|USFDC Home | USF Electronic Theses and Dissertations||| RSS|
This item is only available as the following downloads:
xml version 1.0 encoding UTF-8 standalone no
record xmlns http:www.loc.govMARC21slim xmlns:xsi http:www.w3.org2001XMLSchema-instance xsi:schemaLocation http:www.loc.govstandardsmarcxmlschemaMARC21slim.xsd
leader nam Ka
controlfield tag 001 001935416
007 cr mnu|||uuuuu
008 080423s2007 flu sbm 000 0 eng d
datafield ind1 8 ind2 024
subfield code a E14-SFE0002256
Kemker, Katherine J.
Technology in low socio-economic K-12 schools :
b examining student access and implementation
h [electronic resource] /
by Katherine J. Kemker.
[Tampa, Fla.] :
University of South Florida,
ABSTRACT: Over the past twenty years, there has been an enormous financial investment in technology for K-12 schools to help bridge the digital divide between high and low socioeconomic (SES) students. These investments have included hardware, software, professional development, and research studies on the effectiveness of technology in the classroom. This dissertation explored the essential conditions for the integration of technology, access, student-centered learning environment, and teacher use in the curriculum. Florida's elementary schools were divided into quartiles based on free and reduced lunch to investigate differences between the top quartile of high SES schools (n=400) and the lower quartile of low SES (schools n=396). The first research question investigated students' access to technology by specifically concentrating on their access to computers in high and low SES schools.A statistically significant difference was found between students' access to modern computer labs and modern laptop computers. However, there were no significant differences in access to modern classroom computers. When looking at software on student computers, a statistically significant difference between high and low SES schools was found in the availability of both basic tool-based software and robust tool-based software. These data mirrored the statistically significant difference for frequency of use for tool-based software at high and low SES schools. Data on the teacher use of technology in the curricular framework of the classroom showed a statistically significant difference in the curriculum-focused use of technology, between high and low SES schools. This study is a building block for further examination of the digital divide that exists between high and low SES schools. This divide appears to include low expectations for students in low SES schools through regular exposure to drill and practice software, while students in schools with high-SES populations have more opportunities to utilize tool-based software.
Dissertation (Ph.D.)--University of South Florida, 2007.
Includes bibliographical references.
Text (Electronic dissertation) in PDF format.
System requirements: World Wide Web browser and PDF reader.
Mode of access: World Wide Web.
Title from PDF of title page.
Document formatted into pages; contains 120 pages.
Advisor: Ann E. Barron, Ed.D.
x Instructional Technology
t USF Electronic Theses and Dissertations.
Technology in Low Socio Economic K 12 Schools: Examining Student Access and Implementation by Katherine J. Kemker A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Secondary Educa tion College of Education University of South Florida Major Professor: Ann E. Barron, Ed.D. Frank Breit, Ph.D Robert Dedrick, Ph.D. Denise Miller, Ph.D. Date of Approval: August 16, 2007 Keywords: low socioeconomic, technology, computers, software, access Copyright 2007 Katherine J. Kemker
ACKNOWLEDGMENTS I want to thank my family, friends and colleagues for all of their support and guidance during the many stages of this dissertation. Thank you to my parents who have stood by me in this pr ocess with their unselfish love, support and encouragement. Thank you to my major professor, Ann Barron, for her ongoing belief that I would complete this dissertation. In particular, I want to thank my friends, cheerleaders, and taskmasters Roy Winkelman and Christine Harmes, who truly pushed me all the way to the end. Finally I want to thank all of my other friends and colleagues that I have made during this long journey to truly make a difference in the world.
i TABLE OF CONTENTS Acknowledgments ................................ ................................ ................................ ................ i List of Figures ................................ ................................ ................................ .................... iii List of Tables ................................ ................................ ................................ ..................... iv Technology in Low Socio Economic K 12 Schools: Examining Student Access a nd Implementation ................................ ................................ ................................ .................. vi Chapter One Introduction ................................ ................................ ................................ ... 1 Role of Technology ................................ ................................ ................................ ......... 2 Defining Technology Integration ................................ ................................ .................... 4 Purpose ................................ ................................ ................................ ............................ 6 Research Questions ................................ ................................ ................................ ......... 7 Significance of the Study ................................ ................................ ................................ 7 Limitations ................................ ................................ ................................ ...................... 8 Definitions ................................ ................................ ................................ ....................... 8 Chapter Two Literature Review ................................ ................................ ........................ 11 Defining Effective Technology Integration ................................ ................................ .. 11 National Educational Technology Standards ................................ ........................ 12 Authentic Instruction ................................ ................................ ............................ 14 Stages of Instructional Evolution ................................ ................................ .......... 19 Summary ................................ ................................ ................................ ............... 21 Essential Conditions ................................ ................................ ................................ ...... 22 Access to Technology ................................ ................................ ........................... 22 Student Centered Learning Environment ................................ ............................. 26 Technology in the Curricula r Framework ................................ ............................. 33 Summary ................................ ................................ ................................ ............... 36 Technology and Students of Low Socioeconomic Status ................................ ............. 37 Title I Schools ................................ ................................ ................................ ....... 37 Characteristics of Students ................................ ................................ .................... 39 Student Achievement ................................ ................................ ............................ 40 Technology in Schools with Low SES Student Population ................................ .. 42 Summary ................................ ................................ ................................ ............... 47 Chapter Three Method ................................ ................................ ................................ ...... 49 Resea rch Design ................................ ................................ ................................ ............ 49 Population ................................ ................................ ................................ ..................... 50 Instrument ................................ ................................ ................................ ..................... 5 1
ii Construct Definition ................................ ................................ .............................. 52 Survey Development ................................ ................................ ............................. 53 Pilot Test Implementati on ................................ ................................ ..................... 54 Current Implementation ................................ ................................ ........................ 56 Data Source and Analysis ................................ ................................ ............................. 57 Chapter Four Results ................................ ................................ ................................ ......... 65 Verification of Data ................................ ................................ ................................ ...... 65 Acc ess to Computers ................................ ................................ ................................ ..... 69 Total Modern and Non Modern Computers ................................ ......................... 70 Location of the Computers ................................ ................................ .................... 74 Summary Access to Computers ................................ ................................ ............ 78 Software Types on S tudent Computers ................................ ................................ ......... 78 Tool Based Software on Student Computers ................................ ........................ 78 Curriculum Based Software on Student Computers ................................ ............. 82 Summary of Software Types on Student Computers ................................ ............ 83 Student Use of Software Types ................................ ................................ ..................... 83 Limited and Regular Use of Software Types ................................ ........................ 83 Frequency of Student Use of Software ................................ ................................ 86 Summary of Student Use of S oftware ................................ ................................ .. 88 Teacher Use of Technology ................................ ................................ .......................... 88 Basic Teacher Use of Technology ................................ ................................ ........ 89 Curriculum Focused Use of Technology ................................ .............................. 90 Summary of Teacher Use of Technology ................................ ............................. 91 Chapter Five Discussion and Recommendations ................................ .............................. 92 Summary of Questions and Results ................................ ................................ .............. 92 Discussion ................................ ................................ ................................ ..................... 96 Student Access to Technology ................................ ................................ .............. 96 Student Centered Learning Environment ................................ ............................. 98 Technology in the Curricular Framework ................................ ............................. 99 Recommendations ................................ ................................ ................................ ....... 101 Recommendations to Policymakers ................................ ................................ .... 101 Recommendations for Modifications to the Survey ................................ ........... 102 Recommendations for Future Research ................................ .............................. 103 Summary ................................ ................................ ................................ ..................... 104 References.......................................................................................................................106 About the Author................................................................................................... End Page
iii LIST OF FIGURES Figure 1. Scale levels of authentic instruction ................................ ................................ .. 18 Figure 2. Student access to modern computers ................................ ................................ 59 Figure 3. Student access to non modern computers ................................ ......................... 59 Figure 4. Tool based software on computers. ................................ ................................ ... 61 Figure 5. Curriculum based software on computers ................................ ........................ 62 Figure 6. Freq uency of student use of software. ................................ ............................... 63 Figure 7. Level of teacher use of technology. ................................ ................................ ... 64
iv LIST OF TABLES Table 1. Contrasting Views of Instruction and Construction ................................ ........... 29 Table 2. STaR Strands and Categories within Each Strand ................................ ............. 53 Table 3. Number of Schools and Student by Free and Reduced Lunch Status ................ 67 Table 4. District Size for Sample Size ................................ ................................ .............. 68 Table 5. Descriptive Statistics for Total Modern Computers by School SES .................. 71 Table 6. Descriptive Statistics for Total Non Modern Computers by School SES .......... 72 Table 7. Descripti ve Statistics for Total Modern Laptop Computers by School SES ...... 72 Table 8. Modern Computers per Classrooms by School SES ................................ .......... 73 Table 9. Modern Computers per Labs by School SES ................................ ..................... 74 Table 10. Student Access to Modern Computers ................................ .............................. 75 Table 11. Students' Access to Non Modern Computers ................................ ................... 76 Table 12. Students' Access to Modern Classroom Computers ................................ ........ 77 Table 13. Students' Access to Modern Lab Computers ................................ .................... 77 Table 14. Tool Based Software Availability on Student Computers ............................... 79 Table 15. Factor Analysis for Tool Based Software ................................ ........................ 80 Table 16. Basic Tool Based Software Availability on Student Computers ...................... 81 Table 17. Robust Tool Based Software Availability on Student Computers ................... 81 Table 18. Curriculum Based Software Availabili ty on Student Computers ..................... 82 Table 19. Student Limited and Regular Use of Software ................................ ................. 85
v Table 20. Frequency of Student Software Use ................................ ................................ 86 Table 21. Factor Analysis for Frequenc y of Student Use ................................ ................. 87 Table 22. Frequency of Student Tool Based Software ................................ ..................... 88 Table 23. Frequency of Basic Teacher Use of Technology ................................ .............. 90 Table 24. Factor Analysis for Teacher Use of Technology ................................ .............. 90 Table 25. Frequency of Teacher Curriculum Focused Use of Technology as a Tool ...... 91
vi TECHNOLOGY IN LOW SO CIO ECONOMIC K 12 SCHOOLS: EXAMININ G STUDENT ACCESS AND I MPLEMENTATION Katherine J. Kemker ABSTRACT Over the past twenty years, there has been an enormous financial investment in technology for K 12 schools to help bridge the digital divide between high and low socioeconomic (SES) students. These investments have included h ardware, software, professional development, and research studies on the effectiveness of technology in the classroom. This dissertation explored the essential conditions for the integration of technology, access, student centered learning environment, and teacher use in the curriculum. Florida's elementary schools were divided into quartiles based on free and reduced lunch to investigate differences between the top quartile of high SES schools ( n= 400 ) and the lower quartile of low SES ( schools n= 396 ) T he first research question investigated students' access to technology by specifically concentrating on their access to computers in high and low SES schools. A statistically significant difference was found between students' access to modern computer labs and modern laptop computers. However, there were no significant differences in access to modern classroom computers. W hen looking at software on student computers, a statistically significant difference between high and low SES schools was found in the av ailability of both basic tool based
vii software and robust tool based software Th ese data mirrored the statistically significant difference for frequency of use for tool based software at high and low SES schools. Data on the teacher use of technology in the curricular framework of the classroom showed a statistically significant difference in the curriculum focused use of technology between high and low SES schools. This study is a building block for further examination of the digital divide that exists b etween high and low SES schools. This divide appears to include low expectations for students in low SES schools through regular exposure to drill and practice software while students in schools with high SES populations have more opportunities to utilize tool based software.
1 CHAPTER ONE INTRODUCTION In a speech to the Commonwealth Club of California, former United States Secretary of Education, Rod Paige, remarked on the soft bigotry of low expectations for low socioeconomic students. He stated t hat educators must let go of the myths and perceptions about who can learn and who cant to ensure that all students despite their level of poverty can reach high academic standards (U.S. Department of Education [USDOE], March 12, 2003, p. 2). The passag e of the federal No Child Left Behind (NCLB) Act of 2001 legislation reinforced the belief that all children can learn through the achievement of high academic standards (USDOE, 2002). This movement in the field of education emphasizes the use of rigorous curriculum for all students with the expectation of a shift in the type of curriculum being offered to students of poverty in low socioeconomic schools (Borman, 2003). Research is revealing a pedagogical gap between schools in low and high socioeconomic s tatus (SES) neighborhoods. Grading standards have been found to be lower in low SES schools because less curriculum material has been covered (Biddle & Berliner, 2002). In low SES schools, curriculum has been implemented that aims at the basic elements o f the content to be learned on the assumption that no more can be managed and that mastery of the basics is the important accomplishment (Knapp, 1995).
2 A pedagogy of poverty" has emerged in which students are given direct instruction in order for the teac her to maintain control in the classroom (Haberman, 1991). An analysis of Title I schools conducted for the U. S. Department of Education found that students performed better in reading when teachers spent less time on basic instruction, followed by stude nts filling out worksheets, and more time in exploration activities, such as problem solving (USDOE, 2001). Educational researchers concur inequities exist in the curriculum offered to low income students, in that cognitively rich tasks that require critic al thinking are often missing (Darling Hammond, 1997; Haycock, 2001; Wenglinsky, 2005). A curriculum must be developed for low socioeconomic schools that helps students to draw upon their strengths, skills, and prior knowledge in the learning environment ( Marlowe & Page, 1999). Newmann (1996) developed a curriculum called authentic pedagogy that requires students to construct meaning or knowledge, engage in disciplined inquiry, and work on products that have value beyond school. Students are given challen ging academic lessons that involve the development of higher order cognitive skills rather than merely responding to lower order skills found in the pedagogy of poverty (Newmann, Wehlage, & Lamborn, 1992). Research has shown positive outcomes when this cur riculum approach has been implemented in the classroom (Avery, 1999; Newmann, 1996). Role of Technology A technology enriched learning environment has the potential to deepen classroom instruction to make it more meaningful and assist in the development o f higher order thinking skills (Niemiec & Walberg, 1992). This type of environment entails
3 a teacher and students seamless access to technology in the classroom. When technology is used in this manner, it empowers students to develop thinking skills that allow them to help themselves (Kelley & Ringstaff, 2002; Mann, 1999). Students in a technology rich classroom tend to become more engaged and more active learners because of greater emphasis on inquiry and less on drill and practice (Bozeman & Baumbach, 19 95; Sandholtz & Dwyer, 1997). Since 1990, the United States has invested over forty billion dollars to provide technology for the K 12 classrooms. These federal dollars have come in various forms, such as E Rate funding, Technology Literacy Challenge Fund s (TLCF ), and Preparing Tomorrows Teachers to Use Technology (PT3) Each source of funding had a specific purpose for the use of technology (McMillan Culp, Honey, & Mandinach, 2003). For example, E Rate supplies funding to schools with the expectation tha t every student will have access to the Internet. The goal of the TLCF program was to provide computers in the classroom, and funding has been provided through PT3 to train pre service teachers on the use of technology in the classroom (Dickard, 2003). NC LB mandates the effective integration of technology into the curriculum of public schools for all students in the United States. This landmark legislation provides federal dollars through Title II Part D, Enhancing Education Through Technology (EETT) with the stipulation that both state and local educational agencies develop a comprehensive plan for the integrated use of educational technology into instruction and curricula to improve teaching and student achievement. The deadline placed on
4 educators is t hat technology will be fully integrated into the curricula and instruction of the schools by December 31, 2006 (USDOE, 2002, p.1676). Defining Technology Integration Unfortunately, a consistent definition for the integration of technology in the curricul um has never been adopted. Even the term technology can have varied interpretations. For example, administrators, teachers and policymakers often make the assumption that technology refers simply to a computer. However, Kelley and Ringstaff (2002) define technology as a variety of digital devices, from computers to digital cameras to software. For the purpose of this study, the term technology will include computers, application software, and digital devices, such as digital cameras, digital microscopes and digital video cameras. The integration of technology in the classroom is a process that involves change in an educational system and occurs over a period of time (National Center for Education Statistics [NCES], 2002). The attainment of this goal en tails a reform in the teachers method for the delivery of instruction with students. The National School Boards Association stated that the integration of technology is as much about change as it is about technology because educators must reform their tea ching to integrate technology (Tiene & Luft, 2002). Researchers have concluded, The magic lay not exclusively in the technology, but in the interweaving of a systematic program of education reform with the judicious use of technology based resources (Cha ng, Henriquez, Honey, Light, Moeller, & Ross, 1998, p. 43).
