errors of In using the SDLRS with older adults of varying educational levels, Bro ckett (1985) concluded that the instrument wa s less effective in measuring self directedness in directed Despite concerns raised in the literature the SDLRS remains the instrument of SDL

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67 (Stockdale & Brockett, 2000). Most reliability estimates are consistently reported as greater than .80 (Stockdale, 2003). Brockett & Hiesmtra (1991) argued that the SDLRS has made a vital contribution to present understanding of the self directed learning phenomenon a nd has helped inspire research, controversy and dialogue Brockett & ion out weighs the limitations that seem to 75). Regardless, identified critiques indicate that a more focused SDL instrument designed specifically for college students may be more appropriate for the purposes of this research study. Personal Responsibility Orientation to Self Direction in Learning Scale (PRO SDLS) Reliance on the older, unrevise d SDLRS instrument has been problematic for inquiry into modern conceptualizations of self directed learning (Stockdale, 2003) According to Merriam, Caffarella & Baumgartner (2007 ), the absence of a richer research agenda in SDL is due in part to a shortage of robust critical discussion and data based studies of later conceptual models. PRO SDLS addresses this concern and is one of the more recent additions to the research base on SDL measurement. instrument to measure self directedness in learning among college students based on an operationalization of the PRO M odel of self 2010, p. 1). Due to its applicability in higher education, t he PRO SDLS was selected for the current study as a measure of SDL among first generation college students

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68 participating in a summer bridge program. The following is a discussion o f the instrument and rationale for utilizing it in t he current study. The PRO SDLS evaluates the two main component s of self direction in learning learning transaction (self directed learning) and characteristics of the learner (learner self direction). Prior to the development of the PRO Model, research in SDL tended to view the c onstructs separately from either the teaching learning context or as being a personality characteristic of the learner (Stockdale, 2003) In selecting the PRO Model as the basis for the development of her scale, Stockdale (2003) sought to (1) identify and operationalize items that reflect the process and learner components of the PRO Model and (2) validate the scale items associated with oth er measures of self direction. Six research objectives guided Stockdale (2003) in the development of the PRO SDLS: 1. Development of a reliable measure of self directedness 2. Content validation established by a panel of experts 3. Congruent validation of the measure of self directedness confirmed by a comparison of scores on the SDLRS and the PRO SDLS 4. Construct validation verified by comparing scores on SDL with logically related behavioral criteria 5. Convergent validity corroborated by the ratings by pro fessors of the self directedness of their students who participated in the studies

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69 6. Demonstration that the PRO SDLS scores added signification and unique variance to the predication of self direction beyond scores from the SDLRS The significance of Stockd teaching designated learner characteristic ( designated LC) framework of the PRO Model. Within each framework of the PRO Model, Stockdale identified two componen ts. In the TL framework, learner control and initiative are described and measured by the PRO SDLS Alternatively, motivation and self efficacy are measured by the LC component of the PRO SDLS (Stockdale, 2003). Adult education literature was cited in the development of items related to the TL component of the PRO Model (Stockdale, 2003) Seminal research by Kasworm (1982), Fellenz (1985), and Long (1990) inspired Stockdale to include learner control as a component of the PRO SDLS. Stockdale (2003) cited Long the psychological variable of active control over the learning process is often an overlooked component in SDL. In addition, Stockdale (2003) cited Fellenz (1985) who indicated that locus of control may influence the outcome of self directed learning. The second item in the TL component of the PRO SDLS is initiative. In Brockett 24). Similarly Knowles (1975) defined self analyzing the two definitions of SDL S between the two definitions seems t o center on Brockett & personal responsibility initiative SDLS,

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70 Stockdale (2003) concluded that had very similar meaning and settled o SDLS. In formulating items for the LC component of the instrument, Stockdale utilized psychology and education psychology literature to inform her research. Stockdale (2003) cited descriptors of motivation types fr om the research of Deci and Ryan (1985, 2000) as helpful in item construction for inclusion within the LC component of the PRO SDLS. In particular, Stockdale indicated that Deci & Ryan motivation orientation was influenc ed by factors in the environment that affect their self perceptions of competence and autonomy. According to Brockett & Stockdale (2010), In addition to motivation, Stockd ale (2003) viewed the psychological construct of self efficacy as vital to operationalizing the LC component of the P RO SDLS. S tockdale confidence re lative to learning a ctivities. In contrast, modern conceptualizations in adult education literature (Jones, 1994; Murphy & Alexander, 2000) contend ed that self cognitive learning theories ( St ockdale, 2003) of self confidence and defined self efficacy as their capacities to organize and execute courses of action required to attain designated

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71 asserted that self efficacy might be more predictive of actual self directed learning than self confidence. self efficacy for s elf direction may be a valuable addition to the PRO As a result, Stockdale (2003) selected self efficacy as an LC component for the PRO SDLS. Stockdale (2003) conducted t hree pilot studies and a final analysis t o answer the research obj ectives previously described The first research objective was achieved as a reli able measure of self directedness was achieved. During the t hird pilot, a 35 item version of the PRO SDLS produced a coefficient alpha of .92. According to Stockdale, high coefficient alpha (.92) indicated that self direction as measured here can be The second research objective was aimed at establishing content validation using a panel of experts familiar with the PRO Model The panel included Brockett & Hiemstra and four other experts in SDL who provided their input relative to the representativeness and appropriateness of the PRO SDLS. Stockdale asked each rater to decide whether the items app ropriately related to the TL or LC component of the PRO Model. While agreement was not 100%, 31 of the 35 items were representative of one or both components of the model Stockdale further compared the results of the ratings by the expert with the psych ometric data for each item. Stockdale concluded that six of the original items should not be included in the final version of the PRO SDLS. Elimination of the six items by the researcher resulted in a final scale with 25 items (Stockdale, 2003). Accordin d item total correlations

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72 greater than .31, and the calculated coefficient alpha for the 25 10). Research objective #3 explored congruent validity of the measure of self direct edness between scores from the SDLRS (Guglielmino, 1977). Utilizing a Pe a rson product moment correlation coefficient, PRO SDLS scores yielded an r value o f <.70 in relation to the SDLRS. The results indicated that this research objective had been met (St ockdale, 2003). The fourth research objective looked at the construct validity of the scale by examining relations between age, gender, GPA, course performance, and previously completed semester hours. Stockdale (2003) obtained this information in the dem ographics survey included in the research questionnaires. Her correlations revealed significant relationships (p<.01) between scores on the PRO SDLS and age, self reported GPA, previously completed semesters hours, and course performance (Stockdale, 2003) The only objective not met was the fifth, which sought to establish convergent directedness and ratings by professors on the self directedness of those same students. Stockdale (2003) reported that there was no directedness and SDLS or the SDLRS. The final research objective examined whether scores on the PRO SDLS would add significant unique variance to the prediction of self direction beyond scores of the SDLRS. Utilizing a hierarchical multiple regression technique, Stockdale (2003)

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73 determined that the PRO SDLS improved on the prediction of GPA, age, and course performance over the SDLRS. Based on the re sults of her study, Stockdale (2003) concluded that a link between self direction, as measured by the PRO (p. 143). Based on this finding, the PRO SDLS is appealing for this study for t hree reasons. First, the PRO concept ualization that personal responsibility is central to the understanding of self direction. According to Brockett & Hiemstra (1991) personal responsibility means personal responsibility for academic success is important for first generation college students entering the university environment. Second, the PRO SDLS is appealing for this study because it is was specif ically developed for class settings at the college level. Stockdale (2003) noted that a delimitation of her study was that her sample was taken from graduate and undergraduate students attending a large, southeastern, public institution. In the current s tudy, the University of South Florida is a large, southeastern, public institution and is similar demographically to the institution stud ied in the original research. In contrast, the population represented in this study is a far more homogenous university population, Lastly, utilizing the PRO SDLS in the current study afford ed an opportunity to test the reliability of a more recent instrument in the field of adult education Previous studies by Stockdale (2003) and Fogerson (2005) indicated a high level of internal

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74 consistency, .92 & .91 respectively. Further research utilizing the PRO SDLS provides a test o f internal consistency and add s more information concerning the reliability of this particular instrument in the measurement of self direction. Follow used the PRO SDLS to d etermine self directedness in university student s completing online course s reliability of the PRO SDLS was confirmed. A coefficient alpha of .91 was achieved based on 314 responses to a questionnaire. This compares favorably with the measure of internal consistency (.92 & .91 ) reported by Stockdale (Fogerson, 2005). sample was a heterogeneous group that differed in age and included both undergraduate and graduate students at varying levels of academic ability. Fogerson (2005) indicated that age had a considerable impact on statistical outcomes. According to Fogerson (2005) direction within the different groupings. For the group as a whole, there was a positive correlation of .29 between age and self researchers who have indicated that self direction tends to increase with age (Bitterman, 1989; Guglielmino, Guglielmino, & Long, 198 7; Hoban & Sersland, 1999; Jones, 1994; Long & Agyekum, 1984; Long & Morris, 1996). In th e current study, the population wa s a homogenous group of traditional aged college students (17 19) with similar levels of high school achievement The use of a h omogenous group of students help ed minimize the impact of age and ability on statistical outcomes.

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75 Summary In this chapter, literature regarding first generation college students, retention and involvement theory, summer bridge programs, and self directed learning was presented. The literature indicated that f irst generation college students have to negotiate a difficult transition into academia and often experience difficul ties remaining enrolled and attaining a degree (Horn & Nunez, 2000). Limited research has been conducted regarding this student population following matriculation at the university. Additio nal research is needed to better inform university administrators in developing strategies to retain and promote academic success among at risk student population s Next, relevant research on student retention and involvement was investigated as a next s tep in understanding the nature of difficulties surrounding first generation college student persistence Although there is a significant body of literature on attrition and how to ameliorate the problem, there is little research on university level retent ion programs. A common retention effort identified in the literature was the summer bridge program designed to increase academic success and degree completion among at risk student populations Despite a heavy investment of institutional resources, lit tle empirical data exists beyond program based evaluations (Kezar, 2000; Santa Rita & Bacote, 1996) The final component of the literature review described self directed learning and instrument s to measure the phenomenon. Research indicates that self dire ction is an important characteristic of learners; however, no research has been identified regarding the self directedness of first generation college students. The current study identified

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76 possible relationships between higher education and adult educati on constructs through the research self directed learning among first generation college students participating in a summer bridge program. Chapter Three presents a description of the methods utilized for measuring self direction among first generation college students p articipating in the Freshman Summer Institute, a summer bridge program at the University of South Florida

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77 CHAPTER THREE METHOD S A rev iew of the literature indicated that research investigating the relationship between self directed learning readiness and first generation college student success is notably absent. In addition, few empirical studies exist concerning the implementation of summer bridge programs as a tool to augment academic success and retention of first generation students Identified gaps in the literature reveal possible interrelationships between theoretical frameworks in the fields of adult and higher education The purpose of this study was to investigate the change in self direction among first generation college students participating in the Freshmen Summer Institute (FSI), a summer bridge program at the University of South Florida. This study wa s designed to answ er the following research questions: 1. What is the relationship between pre test scores of the Personal Responsibility Orientation to Self Direction in Learning Scale and previous academic achievement as measured by university admissions grade point average? 2. What differences in scores were measured between pre test (given July, 2009) and post test (given January, 2010) administration of the Personal Responsibility Orientation to Self Direction in Learning Scale ? 3. What is the relationship between post test sc ores of the Personal Responsibility Orientation to Self Direction in Learning Scale and academic

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78 achievement as measured by university grade point average at the end of the third full semester? 4. How are participants' levels of self direction following invol vement in a summer bridge pro gram, as indicated by post test scores of the Personal Responsibility Orientation to Self Direction in Learning Scale, different for participants' based on gender and ethnicity? 5. How is the impact of a summer bridge program, as indicated by a change in self direction scores on the Personal Responsibility Orientation to Self Direction in Learning Scale, different for participants' based on gender and ethnicity? Research Design T o answer the research questions proposed, a quantitative research design was used to analyze secondary data A correlational design wa s selected to determine if statistically significant differences exist in variables measured by the Personal Responsibili ty Orientation to Self Direction in Learning Scale ( PRO SDLS). Data were previously gathered through a cooperative effort between Tutoring and Learning Services (TLS) and the Freshman Summer Institute (FSI) at the University of South Florida. Located in The purpose and goals of the FSI program were discussed in detail in chapter two.

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79 During the summer 2009 semester, the Director of TLS partnered with the Director of FSI to offer all incoming FSI students a one credit hour course called Strategic Learning The purp ose of the Strategic Learning course was to assist students in the development of effective academic strategies and to enhance success during college and for lifelong learning. With consent from the USF Division of Research Integrity & Compliance (see App endic es B, C, & D ) the PRO SDLS was distributed to all student participants in the FSI program The Principal Investigator in the Institutional Review Board (IRB) application was the Director of TLS, with the researcher in the current study named as c o I nvestigator. As referenced above, the current study employed a secondary data analysis using an existing dataset collected by the researcher and Director of TLS According to McMillan & Schumacher (2010), secondary data analysis is the process of statisti cally examining data collected by some other organization, group, or individual at some prior time. Secondary data analysis is often chosen by researchers because of data quality and increased sample size (McMillan & Schumacher, 2010). The decision to us e secondary data for this study was certainly intentional given the quality of data a nd large sample size of the summer 2009 cohort of FSI students. Population and Sample The population for this study was from the University of South Florida (USF) a lar ge, metropolitan public, multi campus research university in the state of Florida. USF is one of three research intensive public universities in the state A final headcount of

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80 47,341 students was reported for the fall 2009 semester by the USF Office of Decision Support. The Tampa campus is the main campus for the university, with a total fall 2009 enrollment of 40,267, of which 30,007 were classified as undergraduate. USF Tampa is located on more than 1,5 00 acres and includes 253 buildings housing ext ensive health, medical, and academic facilities, residence halls, research facilities, as well as student services and recreational facilities. The Tampa campus was founded in 1956 to address the needs of a rapidly growing population in the Tampa Bay area In 2008, the population of Hillsborough County, where USF Tampa is located, was reported as 1.2 million (Bureau of Economic and Business Research, 2009). According to the Princeton Review (2010), USF is one of the most ethnically diverse universitie s in the nation In fall 2009, 61.5% of undergraduate students at USF Tampa identified themselves as white, 12.6% black, 15.5% Hispanic, 6.7% Asian and 3.7 % represented other minority groups or did not report During the same term, 56.3% of undergraduate students were female and 43.7% were male (USF Office of Decision Support, 2010). A purposeful sample was used for this study and wa s drawn from participants in the 2009 Freshman Summer Institute (FSI) at the USF Tampa campus. Students were selected for the FSI program through their fall admission application to the university. The university used academic success predictors (high school grade point average, SAT/ACT test score results) to make admissions decisions f or applicants. If a stud ent fell nts, the admissions office flagged

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8 1 students who identified themse lves as being first generation, admission s application as neither parent having completed a baccalaureate degree N ext, the flagg ed first generation applicant s were notified that although they have been denied admission for the fall semester, they ha d been accepted for admission for the summer term with the condition that they successfully complete the Free Application for Federal Student Aid (FAFSA). Students with the lowest expected family contributions (EFC) scores, determined by the FAFSA, we re referred to FSI or a federally funded TRIO program known as Student Support Services (SSS) Depending on the year and re sources, between150 250 students enter the U niversi ty through the FSI program. All F SI participants are traditional aged first year college students (17 19). A total of 224 students participated in FSI during the summer 2009 semester. Of those, 193 (86 .2%) completed a pre test administration of the PRO SDLS. Table 1 contains the demographic data which were taken from the pre test administration of the PRO SDLS.

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82 Table 1 PRO SDLS Pre test Demographics Description N Percentage Males 72 37.3 0 % Females 121 62.7 0 % Totals 193 100% Asian or Pacific Islander 9 4. 67 % Black, non Hispanic 58 30.05 % Hispanic 53 27. 47 % American Indian/Alaska Native 4 2.07 % Race/ethnicity unknown 3 1.55 % White, non Hispanic 66 34.20 % Totals 193 100% FSI students who completed the pre test were expected to complete a post test distributed during January 2010 during a large group meeting of FSI students Unfortunately, not all students in the program completed the second administration. Several studen ts submitted incomplete instrument s and were not included. A total of 122 (54.4%) students completed both the pre and post test administration of t he PRO SDLS. This study, however, limit ed d ata analysis to the 110 students who completed both the pre and p ost test assessment of the PRO SDLS and were categorized as black, Hispanic, or white The students representing the final analysis represent 49.1 % of the entire FSI population and their demographic data is contained in Table 2

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83 Table 2 PRO SDLS Post tes t Demographics Description N Percentage Males 37 33.6 4 % Females 73 6 6. 36 % Totals 110 100% Black, non Hispanic 36 33.7 2 % Hispanic 40 36.3 6 % White, non Hispanic 34 30.92 % Totals 110 100% Variables The following variables are represented in this study: 1. Admissions GPA: Also known as high school GPA. This is a measure of the prior academic performance of first year students participating in the FSI using a 4.0 scale. Ext ra points for advanced placement, honors, or gifted courses given by school districts are not included in the admissions GPA. 2. Ethnicity: A categorical measure which distinguishes between the following : black, Hispanic, and white 3. Gender: A categorical measu re which distinguishes between males and females. This independent variable is dichotomous. Males were coded with a value of 1 and females with a value of 0