5 This process of change takes time, as a culture must be developed in the classroom that embraces technology as a natural part of the everyday work routine (NCES, 2003). According to Sandholtz, Ringstaff, and Dwyer (1997), there is an evolution of thought and practice that occurs with teachers during the process of change that teachers undergo with the integration of technology. At each stage of the process, the teacher adopts and implements tech nology in the curriculum (Dooley, 1999; Hall & Hord, 2001; Painter, 2001; Sandholtz, Ringstaff, & Dwyer, 1997). The integration of technology in the educational system is a time consuming process in which teachers must change their beliefs about teaching a nd learning. Regrettably, the pedagogical divide is also reflected in the use of technology. Low SES schools frequently use technology for drill and practice, while high SES schools use technology for higher order thinking skills (Becker & Ravitz, 1998; B ecker, Ravitz, & Wong, 1999; Wenglinsky, 2005). For example, students in low SES schools most commonly use technology for lower order tasks, such as drill, practice, and test taking, whereas students in affluent, high SES schools have more opportunity to c reate web sites and multimedia presentations (Reid, 2001). Wenglinsky (1998) found that students of poverty use computers for drill n skill purposes. Likewise, when Herbert Kohl examined the use of computers with inner city students, he found a covert racism in the limits to the types of activities being done with students of poverty (Reid, 2001). Recent studies suggest that research should move beyond a focus on the technology to an interest in designing an environment that fosters the disposition for critical thinking (Kelley & Ringstaff, 2002; Wenglinsky, 2005 ). The restructuring of the
6 classroom should include the use of technology to provide active learning, authentic tasks, challenging work, problem solving, and higher order thinking skills (Dalto n & Goodrum, 1991; David, 1993). Purpose In the last four years, the state of Florida has received over $175 million dollars of federal funds through the EETT program for an average of 2.5 million students per year. However, research on the use of techno logy with low socioeconomic students has been very limited. The legislation of No Child Left Behind demands more resources for students in low socioeconomic classrooms. With more technology funds being provided to the schools, it is important to recogniz e and understand how effectively technology is being integrated that promotes higher order thinking skills. The purpose of this study is to examine the use of technology with low and high socioeconomic students in the state of Florida. In response to the NCLB legislation, the Florida Department of Education significantly revised its annual technology survey to provide more meaningful information about technology integration and capacity in Floridas schools. The implementation of the survey is an iterativ e process that entails a variety of individuals from the district level personnel to the school based technology specialist. During this process, the district and schools are able to be in constant communication to verify the accuracy of the data. In 2002 the technology resource survey was piloted with schools in Florida based on benchmarks to provide schools with a tool for use in goal setting and technology planning. Revisions to the survey were based upon these benchmarks and the pilot study.
7 Historic al data from the 2005 06 Florida Technology Resource Survey (TRS) were analyzed to examine the use of technology with low SES students in Florida. The TRS is divided in five sections, Digital Learning Environment, Instructional Leadership, Florida's Digita l Educators, and Access to Technology with 78 survey items. Research Questions This study used data from the last three years of the Technology Resource Survey to examine the following questions in elementary schools: 1. Is there a significant difference bet ween students access to computers in high and low socioeconomic schools? a. Is there a significant difference in the total number (desktops and laptops) of modern and non modern computers? b. Is there a significant difference in the locations of the computers f or student use? 2. Is there a significant difference in the types of software available on computers between high and low SES schools? 3. Is there a relationship between the socioeconomic status of an elementary school and the frequency of student use by softwar e type? 4. Is there a relationship between socioeconomic status of an elementary school and the teacher use of technology in the classroom? Significance of the Study Findings from this study expand our knowledge about the use of technology in high and low SE S schools. Data from this study informs policy makers and state/district
8 level administrators on how to plan for the high level integration of technology in all public schools. In addition, the data provide guidance on professional development for teachers on the use of technology with low SES students. Limitations A limitation of this study is the use of survey data in which the purpose is to provide information on the accessibility, use, and accountability of technology in schools. These data represent o ne moment in time from the individual who submits the survey and do not represent the actual classroom implementation by the teacher. The data collected reflects the interpretation of an individual who reports the data based on their perception of the use of the technology in the classroom. Another limitation is a clear definition for the types of software programs being reported on the computers and the pedagogical use with students in the classroom. The data on the software programs is a generic represe ntation for types of software programs being purchased for the computers. This data does not reflect how the software is actually being integrated into curriculum by the teachers. In addition, these data are reported by individuals, such as principals, te chnology coordinators, and media specialists that are not in the actual classroom. Definitions Authentic instruction A model for high quality instruction that incorporates five main components of higher order thinking, depth of knowledge, connectedness to the world beyond the classroom, substantive conversation, and social support for student achievement.
9 Authentic standards The five standards for authentic instruction: higher order thinking; 2) depth of knowledge; 3) substantive conversation; 4) conne ctions to the world beyond the classroom; and 5) social support for student achievement. Curriculum based software Program based software that focuses specifically on curriculum and content areas. High socioeconomic status The top quartile of schools in which less than 40% of the students qualify for free and reduced lunch. Low socioeconomic status The bottom quartile of schools in which more than 75% of the students qualify for free and reduced lunch. National Educational Technology Standards The technology standards established for teachers and students. The six standards are premised on the use of technology to develop students: basic operations and concepts; social, ethical, and human issues; productivity tools; communication tools; research too ls; and problem solving and decision making tools. Pedagogy of poverty A series of basic traditional core functions, such as teachers giving information, assigning homework, and monitoring seatwork that constitutes an instructional method. Project base d learning An instructional method organized around projects that allow students to encounter and learn the central concepts of the subject through real world projects. Technology integration The seamless use of technology as a tool to accomplish a give n task in a disciplined study that promotes higher order thinking skills.
10 Tool based software Application based software that provides opportunity for teachers and students to perform a task. 21 st century skills Those skills that have been i dentified a s information and communication skills, thinking and problem solving skills, and interpersonal and self directional skills.
11 CHAPTER TWO LITERATURE REVIEW The review of the literature is organized into three sections. The first section provides a theore tical understanding for defining technology integration technology in the classroom. The second section examines research on the essential conditions for integrating technology in the classroom. The final section provides an overview of research related to technology use with students from low socioeconomic status. Defining Effective Technology Integration Technology integration entails the students seamless use of technology as a productivity tool to accomplish a given task in a disciplined study that pr omotes higher order thinking skills. Integration is the assimilation of technology resources and technology enabled practices as a routine and seamless element of the day, so that students are prepared to use technology for the workforce (NCES, 2002). Tech nology is a tool to be employed by students in all curriculum areas to acquire new knowledge and skills, analyze and synthesize data, then construct a product that demonstrates their knowledge (NCES, 2003; Partnership for 21 st Century Skills, 2002). This definition of technology integration in the classroom is based on NETS (the National Technology Standards for Students that were developed by ISTE in 2004), authentic instruction (Newmann & Wehlage, 1995), and Stages of Instructional Evolution (Sandholtz, Ringstaff & Dwyer, 1997).
12 National Educational Technology Standards In the last century, there has been a move toward identifying and implementing state and national technology standards for students and teachers. In 1998, the International Society for Te chnology in Education (ISTE) published their National Educational Technology Standards (NETS) for Students. Two years later, ISTE published standards for teachers, which was followed in 2001 by standards for administrators. As of May 19, 2004, 33 states ha d adopted, adapted, or aligned with the ISTE standards for students. In addition, 34 states were using the ISTE standards for teachers and 29 states were implementing the ISTE standards for administrators (International Society for Technology in Education [ISTE], 2004). In addition to basic computer operations and social/ethical issues, the NETS standards for students focus on using technology as a classroom tool for productivity, communication, research, and problem solving/decision making. Specifically, the NETS for students categorize technology competencies into six areas: 1. Basic operations and concepts 2. Social, ethical, and human issues 3. Technology productivity tools 4. Technology communication tools 5. Technology research tools 6. Technology problem solving and decision making tools The categories provide a framework for linking performance indicators to the standards for Grades PreK 2; 3 5; 6 8; and 9 12. It is recommended that the skills be
13 integrated into a student's personal learning and social framework, by being introduced in the classroom, reinforced, and finally mastered. The intent of the standards is for technology to be an integral component or tool for learning within the context of academic subject areas (ISTE, 2000). In the first category, basic o perations and concepts students should be able to demonstrate a basic understanding of technology, such as computers, television, digital cameras and additional peripherals. Performance indicators at various grade levels might include being proficient in the use of computer terminology, proper care of the monitor, and selection of a printer (ISTE, 2004). The second category, social, ethical and human issues pertains to various issues involving the use of technology. Students should demonstrate technology literacy (in areas such as legal and ethical behaviors). A sample performance indicator would include students being able to evaluate the media images that surround them to make responsible choices about what they see and hear (ISTE, 2004). In the third category, technology productivity tools students use various forms of technology to create different types of media such as documents, movies, pictures, and spreadsheets. In addition to using the computer, students demonstrate proper use of an assortment of hardware such as probes, scanners, and digital cameras to construct their knowledge (ISTE, 2004). The fourth category, technology communication tools focuses on students use of various media to converse and interact with their peers, experts, and oth er audiences. Performance indicators include being able to collaborate, communicate, and interact
14 effectively with a range of audiences for both direct and independent learning (ISTE, 2004). In the fifth category, technology research tools students use d iverse forms of technology as a tool to locate, evaluate, and collect information from a variety of sources. Performance indicators include the ability to evaluate the accuracy, relevance, appropriateness, comprehensiveness, and bias of online information (ISTE, 2004). In the sixth category, technology problem solving and decision making students use technology to develop strategies for recognizing and solving real world problems. They should be able to identify a problem, ascertain if technology is usef ul in solving the problem, and, if so, select and implement the appropriate tools (ISTE, 2004). Authentic Instruction The NETS for students provide a basic framework for the use of technology in the classroom, while the theory of authentic instruction aim s to engage students in authentic activities (Newmann & Wehlage, 1995). In 1990, the Office of Educational Research and Improvement of the U.S. Department of Education funded the University of Wisconsins Center on Instructional and Restructuring of School s to conduct a five year program of research on School Restructuring to support authentic learning (Newmann & Wehlage, 1995; Newmann, 1996). Researchers visited over 60 schools across the country that were involved in restructuring efforts, and observed in structional activities, assessments, and student work in mathematics and social studies in 24 of those schools (Newmann & Wehlage, 1993).
15 Because of the dynamic and social nature of the activities, researchers noticed in the schools a widespread use of in structional techniques that resembled both social and psychological constructivism. Students were engaged in activities to create projects, compile portfolios, produce performances, complete experiments, and analyze data from surveys. The researchers recog nized and appreciated the active learning in the classrooms but were concerned: Assuming the central purpose of teaching is to help students to use their minds well, then education reform must involve more than innovation in teaching technique, method, or procedure. The merit of any technique, whether conventional or innovative must be judged on its capacity to improve the intellectual quality of student performance (Newmann, Secada, & Wehlage, 1995, p.3). Authentic Pedagogy The theory of authentic pedagog y is based on the premise that students work in the classroom should prepare them for the intellectual work that will be demanded of them as adults (Newmann & Wehlage, 1995). Newmann (1996) states that authentic pedagogy stands for intellectual accomplis hments that are worthwhile, significant, and meaningful, such as those undertaken by successful adults (p. 23). He goes on to elaborate this theory with three criteria for authentic intellectual work: student construction of knowledge, discipline inquiry, and value beyond the classroom (Newmann, 1996). First, students need to be constructors of knowledge, built on what they already know. The goal of instruction should be deep understanding of concept development
16 through cognitive development or knowledge building rather than the development of behaviors or skills (Fosnot, 1996). From this perspective, learning is viewed as an active process and teaching as a means of facilitating active student mental processing (Gagne, 1985). When students construct knowl edge it involves organizing, interpreting, evaluating, or synthesizing prior knowledge to solve new problems (Newmann, Bryk, & Nagaoka, 2001, p. 19). The second criterion for significant authentic pedagogy requires disciplined inquiry that involves the use of a prior knowledge base, in depth understanding, and elaborated communication. Students must acquire the knowledge base of facts, vocabularies, concepts and theories necessary for their inquiry. Knowledge becomes most powerful when students can use i nformation to gain a deeper understanding of specific problems. Students must rely upon complex forms of communication, both to conduct their work and to present their ideas (Newmann, Bryk, & Nagaoka, 2001). The final criterion of authentic pedagogy is fo r the task to have meaning or value apart from documenting the competence of the learner (Newman, Secada, & Wehlage, 1995, p.11). Instructional activities might include allowing students experiences outside of school to be integrated into those activiti es in the classroom. The concept of value beyond school involves the transfer of knowledge to an area that has personal significance for the students. When students are involved in an activity that has no value beyond measuring their success in school, su ccess in these tasks often carries no adaptive value, because large numbers of students consider school to only be a restricted,
17 even an insignificant, arena of personal experience (Newmann, Marks, & Gamoran, 1996, p. 286). Authentic Standards The stand ards for authentic instruction describe the cognitive connections that authentic tasks promote. For example, Wiggins (1998) defines authentic instruction as the situational or contextual realism of the proposed tasks (p. 21). Similarly, Darling Hammond ( 1995) contends that authentic tasks should be set in a meaningful context that provides connections between real world experiences and school based ideas (p. 4). Authentic instruction involves tasks that generally engage students in ill structured chal lenges and roles that guide students for real world situations (Wiggins, 1989). Authentic instruction is meaningful instruction. Teachers are able to move students beyond memorization of facts by creating experiences that demand sustained, disciplined, an d critical thinking on topics that have relevance to life beyond school. The scale for the five standards of authentic instruction is shown in Figure 1.