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84 4. Pre test score on PRO SDLS: The pre test was administered during the participants first week of co llege in July, 2009. 5. Academic performance: This study use d cumulative university GPA at the end of the spring 2010 semester as a measure of academic perf ormance. This variable included three semesters of college coursework. 6. Post test score on PRO SDLS: The post test was administered January, 2010 or approximately six months after the pre test. Instrument ation For the purposes of this study, the Personal Responsibility Orientation (PRO) M od el (Brockett & Hiemstra, 1991) wa s used as a foundation for investigating self directed learning characteristics of first generation college freshman participating in the FSI program at USF The instrument chosen for this research wa s the Personal Responsibility Orientation to Self Directi on in Learning Scale ( PRO SDLS) described in detail in Chapter Two The PRO SDLS (see Appendix F) was developed by Stockdale (2003) as her doctoral dissertation at the University of Tenness ee. The instrument was an attempt to develop a reliable and valid instrument to measure self directedness in learning among college students based on an operationalization of the PRO Model of self The PRO SDLS scale consists of 2 5 questions representing two subcomponents: a teaching learning transaction component and a learner characteristic component. Within the two subcomponents are four factors: initiative, control, self efficacy, and motivation. Likert scale responses were use d for these questions and represented the values strongly

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85 disagree (1) to strongly agree (5). Total possible score on the instrument is 125 with a higher score indicating a higher level of overall self direction. Contributing to the total score are the in itiative, control, and self efficacy factors which have a m aximum sum score of 30. The final factor, motivation has a maximum score of 35. The scale, scoring rubric, and permission from Stockdale to use the instrument for this study are included in App endix E Based on the results of her study, Stockdale (2003) concluded that a link between self direction, as measured by the PRO (p. 143). Based on this finding, the PRO SDLS wa s appealing for this study for t hree reasons. First, the PRO concept ualization that personal responsibility is central to the understanding of self direction. According to Brockett & Hiemstra (1991) personal responsibility means personal responsibility for academic success is important for first generation college students entering the university environment. Second, the PRO SDLS wa s appealing for this st udy because it is was specifically developed for class settings at the college level. Stockdale (2003) noted that a delimitation of her study was that her sample was taken from graduate and undergraduate students attending a large, southeastern, public in stitution. In the current study, the University of South Florida is a large, southeastern, public institution and is similar demographically to the institution stud ied in the original research. In contrast, the

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86 population represente d in this study wa s a f ar more homogenous university population, Lastly, utilizing the PRO SDLS in the current study afford ed an opportunity to test the reliability of a more recent instrument in the field of adult education Previous studies by Stockdale (2003) and Fogerson (2005) indicated a high level of internal consistency, .92 & .91 respectively. Further research u tilizing the PRO SDLS provides a test of internal consistency and add s more information concerning the valid ity of the instrument in the measurement of self direction. Data Collection Procedures As stated earlier, secondary data collected by the Directors of TLS and FSI w as analyzed for this study. The first data collection point occurred in July 2009 when FS I students completed the pre test administration of the PRO SDLS. During the first week of the Summer B semester, students were asked to sign the IRB informed consent and complete the PRO SDLS during the first class session of Strategic Learning Comple te d PRO SDLS instruments were entrusted t o academic advisor in the FSI program. The advisor scored and coded each instrument so that the researchers could not identify students. In addition to PRO SDLS scores, the advisor entered additional n on identifying student information including variables such as gender, ethnicity, and admissions GPA into the database. The second data collection point occurred January, 2010. During a large group meeting to celebrate the start of the spring 2010 semester FSI students were asked to complete the post test administration of the PRO SDLS. Once again, an advisor in the

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87 FSI program coded all completed instruments, scored them, and inputted them into an electronic database. At the end of the spring 2010 semester, university GPA was recorded in the database. Data Analysis A statistical analysis of the data was completed using SAS software. Descriptive statistics such as appropriate measures of central t endency, variability, standard deviation, minimum/maximum values, skewness, and kurtosis were reported for all variables in this study. lpha was conducted as a measure of reliability and internal consistency of the PRO SDLS score s The appropriate inferential tests were conducted to address each research question Below is an overview of the analysis procedure that was applied to each research question in addition the descriptive statistics outlined above. Question 1: A Pearson Product Moment Correlation was conducted to analyze the relationship between pre test scores of the PRO SDLS and previous academic achievement (high school GPA) Question 2: A dependent means t test was conducted to analyze differences measured in the pre and post test administration of the PRO SDLS. Question 3: A Pearson Product Moment Correlation was conducted to analyze the relationship between post test scores of the PRO SDLS and academic achievement (u niversity GPA). Question 4: A factorial ANOVA was conducted to analyze the relationship between post test scores of the PRO SDLS and both gender and ethnicity

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88 Question 5: A factorial ANOVA was conducted to analyze the change in score s on pre and post test administration of the P RO SDLS and both gender and ethnicity Summary The methodology of this study included both presentation of the design and setting in which the study occurred Utilizing secondary data, the study includes analysis of a pretest and posttest design of first generation college studen ts participating in the Freshmen Summer Institute at the University of South Florida. The student sample was described and consists of 1 10 FSI students. The PRO SDLS instrument was utilized to measure self direction and data collection procedures were desc ribed. Finally, a description of the data analysis techniques was described in detail.

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89 CHAPTER FOUR ANALYSIS OF DATA The purpose of this research was to investigate self direction among first generation college students participating in the Freshmen Summer Institute (FSI), a summer bridge program at the University of South Florida (USF). The study sought answers to five research questions through statistical analysis of pre test and p ost test scores on the PRO SDLS and the interactions between gender, ethnicity, admissions grade point average and university GPA R eliability of the PRO SDLS scores as a measurement of self directedness among the sample population was also examined. The following sections in this chapter will consider: (a) the sample and demographic profile of the respondents, (b) descriptive survey data and reliability of PRO SDLS scores and (c) analysis of the five research questions. Sample Popula tion and Demographic Profile of the Respondents Descriptive statistics are used to describe the main features of a collection of data in quantitative terms. The text in this section present s data that describe the research sample. The variables in this stu dy included admissions GPA, ethnicity, gender, pre test scores on the PRO SDLS, university GPA, and post test scores on the PRO SDLS. As indicated in Chapter Three the sample included 110 first year college students participating in the Freshman Summer Institute (FSI), a summer bridge program at the Un iversity of South Florida (USF) during the summer 2009 semester. A total of 224

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90 students participated in the FSI program during the summer 2009 semester. Of those, 193 (86.2%) completed the pre test adm inistration of the PRO SDLS distributed July, 2009 (see Table 1) In the spring 2010 semester (January), a post test administration of the PRO SDLS was distributed with 122 of 224 (54.4%) participants completing both pre and post test administrations of the PRO SDLS. Of the 122 students with both pre test and post test scores, a final sample size of 110 (49.1% of the entire population) was determined through the inclusion of students who were described themselves as either black, Hispanic, or white. Limiting data analysis to these ethnic groups permitted the use of a factorial ANOVA to answer the fourth and fifth research questions. In Chapter Three, the data cited in Table 2 presented d emographic information of the final sample There were 73 (66. 36 %) females and 37 (33.64%) males in the sample. Broken down by ethnicity, t he largest proportion of participants, 40 students (36.36%), was identified as Hispanic. There were al so 36 b lack students (32.72%) and 34 ( 30.92 %) w hite students. Information on participant age was not collected as all FSI pa rticipants were traditional age d (17 19), first year college students. In addition to gender and ethnicity, information on academic achievement was gathered. Previous academic achievement is indicated by USF admissions GPA, while university academic achievement is indicated by cumulative GPA at the conclusion of the spring 2010 semester, wh ich represents the third semester of college. Table 3 summarizes academic achievement information for the study sample.

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91 T able 3 Descriptive Statistics for Academic Performance Measures Description N Mean Minimum Maximum Std. Dev Skewness Kurtosis Admissions GPA 110 3.35 2.82 3.95 .28 0.79 0.55 University GPA 1 10 2.74 0.84 3.79 .63 0.11 0.66 Descriptive Survey Data This section includes descriptive data based on pre test and post test administrations of the PRO SDLS. The first subsection reports response totals for both administration s of the PRO SDLS and compares these findings with past studies by Stockdale (2003) and Fogerson (2005) Next means are analyzed in order to ensure there is not a systemat ic difference between those who completed the pre test but not the post test administration of the PRO SDLS. The final subsection addresses the reliability of scores for both administrations of the PRO SDLS. PRO SDLS Response Totals and Comparison to Previous Studies Descriptive data for the pre test administration of the PRO SDLS are represented in Table 4 and p ost test data are presented in T able 5 Total PRO SDLS scores are broken down into the four subcomponents measured by the instrument: Learner initiative, control, self effica cy, and motivation. The minimum total score possible on the PRO SDLS is 25 with a maximum score of 125. Three subcomponents, learner initiative, control, and self efficacy each ha ve a minimum possible score of six and a maximum of 30. For motivatio n, th e lowest possible minimum score is seven with a maximum of 35.

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92 Measures of skewness and kurtosis for both administrations of the instrument indicate a n approximately normal distribution. Table 4 Descriptive Data for Pre test Administration of PRO SDLS Description N Mean Minimum Maximum Std. Dev Skewness Kurtosis Total Score 110 89.62 62.00 113.00 10.03 0.24 0.27 Learner Initiative 1 10 19.03 9.00 27.00 3.50 0.25 0.21 Learner Control 110 22.61 14.00 30.00 3.64 0.22 0.60 Learner Self Efficacy 110 24.02 12.00 30.00 3.61 0.57 0.54 Learner Motivation 110 23.96 17.00 32.00 2.91 0.20 0.27 Table 5 Descriptive Data for Post test Administration of PRO SDLS Description N Mean Minimum Maximum Std. Dev Skewness Kurtosis Total Score 110 91.17 60.00 116.00 10.92 0.01 0.13 Learner Initiative 1 10 19.33 8.00 30.00 3.37 0.02 1.07 Learner Control 110 22.89 11.00 30.00 4.01 0. 47 0. 03 Learner Self Efficacy 110 24.40 15.00 30.00 3. 49 0. 50 0.02 Learner Motivation 110 24.55 13.00 34.00 3.90 0.46 0 .29 Mean s cores reflecting self direction as measured by the PRO SDLS fall midway between averages from previous studies by Stockdale (2003) and Fogerson (2005) on both the pre test and post test The mean score s on the PR O SDLS for the current study were 89.62 and 91.17 ( SD = 10.03 and 10.92 ) respectively, out of a possible range of 25

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93 to 125. Analysis by Stockdale (2003) for her study sample revealed a mean score on the PRO SDLS of 84.05 ( SD = 12.47). A more recent study by Fogerson (2005) revealed a mean score of 96.91 ( SD = 11.82) These find ings are represented in Table 6 Table 6 Comparison of Descriptive Statistics for PRO SDLS: Previous and Current Study Description N Mean Std. Dev PRO 194 84.05 12.47 PRO SDLS Total 217 96.91 11.82 PRO SDLS (Current Study Pre test) 110 89.62 10.03 PRO SDLS (Current Study Post test) 110 91.17 10.92 Data Compar ison between Pre test only Group A total of 74 participants completed the pre test administration of the PRO S DLS but did not complete the post test. T o ensure that those who did not complete the post test were not significantly less self directed than those who completed both administrations, a comparison of the means on the pre test as well as admissions and university GPA are presented in Table 7. Table 7 Comparison of Pre test o nly Group to Sample Population Description N Pre Test Mean Admissions GPA University GPA Pre Test Only Group 74 88.97 3.30 2.78 Pre Test and Post Test Group 110 89.62 3.35 2.74

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94 Reliability of PRO SDLS Scores PRO SDLS scores since it is one of the few studies to utilize the instrument. The 25 item PRO SDLS yielded a coefficient alpha on test) and .87 (post test) based on the 110 respo nses to the questionnaire. These coefficient alphas compare favorably with the measures of internal consistency discovered by Stockdale (2003) and Fogerson (2005), which were coef ficient alphas of .91 and .92 respectively. Reliability for each subcomponent score (learner initiative, control, self efficacy, and motivation) was also determined for the current study but was unavailable from previous research studies Data analysis of the reliability of the PRO SDLS is presented in Table 8 Each sub particular, question 16, significantly affected the reliability of the motivation component. Question 16 states: The primary reason I complete course requirements is to obtain the grade expected of me. mo tivation component of the PRO SDLS to .53 (pre test) and .74 (post test).

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95 Table 8 Reliability Data for the PRO SDLS Scores Description N Total Score (Pre test) 110 .84 Initiative (Pre test) 1 10 .76 Control (Pre test) 110 .78 Self Efficacy (Pre test) 110 .79 Motivation (Pre Test) 110 .41 Total Score (Post test) 110 .87 Initiative (Post test) 110 .72 Control (Post test) 110 .83 Self Efficacy (Post test) 110 .79 Motivation (Post Test) 110 .67 Total Score Stockdale (2003) 194 .91 Total Score Fogerson (2005) 217 .92 Analysis of Research Questions The study sought answers to five research questions through statistical analysis of pre test and post test scores on the PRO SDLS and the relationships between gender, e thnicity, admissions grade point average and university GPA Following is a summary of the findings for each of the questions based on the dat a collected

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96 Question One What is the relationship between pre test scores of the Personal Responsibility Orientation to Self Direction in Learning Scale and previous academic achievement as measured by university admissions grade point average? A Pearson Product Moment Correlation was conducted to analyze the relationship between pre test scores of the PRO SD LS and previous academic achievement ( admissions GPA). Correlation is a measure of the relation between two or more variables. Correlation coefficients can range from 1.00 to +1.00. The value of 1.00 represents a perfect negative correlation while a valu e of +1.00 represents a perfect positive correlation. A value of 0.00 represents a lack of correlation or relationship (Cohen, 1988) From the correlat ion values presented in Table 9 all correlations with admissions GPA are positive with three components of the PRO SDLS statistically significant at the 0.05 level : Total score learner control, and self efficacy. Table 9 Correlations between Admissions GPA and PRO SDLS Pre test Scores Description PRO SDLS Total PRO SDLS Initiative P RO SDLS Control PRO SDLS Self Efficacy PRO SDLS Motivation Admissions GPA Pearson r .2 6 10 .26 .29 .08 p value < .01 .30 < .01 < .01 .43 N = 110 While significant, the magnitude of effect between admissions GPA and the above components is not strong. According to Cohen (1988), r values between .10 and .29 are considered a small effect size. The Pearson r values ( effect size) range between .26 and

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97 .29 for the total score and two sub components of the PRO SDLS considered statistically significant. Question Two What differences in scores were measured between pre test (given July, 2009) and post test (given January, 2010) administration of the Personal Responsibility Orientation to Self Direction in Learning Scale ? A dependent means t test was conducted to analyze differences measured in the pre and post test administration of the PRO SDLS. The FSI participants (n = 110) had scores on two vari ables, the pre test PRO SDLS and the post test PRO SDLS The data presented in Table 10 indicate that pre test PRO SDLS scores demonstrated a mean of 89.62 and the post test scores demonstrated a mean of 91.17. Table 10 Pre test and Post test Mean PRO SDLS Scores Description N Mean Std. Dev PRO SDLS (Pre test) 110 89.62 10.03 PRO SDLS (Post test) 110 91.17 10.92 It was noted the post test mean scores were higher, however, a review of the data in Table 11 indicates there was no significant ( p< .05) difference between the mean of pre test PRO SDLS scores and the mean of post test PRO SDLS scores. Table 11 t test Results for Differences in Pre test and Post test Mean PRO SDLS Scores Description N Mean Std. Dev Std. Error Mean t df p value PRO SDLS (Post test) PRO SDLS (Pre test) 110 1.55 10.14 0.97 1.61 108 0.11

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98 Question Three What is the relationship between post test scores of the Personal Responsibility Orientation to Self Direction in Learning Scale and academic achievement as measured by university grade point average at the end of the third full semester? A Pearson Product Moment Correlation was conducted to anal yze the relationship between post test scores of the PRO SDLS and university grade poin t average From the correlat ion values presented in Table 12 all correlations with university GPA are positive with three components of the PRO SDLS statistically significant at the 0.05 level : Total score, learner control, and self efficacy. Table 12 Correlations between University GPA and PRO SDLS Post test Scores Description PRO SDLS Total PRO SDLS Initiative PRO SDLS Control PRO SDLS Self Efficacy PRO SDLS Motivation University GPA Pearson r 30 .12 .42 .30 .03 p value <. 01 .20 < .01 < .01 .76 N = 110 According to Cohen (1988), r values between .10 and .29 are considered a small effect size while values between .30 and .49 are considered a medium effect size. The correlations between total PRO SDLS scores (.30), universit y GPA and learner control (.42) and self efficacy (.30 ) show a moderately strong relationship Question Four How are participants' levels of self direction following involvement in a summer bridg e program, as indicated by post test scores of the

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99 Personal Responsibili ty Orientation to Self Direction in Learning Scale, different for participants' based on gender and ethnicity? A factorial ANOVA was conducted to analyze the relationship between post test scores of the PRO SDLS and both gender and ethnicity. Sheng ( 2008) described the ANOVA F 324). Categorical independent variables in this study include gender (ma le, female) and ethnicity (black, Hispanic, white). Table 13 contains data regarding PRO SDLS post test means related to gende r, ethnicity, and the interaction between gender and ethnicity. Table 13 PRO SDLS Post test Means and the Relationship of Gender & Ethnicity Description N Mean Total Std. Dev Male 37 89.35 10.07 Female 73 92. 10 11.28 Black 36 91.97 10.79 Hispanic 40 89.40 11.22 White 34 92.41 10.74 Black Males 9 90.44 11.46 Hispanic Males 16 87.94 10.18 White Males 12 90.42 9.47 Black Females 27 92.48 10.73 Hispanic Females 24 90.38 11.97 White Females 22 93.50 11.44

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100 Accord ing to the findings in Table 14 there was no significant interaction between gender and ethnicity scores, F = 0.02, in relation to the post test score on the PRO SDLS. Additionally, t he results of the ANOVA showed there was no significant difference in the main effect of gender F = 1.23 nor was there significant difference in the main effect of ethnicity F = 0.64 Table 14 Factorial ANOVA of PRO SDLS Post test Scores with Gender & Ethnicity Description df F Value p value Main effect of gender 1 1.23 0.27 Main effect of ethnicity 2 0.64 0.53 Interaction between gender and ethnicity 2 0.02 0.98 Question Five How is the impact of a summer bridge program, as indicated by a change in self direction scores on the Personal Responsibility Orientation to Self Direction in Learning Scale, different for participants' based on gender and ethnicity? Table 15 contains d ata regarding PRO SDLS change score means related to gender, ethnicity, and the interaction between gender and ethnicity.