18 Higher order Thinking Lower order thinking only ..3.4 Higher order thinking is cent ral Depth Knowledge Knowledge is shallow .2.4Knowledge is deep Substantive Conversation None .....4 High level Connections to the world beyond the classroom No connection .2..3...4. Connected Social Support for Student Achievement Negative social support ....4.... Positive social support Figure 1 Scale levels of authentic instruction The standards of authentic instruction are continuous c onstructs of quality from less to more rather than categorical variables (Newmann & Wehlage, 1995). Each of the constructs uses a five point scale to measure the extent of authentic instruction in a particular lesson in the classroom. For example, the first construct measures the degree of higher order thinking in a task given to students. In lower order thinking, students are recipients of information; in this role, students simply recite information. However, at higher order thinking, students are man ipulating information and are involved in tasks that require problem solving and decision making skills (Newmann, Marks, & Gamoran, 1996). The framework for the five standards of authentic instruction can be used as a research tool or a reflective critiq ue of teaching. In the role of research, the framework has been utilized to estimate the levels of authentic instruction in social studies and
19 mathematics in K 12 schools. Researchers were able to examine the conditions under which restructuring improv es instruction in teachers instructional skills. The framework also allows teachers, either alone or with peers, to reflect upon their teaching. The standards provide criteria for examining the extent to which certain lessons and activities provide for aut hentic learning (Newmann & Wehlage, 1995). Stages of Instructional Evolution Over a decade ago, the Apple Classrooms of Tomorrow (ACOT) project began with the assumption that computers and technology would impact the classrooms (Office of Technology Asses sment [OTA], 1995). The ACOT project trained teachers on the integration of computer technology in the classroom. This project was a collaboration among public schools, universities, research agencies and Apple Computer. Over one hundred schools around the United States were selected to begin integrating computer technology in the classroom (Fouts, 2000). The majority of the research was qualitative in nature. In the ACOT classrooms, students and teachers had immediate access to a wide range of technologi es including computers, videodisc players, video cameras, scanners, CD ROM drives, modems and online communication services. Technology in the ACOT classroom was viewed as a tool for learning and a medium for thinking, collaborating, and communicating (San dholtz, Ringstaff, & Dwyer, 1997). The teachers were trained on basic troubleshooting and tool based software such as spreadsheets, databases, and graphic programs (Kelley & Ringstaff, 2002).
20 Using technology in this context changed the role of the teache r (Ringstaff, Sandholtz, & Dwyer, 1997). The teachers involved in the study began to reexamine their instructional strategies, and began to see themselves as facilitators. Teachers began to take a constructivist approach, realizing that teaching is more th an transferring ideas from one who is knowledgeable to one who is not. Instead, learning was perceived as a personal, reflective, and transformative process (Sandholtz, Ringstaff, & Dwyer, 1997). As the role of the teacher changed, the role of the student also changed, moving from a passive role to an active role in learning. Students were provided with the opportunity to collaborate with other students on projects and given tasks. Teachers in the study observed numerous changes in student engagement with the introduction of technology in the classroom. Students became empowered when they were able to take responsibility for their learning as they interacted with the computer, designing and using their own instructional materials (Dixon Krauss, 1996). Often students displayed increased initiative by going beyond the requirements of assignments to independently explore new applications (Sandholtz, Ringstaff, & Dwyer, 1994). Research from the ACOT project demonstrated that the introduction of technology to t he classroom could increase the power for learning. (Sandholtz, Ringstaff, & Dwyer, 1990). However, the mere placement of the computer in the classroom did not change the learning environment; computer based activities had to be related closely to those ac tivities already taking place in the classroom for change to happen (Reilly, 1992). Even when classrooms were drastically altered and teachers were willingly immersed in innovation, the change was slow, and often filled with temporary regression.
21 The ACOT study focused on both the process of the integration technology in the curriculum, as well as the adoption of the innovation, access to computers at the classroom level (Coley, Cradler, & Engel, 2000). This ten year study produced an adoption model for th e use of computer technology in the classroom known as the Stages of Instructional Evolution. This model states that educators go through five stages when adopting technology in the classroom: (a) entry, (b) adoption, (c) adaptation, (d) appropriation, and (e) invention (Sandhotz, Ringstaff, & Dwyer, 1997). Researchers found that at each phase during this model, teachers varied their utilization of technology both personally and pedagogically. For example, at the entry stage, teachers were still not comfor table using technology and tended to use direct instruction and whole class activities. However, at the adaptation stage, teachers began to make the transition to using technology within the context of the curriculum. Finally, at the innovation stage, teac hers and students were engaged in activities where technology was used to construct knowledge in a meaningful context (Sandholtz, Ringstaff & Dwyer, 1997). Summary The Stages of Instructional Evolution (Sandholtz, Ringstaff, & Dwyer, 1997), the NETS (ISTE 2004) and authentic instruction (Newmann & Wehlage, 1995) provide the framework for defining the levels of technology integration in the classroom. The NETS for students (ISTE, 2004) establishes the role of technology in the classroom, while the standard s for authentic instruction define the instructional methods for engaging students in authentic tasks (Newmann & Wehlage, 1995). Finally, the Evolution of Thought and
22 Practice establishes the stages of teachers use of technology in the classroom (Sandholtz Ringstaff, & Dwyer, 1997). Hence, the optimal integration of technology in the classroom entails students seamless use of technology as a productivity tool to accomplish a given task in a disciplined study that promotes higher order thinking skills. Ess ential Conditions The integration of technology in the classroom requires a reformation of instructional practice by all individuals in the educational system. Three essential conditions have consistently emerged from the research on technology integratio n in the classroom (Glennan & Melmed, 1996; Kelley & Ringstaff, 2002; Sandholtz, Ringstaff, & Dwyer, 1997). First, teachers must have access to technology, such as laptop computers and tool based software, in the classroom ( Fisher, Dwyer, & Yocam, 1996; Ma nn, 1999; and Stratham & Torell, 1999). Second, teachers must develop a student centered learning environment in which technology can be utilized for a higher level of integration (Means, Blando, Olson, & Middleton, 1993; Penuel & Means, 1999; Silverstein, Frechtling, & Miyoaka, 2000; Statham & Torell, 1999). Finally, technology tasks must be embedded in the curriculum framework (Coley, Cradler, & Engle, 1997; Sandholtz, Ringstaff, & Dwyer, 1997; Silverstein, Frechtling, & Miyoaka, 2000). Access to Technol ogy In the last 20 years, there has been a major transformation in the access to computers for students in the classroom (NCES, 2005; Trotter, 2005). Since the early 1980s, the primary intent of federal funding has been to reduce the student to computer ratio. Nationally and locally there has been a significant progress toward this goal. In
23 1983, the ratio was 92 students per computer (at school); five years later, the ratio decreased to 27 students per computer, and ten years later, the ratio was just un der 6 students for each computer (Technology Counts, 2005). While studies have shown a decrease in the student/computer ratio, teachers continue to report that the lack of access to computers in the classroom is a significant barrier to technology integra tion. The location of the computers affects the accessibility for teachers in the classroom ( Livingston & Wirt, 2005 ; Statham & Torrell, 1999). Computers maybe distributed among the classrooms, centralized in one area of the school, or a combination of the two approaches (Mann, 1999). However, computers in the classroom rather than in a separate computer lab are more likely to facilitate positive social interactions and students use of technology a tool in the curricula (Clements, 2002; Knapp & Glenn, 1996 ). Laptop computers Almost a decade ago, education reformers began implementing initiatives in which both students and teachers were provided their own laptop computer. In 1996, the Learning with Laptops program began in Beaufort County School District in South Carolina when 300 sixth grade students were given laptop computers (Stevenson, 1999). That same year, Microsoft and Toshiba began the Anytime Anywhere Learning project with 19 pioneer schools in the United States receiving laptop computers (Rockma n et al., 1998). Large scale initiatives, such as those in Henrico County, Virginia, and the state of Maine, have placed 24/7 accesses to laptop computers in the hand of tens of thousands of teachers and students (Rockman, 2003; Zucker, 2004).
24 Another st rategy has been to obtain a classroom set of laptop computers that is stored on a recharging cart, allowing access for any classroom in a school (Rockman, 2003). These class sets serve as portable labs that provide access to laptop computers for classroom use to complete a given task that utilizes technology in the curricula. While access is limited to the availability of the laptop cart in this strategy, this approach does overcome one barrier to the integration of technology by providing all students acce ss to laptop computers. The emergence of laptop computers in the classroom has enabled a shift in the focus of research for technology. Rather than focusing on the adoption of technology in the classroom, researchers have begun to examine the integration of laptops in the school infrastructure, the unique advantages of laptops in the classrooms, and student motivation with laptop computers (Rockman, 1998; Fouts & Stuen, 1997; Stevenson, 1998). Unfortunately, the majority of this research has been limited t o those universities and districts implementing laptop computers in classrooms of students with high achievement and high socioeconomic status. For three years, Rockman and an independent research organization evaluated Microsofts Anytime, Anywhere Lear ning Program. This three year study included eight school sites with three of the schools matched and five of the schools unmatched. A total of four hundred sixty students from grades 7 through 11 and 47 teachers from both public and private schools were i nvolved in the study (Rockman, 1998). The first year of the study examined the challenges and success of the implementation of laptops in the classroom. The second and third year focused on the
25 impact of teaching and learning in the laptop classroom and h ow it supported the constructivist pedagogy. The data collected for this study included student and teacher questionnaires, test scores, student and teacher computer usage logs, interviews with administrators, and the analysis of standardized test scores ( Rockman, 1998). The study found that students who had access to technology increased their use of technology. In addition, more opportunities existed for individual access; laptop students used the Internet more frequently and for longer periods of time t han non laptop students. The laptop users spent more time doing their homework and used the computer at home for a variety of tasks (Rockman, 1998). Finally, researchers noted a move toward constructivist ideals and pedagogy. The ubiquitous technology was a catalyst for the change to more independent efforts and less direct instruction (Fouts, 2000; Rockman, 1998; Rockman, 2000). In another study that focused on Title I schools, Ross studied the effectiveness of providing fifth and sixth grade students in Walled Lake Consolidated schools with access to laptop computers. This study concentrated on the use of laptop computers with regard to learning activities, technology usage, and writing achievement. The district had been part of the Anytime Anywhere Lear ning program since 1996. The laptop computers were available for a lease of fifty dollars a month. The training of teachers was based on framework to develop problem based lessons that utilize real world resources, student collaboration, and the use of com puter tools to reach solutions. The lessons were structured around projects which engage students in examining community and global issues (Lowther, Ross, & Morrison, 2001).
26 The data collected included classroom observations, student writing test scores, student surveys and focus groups, teacher surveys and interviews, and parent surveys and interviews. Observation instruments included the School Observation Measure (SOM) and the Survey of Computer Use (SCU). The SOM was based on 60 continuous minutes of observation, divided into four, fifteen minute segments. The four observation periods were then summarized on one SOM Data Summary form. The SCU was completed as part of the 60 minute observation sessions, only if students were using technology during that time (Lowther, Ross, & Morrison, 2001). Researchers found that teachers in the laptop classrooms tended to integrate technology more in their curriculum with student centered strategies such as project based learning, inquiry research, and cooperative l earning. Observers found that laptop classrooms were engaged with the computer as an educational tool. The majority of teachers reported they were teaching differently than before by integrating technology into both existing lessons and newly developed les sons. The implication from the multiple data sources was as a consequence of all students having continual access to individual computers an active learning environment was created in the classroom (Lowther, Ross, & Morrison, 2001). Student Centered Learni ng Environment The second condition for effective technology integration is the development of a student centered learning environment. Kelley and Ringstaff (2002) stated that if technology is to be used in powerful ways, the role of the teacher in the cl assroom must change. The traditional model of instruction is the lecture recitation seat work model in
27 which the teacher is the focus of the instruction and the only source of knowledge. In a student centered environment, the teachers role becomes one of a facilitator for learning as students were guided to take an active role in constructing their knowledge (Penuel & Means, 1999; Silverstein, Frechtling, & Miyoaka, 2000; Statham & Torell, 1999). Students become active learners because they are engaged in collaborative and project based learning environments with an emphasis on higher order thinking skills (Penuel, Golan, Means, & Korback, 2000). In a 1995 report to the U.S Congress, the Office of Technology Assessment (1995) proposed that using computers could change the way teachers teach. This report stated that some teachers use computers in a traditional teacher centered method, such as drill and practice for mastery of basic skills, or to supplement teacher controlled activities. However, other teac hers use technology to support more student centered approaches to instruction, such as providing students with the opportunity to conduct their own scientific inquires and engage in collaborative activities while the teacher assumes the role of facilitato r or coach. The learner centered framework focuses on the learners and their perceptions, needs and motivations (McCombs, 2000). Teachers who fall into this second group are the more enthusiastic technology users, because technology is particularly well su ited to support this style of instruction (OTA, 1995). Ausubel (1963) contends that meaningful learning occurs when new experiences are relative to what a learner already knows. While developing expert systems, Jonassen, Wilson, Wang, and Grabigner (1993) found that the people who seem to learn the most from the design of instructional materials are the designers themselves. When students
28 are provided the opportunity to produce their own knowledge, it becomes a powerful learning experience. The use of tech nology as a cognitive tool requires students to think in meaningful ways about the applications capabilities (Jonassen & Reeves, 1996). The traditional use of the computer in the classroom has been to teach or tutor students. Thomas Reeves (1998) descri bes this process as learning from computers in which the primary goal is to increase students basic skills and knowledge. Learning from computers takes on a variety of tutoring systems, such as computer based instruction (CBI), computer assisted instru ction (CAI), and integrated learning systems (ILS), in which content is provided from the computer (Ringstaff & Kelley, 2002). The term learning with computers refers to students using technology as a tool for problem solving, conceptual development, an d critical thinking (Culp, Hawkins, & Honey, 1999; Sandholtz, Ringstaff, & Dwyer, 1997; Means, 1994; Reeves, 1998). When the computer takes on the role of a cognitive tool, it is an intellectual partner to enable higher order learning (Reeves, 1998). Rathe r than treating students as recipients of knowledge, Jonassen and Reeves (1996) proposed that computers be used as a cognitive tool allowing students to use the computer to interpret and organize their knowledge. The computer as a tool allows teachers and students flexibility to creatively enhance the curriculum and instruction. Examples of tool based software include: spreadsheets, databases, and multimedia software (Ringstaff & Kelley, 2002). The more advanced uses of the computer support the constructiv ist view of learning in which the teacher is a facilitator of learning rather than the classrooms only source of knowledge (Trilling & Hood, 1999; Penuel & Means, 1999; Statham & Torrell,
29 1999). In studies of students using the computer as a tool for inst ruction, teachers have reported it provides them the opportunity to create a student centered environment. The teachers become more open to multiple perspectives on problems and are willing to experiment in their teaching (Knapp & Glenn, 1996). Table 1 il lustrates the contrasting views of instruction with technology and construction with technology as a tool (Sandholtz, Ringstaff, and Dwyer, 1997): Table 1 Contrasting Views of Instruction and Construction Instruction Construction Classroom activity teacher centered didactic learner centered interactive Teacher role fact teller always expert collaborative sometimes learner Student role listener always learner collaborators sometimes expert Instructional emphasis facts memorizat ion relationships inquiry and investigation Concept of knowledge accumulation of facts transformation of facts Demonstration of success quantity quality of understanding Assessment norm referenced criterion referenced portfolios and performances Techno logy use drill and practice communication, collaborative, information, access, expression
30 Project based learning One method for integrating cognitive technology tools in a student centered environment is to implement a project based learning environment Project based learning is organized around projects that allow students to encounter and learn the central concepts of the subject. A project involves students in a constructive goal directed process that entails inquiry, knowledge building, and resolut ion. The most important aspect of project based learning is that the projects are realistic and give the students a feeling of authenticity (Thomas, 2000). Project based learning gives control to the learner to make decisions about the content, style and d esign. Research on project based learning has reported gains in student achievement, problem solving capabilities, understanding of the given subject matter, comprehension of specific skills and strategies in introduced in the project, and changes in pro blem solving skills (Thomas, 2000). The Challenge 2000: Multimedia Project is an example of project based learning. The Multimedia Project was developed through a joint venture with the San Mateo County Office of Education and Silicon Valley, a partnership of area businesses focused on improving the quality of life in the Silicon Valley region in northern California. The projects were expected to include seven components: Be anchored in core, curriculum; multidisciplinary Involve students in sustained effor t over time Involve student decision making Be collaborative Have a clear real world connection Use a systematic assessment: both along the way and end product