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101 Table 15 PRO SDLS Change Score Means and the Relationship of Gender & Ethnicity Description N Change in Mean Std. Dev Male 37 0.41 10.26 Female 73 2.14 10.09 Black 36 2.53 9.23 Hispanic 40 1.30 10.64 White 34 0.82 10.66 Black Males 9 1.33 6.50 Hispanic Males 16 1.31 13.25 White Males 12 1.50 8.3 1 Black Females 27 2.93 10.05 Hispanic Females 24 1.29 8.80 White Females 22 2.09 11.73 According to the findings in Table 16 t here was no significant interaction between gender and ethnicity scores, F = 0.26, to the change score on the PRO SDLS. Furthermore, t he results of the ANOVA showed there was no significant difference in the main effect of gender F = 0.66 nor was there a significant difference in the main effect of ethnicity F = 0. 23

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102 Table 16 Factorial ANOVA of PRO SDLS Change Scores with Gend er & Ethnicity Description df F Value p value Main effect of gender 1 0.66 0.42 Main effect of ethnicity 2 0.23 0.79 Interaction between gender and ethnicity 2 0.26 0.77 Summary The purpose of this chapter was to analyze the results using statistical techniques consistent with the research questions. The study sought answers to five research questions through statistical analysis of pre test and post test scores on the PRO SDLS, gender, ethnicity, admissions GPA, and university grade point average. Reliability of the PRO SDLS scores to measure self directedness among the sample population was also examined. The 25 item PRO SDLS yielded a coefficient test) and .87 (post test) based on the 110 responses to the q uestionnaire. This compares favorably with the measures of internal consistency reported by Stockdale (2003) and Fogerson (2005), which were coefficient alphas of .91 and .92 respectively. For the first research question, a Pearson Product Moment Correlation was conducted to analyze the relationship between pre test scores of the PRO SDLS and previous academic achievement ( admissions GPA). S ignificant relationships at the .05 level were found between admissions GPA and the following components of the PRO SDLS: Total score, learner control, and self efficacy. While statistically significant, the

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103 strength of the relationship for all three components was considered small according to having a high correlation, the strength of the relationships between the components and admissions GPA was small. To answer the second research question, a dependent means t test was conducted to analyze differences measured in the pre and post test adm inistration of the PRO SDLS. While post test mean scores were higher, there was no significant ( p< .05) difference between the mean of pre test PRO SDLS scores and the mean of post test PRO SDLS scores. There were no significant results found for this research question. For the third research question, a Pearson Product Moment Correlation was conducted to anal yze the relationship between post test scores of the PRO SDLS and university grade point average Significant relationships at the .05 level were found between university GPA and the following components of the PRO SDLS: Total score, learner control, and self efficacy. The effect size was moderate for the three significant components using relation, the strength of the relationships between the components and university GPA was moderately strong. T o answer the four th research question, a factorial ANOVA was conducted to analyze the relationship between post test scores of the PRO SDLS and bo th gender and ethnicity. While differences in means were discovered, there were no significant difference s in the main effect of gender and ethnicity. There was also no significant interaction between gender and ethnicity scores in relation to the post tes t score on the PRO SDLS. This research question yielded no significant findings.

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104 In order to determine the fifth research question a second factorial ANOVA was conducted to analyze the relationship between the change score of the PRO SDLS and both gend er and ethnicity. While differences in means were discovered, there were no significant difference s in the main effect of gender and ethnicity. There was also no significant interaction between gender and ethnicity scores in relation to the change score on the PRO SDLS. This research question yielded no significant findings. The following chapter will address the findings of this study including possible explanations for the lack of significance between the variables. Also included will be a discussion of the importance and possible implications of this research as well as recommendations for further study and research.

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105 CHAPTER FIVE FINDINGS, IMPLICATIO NS AND RECOMMENDATIONS Introduction Increased access to higher education over the past forty years ha s resulted in a greater diversity of incoming student s. Of particular interest is the one quarter to one half of f irst year students whose parents are are more likely to be minority, low income, and experience other disadvantages and possible deficits compared to their non first generation peers (Berkner & Choy, 2008; Horn & Nunez, 2000; Pascarella, Pierson, Wolniak, & Terinzini, 2004). In response to greater student diversity and ot her factors such as decreased graduation rates and increased accountability, retention programs have become popular at higher education institutions across the country (Kezar, 2000). Informed by student development and retention theory and research, summe r bridge programs are but one example of programs created to address academic preparation and social adjustment issues experienced by many first year college students (Kezar, 2000; Pantano, 1994; Santa Rita & Bacote, 1996). One possible solution proposed to increase retention among first year college students is to assist them in becoming more highly self directed learners who take greater responsibility for their learning (Kreber, 1998; Maher, 2005) Researchers have proposed

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106 that highly self directed learners are more interested in academic subjects, have more positive attitude s and exhibit a greater sense of self concept, ultimately leading to increased retention (Brockett & Hiemstra, 1991). Programs exist in higher education to foste r the development of personal responsibility and self directedness among first generation first year college students; however, discussion of relationships between self directed learning readiness and academic success among these students are notably miss ing from the literature. For the purposes of this study, the concept of self directed learning is examined through the lens of retention and student involvement theory in order to examine the self directedness of a sample population of first generation, first year college students. This chapter offer s a summary of the relationships between self direction as measured by the PRO SDLS and the interactions between gender, ethnicity, admissions GPA, and university grade point average among first year college s tudents participating in the Freshman Summer Institute, a summer bridge program at the University of South Florida. Sections in the chapter include: (a) Summary of the Study, (b) Principle Findings, (c) Implications an d Discussion of the Results, (d) Rec o mmendations for Future Research, and (e) Concluding Remarks. Summary of the Study This section contains a summary of the research problem, context, and methodology employed to answer the proposed research questions.

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107 Problem Statement This research explored possible relationships and interactions between self directed learning readiness and a number of variables associated with a population of first generation, first year college students. These variables included pre test and post tes t scores on the PRO SDLS instrument, gender, ethnicity, previous academic achievement (admissions GPA), and university GPA. The study sought to answer five quantitative research questions. 1. What is the relationship between pre test scores of the Personal Responsibility Orientation to Self Direction in Learning Scale and previous academic achievement as measured by university admissions grade point average? 2. What differences in scores were measured between pre test (given July, 2009) and post test (given Jan uary, 2010) administration of the Personal Responsibility Orientation to Self Direction in Learning Scale ? 3. What is the relationship between post test scores of the Personal Responsibility Orientation to Self Direction in Learning Scale and academic achie vement as measured by university grade point average at the end of the third full semester? 4. How are participants' levels of self direction following involvement in a summer bridge pro gram, as indicated by post test scores of the Personal Responsibility Ori entation to Self Direction in Learning Scale, different for participants' based on gender and ethnicity?

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108 5. How is the impact of a summer bridge program, as indicated by a change in self direction scores on the Personal Responsibility Orientation to Self Dire ction in Learning Scale, different for participants' based on gender and ethnicity? Research Setting The population for this study came from participants in the Freshman Summer Institute, a summer bridge program at the University of South Florida Tampa campus. Enrolling more than 47,000 students over four campuses, USF is a large, metropolitan, public research university and one of three research intensive institutions in the state. FSI is an alternative admissions program which supports first genera tion, low intensive six week summer term where they complete nine semester hours of coursework. Part of the required curriculum for all 224 participants during the summer 2009 semester was a one credit course called Strategic Learning The purpose of the course was to develop autonomous learners through their understanding of concepts related to motivation, attitude, goal planning, and the process of learning. Thr ough the process of reflective practice, students had the opportunity to develop a deep u nderstanding of themselves as learner s and then intentionally apply that understanding to the development of the most effective strategies for success in both college learning and beyond. Successful completion of Strategic Learning and other required summer coursework allowed students to continue their education into the fall semester.

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109 A total of 224 students participated in the FSI program during the summer 2009 s emester. Of the total population 110 (49.1%) comprise d the final sample size for the current study. Those included in the final sample completed both a pre test and post test administration of an instrument (PRO SDLS) designed to measure self direction. Method s A correlational research design was selected to analyze the following secondary data: Pre test and post test scores on the PRO SDLS instrument, gender, ethnicity, previous academic achievement (admissions GPA), and university GPA. T o answer the proposed research questions, a series of statistical analyses were conducted using SAS software. A Pearson Product Moment Correlation was used to analyze the first and third research questions while a dependent means t test was conducted to a nalyze differences between pre test and post test scores of the PRO SDLS (question two). Lastly a series of Factorial ANOVA analyses were completed to answer the fourth and fifth research questions. The use of Factorial ANOVA permitted the isolation of ethnicity into three distin ct groups: Black, Hispanic and W hite. Principle Findings This research used five research questions to determine the relationships between the variables previously described A summary of t he findings are presented in this sec tion Findings for Research Question One The first research question focused on previous academic achievement (admissions GPA) and the relationship to pre test scores on the PRO SDLS The research

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110 question was stated as follows: What is the relationship be tween pre test scores of the Personal Responsibility Orientation to Self Direction in Learning Scale and previous academic achievement as measured by university admissions grade point average? A Pearson Product Moment Correlation was used to analyze the da ta in an effort to identify relationships among pre test scores on the PRO SDLS and previous academic achievement as measured by admissions GPA. There were three significant relationships found ( p< .05) between the total and subcomponent score s of the PRO SDLS and admissions GPA There was a significant, positive correlation between total pre test PRO SDLS score s ( r = .26, p< .01) and admissions GPA. The correlation coefficient suggests a low magnitude of effect While significant, the low effect size indicates that the relationship between total PRO SDLS scores and admissions GPA is not a strong relationship. Significant, positive relationships to admissions GPA were found in the learner control and self effica cy subcompone nts of the PRO SDLS while no significant correlations were determined for the initiativ e and motivation components. Participants with a higher score on the learner control and self efficacy components on the PRO SDLS were found to have a highe r admissions GPA. As with pre test total score, both the learner control (r = .26, p< .01) and self efficacy (r = .29, p< .01) components had a low effect size.

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111 Findings for Research Question Two The second research question measured the difference in sc ores between pre test and post test administration of the PRO SDLS and was stated as follows: What differences in scores were measured between pre test (given July, 2009) and post test (given January, 2010) administration of the Personal Responsibility Ori entation to Self Direction in Learning Scale ? Despite a mean increase of 1.55 or 1.7% a dependent means t test indicated that the change (t = 1.61, p > .05) in PRO SDLS scores was not significant. With 125 total possible points, the pre test mean was 89.62 while the post test was 91.17. Despite an increase between pre test and post test administrations of the PRO SDLS measured increases were not considered statistically significant Findings for Research Question Three The third research question f ocused on academic achievement after three semesters of college coursework ( u niversity GPA) and the relationship to post test scores on the PRO SDLS. The research question was stated as follows: What is the relationship between post test scores of the Personal Responsibility Orientation to Self Direction in Learning Scale and academic achievement as measured by university grade point average at the end of the third full semester? A Pearson Product Moment Correlation was used to analyze the data in an ef fort to identify relationships among post test scores on the PRO SDLS and academic achievement as measured by university GPA. There were three significant relationships

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112 found ( p< .05) between the total and subcomponent scores of the PRO SDLS and admissions GPA. There was a significant, positive correlation between total post test PRO SDLS scores ( r = .30, p< .01) and university GPA. The correlation coefficient suggests a The medium effect size indicat es that the relationship bet ween total PRO SDLS scores and university GPA is a moderately strong relationship. Significant, positive relationships to university GPA were found in the learner control and self efficacy subcomponents of the PRO SDLS while no significant correlations were determined for the initiative and motivation components. Participants with a higher score on the learner control and self efficacy components of the PRO SDLS were found to have a higher university GPA. As with post test tot al score, b oth the learner control (r = .42 p< .01) and self efficacy (r = .30 p< .01) components had a medium effect size with learner control having the largest correlation coefficient in the study Findings for Research Question Four The fourth research question examined the relationships between gender, ethnicity, and post test scores on the PRO SDLS. A factorial ANOVA was conducted to answer the following research question : How are participants' levels of self direction following involvement i n a summer bridg e program, as indicated by post test scores of the Personal Responsibility Orientation to Self Direction in Learning Scale, different for participants' based on gender and ethnicity?

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113 Differences in mean PRO SDLS scores were measured based o n ethnicity, gender, and the interaction of each element. For gender, while females had higher post test scores (92.10) than males (89.35), these differences (F = 1.23, p > .05) were not considered statistically significant. In addition to gender differ ences, means varied between B la ck, Hispanic, and W hite participants. White students had the highest PRO SDLS mean (92.41) while Hispanics had the lowest (89.40) average score. Differences measured between ethnic groups were not considered statistically s ignificant (F = .64, p > .05) following the factorial ANOVA. The interaction of gender and ethnicity was also examined as part of this research question. Mean differences were found between PRO SDLS scores based on the combination of gend er and ethnicity. Scores varied from 87.93 for Hispanic males to 93.50 for white females. Results of the factorial ANOVA indicated that these differences (F = .02, p > .05) were not statistically significant. Findings for Research Question Five The final rese arch question examined the relationships between gender, ethnicity, and the change in score on the PRO SDLS. A factorial ANOVA was conducted to answer the following research question : How is the impact of a summer bridge program, as indicated by a change i n self direction scores on the Personal Responsibility Orientation to Self Direction in Learning Sc ale, different for participants based on gender and ethnicity?

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114 Differences in the change in mean of PRO SDLS scores was measured based on ethnicity, gender and the interaction of each element. For gender, whi t e females had a greater change in mean score between pre test and post test (2.14) than males (.41) these differences (F = .66, p > .05) were not considered statistically significant. In addition t o gender differences, the change in mean varied between black, Hispanic, and white participants. Black students had the highest change in mean (2.53) between pre test and post test administrations of the PRO SDLS while white students had the lowest (.82) change score. Differences measured between ethnic groups were not considered statisti cally significant (F = .23, p > .05) following the factorial ANOVA. The interaction of gender and ethnicity was also examined as part of this research question. Differ ences in the change score was found between pre test and post test PRO SDLS scores based on the combination of gend er and ethnicity. Scores varied from a positive change of 2.93 for black females to a decrease in mean of 1.50 for white males Despite a d ifference in change of nearly five points between these two groups r esults of the factorial ANOVA indicated that these differences (F = .26 p > .05) were not statistically significant. Implications and Discussion of the Results The findings of this res earch study indicate that institutions of higher education may have a difficult time having a direct, immediate impact on student self direction. The level of self direction among the first year, first generation

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115 students in this study did not change sign ificantly d espite participation in a summer bridge program and the completion of a Strategic Learning course which was designed to instill in students the values of a self directed learner who understands the process of learning and its relationship to th e concepts of motivation, attitude, and goal planning. Scholars in the field of adult education have indicated that self direction tends to increase with age and develops over time (Bitterman, 1989; Guglielmino, Guglielmino, & Long, 1987; Hoban & Sersland, 1999; Jones, 1994; Long & Agyekum, 1984; Long & Morris, 1996). The expectation that a summer bridge program could have a significant effect of self direction may be a lofty, unattainable short term goal; however, the long term impact of such a program may assist in the development of autonomous, lifelong learners who take responsibility for their own learning. A longer term study may have reveal ed more significant change in self direction among the participants as they would have more time to ma ture and engage more meaningfully in the ir academic careers. Learner Control Despite a lack of significant increase in self directedness among the participants in the study, there are other important correlations that were discovered through administration of the PRO SDLS. The first, and most significant, was the correlation between learner control and academic success. Learner control was highly correlated to both previous (admissions GPA) and

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116 current (university GPA) academic achievement. The strength of relationship between academic achievement and scores on the learner control component of both administrations of the PRO SDLS had a medium effect size The concept of learner control is at the heart of the PRO Model develope d by Brockett & Hiemstra (19 91) and t heir definition of personal responsibility cites learner control as a central component. According to Brockett & Hiemstra (1991), personal responsibility the ability and/or willingness of individuals to take control of their own learning tha t determines their potential for self 26). In addition to Brockett & Hiemstra other scholars in the field of adult education wro te about the importance of learner control. Long (2000) emphasized the concept of learner control when he referred to his four conceptualizations of self directed learning. The first conceptualization was the learning projects. Next Long discusses self directed learning as a technique the teaching format. The third conceptualization, methodological, is based on the distance method of delivering instruction. The last and most important conceptualization is the ps ychological concept ualization, which was control over the cognitive process of learning. According to Long (2000), self direction indicates proces s and is able to apply the self (consciousness) to those elements for