31 Take advantage of multimedia as a communication tool (Penuel, & Means, 1999). To accomplish the goals of the Multimedia Project, support was provided to teachers in learning how to implement projects by forming a peer community of learners. This community was comprised of veteran teachers, who could share their teaching experiences, and technology co ordinators, who brought their technology related skills. Through the implementation of a peer learning community, student centered projects, and the provision of support for teachers, the classroom processes were expected to change. The design called for c lassrooms in which: Students engage in longer term, more complex assignments Teachers act as coaches and facilitators of student learning Students engage in more small group collaborative activities Greater involvement with external resources is emphasized including heightened attention to external audiences for student work These benchmarks were established to indicate that the project would meet its objectives for student learning (Penuel, & Means, 1999). The Stanford Research Institute (SRI) used case studies, interviews, teacher surveys, classroom observations, and school wide indicators of achievement and performance assessment to evaluate the Multimedia Project. In year three of the study, nineteen classrooms were chosen from among the Challenge 2000 classrooms across grade levels. Principals from these schools then nominated three technology classrooms engaged in the project and three non technology classrooms. The results of the study
32 established that technology had made a difference in the classroo m (Penuel & Means, 1999). In the fall, students in the technology classrooms were only slightly more likely than students in non technology classrooms to be engaged in long term projects. However, by spring the gap was very wide, with 67% of technology cl assrooms involved in extended projects compared to 14% in the non technology using classrooms. Similarly, in the fall, only 56% of technologies using classrooms were involved in the constructing products, while 39% of non technology using classrooms were i nvolved in constructing products. In the spring, 73% of technology using classrooms engaged students in small group collaboration, compared to the 38% in the non technology using classrooms (Penuel & Means, 1999). The study determined that teachers in Mul timedia Project classrooms assumed the role of coaches or facilitators. In the spring semester of the study, only 29% of time in the technology classrooms was devoted to teacher led activities, versus 62% in non technology classrooms. Teachers in the techn ology classrooms were more likely to spend time explaining concepts, providing information, or questioning students about their understanding of material. Finally, the study found that students in the Multimedia Project classrooms were more likely to spend time engaged in small group collaboration. In the spring portion of the study, only 3% of the time in the technology classrooms was devoted to instructional or known answer questions compared to 72% of the time in the non technology classrooms. The study found that teachers had an eagerness to use
33 collaborative learning as a tool to promote a greater mastery of content and skill in cooperating and working with others (Penuel & Means, 1999). Technology in the Curricular Framework The final condition for ef fective technology integration involves teachers development of student tasks that make use of technology as a tool within the curriculum framework (Coley, Cradler, & Engle, 1997). Before technology can become a critical element of the curriculum process, teachers must move beyond using it to simply improve students basic skills. Teachers need to understand and appreciate the role technology can perform within their curriculum. Hence, students will be given tasks that employ the use of technology to const ruct their knowledge on a given topic, rather than being an add on to the curriculum (Sandholtz, Ringstaff, & Dwyer, 1997). Researchers (Sandholtz, Ringstaff, & Dwyer, 1997) from ACOT recognized that as teachers transformed their instructional methods, they progressively changed their use of technology in the curriculum framework. For example, in the entry stage, technology was used sparingly in the curriculum. Teachers used computer based or tutorial programs to reinforce concepts taught in the curricul um. However, in the appropriation stage, students used technology as a tool to complete interdisciplinary, project based tasks. Students were provided access to tool based programs, such as spreadsheets, presentation, and multimedia authoring programs to c omplete their projects (Ringstaff, Yocam, & Marsh, 1997). Means and Olson (1995) anticipated that technology could expand learning opportunities for all students when teachers utilized it as a tool in the curriculum and
34 instructional framework. As teacher s develop an understanding for the impact technology can have on the curriculum, there tends to be a shift in learning from technology to learning with technology. In this manner, teachers and students have flexibility to creatively enhance the curricu lum and instruction, as technology is used as a tool to gather, organize, and analyze that is used to solve problems and construct knowledge (Means, Blando, Olson, & Middleton, 1993). Effective integration of technology depends on the teachers understandi ng and development of tasks for students in the curriculum framework (Baker, Herman, & Gerhart, 1996). North Central Texas When technology is implemented as a tool within the curriculum, students are encouraged to develop higher order thinking skills. Ho pson, Simms, and Knezek (2002) examined fifth and sixth grade students in a suburban school district to determine if students in technology enriched classrooms demonstrated better use of high order thinking skills than students in a traditional classroom. The treatment group was composed of students enrolled in a technology enriched magnet school in a North Central Texas school district during the 1996 97 and 1997 98 school years. The treatment group included students selected at random from the students wh o applied for the program. The comparison group included students not accepted to the magnet program, along with students from comparable campuses without a technology enriched classroom (Hopson, Simms, & Knezek, 2002). The research design for this study was a posttest and quasi experimental design. The treatment and comparison groups were given the Ross Test of Higher Cognitive Process. The Ross Test is a 105 item test to determine the effectiveness of curricula or
35 instructional methodology to teach the h igher order thinking skills of analysis, synthesis, and evaluation. Four distinct groups were identified for the study based on demographics and grade level (Hopson, Simms, & Knezek, 2002). The treatment groups were provided access to the computer as a to ol for learning, and instructed using the fifth grade curriculum for a technology rich environment. Each of the treatment classrooms was equipped with one computer for every two students allowing the students to use spreadsheet, database, and word processi ng software as tools to take notes, produce assignments, and construct projects. In addition, the teachers were equipped with a multimedia teaching station and trained on the use of technology as a tool for learning (Hopson, Simms, & Knezek, 2002). The c omparison groups were instructed in a traditional classroom setting with the districts curriculum for fifth grade. The comparison classrooms had no access to computers in the classroom and only limited access to computers in the campus computer lab for co mputer literacy and remediation. The teachers did not have access to computer based teaching stations and received no training in the use of technology as a tool for learning (Hopson, Simms, & Knezek, 2002). A univariate analysis of variance was used to e stablish initial equivalence for the treatment and comparison groups on the Ross Test. The results of the ANOVA indicated no significant difference between the fifth grade treatment and comparison groups. A one way ANOVA on the posttest data indicated diff erences between the sixth grade groups was significant, so an analysis of covariance was required. The fifth and sixth grade treatment group students exhibited a higher level of evaluation skill as measured by
36 the Ross Test. No significance difference was noted in the performance of the two groups on the analysis and synthesis subtests. The technology enriched classroom environment had a minimal but positive effect on student acquisition of higher order thinking skills (Hopson, Simms, & Knezek, 2002). This study adds to the limited research on the use of computers to enhance the development of higher order thinking skills. The teachers reported that learning in the technology enriched classrooms was more student centered and less teacher/textbook driven. An environment was created that facilitated the use of cooperative groups with students focused on knowledge construction rather than acquisition (Hopson, Simms, & Knezek, 2002). Summary Computers and technology can provide students with the opportunity to construct their own knowledge. However, computers in and of themselves do very little to aid in the learning process, and the presence of technology in the classroom does not automatically inspire teachers to rethink their teaching or students to adopt ne w modes of learning. In order for a change to occur, both teachers and learners need to learn how to take advantage of technology to enhance learning (Coley, Cradler, & Engel, 2000). It is the role of the teachers to implement and use technology as a tool for learning with their students.
37 Technology and Students of Low Socioeconomic Status Research has found that poverty is one factor associated with student achievement and school dropout rates (Leroy & Symes, 2001). Other factors associated with low achi evement include racial or ethnic background, single parent family, mother with low education, limited proficiency in English, and low expectations for success (Miller, 1993; Huang & Waxman, 1996; Strahan, 2003). Often, students who are likely to fail in sc hool or in life because of their lifes social curriculum are generally termed at risk students (Slavin & Madden, 1983). These students are at risk of failure because of broken homes and a culture of poverty. In the Conditions of Education (NCES, 2002) researchers stated that poverty has the potential to be a serious threat to students access to quality learning opportunities and their success in school. According to the 2000 Census, 15% of all children lived in households where the annual income in th e previous year was below poverty level. Students from low SES can be found throughout the United States; the state of Florida has an average with 18% of students living at the poverty level (Kidscount Snapshot, 2004). Title I Schools In 1965, Congress es tablished the Elementary and Secondary Education Act The goals of the Title I program were to improve schooling in areas of high poverty areas and to advance the equality of education outcomes (Borman, 2003). Since that time, the federal government has ap propriated nearly eight billion dollars each year for Title I programs designed to assist economically disadvantaged students (Citizens Commission
38 on Civil Rights, 1998). Recently, the largest funding in history was given to the Title I program calling fo r stronger accountability mandates and holding schools and districts responsible for the achievement of outcomes of minority students, low income students, and English language learners (Borman, 2003). Several critical issues have been associated with the low achievement of students from high poverty Title I schools. While researchers realize the most serious concerns are basic funding and political beliefs, there are still several amendable issues with possible improvements for at risk students (Melendez, 1993). Three critical issues are: shortage of qualified teachers; teachers' low expectations; and teaching practices that consist predominantly of basic skills (Waxman, Padron, & Arnold, 2001). Frequently, the most under prepared teachers are assigned to schools and classrooms in areas with the highest level of poverty (Darling Hammond, 1992, 1997). Teachers from high poverty schools tend to have lower literacy skills on teacher certification tests than teachers from advantaged schools. Those differences in teacher qualities often have an impact on student achievement (Ferguson, 1998). Furthermore, teachers of at risk students have been found less skilled at implementing more complex approaches to instruction and less capable of identifying students needs (Darling Hammond, 1992, 1997). The second critical issue has to do with teacher expectations of students from high poverty Title I schools. Lower teacher expectations result in an overemphasis on repetition of content through drill and practice (Knapp & Shields, 1990; Lehr & Harris, 1988). Often, at risk students are not provided tasks to promote higher order thinking
39 skills because it is believed they must first demonstrate lower levels of knowledge before they can be taught higher level skills (Rivera & Zehler, 1991; Waxman, Padron, & Knight, 1991). When teachers underestimate the potential of students from high poverty schools, the achievement gap widens between minority and majority students (Ferguson, 1998). The final issue is the teacher centered model of instruction found in schools that serve at risk students (Brookhart & Rusnak, 1993; Haberman, 1991, Waxman, Huang, & Padron, 1995). The teacher centered model of instruction emphasizes lecture, drill and practice, remediation, and student seatwork consisting mostly of worksheets (Stephen, Varble, & Taitt, 1993). Haberman (1991) calls this emphasis on teacher directed instruction in high poverty schools with at risk students a pedagogy of poverty. He went on to state that when teachers are makin g students learn, the students usually assume a passive role with low engagement in tasks that are not generally authentic (Haberman, 1991). Characteristics of Students Often, students living in poverty have different basic value and belief systems fro m the schools they attend, which operate within middle class norms. It becomes the role of teacher and school to mediate the students belief system with the teachers belief system. Payne (2001) describes poverty as the extent to which an individual does without resources (p. 16). These resources include financial, emotional, mental, spiritual, physical, support systems, relationships, and knowledge of hidden rules of class.
40 Schools become the only environment in which students of poverty have the opport unity to access a number of these resources (Payne, 2001). Students of poverty often come to school with different expectations than their peers and teachers. Researchers have found that poverty affects a students growth, cognitive development and acade mic achievement (Alaimo, Olson, & Frangillo, 2001). Often, students of poverty lack a motivation to learn, because they live in environments over which they have little control. When they are at school, they lack a vision for their future and see themselve s remaining in this situation for the rest of their lives (Bandura, 2001; Brophy, 1998). The students are unable to make the connection between acquiring an education and creating an opportunity out of their situation. In addition, a lack of readiness to learn exists because of their social situation. They are deficient in the social and cognitive skills that are needed to succeed in life because their social environment has affected their development (Renchler, 2000). Their home environment has limited o pportunity to organize perceptions and to develop higher order cognitive process. The students lack the ability to solve problems because of a lack of parental involvement, which supports the development of independent thinking (Benson, 1995; Bowman, 1994) Student Achievement Research has shown that poverty has a negative impact on student learning and future success (Payne, 1998). In a research study on students of poverty in inner cities, Renchler (1993) found those living in low socioeconomic areas wer e more likely to have educationally damaging circumstances as part of their life experiences than higher SES
41 children. Poverty negatively effects a students cognitive development, academic achievement, and physical and emotional health (Alaimo, Olson, & F rangillo, 2001). Researchers conducted a two year study in high poverty schools with a focus on instructional practices designed to promote childrens understanding and build meaning into the learning experience. The study included three states (Californi a, Maryland, and Ohio) and approximately 140 classrooms in 15 schools. During the first year, grades one, three, and five were studied; the second year included grades two, four, and six. The study focused on three subject areas (mathematics, reading, and writing) since these subjects accounted for the majority of instructional time in elementary grades (Knapp, Shields, & Turnbull, 1995). After an analysis of instruction and student learning gains, the researchers concluded that meaning oriented instructi on produces superior learning to more traditional skills with both low performing and high performing students. Instruction that emphasizes reasoning and problem solving is more effective at teaching advanced skills, as effective at teaching basic skills, and better at engaging students in learning. Their research offered a three pronged argument for teaching meaning in high poverty classrooms. First, teachers construct and maintain a rich academic learning environment, which reflects the students diverse cultural and SES backgrounds. Second, teachers approach instruction in a method that produces a higher level of thinking by constructing cognitively demanding tasks. Third, teachers are able to sustain this form of teaching because of the instructional lea dership, peer support, and uninterrupted blocks of instructional time (Knapp, Shields, & Turnbull, 1995).
42 Technology in Schools with Low SES Student Population Research examining the use of technology in high poverty, Title I elementary schools is very li mited. Some studies reveal that although millions of dollars have been appropriated for technology in high poverty, Title I schools, it is not being utilized effectively as a tool for higher order thinking skills (Waxman & Huang, 1996; Wenglinsky, 1998; Wa xman, Padron, & Arnold, 2001). Additional research findings concluded that teachers from high poverty schools were less prepared to use computers than teachers from economically advantaged schools (Wenglinsky, 1998). Wenglinsky (1998) examined the relatio nship between educational technology and student achievement in mathematics. Data were gathered from the 1996 National Assessment of Education Progress in mathematics consisting of national samples of 6,227 fourth graders and 7,146 eighth graders. Data inc luded the frequency of use for mathematics in schools, access to computers at home and in school, professional development of mathematics teachers in computer use, and the kinds of instructional use of computers in the schools. Wenglinsky (1998) conclude d that the impact of technology depended on how teachers used it and integrated in the curriculum with their students. The overall frequency of school computer use for simulation and higher order thinking skills had a positive impact to academic achievemen t. He concluded that disadvantaged eighth graders seem to be less likely to be exposed to higher order learning through computers and that using computers for drill and practice, the lower order skills, is negatively related to academic achievement" (19 99, paragraph 4).
43 In the spring of 1998, the Teaching, Learning, and Computing (TLC ) survey was conducted with a national sample of principals, technology coordinators, and teachers (Ravitz, Becker & Wong, 2000). Altogether, over 4,000 teachers from grade s 4 through 12 in over 1,100 schools from across the United States chose to participate in the survey. Between the fourth through sixth grade teachers in this sample who use computers for instruction, teachers in predominately minority, poorer schools (N=1 20) showed no real difference in the use of skill/game software than teachers in wealthier schools (N=189). The use of word processing, however, was much more prevalent in economically advantaged schools. Teachers in wealthier schools were also more likely to use simulations, reference materials, spreadsheets/databases, multimedia, and web browsers than were teachers who taught minority students in low SES schools (Becker, Ravitz, & Wong, 1999). Students of poverty need to develop cognitive skills and beco me designers of their learning. Students learn and retain the most from thinking in meaningful ways. Using technology as a cognitive tool enables students to apply concepts in a variety of contexts. Those tools need to engage students in the cognitive str ategies of learning such as construct, collaborate, interact, and contextualize process (Roschelle, Pea, Hoadley, Gordin, & Means, 2000). The more advanced uses of technology support the constructivist view of learning in which the teacher is a facilitator of learning rather than the classrooms only source of knowledge (Penuel & Means, 1999; Statham & Torrell, 1999; and Trilling & Hood, 1999). However, little research exists on the use of this type of technology with at risk elementary students
44 In a recen t study Page (2002) investigated the effects of technology on the academic accomplishments of elementary students of low SES, the sense of worth for those students, and the classroom among between students and with teachers, which resulted from the exposur e. The purpose of the study was to compare elementary students attainments, such as self esteem, classroom interactions with peers, and academic achievement, in a technology enriched environment with those of students in traditional elementary classrooms. This quasi experimental study was designed to examine if the outcomes would be significantly different between the two groups (Page, 2002). Participants in this study included 211 students from 10 classrooms (five technology enriched and five without tec hnology) at five elementary schools in Louisiana. Two of the schools included third grade classes for the experimental and control groups, while three of the schools included fifth grade classrooms for the experimental and control groups. Each of the class rooms involved in the study was a self contained classroom, with the students in each class typical of other students at their particular schools. Most of the students were from lower income families, and all were classified as being low SES (Page, 2002). The experimental and control classrooms were selected by each schools principal based on their teaching performance. The teacher in each of the experimental group classrooms was fully trained in the use of technology and was made aware of innovative us es of technology through workshops, in service days, and university coursework. Throughout the year, the experimental teachers began to integrate technology as a tool
45 into the curriculum. Experimental teachers made an effort to integrate technology into ev ery lesson, whether or not the learning opportunity appeared to be technology based. The teachers were provided assistance from staff members on the Louisiana Challenge with technical assistance, training in operational procedures, and technology integrati on (Page, 2002). The hardware in each of the experimental group classrooms included at a minimum: a teacher computer; four student computers; five Internet connections; a network laser printer; an inkjet printer; a large TV monitor; a presentation device; a digital camera; a VCR; a classroom set of calculators; a laserdisc player with laserdiscs; and a mini computer camera for videoconferencing. The software package included: an integrated office suite; a multimedia authoring tool; math curriculum programs ; reference programs; and a portfolio assessment. Teachers in both the experimental and control groups followed the same curriculum throughout the school year with different approaches (technology or traditional). The control group teachers conducted thei r classroom in the traditional manner. The majority of the classrooms contained a computer for teacher use, but with little or no access for students to technology (Page, 2002). Data for this study were collected from standardized achievement scores. Four schools used the Iowa Tests of Basic Skills (ITBS) to measure student achievement in math and reading (Hoover, Hieronymus, Frisbie, & Dunbar, 1996; Page, 2002). Pretests were administered in April 1998 and posttests in March 1999. One school administered the California Achievement Test (CAT, 1996) for reading and mathematics achievement.
46 CAT pretests were administered to all students in September 1998 and posttests in April 1999. All schools completed the Coopersmith Self Esteem Inventories (CSEI) during t he two observation sessions for each school (Page, 2002). Two additional observations in the experimental and control group classrooms utilized an adapted version of the Flanders Interaction Analysis System (FIAS) instrument to measure initiation/reaction patterns among students and teachers. A form was developed that allowed the researcher to record both classroom activities and the instances of classroom verbal exchange. The observation period was for an entire day in which the researcher listened and re corded for a three minute time period, then took a three minute rest from recording interactions. This process was repeated for the entire observation period (Page, 2002). Data from the ITBS, the CAT, and CESL were analyzed to determine if significant dif ferences existed between the adjusted posttest means of the experimental and control groups. The ANCOVA analyses related to reading showed mixed results, as there was no significant difference between the adjusted posttest means of the ITBS Reading Total. However, there was a statistically significant difference on the adjusted post means of the CAT Vocabulary, p <.001, and the CAT Comprehension, p <.001. In regards to the math, the ANCOVA analyses were much more consistent than the reading. There as a statis tically significant difference, p <.05, with the ITBS Mathematical Total scores and the CAT Mathematical Concepts and Applications, p <.001, and the Analytical Mathematics, p <.001 (Page, 2002).
47 The researchers found a statistically significant difference, p <.05, with the ANCOVA analysis between the experimental and control groups on the composite self esteem scores. The experimental group had a higher adjust pot means score with general self esteem, p <.001, and school self esteem, p <.05. A statistically sign ificant difference was found in the analysis on classroom interactions, p <.001, between the experimental and control group. In particular, the differences were found between the types of classroom interactions. In the experimental group, the frequency of s tudent to student interaction was observed 51% of the time with the teacher to student frequency 31% of the time. A contrast was seen in the control group, in which the student to student interaction frequency was 16% of the time and the frequency of the t eacher to student interaction was 58% of the time. The study concluded that when students of low SES have a technology enriched elementary classroom, levels of mathematics achievement, self esteem, and student centered learning environments. Students in technology enriched classrooms scored higher on standardized tests in mathematics. In addition, those same classrooms, students were provided the opportunity to take control of their own learning environment, through computer work groups and student to stu dent interactions. The researcher recognized that other variables were involved in the study, thus could not conclude that the computers and technologies caused these positive outcomes (Page, 2002). Summary Research has demonstrated that students of pover ty are more likely than other students to attend schools with limited access to technology and receive only traditional
48 instruction consisting of lecture and drill and practice activities. Much of the research involving students of poverty mirrors the sof t bigotry of low expectations (U.S. Department of Education, 2002) and has therefore focused on learning from technology rather than learning with technology. A clear gap exists in the research on use of technology as a tool for learning with students of poverty. The school must take on the role of creating a culture in which all students from various ethnicities and SES backgrounds can understand the vital role education plays in their lives and futures (Bowman, 1994; Marlowe & Page, 1999). A social env ironment must be created in which all students feel a part of the learning so they can take responsibility for their lives. The students must be educated to reach beyond their current poverty and given intellectual and social skills, to succeed more fully in life (Renchler, 2000). This goal will be achieved when school principal and instructional staff set high expectations that are attainable for all students. Schools provide the opportunity to increase students academic achievement and allow them to re ach their potential when students are viewed at promise rather than at risk (Slavin, 1998). A learning environment at the school must be created that allows for students to become more independent learners as they assume responsibility for their learning. In order for schools to be successful in student learning, a pattern of attitudes, beliefs and behaviors by the principal, teachers, and support staff needs to be developed for high expectations and commitment to bring about student achievement (Good & Bro phy, 1996). In addition, schools with low SES students must have access to the same levels of hardware and software tools as high SES schools.
49 CHAPTER THREE METHOD This chapter is divided into three major sections. The first section outlines the researc h questions to be examined. The second section is background on the Technology Resource Survey instrument. The final section is the research method that was employed to answer the research questions. Research Design This study used secondary data to exam ine the use of technology in low and high socioeconomic in public elementary schools in the state of Florida. Data from statewide surveys related to technology were used to investigate differences in access and use of technology in low and high SES schools Specifically, this study used data from the 2005 06 School Technology Resource Survey to examine the following questions: 1. Is there a significant difference between students access to computers in high and low socioeconomic schools? a. Is there a signific ant difference in the total number (desktops and laptops) of modern and non modern computers? b. Is there a significant difference in the locations of the computers for student use? 2. Is there a significant difference in the types of software available on compu ters between high and low SES schools?
50 3. Is there a relationship between the socioeconomic status of an elementary school and the frequency of student use by software type? 4. Is there a relationship between socioeconomic status of an elementary school and the teacher use of technology in the classroom? Population To answer the research questions, enrollment data for all elementary schools in Florida were tabulated then categorized according to poverty level. These data were provided from the Division of Accou ntability, Research and Measurement (ARM) in the Florida Department of Education. It is the role of ARM to assist school districts in the legislative requirement for reporting accurate information to the Master School Identification Database (MSID). This f ile contains information for all public school PK 12 schools such as school type, primary service, and Title I status. The Title I status is designated by the school district based on the criteria in No Child Left Behind legislation based on the students who were eligible to receive free and reduced lunch. Under the National School Lunch and Breakfast Programs, students unable to pay the full price of meals served have the opportunity to apply for free or reduced price meals. Students eligible for this p rogram come from households whose household income is at or below the poverty level. The data on the students that are eligible for the program are then aggregated to provide accurate information on the poverty level of each of the schools. For this stud y, data on elementary schools were used as it reflects the most accurate information on the poverty level of a school. A factor for using elementary
51 schools over secondary was the higher concentration of schools that qualify for the program. In addition, u sing eligibility for secondary schools is not as reliable because the students may attach a stigma to receiving a school lunch subsidy (National Science Foundation, 2006). The data focused only on those public elementary schools with grade levels of PK 6 ( N =1591) for the school year of 2005 06. The elementary schools in the MSID file were ranked from highest poverty level to the lowest poverty level. These data were then broken into four quartiles with a focus on the top 25% of schools that have a larger s tudent population at poverty level and the bottom part 25% of schools that have a smaller student population a poverty level. For this study, low SES schools were defined as the top quartile of elementary schools with a high poverty, and high SES schools w ere defined as those schools in the bottom quartile with low levels of poverty. Instrument For more than twenty years, the state of Florida, Office of Educational Technology, has collected information from the 67 school districts about the access to techno logy in the schools. Historically, the Computer Survey has focused on counting the boxes reporting how many computers were available in each school and the age/type of each computer. In response to the NCLB legislation, the Florida Department of Educat ion significantly revised its annual technology survey in 2002 to provide more meaningful information about technology integration and capacity in Floridas schools. The result of those modifications culminated in the School Technology and Readiness (STaR) Chart and updated Technology Resource Survey.
52 Construct Definition In the summer of 2002, educational technology leaders in Florida expressed a desire to modify the Computer Survey, shifting the focus from simply the numbers and types of computers per st udent to areas such as technology planning, integration, and accountability. Content experts from across the state were assembled to define key elements for the revised survey. Using a model from the state of Texas as a starting point, strands and categori es were identified and discussed. The result was the School Technology and Readiness (STaR) chart, which was comprised of five strands, each made up of 3 to 5 categories (See Table 2).
53 Table 2 STaR Strands and Categories within Ea ch Strand Strand I Strand II Strand III Strand IV Strand V Technology Administration and Support Technology Capacity Educator Competency and Professional Development Learners and Learning Accountability Technology Planning Student Compute r Access School Administrators Student Use of Technology Student Technology Standards Technical Support Teacher Computer Access Professional Development Budget 21 st Century Classroom Teacher Technology Standards Instructional Technology Support Interne t Access (not in analysis) Models of Professional Development Secondary Technology Courses School Budget Video Capacity Content of Professional Development Community Outreach Funding LAN/WAN Category Name Curriculum based Tools Survey Development The S TaR chart provided the initial structure for the subsequent Technology Resource survey, which was developed with input from the Office of Educational Technology (OET) and technology leaders from around the state. First, the Florida Center for Instructional Technology (FCIT) staff at the University of South Florida worked closely with OET representatives to create survey items and verify alignment with the Florida STaR chart. Next, several work group sessions were held with content experts in
54 order to write and revise items. Item formats included multiple choice, check box, and open ended response. These review sessions began in August 2002, and continued through December 2002. In December 2002, experts in survey development at the University of South Fl orida reviewed the draft survey, which was then posted on an FCIT server for comment from technology leaders across the state. Throughout January 2003, FCIT staff visited principals in their schools to observe them and elicit comments as they worked throug h the draft survey. Each page of the draft survey also allowed for immediate email feedback from principals or other content experts who were field testing the survey. Survey items were revised based on feedback from field testing and emailed comments from participants. Pilot Test Implementation The survey was initially programmed using open source software such as Apache, PHP, and MySQL. An Xserve server was used, to ensure that the survey could withstand hundreds of simultaneous connections with schools entering data. The survey was designed so that a school could log on and answer some of the questions and then quit. When the school logged back on, all of the previous answers were available for viewing or revision. A menu page was provided to indicate the questions that had been answered and those that had not yet been answered. Survey Structure The initial survey contained a base set of 47 items. The answer for each of the initial questions determined which, if any, contingency (follow up) questions were asked. The contingency questions allowed for a more precise placement
55 within the STaR chart categories, and provided additional information for future item development. Questions were presented one at a time; schools could skip and go back to question s multiple times. District Review When a school finished with the survey, the district was informed that the survey was ready for review. The appropriate district representative was then able to review and accept or reject the survey. If it was accepted, the results were written into a permanent database and were not available for revision. If rejected by the district for inaccurate data, the school was able to revise the answers as directed by the district. Data Review and Verification Schools were giv en approximately six weeks to complete the survey. Districts then had an additional three weeks to accept the completed surveys. At this point, the data were downloaded and screened for missing, extreme, and anomalous responses. A spreadsheet was provided to each district technology supervisor with the response cells coded yellow for a response that was outside of the normal range, orange for an anomalous or unusable response, and red for missing. Surveys were re opened so that corrected information could be recorded and the surveys re accepted. The implementation of the survey was an interactive process that entailed a variety of individuals, from the district level technology personnel to the school based technology specialist. The district received the password for the survey system and had the responsibility of sharing it with the individual schools. It was then the role of individuals at the school level to gather and input all of the data necessary for completing the survey. During this process, the d istrict and schools were in constant communication through email and phone calls to verify the accuracy of the data being reported. Accuracy
56 was determined when the data reflected the purchase invoices for the hardware and software at the district level. C urrent Implementation The Technology Resource Survey has been implemented over the last three years with all public schools in the state of Florida, which includes elementary, middle school, combination, junior high school, high school and charter. In 20 03 04 there were 2,514 respondents (97% response rate); in 2004 05 there were 2,553 respondents (96% response rate); and in 2005 06 there were 2,667 respondents (97% response rate). Each year, the survey has been implemented in the fall with districts b eing provided with secure passwords for the district administrator and the school administrator. The respondents are provided a nine week time span to complete the survey with guidance from the DOE on item verification. The district administrator coordinat es with all public schools, including charter and special centers to complete the survey and verify data provided. At the end of the data collection, the DOE provides districts with a spreadsheet on the school data to examine any anomalies in the reporti ng. For example, a school may have reported 5,000 modern computers in the general education lab, when it is actually 50 modern computers. The district administrator contacts the school to verify the data and correct the information. Over the past three ye ars, revisions to the Technology Resource Survey have been limited to ensure the integrity of the data for reporting purposes to the United States Department of Education. At the end of each year, items are analyzed and revised with
57 input from experts in i tem development, technology, school leadership, and programming. The majority of items have remained the same with only a tweaking of terminology to ensure the item reflects the data that are required for a particular section. A validation report was don e in the summer of 2006 (Florida Center for Instructional Technology, 2006), on the integrity of the web based reporting tool. The Structured Query Language (SQL) used to access the response data from the database was analyzed for accuracy. The researchers examined the records for county level analysis and school level analysis, and determined the reporting utility produced valid records. In addition, the SQL statements appear to provide the correct grouping information: elementary, middle/junior, high, and combination classifications. The researchers concluded that the reporting software for the STAR online reporting utility provides reliable information. Data Source and Analysis Data from 2005 06 School Technology Resource Survey were the primary source o f data for the research questions. Selected items from the Administrative and Technical portions of the survey were analyzed for elementary schools. The data from the survey were merged with the statewide data for student enrollment from public elementary schools. Other factors that were considered in this analysis include issues such as urban and rural settings, size of school, and ethnicity of the student population. This section will address the reliability and validity of the items to be used from the s urvey and the type of statistical analysis and descriptive statistics.