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117 conceptualizations are not possible without the psychological conceptualization because the learner must ha ve both control and motivation to engage successfully in the learning process (Long, 2000). Long further argued that choice is a consequence of control and that learners are not capable of making a choice in the teaching learning situation without feeling a sense of control, or responsibility, over the process (Long, 2000). In further distinguishing the ideas of choice and control, Long (2000) described choice in the learning environment leading to learner control and enabling learners to take personal re sponsibility for their decisions. According to Long, choice is provided by circumstances in the learning environment but learner control is what changes the circumstances (Long, 2000). The viewpoint that learner control is a key component of self directi on has implications for practitioners in higher education. While promoting the ability for lifelong learning has been proposed as a goal of higher education by many administrators and faculty, this has not yet been translated into changes in the process o f higher education teaching. With the emphasis on assessment, evaluation and passing evaluations in a regulated classroom environment of time blocks and rigorous schedules often the importance is placed on the content of the material and the regurgitation of it instead of the process of learning the material with understanding. Students are rewarded for the "correct answers" instead of the problem solving process which is what they will experience in the work place. Candy and Crebert (1991) put it well in stating: "It is hardly surprising, therefore, that the new graduate should feel confused and inadequate and is

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118 likely to falter in the transition from ivory tower to concrete jungle" ( p. 579). Providing more choice and control over the learning process wi ll lead to the development of the Self Efficacy In addition to learner control, self efficacy was significantly correlated to previous academic achievement (admission s GPA) and university GPA. Bandura (1977) refers to self organize and execute courses of action required to attain designated types of asserted that if one b elieves that engagement in a particular activity will lead to desirable outcomes and feels capable of successfully performing that task, self efficacy should precede that task (Ponton, Derrick, Hall, Rhea, & Carr, 2005) Ponton et al. (2005) suggested lf efficacy is a domain specific assessment that must be contextualized to the The works of Astin (1972), Pantages and Creedon (1978), Stampen and Cabrera (1987), and others indicate that pre college characteristics, such as high school GPA, are strong predictors of academic success and persistence. Nevertheless, it should be noted that in terms of psychology and education as related to self efficacy, Graham and Weiner (1996) ities serves as a stronger indicator As administrators continue to respond to questions pertaining to institutional effectiveness with regard to student persistence, a better understan ding of self efficacy as it relates to student

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119 persistence may be helpful. Designing enrichment and academic programs that facilitate greater self efficacy could result in increased persistence at institutions that intentionally focus on the development of self efficacy through its educational offerings. Reliability of the PRO SDLS While t he results of this research showed a high correlation among total score and the learner control and self efficacy subcomponents of the PRO SDLS, the initiative and motiv ation subcomponents did not correlate to academic achievement. It must be noted that the purpose in developing the PRO SDLS was not to predict academic achievement, directedness in learning am ong college students based on an operationalization of the PRO Model of self of this study was that the P RO SDLS proved to be a reliable instrument in the measureme nt of self direction. The total scores on both the pre test a nd post test administration of the PRO each subcomponent was also assessed with three of the four components achieving high reliabi reliability of scores SDLS. A possible reason for this is that Stockdale (2003) may have intended to use this instrument wit h adult learners of varying ages as was used in her study and follow up research by Fogerson (2005) In the current study, the population sampled was a homogenous group of traditional age (17 19), first year college students who recently transitioned from the high school environment. It is possible that motivation among first year college students

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120 has a different meaning than motivation among adult learners of increasing age Coming from a and home environment may indicate that ext rinsic motivation is more powerful than intrinsic motivation among this age group. Research has shown that motivation is related to age, with younger learners being more extrinsically motivated while mature learners tend to be more intrinsically motivated as age increases (Bye, Pushkar & Conway, 2007; Pintrich & Schunk, 1996; Pintrich, Smith, Garcia & McKeachie, 1993) As a result, in its current form, the PRO SDLS may not be an appropriate instrument to measure the construct of motivation among first yea r college students. A revision of the instrument is recommended for use with traditional aged college students. Ethnicity and Gender Despite a lack of statistically significant differences among ethnicity and gender in this study, themes emerged that are worthy of discussion Females were more self directed than males with white females the most self directed among all groups. Hispanics were the least self directed with Hispanic males as the least self directed among all groups. The difference between white females and Hispanic males was 4.1 points on the 125 point PRO SDLS scale. Black females had the greatest positive change in self direction (2.93 points) while white males were the only group to decrease in overall self direction, with an average 1.5 decrease in PRO SDLS scores This phenomenon cannot be explained but is worthy of mentioning. It may be possible that white males come into the collegiate environment overconfident in their abilities and that the college experience cause s the m

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121 to decrease in self environment that hampers the development of white males and causes them to decrease in self direction compared to other groups. In recent decades, much of the emp hasis in higher education has focused on underrepresented and minority groups with white males seen as the majority group that has enjoyed dominance in higher education for hundreds of years. The lack of focus on the white male experience may be worth inv estigation to social and intellectual needs of this group of students. Summer Bridge Programs Realizing both the limit of institutional resources and the desire to ret ain students, Tinto (1993 ) urged that institutions of higher education place those resources at the beginning of the colle ge experience. He further stated that the biggest impact on retention will occur during the first months of the college experience. Universities and colleges c oncerned with how to incorporate retention strategies as early as possible have turned to pre enrollment or summer bridge programs as a means of achieving many of the objectives associated with increased retention of students. The Freshmen Summer Institute at the University of South Florida is just one of many example s of summer bridge program s in the United States An extensive review of the literature revealed a lack of information regarding the structure and effectiveness of most summer bridge programs. Unlike federally funded TRIO programs, the majority of summer bridge programs are created to meet the needs of the students at a particular institution making comparison across institutions difficult, if not impossible Evaluation

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122 of these programs is t ypically performed at the institution level with inconsistent measurements and varying standards of success. The purpose of all summer bridge programs is to help prepare students so that they may succeed in college to the point of graduation. Yet, the cu rrent research found little information concerning both the definition and measurement of long term student A clear set of national benchmarks and guidelines for summer bridge programs is needed in order to more effectively evaluate their succes s. A lack of clarity and purpose muddies the waters in effectively evaluating these expensive retention programs and creating a benchmarking system will be a difficult task due to their diversity may be defined different ly between institutions. One institution may only look at first year to second retention while others may consider four year graduation rates as most important. Additionally, s ome programs offer remedial education courses while others do not. Different still are institutions who offer major specific summer bridge programs and others that offer them to all majors. Furthermore, participation in a summer bridge program is mandatory at some institutions while voluntary at others. Finally some programs are based on minority, first generation, low income, or any combination of these factors. The sheer diversity of summer bridge programs calls for leadership in the development and assessment of these programs that will properly serve the needs of an institution. Allowing college s and universities to operate summer bridge programs in isolation results in an inefficient use of resources and does not allow for authentic

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123 assessment of their effectiveness. Practitioners in the field must come together and determine best practices in the delivery and evaluation of these programs. Most of the evaluations currently being conducted involve students during the college experience, however, little research was found that discussed completion of the f our year degree and life beyond This begs the question: Are former summer bridge participants successful in the workforce? Blending a variety of experience and perspectives in the development and implementation of benchmarks standards and best practices will assist professionals who are managing these programs to design and revise them based on sound educational pr actice and research while also meeting the unique needs of their particular institution s Recommendations for Future Research Following are several recommendations for future research that would enhance understanding of the phenomena presented in this dissertation. 1. Realizing the limitations that are inherent in single institution studies, future researchers are encouraged to replicate th is study with a similar group of first generation, low income students Studies at other institutions could lead to greater generalizability of findings. 2. Further research with the PRO SDLS would aid the field of adult education with data on the reliability of a relatively new scale in the measurement of self direction. A factor analysis of the PRO SDLS questions would provide further evidence of the validit y of the instrument

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124 3. A follow up assessment of the self directedness of the students in this study may have le d to more significant findings. Students were given the post test administration of the PRO SDLS six months after the pre test. A longer durati on between pre test and post test may have yielded more significant results given research that indicates self direction develops over time. 4. A review of the motivation component of the PRO SDLS will help determine whether this instrument is reliably measu ring motivation as intended. A comparison of reliability with a group of older adult learners in comparison to traditional first year college students will help future researchers determine the value of the instrument among varying groups of adult learner s. 5. was a qualitative analysis of writing assignments undertaken by the students over the course of the six week summer semester Students in the FSI program during the summer 2009 al so completed similar reflective writings during the first and last week of the semester. The purpose of these writings was to help students describe themselves as a learner, discuss learning strengths and weaknesses, set goals for improvement, and discuss past approaches to academic tasks The se two writing assignments should be analyzed for evidence of growth in the ability of students to

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125 analyze immediate academic demands and acceptance for increased responsibility for learning. 6. In addition to the PRO SD LS, all students in the 2009 FSI cohort complete d an instrument called the Learning Connections Inventory (LCI) developed by Johnston & Dainton (1997) The LCI is based on the Let Me Learn Process (LMLP) a model of describing how learning takes place and a means to improve instruction in the postsecondary classroom. The foundation of the process is the belief that in order to take control over their learning, the learner must have an awareness of oneself (Johnston, 2010). T he LCI operationalizes the L MLP and is a major component of the Strategic Learning course that FSI students completed in summer 2009 A study to identify the relationship of scores on the LCI to scores on the PRO SDLS may contribute to the body of know ledge on self direction and its relationship to a process whose purpose is to develop learners that take greater responsibility for their learning through an understanding of their own cognitive processes. 7. In addition to comparing the scores on the PRO SDLS and LCI, one could replicate the current study by substituting the LCI scores of the participants in place of the PRO SDLS scores. Identification of the relationship between LCI scores and academic achievement may prove promising in understanding the current population.