58 Research Question #1 : Is there a significant difference between student access to computers in high and low socioeconomic schools? Is there a significant difference between total numb er (desktops and laptops) of modern and non modern computers? Is there a significant difference between the locations of the computers for student use? Item #2 and #3 within the Access to Technology category of the Technology Resource Survey provided the d ata on students' access to computers in the schools. These two items have strong face validity in reporting the modern (purchased within the last three years) and non modern (purchased more than three years ago) computers, the types of computers, and instr uctional location. Specifically, item #2 requires respondents to provide data on the exact number of modern laptop and desktop computers available for students in specific areas of instruction (See Figure 2).
59 Figure 2 Student a ccess to modern computers Item #3 asked respondents to provide data on the exact number of non modern laptop and desktop computers available for students in specific areas of instruction (See Figure 3). Figure 3 Student access to non modern computers Data from items #2 and #3 were analyzed to answer the difference between the total number of modern and non modern computers available for student access. The two categories of desktops and laptops were combined for each room loca tion within the modern computer group and the non modern computer group. The next step was to compute by dividing the school size by number of computers. The two variables of
60 modern computers and non modern computers were then analyzed will an independent t test. The poverty level of students was a dichotomized as the median was used for the high socioeconomic schools and the low socioeconomic schools. To answer the question of location for computers, the data from the total number of modern computers was split into two groups of classroom versus non classroom or labs. The classroom variable was the total number of modern computers in the classroom, mobile labs, and computers assigned to students. The non classroom variable was a combination of the media c enter, general education lab, special education lab, vocational education lab, and other student gathering places. A independent t test was used to examine the difference in high and low socioeconomic schools. Additional analysis included descriptive stati stics with graphical descriptions of the median and mode for the high and low SES schools. Research Question #2: Is there a significant difference between the types of software available on the computers for high and low SES schools? In order to answer this question, item #16 from Access to Technology section was used to provide data on tool based software (See Figure 4). Cronbachs alpha measure of internal consistency reliability of scores on this group of nine survey items was .76.
61 Figure 4 Tool based software on computers. The item to answer this question comes from the category Access to Technology for curriculum based software (See Figure 5). Cronbachs alpha measure of internal consistency reliability of scores on this g roup of seven survey items was .70.
62 Figure 5 Curriculum based software on computers The two variables for this question are tool based software and curriculum based software. The first item represents the availability of tool based software on student computers and the second item represents the availability of curriculum based software. The data from the tool based software was collapsed down to one category of availability on the computers in which the midpoint was calculated for the tool based software. The same procedure was then done for the curriculum based software. A t test was done to examine the difference in the types of software available for high and low socioeconomic students. Research Question #3: Is there a r elationship between the socioeconomic status of an elementary school and the frequency of student use by software type? In order to answer this question, data from item #43 in the category for Digital Learning Environment was used. This item asks responden ts for data on the frequency of student use of various software types (See Figure 6).
63 Figure 6 Frequency of student use of software. The indicators from this item represent the frequency of student use for the variables of tool based software and curriculum based software. Cronbachs alpha measure of internal consistency reliability of scores on this group of seven survey items was .77. Data from the indicators for multimedia, tool based software, research, and presentation soft ware were collapsed into the tool based category. Data from the indicators for drill and practice software, integrated learning system, and simulation software were collapsed into the curriculum based category. The categories for frequency were broken dow n to limited and regular. The indicators, not at all and once a month were collapsed for the limited category. The indicators, once a week, several times a week, and everyday were collapsed for the regular category. A t test was used to analyze t he types of software, frequency of student and use, and socioeconomic status. Research Question #4: Is there a relationship between the socioeconomic status of an elementary school and the teacher use of technology in the classroom? In order to answer thi s question, data from item #1 in the category Digital Learning Environment from the principal survey were used (See Figure 7).
64 Figure 7 Level of teacher use of technology. The indicators from this item represent the teachers u se for both tool based software and curriculum based software with students. In addition, the indicators reflect the levels of the technology integration for the teacher from entry to innovation. Cronbachs alpha measure of internal consistency reliability of scores on this group of five survey items was .79. The midpoint for each composite variable was analyzed as a continuous variable. Descriptive statistics were used to examine the levels of technology integration in high poverty schools and low poverty schools.
65 CHAPTER FOUR RESULTS This chapter presents the analysis of the data from the 2005 06 Technology Resource Survey. This chapter is divided into five sections to represent the verification of the data and the research questions. The first section explains the process for the verification of the data. The next section examines students' access to technology in high and low socioeconomic schools. The third section examines the types of software available on the computers. The next section examines t he relationship between the socioeconomic status of the school and frequency of student use by software type. The final section examines the teacher use of technology in the classroom. Verification of Data The first step in the process for the data analy sis was to acquire the data from the Florida Innovates website. This website provides district contacts with access to results from the Technology Resource Survey through a secure log in. These data can be provided through a web interface and/or can be do wnloaded in raw format of a comma separated values file. Once the data were acquired from the 2005 06 survey, it was then delineated to include only elementary schools as defined to include all schools with grades PREK 5, PREK 6, K 5, or K 6. A new file was created to include only the schools that met the parameters of the research.
66 This file was then organized by school code to keep all of the data in an organized format. Fields were then deleted that did not pertain to the data, such as the school name county, number of teachers, and additional items from the survey. This file was then merged with student population information from the Survey 2, the October report from schools for the Master School ID (MSID) maintained by the Division of Accountabil ity, Research, and Measurement in the Florida Department of Education. The MSID file contains information on every public school in the state, such as active school status, school ID number, school type(s), percentage of students on free and reduced lunch, grade levels taught, and other data that were used to amplify school based analyses. A new file was created that included the variables for grade levels taught, free and reduced lunch, instructional rooms, computer labs, access to modern and non modern co mputers, software installed on computers, teacher use of technology, and student frequency use of technology. The total number of schools that met all of the parameters for the study was N =1591, out of 1897. Schools were divided into quartiles based on th e percentage of student receiving free and reduced lunch. The top quartile ( high socioeconomic schools ) was labeled number 1, followed by the second quartile being labeled number 2, the third quartile was labeled number 3, and the bottom quartile, low soci oeconomic schools, was labeled number 4. The n for quartile 1 was 400 schools, with the range of free and reduced lunch from 1.49 37.5%. The n for quartile 4 was 396 schools, with the range of free and reduced lunch from 77.8 100% (See Table 3).
67 Table 3 Number of Schools and Student by Free and Reduced Lunch Status N Minimum Free/Reduced Maximum Free/Reduced Total Students Quartile 1 400 1.49 37.5 324,089 Quartile 2 397 37.6 59.7 294,774 Quartile 3 398 59.8 77.7 271,436 Q uartile 4 396 77.8 100 251,784 Total 1591 1,142,083 The sample size of N =796 had 400 high SES schools and 396 low SES schools. An additional parameter was to only include grades Kindergarten through 6, hence those schools with students in Prekinderg arten classrooms were excluded from the student membership totals. The Florida Department of Education classifies school districts as small, medium, or large based on the number of students that are served. A small district serves a student population from 1 10,000; a medium district serves 10,000 100,000 students; and a large district serves over 100,000 students. The high SES sample schools had a frequency of 7 percent from small districts, 40 percent from medium districts, and 53 percent from large distr icts (See Table 4). The low SES sample schools had 5 percent from small districts, 24 percent from medium districts, and 71 percent from large districts.
68 Table 4 District Size for Sample Size Small Medium Large High SES 7% 40% 5 3% Low SES 5% 24% 71% After the data w ere organized into the data sets, abnormalities were noticed with the data, such as schools that report ed the same number for more than one variable. For example, one school reported a value of 232 for classroom la ptop computers and the same value (232) for the number of mobile laptop computers. This irregularity was more apparent when analyses were run with SPSS on the ratio of students to computers in which the results reported that each student had access to two computers. Since most of the irregularities found in the data related to the number of computers further analysis was conducted to compare the value for modern desktop classroom computers to the value for non modern desktop computers ; the value for mode rn classroom laptop computers to the value for mobile computer labs ; and the value for modern classroom laptop computers to the value for laptop computers assigned to students. This analysis was conducted using SPSS and the different variables were compar ed for equivalent values. All of the cases were sorted by total modern computers, so that the values could be examined for extreme cases and /or duplicate values for different variables. In addition, new variables were created for the total number of modern classroom computers, total number of modern lab computers, total modern
69 computers, total non modern classroom computers, total non modern lab computers, and total non modern computers. The final step of verification was to compare the values for each of the new variables with data collected from the 2006 07 Technology Resource Survey. The method of gathering the data was designed differently for the 2006 07 survey than it was for the 2005 06 survey The revised 2005 06 06 survey placed the fields for the total number of modern and of non modern computers in two separate sections increasing the potential for error in data entry. The new design would allow the users to view all of the access to student computers in one location on the survey. In addition, th ere was a field for the total number of computers for student use. This final step of the process helped to ensure the accuracy of the data for the 2005 06 surveys. Access to Computers An essential condition for effective integration of technology in the curriculum is students' access to computers. The first research question asked Is there a significant difference between students access to computers in high and low socioeconomic schools? Two components to the question of student access we re considered The first component of the access question explore d the type of computers that students access in high and low SES schools. A comparison wa s made between access to modern and non modern computers. The second component of the technology access question co nsider ed the location of the computers available to high and low SES schools. Data to answer this question w ere organized according to student access to modern computers and non modern computers in both classrooms and lab type settings.
70 Variables to exam ine classroom access included : student access to desktop computers, laptop computers, mobile computer labs, and computers assigned to students. Variables for lab type settings included : desktop and laptop computers in media centers, general education labs, special education labs, and other student gathering places. The number of computers used for vocational education was excluded from the data set of elementary schools, as this type of program is not universally offered in all schools. Total Modern and No n Modern Computers The fi rst component of the access to technology question asked, Is there a significant difference between total number (desktops and laptops) of modern and non modern computers? V ariables used to examine modern computer access for stude nts included modern classroom computers and modern lab computers which were then combined for the variable total modern computers The same procedure was done for non modern computers, in which the variables non modern classroom computers and non modern lab computers were created and then combined to form the variable total non modern computers The descriptive statistics for total modern c omputers in high SES Schools ( n =400) resulted in a median of 121 ( SD =105.91) and a mean of 145.12, with a range of three to 532 modern computers available at schools (See Table 5). There was a positive skewness of 1.12 with a kurtosis of 0.74. The low SES Schools ( n =396) resulted in a median of 114 ( SD =90.16) and a mean of 114 as there was a range from zero to 448 fo r computers. There was a positive skewness of 0.97 with a kurtosis of 0.41. An independent t test showed there was no significant difference ( p =.005) with an extremely small effect size
71 ( d =0.08) for the total number of modern computers at the schools. The results in the data reveal there are extremes in the number of computers available at all schools. There was variability in the number of modern computers in both the high and low SES schools, for example one low SES school reported having zero modern com puters. Table 5 Descriptive Statistics for Total Modern Computers by School SES School SES N Min Max M Mdn SD Skewness Kurtosis High SES 400 3 532 145.12 121 105.91 1.12 0.74 Low SES 396 0 448 136.82 114 90.16 0.97 0.41 *. p=. 005. ** d =0.08. The total n on modern c omputers in high SES Schools ( n =400) had a median of 93 ( SD =84.56) and a mean of 102.32 compared to low SES Schools (n=396) in which the median was 73.50 ( SD =80.36) and a mean of 89.39 ( see Table 6). The range of com puters in the high SES schools went from zero to 510 with a positive skewness of 1.02 and a kurtosis of 2.06. The range of computers in the low SES schools went from zero to 527 with a positive skewness of 1.31 and a kurtosis of 2.63. In addition, t here wa s no significant difference ( p =.188) for total non modern computers (relative to SES level) at p <.05 level with a small effect size ( d =0.18). There continued to be variability in the number of computers reported at the individual school level for both high and low SES schools.
72 Table 6 Descriptive Statistics for Total Non Modern Computers by School SES School SES N Min Max M Mdn SD Skewness Kurtosis High SES 400 0 510 102.32 93.00 84.56 1.02 2.06 Low SES 396 0 527 89.39 73.50 80. 36 1.31 2.63 p =.188. ** d =0.18. A variable was created for total modern laptop computers by combining classroom laptop computers, mobile lab laptops, and computers assigned to students. This variable was limited to those schools that reported having m odern laptops at high SES schools. In high SES schools, there was a median of one ( SD =76.28) with a mean of 39.96 (See Table 7). While in low SES schools, there was a median of zero ( SD =47.05) with a mean of 19.85. An independent samples t test analysis in dicated significant difference for total modern laptop computers ( p =.000) in high and low SES schools. The magnitude of the difference was medium ( d= 0.32). This descriptive data reveals there are more modern laptop computers at high SES schools than at low SES schools. Table 7 Descriptive Statistics for Total Modern Laptop Computers by School SES School SES N Min Max M Mdn SD Skewness Kurtosis High SES 400 0 480 39.96 1 76.28 2.56 7.47 Low SES 396 0 348 19.85 0 47.05 3.49 14.41 p =.000. ** d =0.32.
73 Additional data were analyzed to examine the number of computers available in classrooms and labs. A ratio was developed using total modern classroom computers divided by the number instructional rooms. High SES schools had a median of 1.53 ( SD =1.88), a mean of 2.04, and a range of zero to 9.93 computers per classroom (See Table 8). Low SES schools had a median of 1.73 ( SD =1.93), a mean of 2.16 and a range of zero to 16.76 computers per classroom. An independent t test found no signif icant difference in the number of computers available in the classroom ( p= .361) and no effect size ( d =0.00). Generally, there is a distribution of two modern computers available in the classroom in both high and low SES classrooms. Table 8 Modern Computers per Classrooms by School SES School SES N Min Max M Mdn SD Skewness Kurtosis High SES 400 0 9.93 2.04 1.53 1.88 1.10 0.83 Low SES 396 0 16.76 2.16 1.73 1.93 1.85 8.11 p =.361. ** d =0.00. The ratio for computers to labs was co mputed using the variable for total modern lab computers divided by the total number of labs. High SES schools had a median of 31 ( SD =24.21), while low SES schools had a median of 31.50 ( SD =23.19) modern computers per lab (See Table 9). An independent t te st found no significant difference in the number of computers for modern computer labs ( p =.153) and no effect size ( d =0.00). There was an equal distribution of modern computers in labs between high and low SES schools.
74 Table 9 Mod ern Computers per Labs by School SES School SES N Min Max M Mdn SD Skewness Kurtosis High SES 400 0 237 30.60 31.00 24.21 2.78 18.36 Low SES 396 0 157 33.18 31.50 23.19 1.14 2.97 p= .153. ** d =0.00. Location of the Computers The second component of the technology access question was, Is there a significant difference between the locations of the computers for student use? To examine student access to modern computers, the variables for student access to modern classroom computers and lab computers we re used. Variables representing modern classroom computers access included: student access to desktop computers, laptop computers, mobile computer labs, and computers assigned to students. Variables representing modern lab type settings included: desktop a nd laptop computers in media centers, general education labs, special education labs, and other student gathering places. Data were analyzed to examine the ratio of students to total modern computers and total non modern computers in high and low SES scho ols. The variable of total number of students was divided by the total modern computers. The median for students to total modern computers in high SES schools was 7.40 ( SD =35.68) with a mean of 16.35 (See Table 10). The ratio for students to modern comput ers ranged from 1 to 461, with an extreme skewness of 7.36 and a kurtosis of 72.93. The median for students to total modern computers in low SES schools was 5.37 ( SD =56.75) with a mean of 12.71.
75 The ratio for students to modern computers extended from 1 to 1053, with a positive skewness of 16.38, and a kurtosis of 294.83. An independent t test showed there was no significant difference ( p =.285) for the ratio of students to modern computers between high and low SES schools. The effect size for ratio of stude nts to modern computers was extremely small ( d= 0.07). Table 10 Student Access to Modern Computers School SES N Min Max M Mdn SD Skewness Kurtosis High SES 400 1.00 461 16.35 7.40 35.68 7.36 72.93 Low SES 396 1.00 1053 12.71 5.37 56.75 16.38 294.83 Note. Ratio of students to total modern computers. p =.285. ** d =0.07 The same method was employed for determining the ratio of students to total non modern computers in which the students were the numerator and the computers were th e denominator. Students' access to non modern computers in high SES schools exhibited a median of 7.34 ( SD =91.29) and a mean of 22.41 (See Table 11). The range for students to non modern computers was large extending from 1.81 to 1172, with an extreme ske wness of 9.35 and a kurtosis of 98.45. The median for students' access to total non modern computers in low SES schools was 6.27 ( SD =38.97) with a mean of 15.92. The ratio of students to non modern computers ranged from 1.42 to 566, with a positive skewnes s of 9.65, and a kurtosis of 12.75. An independent t test showed there was no significant difference ( p =.228) for the ratio of students to non modern computers
76 between high and low SES schools. The effect size for students to non modern computers was extre mely small ( d= 0.09). Table 11 Students' Access to Non Modern Computers School SES N Min Max M Mdn SD Skewness Kurtosis High SES 400 1.69 1172 21.99 7.23 91.23 9.38 98.89 Low SES 396 1.06 566 15.24 6.74 36.98 10.67 147.61 Note. Ratio of students to total non modern computers. p =.228. ** d =0.09 Students access modern classroom computers in high SES schools ( n =400) had a median of 9.19 ( SD =76.19) and a median of 7.92 ( SD =68.21) in low SES schools ( n =396) (see Table 12). The r ange for students' access to modern classroom computers in high SES schools went from 1.56 to 630, with a positive skewness of 5.79 and a peaked distribution with a kurtosis of 37.01. The range in low SES schools went from 2.79 to 751, with a positive skew ness of 6.76 and peaked distribution with a kurtosis of 55.33. An independent samples t test analysis indicated no statistically significant difference for student access to modern classroom computers between high and low SES schools ( p =.376 ) with a no ef fect size ( d =0.00). The high SES schools have a ratio of one student per 8.67 computers, while in low SES schools the ratio is one student per 7.43.
77 Table 12 Students' Access to Modern Classroom Computers School SES N Min Max M M dn SD Skewness Kurtosis High SES 400 1.47 630.00 23.27 8.67 65.05 6.89 54.01 Low SES 396 1.37 751.00 22.97 7.43 66.66 7.05 59.57 Note. Ratio of students to modern computers in classroom settings. p =.376. ** d= 0.00 The median for students' access to modern lab computers in high SES schools was 25 ( SD =143.54) with a mean of 68.36 (See Table 10). The range for students to modern computers went from 4.01 to 1112, with a positive skewness of 4.63 and a peaked distribution with a kurtosis of 23.93. The me dian for students' access to modern lab computers in low SES schools was 16.92 ( SD =99.03) with a mean of 42.07. The range for students to modern computers was large as it went from 2.79 to 1053, with a positive skewness of 6.75, and peaked distribution wit h a kurtosis of 55.38. An independent t test showed t here was a statistically significant difference in student access to modern lab computers ( p =.004). The effect size based on Cohen's d revealed the difference in the means was small ( d =0.21). Table 13 Students' Access to Modern Lab Computers School SES N Min Max M Mdn SD Skewness Kurtosis High SES 400 4.59 1112.00 58.95 24.43 125.64 5.47 34.93 Low SES 396 2.79 940 3.92 15.39 76.39 7.11 66.76 Note. Ratio of students to modern computers in lab settings.* p =.004. ** d =0.21.
78 Summary Access to Computers The first research question examined students access to technology by focusing specifically on their access to computers in high and low SES schools. Data analysis between high a nd low SES schools found no significant difference between total modern computers, total non modern computers modern classroom computers, and modern computers labs. There was a higher distribution of modern laptop computers in high SES schools than low SES schools. However, low SES schools have a significantly higher ratio student to total modern computers, total non modern computers, modern computers in classroom settings, and modern computers in lab settings. Software Types on Student Computers Another e ssential condition for the integration of technology in the curriculum is development of a student centered learning environment. The second research question was, Is there a significant difference between the types of software available on computers betwe en schools serving high and low SES schools? This question focused on the availability of types of software ( tool based and curriculum based ) on student computers. Tool based software refers to application based programs that provide opportunity for teache rs and students to perform a task. Curriculum based software refers to those programs that deliver content for student consumption. Tool Based Software on Student Computers The data analyzed for this question came from items on the survey that required t he respondent to report the approximate percentage range of software types on each given computer. The response scales used were: 0%; 1 24%; 25 49%; 50 74%;
79 and 75 100%. The midpoint percentage of each option selected was used to create a continu ous variable. Variables for tool based software included: concept mapping, graphics, multimedia authoring, presentation, spreadsheet, video editing, web authoring, basic word processing, and robust word processing. A Cronbachs alpha of .74 for the group o f nine survey items was used to measure the internal consistency reliability for tool based software, which is an acceptable value for this portion of the survey. The mean for the availability of tool based software on students computers for high SES sc hools ( n =400) was 57.18 ( SD =13.94) and for low SES schools ( n =396) was 50.59 ( SD =16.90) (See Table 14). An independent samples t test was conducted to compare the availability of tool based software at high and low SES schools. There was a significant diff erence related to the availability of the tool based software ( p =.000). The magnitude of the differences in the means tended toward a medium effect size ( d =0.43). There was a higher distribution of tool based software on student computers for high SES scho ols than the low SES schools. Table 14 Tool Based Software Availability on Student Computers School SES N Min Max M Mdn SD Skewness Kurtosis High SES 400 0 87.50 57.18 58.22 13.94 0.54 0.98 Low SES 396 0 87.50 50.59 51.38 16.90 0.37 0.10 p= .000. ** d =0.43. A factor analysis was performed on the nine survey items for tool based software to create two different variables, basic and robust (See Table 15). The basic tool based
80 software variable consisted of three items: presenta tion, spreadsheet, and robust word processing software. The basic tool based software reflects the average suite of tools which are available on the computer when purchased. A Cronbach's alpha for the three items was .81, which was adequate for these three items. The robust tool based software variable consisted of multimedia authoring and video editing with a Cronbachs alpha of .69 which was adequate. Table 15 Factor Analysis for Tool Based Software Factor 1 Factor 2 Spreadshee t .927 .023 Presentation .784 .060 Robust word processor .594 .084 Basic word processor .345 .067 Graphics .318 .268 Multimedia .053 .815 Video Editing .007 .661 Concept Map .065 .457 Web authoring .126 .290 Extraction Method: Principal Axis Fa ctoring. Rotation Method: Promax with Kaiser Normalization. An independent samples t test was conducted to compare the availability of basic tool based software on high and low SES schools computers. There was significant difference in availability for high SES schools ( M =77.29, SD =17.21) and low SES schools ( M =70.71, SD =24.90) with p=.000 (See Table 16). The magnitude of the
81 differences in the means tended toward a medium effect size (d=0.30). The data show that computers in high SES schools have a hig her distribution of basic tool based software than in low SES schools. Table 16 Basic Tool Based Software Availability on Student Computers School SES N Min Max M Mdn SD Skewness Kurtosis High SES 400 0 87.50 77.29 87.50 17.22 2.12 4.88 Low SES 396 0 87.50 70.72 87.50 24.90 1.40 .728 Note. Basic tool based software consisted of presentation, spreadsheet, and robust word processor. p= .000. ** d =0.30. An independent samples t test was conducted to compare the availability o f robust tool based software at high and low SES schools computers. There was significant difference in availability for high SES schools ( M =33.27, SD =28.68) and low SES schools ( M =25.02, SD =27.00) with the p =.000 (See Table 17). The magnitude of the diffe rences in the means tended toward a medium effect size ( d =0.30). There is a significant distribution in the availability of robust tool based software on computers in high SES schools than on computers in low SES schools. Table 17 Robust Tool Based Software Availability on Student Computers School SES N Min Max M Mdn SD Skewness Kurtosis High SES 400 0 87.50 33.27 24.75 28.68 0.62 0.83 Low SES 396 0 87.50 25.02 12.50 27.00 1.02 0.05 Note. Robust tool based software consisted of multimedia authoring and video editing p= .000 ** d =0.30
82 Curriculum Based Software on Student Computers Variables for curriculum based software included: FCAT Explorer, other test prep tools, Integrated Learning Systems, content specific skills pract ice/tutorials, content specific simulation, other content specific resources, and general reference tools. The composite variable for each software type was created by finding the mean of all options selected. Cronbachs alpha for the seven survey items wa s used to measure the composite variables for curriculum based software was .68, which is acceptable for internal consistency with the items. An independent samples t test was conducted to compare the availability of curriculum based software at high and low SES schools. There was no significant difference in availability for high SES schools ( M =55.36, SD =17.80) and low SES schools ( M =54.74, SD =18.45) with the p =.628 (See Table 18). The effect size was extremely small ( d =0.03). There tends to be an equal d istribution of curriculum based software on computers in high and low SES schools. Table 18 Curriculum Based Software Availability on Student Computers School SES N Min Max M Mdn SD Skewness Kurtosis High SES 400 00 87.50 55.36 55.35 17.80 0.22 0.33 Low SES 396 00 87.50 54.74 53.57 18.45 0.12 0.47 p= .628. ** d= 0.03.
83 Summary of Software Types on Student Computers The second research question focused on the availability of tool based and curriculum based software on studen t computers. There was a statistically significant difference in the basic tool based software, with a mean of 77.29 for high SES schools and 70.72 for low SES schools. In addition, there was a statistically significant difference with the robust tool base d software, with a mean of 33.27 in high SES schools and 25.02 in low SES schools. A single variable was created for curriculum based software available on students computers. There was no statistically significant difference in the curriculum software, w ith the mean of 55.36 in high SES schools and 53.57 in low SES schools. Student Use of Software Types The third research question was, Is there a relationship between the socioeconomic status of an elementary school and the frequency of student use by sof tware type? This question expanded on the availability of the software types on the computers, to examine the frequency of use by the student. The data analyzed for this question came from the item on the survey that require d the respondent to report the f requency of student use of software types. A composite score was developed for each of the variables by adding the categories for frequency of student use. Limited and Regular Use of Software Types Two categories "limited" and "regular" were used to exa mine the frequency of student use of software. The limited category included the total frequencies for not at
84 all, once a month, and once a week while the regular category included the total frequencies for several times a week and everyday. A fr equency table was developed to examine these two categories for each of the variables for student use of software types (See Table 19). The frequency table reveals that students in low SES schools use Integrated Learning Systems and drill software on a reg ular basis with limited use of other software types. The high SES schools had regular use of drill and research based software with limited use of the other software types.
85 Table 19 Student Limited and Regular Use of Software Hig h SES Low SES Multimedia Limited 76% 85% Regular 24% 15% Presentation Limited 79% 90% Regular 21% 10% Tool based Limited 50% 64% Regular 50% 36% Research Limited 41% 46% Regular 59% 54% Simulation Limited 87% 97% Regular 13% 03% Integrated Learning System Limited 42% 24% Regular 58% 76% Drill Limited 36% 25% Regular 64% 75% Note. Limited category consisted of the total frequencies for not at all, once a month, and once a week. The Regular category consisted of the total frequenci es for several times a week and everyday. The data in each of the software types exhibits there is extreme use of software types within high and low SES schools. The use of simulation type software in high SES
86 schools was limited to 87% with only 13% regular use. While presentation types of software in low SES schools was limited to 90% with regular use of 10%. The tool based and research types of software showed an even distribution of student use within the high and low SES schools. Frequency of St udent Use of Software The indicators, not at all, once a month, once a week, several times a week, and everyday were assigned a corresponding value of 0, 25, 50, 75, and 100. A variable was created for frequency of student software that included : tool based, presentation, multimedia, research, simulation, drill and skill, and Integrated Learning System. An independent samples t test was conducted to compare the frequency of student software use between high and low SES schools. There was a signif icant difference in frequency of student use for high SES schools ( M =51.01, SD =17.50) and low SES schools ( M =47.42, SD =16.07) with the p =.003 (See Table 22). The magnitude of the differences in the means was small ( d =0.24). Table 20 Frequency of Student Software Use School SES N Min Max M Mdn SD Skewness Kurtosis High SES 400 0 100 51.01 50.00 17.50 0.18 0.10 Low SES 396 0 100 47.42 46.42 16.07 0.32 0.09 p= .003. ** d =0.24. Factor analysis was performed on the frequency o f use for software types to create a variable for frequency of student tool based software use (See Table 21).
87 Variables for frequency of student tool based software included: tool based, presentation, multimedia, and research with a Cronbachs alpha of .7 8. Table 21 Factor Analysis for Frequency of Student Use Factor 1 Factor 2 Tool based .765 .023 Presentation .712 .101 Multimedia .638 .027 Research .620 .132 Simulation .398 .033 Drill and skill .027 .720 Integrated Lear ning System .022 .497 Extraction Method: Principal Axis Factoring. Rotation Method: Promax with Kaiser Normalization. An independent samples t test was conducted to compare the frequency of student tool based software use between high and low SES scho ols. There was a significant difference in frequency of student use for high SES schools ( M =50.18, SD =20.55) and low SES schools ( M =41.84, SD =20.30) with p =.000 (See Table 22). The magnitude of the differences in the means was medium ( d =0.40).