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126 8. While stud ents were given explicit instruction on the habits of highly self directed learners in the one credit hour Strategic Learning course, there was no programmatic coordination to intertwine the concept of self directedness in the other eight credit hours of c oursework FSI students completed. A more intentional approach by the leadership of the FSI program to encourage self directed learning principles throughout the summer curriculum may have achieved different results. Replication of this study with greater support from all stakeholders to develop self direction in students should be conducted. 9. Further inquiry in to the experience of white male self directedness should be conducted. A larger sample size of white males should be surveyed to determine if dec reased self direction among white males as found in this study was an anomaly or a trend. 10. Further studies should be conducted regarding learner control in the college classroom. The PRO SDLS instrument could be used to measure the change in self direction between a classroom environment that encourages learner autonomy and control versus an environment that is more traditional and teacher led. 11. Additional research is needed on the use of summer bridge programs as a retention tool in higher education. Ther e is limited research available that discusses the ef fectiveness of these programs and no research was found that tracked success after the collegiate experience.

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127 Concluding Remarks This study was intended to advance understanding of self directed learn ing characteristics of first year, first generation college students participating in a summer bridge program. Understanding the experience of these students in higher education can lead to the development of programs that better meet the needs of this at risk student population. Theoretical frameworks from higher education and adult education literature merged to provide an understanding of self direction for the context of this study. Student retention and social integration theories from Tinto and A stin were studied as they have been widely used to assist higher education professionals in understand ing why students leave college and to help them d evelop strategies and programs to aid in the retention of at risk students. The adult education theory of self directed learning complements higher education theory by providing insight into the academic environment that was experienced by students in the current study. In the context of the Personal Responsibility Orientation Model, r esults of this study indicated that a fundamental shift in teaching pedagogy may be an integral component of increasing the Higher education faculty should be challenged to design curriculum that relies less on rote memorization themselves to the notion that learning is more effective when the learner is allowed to control and construct their own meaning of the material. Stinson & Miller (1996) advocated a paradigm shift away f rom the teacher centered mentality of instruction t o a

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128 student centered philosophy Stinson & Miller (1996) best state d the need for faculty to re examine their role in the teaching process: tive listening, coaching, mentoring, and facilitation) are not characteristic 40) Faculty who are willing to learn and teaching philosophy can create an environment where students take control of their own learning through interaction with their peers and through an instructor who provides support to students through constructive feedback and scaffolding the learning experience. Faculty would be well served to gradually relinquish their position of power, introducing choices for students, and having them assume more responsibility for their learning. In order to help transition responsibility and control of the learning process from faculty to student s, the use of an advanced learning system such as the Let Me Learn Process (LMLP) used in the Strategic Learning course, would be invaluable in the college classroom. The purpose of a system such as LMLP is to help students understand their own learni ng process es and provide them the cognitive tools for task analysis and to ultimately customize strategies for increase academic efficiency and ultimately, success. Simply relinquishing control over the learning environment is not the solution to increasi ng learner control and responsibility. In order to be more successful, a tool like the LMLP must be provided for the learners to understand themselves as learners and develop individualized strategies for success that will ensure their adaptation to a mo re self directed college environment. It will take an intentional

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129 classrooms from teacher led to an environment conducive for self directed learning. At a January, 2010 p resentation of the Student Success Task Force at the University of South Florida, a faculty focus group was quoted: active in contributing to their own success. This is an institutional issue. There is a socialization process. We need to create a culture in which our students are socialized to understand that learning is an active process and that they are in control of their own education (p. 14). The above quote is encouraging for those holding the bel ief that a fundamental shift from a teacher centered to a learner centered college classroom is a central component to increasing self directedness among college students. A student led curriculum will help transition college students into lifelong autono mous lea rners who take responsibility for their lear ning.

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148 APPENDICES

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149

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150 Appendix B: IRB Initial Consent from Secondary Data Source

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151 Appendix B Continued

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152 Appendix C: Informed Consent from Secondary Data Source

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153 Appendix C Continued

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154 Appendix C Continued

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155 Appendix D: IRB Modification Approval

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156 Appendix D Continued

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157 Appendix E: Permission to use PRO SDLS

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158 Appendix F: PRO SDLS

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159 Appendix F Continued

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ABOUT THE AUTHOR Jeffrey Hall is a first generation college student with over a decade of progressive professional experience in both K 12 and higher education. In 2000, Jeff began his higher education career at the University of South Florida (USF) and spent eight years in the Department of Educational Leadership & Policy Studies as an academi c advisor for graduate students. In 2008, Jeff changed his career focus and began a two year career as an Instructional Designer at both the USF College of Nursing and the University of Nevada Las Vegas Office of Distance Education. Currently, Jeff is se rving as a Graduate Teaching Assistant in the College Student Affairs program at USF. Jeff earned an Associate of Arts with Honors Program endorsement from the University of North Florida in 1997. Afterward, he obtained a Bachelor of Science from USF in Instructional Technology (2003), also from USF.


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Self-directed learning characteristics of first-generation, first-year college students participating in a summer bridge program
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ABSTRACT: The purpose of this study was to advance understanding of self-directed learning characteristics of first-year, first-generation college students participating in a summer bridge program. Understanding the experience of these students in higher education can lead to the development of programmatic and pedagogical strategies to better meet the needs of this at-risk student population. This study was conducted at the University of South Florida (USF), a large, public research university in Tampa. Participants were recruited from the Freshman Summer Institute (FSI), a summer bridge program for first-generation students at USF. Theoretical frameworks from higher education and adult education literature merged to provide an understanding of self-direction for the context of this study. Student retention and social integration theories from Tinto and Astin were studied, as they have been widely used to assist higher education professionals in understanding the reasons students leave college and to assist administrators in the development of strategies and programs to aid in the retention of at-risk students. An example of a retention strategy is the summer bridge program, used by a variety of colleges and universities to increase persistence of at-risk student populations. The adult education theory of self-directed learning complemented Tinto and Astin's theories. The Personal Responsibility Orientation (PRO) Model (Brockett & Hiemstra, 1991) served as a theoretical framework for understanding self-direction among the participants in the study. The PRO Model posits that learners utilize personal responsibility through the characteristics of the teaching-learning transaction along with their own personal learning characteristics to achieve self-directed learning within a broader social context. The Personal Responsibility Orientation to Self-Direction in Learning Scale (PRO- SDLS), based on a conceptualization of the PRO Model, was used to quantitatively measure self-directed learning among participation in the FSI Program. A series of correlations, dependent means t-tests, and factorial ANOVA's were conducted to examine the relationship between scores on both pre-test and post-test administrations of the PRO-SDLS. In addition to an investigation of the change in self-direction, relationships between academic achievement, gender, and ethnicity was also examined in the study. Measured increases in overall self-directedness as measured by the pre-test and post-test administrations of the PRO-SDLS were not considered statistically significant, however, significant correlational relationships (p<.01) were found between academic achievement and total PRO-SDLS scores. Subcomponent measurements of learner control and self-efficacy were also highly correlated to both admissions GPA and university GPA. No significant relationships were found between ethnicity, gender and scores on the PRO-SDLS. An implication for practice indicates that a shift in teaching pedagogy may be an integral component to increasing the academic success of first-year college students. Higher education faculty should be challenged to design curriculum that relies less on rote memorization and "spoon feeding" information to students. Instead, a learner-centered curriculum which gives control of the learning process to students is vital to instilling the habits of highly self-directed learners. In addition to revamped pedagogical strategies, this study calls for the development of national benchmarks and guidelines to more effectively evaluate the quality and impact of summer bridge programs.
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