88 Table 22 Frequency of Student Tool Based Software School SES N Min Max M Mdn SD Skewness Kurtosis High SES 400 0 100 50.18 50.00 20.55 0.20 0.32 Low SES 396 0 100 41.84 43.75 20.30 0.39 0.31 Note. Student tool based software consisted of tool based, presentation, multimedia, and research. p= .000. ** d =0.40. Summary of Student Use of Software The third research question examined the essential condition of the frequency of student use of software types. The frequency table of limited and regular use highlighted there were extreme differences with all software types within high and low SES schools. In addition, there was regular use of Integrated Learning Systems and drill software with high SES schools. There was a statistically sign ificant difference in the variable for frequency of student software use ( p= .003), however, the magnitude of the effect size was small ( d =0.24) with high SES schools ( M= 51.01, SD =17.50) and low SES schools ( M= 47.42, SD= 16.07). There was also a statistical ly significant difference in the variable for frequency of student tool based software use ( p =.000) with the magnitude of the effect size medium ( d =0.40) in high SES schools ( M= 50.18, SD= 20.55) and low SES schools ( M= 41.84, SD= 20.30). Teacher Use of Tech nology The final essential condition for the integration of technology in the curricular framework is dependent on the teacher use of technology in the classroom. The fourth research question, Is there a relationship between the socioeconomic status of an
89 elementary school and the teacher use of technology in the classroom? examine d the teachers level of use of technology in the classroom with students. Basic Teacher Use of Technology The data analyzed for this question came from the item on the survey t hat required the respondent to report the percentage of teachers' use of technology in the classroom. A midpoint score was given for each of the responses, 0%, 1 24%, 25 49%, 50 74%, and 75 100%, for percentage of time in which the teacher uses t echnology in the classroom. A basic teacher use of technology variable was developed that encompassed the sum of the means for the variables that included: delivery method, supplement to instruction, instructional tool, core curriculum, and daily curriculu m tool. The basic teacher use of technology variable represents the teacher's overall method for the use of technology in the classroom with students. An independent samples t test was conducted to compare basic teacher use of technology between high and low SES schools. There was a significant difference in the combined teacher use for high SES schools ( M =51.56, SD =19.96) and low SES schools ( M =48.55, SD =20.51) with p =.036 (See Table 23). The magnitude of the differences in the means was extremely small (d=0.14). The mean for high SES schools was 51% of the time, while in low SES schools it was 48% of the time.
90 Table 23 Frequency of Basic Teacher Use of Te chnology School SES N Min Max M Mdn SD Skewness Kurtosis High SES 400 0 87.50 51.56 52.10 19.96 0.05 0.72 Low SES 396 0 87.50 48.55 47.20 20.50 0.05 0.90 p= .036 ** d =0.14 Curriculum Focused Use of Technology A factor analysis on the teacher use of technology examined those variables with similar characteristics (see T able 24). The daily instructional tool variable and core curriculum tool variable were closely aligned to form a new variable curriculum focused use of technology as a tool with Cronbach's alpha of .86. Table 24 Factor Analysi s for Teacher Use of Technology Factor 1 Factor 2 Daily instructional tool .981 .085 Core curriculum tool .759 .099 Delivery of instruction .387 .229 Instructional tool .027 .728 Supplement to instruction .025 .528 Extraction Method: Principal Axis Factoring. Rotation Method Promax with Kaiser Normalization. An independent samples t test was conducted to compare curriculum focused use of technology as tool between high and low SES schools. There was a significant
91 difference in frequency of stude nt use for high SES schools ( M =47.46, SD =26.29) and low SES schools ( M =43.06, SD =27.31) with p =.021 (See Table 25). The magnitude of the differences in the means was small ( d =0.16). Table 25 Frequency of Teacher Curriculum Focused Use of Technology as a Tool School SES N Min Max M Mdn SD Skewness Kurtosis High SES 400 0 87.50 47.46 46.62 26.29 0.12 0.72 Low SES 396 0 87.50 43.06 37.00 27.31 0.31 1.20 Note. The curriculum focused use of technology as tool consisted of "daily instructional tool" and "core curriculum tool. p= .021 ** d =0.16 Summary of Teacher Use of Technology Data for this question came from an item that examined the percentage of teachers' use of technology in the classroom. Two variables were developed from the items: the basic teacher use of technology in the classroom and the curriculum focused use of technology as a tool. There was a statistically significant difference with the basic teacher use of technology in the classroom variable ( p =.036), however, the magnitude of the difference was extremely small ( d= 0.14). There was also a significant difference in the curriculum focused use of technology as a tool ( p=. 021), however, the magnitude of the difference was small ( d =0.16), with high SES schools ( M= 47.4 6, SD= 26.29) and low SES schools ( M= 43.06, SD= 27.31).
92 CHAPTER FIVE DISCUSSION AND RECOM MENDATIONS In response to the NCLB legislation, the Florida Department of Education (FLDOE) defined technology integration in the classroom as the students seamles s use of technology as a productivity tool to accomplish a given task in a disciplined study that promotes higher order thinking skills. The FLDOE put into practice an annual technology resource survey to provide more meaningful information about technolo gy integration in Floridas schools. The purpose of this study was to examine the use of technology with low and high socioeconomic students in the state of Florida. Findings from this study expand our knowledge about the use of technology in high and lo w SES schools. Data from this study informs policy makers and state/district level administrators on how to plan for the high level integration of technology in all public schools. In addition, the data provide guidance on professional development for teac hers on the use of technology with low SES students. Summary of Questions and Results Data for this study w ere from the 2005 06 Technology Resource survey with elementary schools ( N =1591) that responded in the fall of 2005. The schools were then ranked by the percentage of students who received free and reduced lunch and divided into four quartiles. High SES schools ( n =400) had a range students who qualified for the free and reduced lunch program with an average of 810 students per school. The low SES
93 scho ols ( n =396) had a range of students who qualified for free and reduced lunch, with an average of 636 students per school. The first research question was divided into two separate components for examining students access to computers in high and low SES elementary schools. The first component was : Is there a significant difference between the total number of modern and non modern computers? T he results showed t here was no significant difference found between total modern computers, total non modern compu ters, and modern computer labs. There was a significant difference for total modern laptop computers ( p =.000), however, the magnitude of the difference was medium ( d =0.32). The descriptive data made known there are more modern laptop computers in high SES schools than at low SES schools. The second component of the question was : Is there a significant difference between the locations of the computers for student use? Items from the survey were categorized into two groups of classroom versus non classroom or labs. The classroom variable was the total number of modern computers, including desktop and laptop computers, in the classrooms, mobile labs, and computers assigned to students. The non classroom variable was a combination of modern computers in the me dia center, general education labs, special education labs, and other student gathering places. Data analyzed for this question found no significant difference in students access to modern computers in the classroom with the p =.376. The ratio for student s to modern classroom computers in high SES schools was 8.67 to 1 and it was 7.43 to 1 in low SES schools.
94 A statistically significant difference was found in students access to modern labs ( p =. 004 ), however, the effect size was small ( d =0.21) which meant the difference was trivial The ratio of students to computers in modern labs for high SES schools using the median was 25.00 to 1 and it was 16.92 to 1 in low SES schools. There was a statistically significant difference for access to the modern l aptop computers ( p= .000, d =0.35) between high and low SES schools. The second research question stated: Is there a significant difference between the types of software available on the computers? The two items examined for this question had the respondent s provide the percentage of tool based software and curriculum based software on student computers. The tool based software variable included: concept mapping, graphics, multimedia authoring, presentation, spreadsheet, video editing, web authoring, basic w ord processing, and robust word processing. There was a significant difference ( p =.000) with a medium effect size ( d =0.43). The curriculum based software variable included: FCAT Explorer, other test prep tools, Integrated Learning Systems, content specific skills practice/tutorials, content specific simulation, other content specific resources, and general reference tools. There was no significant difference ( p =.628) i n the availability of curriculum based software on student computers in high and low SES schools. The third research question stated: Is there a relationship between the socioeconomic status of an elementary school and the frequency of student use by software type? Data for this question came from an item that examines the frequency of studen t use of computer software programs. The variable for frequency of student
95 software use included: tool based, presentation, multimedia, research, simulation, drill and skill, and Integrated Learning System. There was a significant difference in the variabl e for frequency of student software use ( p= .003), however, the magnitude of the effect size was small ( d =0.24). A factor analysis developed a second variable for student tool based software that included: tool based, presentation, multimedia, and research. There was also a statistically significant difference in the variable for frequency of student tool based software use ( p =.000) with the magnitude of the effect size medium ( d =0.40) in high and low SES schools. The final question stated : Is there a rel ationship between the socioeconomic status of an elementary school and the teacher use technology in the classroom? Data for this question came from an item that examined the percentage of teachers' use of technology in the classroom. The basic teacher use of technology in the classroom variable included: delivery method, supplement to instruction, instructional tool, core curriculum, and daily curriculum tool. There was a statistically significant difference with the basic teacher use of technology in the classroom variable ( p =.036), however, the magnitude of the difference was extremely small ( d= 0.14). A factor analysis developed a the curriculum focused use of technology as a tool variable that included daily instructional tool and core curriculum tool. There was also a significant difference in the curriculum focused use of technology as a tool ( p=. 021), however, the magnitude of the difference was small ( d =0.16).
96 Discussion The following discussion addresses the results with regard to the essential co nditions for technology integration in the classroom in Floridas elementary schools. Researchers have identified the conditions to be: (a) students' access to technology, (b) the development of a student centered learning environment, and (c) integration of technology in the curricular framework (Glennan & Melmed, 1996: Kelley & Ringstaff, 2002; Sandholtz, Ringstaff, & Dwyer, 1997) All three conditions are crucial aspects to the effective integration of technology in the classroom. The final section will address the essential conditions impact in the low SES schools. Student Access to Technology The process for the collection and analysis of the data for this study was much more rigorous than current national studies on student access to computers. For example, in the Technology Counts 2007 report, the process for gathering data was to interview selected 4 th and 8 th math teachers across the state of Florida on their access to computers ( Bausel and Klemick, 2007) In contrast, t he data from Floridas Tech nology Resource Survey involves a coordinated effort between the survey programmer, the state technology director, district personnel, and the school technology specialists to ensure the accuracy of the data. The data from the Technology Counts 2007 stated that Florida had 3.2:1 ratio of students to computers in the classroom ( Bausel and Klemick, 2007). This study showed that median access for computers in the classroom for high SES schools was 8.67:1 and in low SES schools was 7.43:1. Although this differe nce was not
97 statistically significant (relative to SES level) it does illustrate a higher ratio than is being reported nationally. In reports on effective integration of technology in the classroom, teachers have stated one of the main barriers for the integration of technology in the classroom is the access to computers (Ringstaff & Kelley, 2002). D ata w ere analyzed in this study to determine the average number of computers per classroom. The average number of computers per classroom in sample in Flori da wa s less than two. This limited access to computers in the classroom affirms teachers' concerns that access is only one barrier to effective integration of technology in the classroom. Nearly a decade ago, education reformers began implementing laptop computer programs across the nation as a method for increasing access to computers in the classroom (Rockman et al, 1998). Strategies for providing access to laptop computers have ranged from providing one computer for every child to providing a set of m obile carts that can travel from room to room. When teachers have access to laptop computers in the classroom, they can be utilized as an educational tool for the movement toward constructivist ideals and pedagogy that engages students in the learning proc ess (Fouts, 2000; Lowther, Ross, & Morrision, 2001; and Rockman, 2000). Results from this study revealed there was a statistically significant difference for the availability of laptop computers with low SES schools ( p =.000, d =0.35). Data from this study s hows that low SES have limited opportunities to use technology as a tool for constructing knowledge in the classroom particularly when considering laptop computers
98 Student Centered Learning Environment The second condition for effective technology integ ration is the development of a student centered learning environment in which technology is used as a cognitive tool for learning (Culp, Hawkins, & Honey, 1999; Means, 1994; and Reeves, 1998). The computer as a tool allows teachers and students the opportu nity to construct knowledge that enhances curriculum and instruction (Ringstaff & Kelley, 2002). In order to facilitate this type of environment, adequate software needs to be available on the computer that enables the use of the computer as a tool. This t ype of software includes tool based software, such as word processing, spreadsheet, presentation, concept mapping, and video editing programs. Studies have shown that technology will have the greatest impact in the classroom if it is used for higher orde r learning activities with students. However, when technology is used for drill and practice, there tends to be a negative impact on student achievement (Wenglinsky, 1998). Students of low SES schools need to develop cognitive skills and independent thinki ng, which are often lacking due to their social situation (Renchler, 2000). Frequently, the students live in environments over which they have little control and lack the vision for their future (Brophy, 1998; Bandura, 2001). The results from this study a ffirmed there is a statistically significant difference in the availability of software types on the computers between high and low SES schools. The high SES schools had an average of basic tool based software on 77% of the computers, while the low SES sch ools had a frequency of 71% (See Table 16). These data do not support the 1998 Teaching, Learning, and Computing (TLC) national survey,
99 that found no significant difference in the availability of skill/game software between high and low SES schools (Becker Ravitz, & Wong, 1999). However, the largest practical difference was in the availability of additionally purchased software, such as robust tool based software. This type of software requires the school to make a conscious effort to purchase specific pr ograms that are not built into the computer or part of a basic software package. The robust tool based software was on approximately 33% of the computers in high SES schools and 25% of low SES schools (See Table 17). When computers are equipped with robust tool based software, such as those in the Multimedia Project Classrooms, a student centered learning environment can be created (Penuel & Means, 1999). In this type of learning environment, there can be an increase in the frequency of student use for rob ust tool based software, such as concept mapping, video editing, and multimedia authoring. Technology in the Curricular Framework The frequency for the utilization of tool based software programs confirmed there was limited use, between none to once a we ek, with low SES schools (See Table 19). For example, in high SES schools the frequency of use for tool based software was 50% limited and 50% regular, while in low SES schools it was 64% limited and 36 % regular. However, the frequency for drill and pract ice software in high SES schools was 36% limited and 64% regular and in low SES schools 25% limited and 75% regular. The discrepancy with the frequency of student tool based software user reveals that students in low SES schools may not be provided the sam e opportunity to develop higher order thinking skills (Knapp & Shields, 1990; Waxman, Padron, &Knight, 1991). The students
100 are learning from the software programs rather than with the software programs, hence the computers are being used as tutor rathe r than a cognitive tool for learning. The results obtained from this study support the need for professional development on the integration of technology in the curricular framework. A significant difference was found with basic teacher use of technology in the classroom ( p =.036, d= 0.14) for high SES schools ( M =51.56, SD =19.96) and low SES schools ( M =48.55, SD =20.51). The data mirrors the information that has been stated by researchers when there is limited access to the technology, the teachers will not be able to make the shift toward the use of technology as a tool for instruction (Sandholtz, Ringstaff, & Dwyer, 1997). In order for change to occur, teachers need to learn how to take advantage of integrating technology in the classroom, hence there needs to be an increase in professional development on the use of technology in the classroom. The role of technology must move beyond the teachers use of technology to improve students basic skills. Teachers beliefs about the use of technology need to tra nsform learning from technology to learning with technology (Ringstaff & Kelley, 2002). The essential condition of technology in the curricular framework depends on teachers understanding and development of tasks that engage students in the learning p rocess (Baker, Herman, & Gerhart, 1996). Data from this study reveals that just having computers in the schools does not ensure effective use of the technology. There needs to be a sustained professional development effort for teachers in high and low SES schools on the use of technology as a tool for learning in curriculum.
101 Recommendations Based on the results of this research and literature review, this section offers recommendations for policy makers, modifications to the survey and future research. Rec ommendations to Policymakers Policymakers at both the federal and state level need to develop a common definition for technology integration in the curriculum that can be disseminated to all stakeholders. A common definition for technology integration wil l address three main issues of student access to technology in the classroom, integration of technology a tool for learning, and professional development of educators. Policy needs to be established that ensures all students have access to technology in th e classroom. There needs to a set of target dates for a student to computer ratio in classrooms at every grade level. In addition, policymakers need to identify innovative methods of students to bring existing computers to the classroom. A second recomm endation for policy is to develop common goals for the use of technology as a tool for learning in the classroom. Data from this study show there is still a gap in student access to computers, however there is a larger gap in the frequency of software type s on the computer. There have been tremendous strides in providing students access to curriculum based software, however, there is amble room for growth in the area of tool based software. Students need to have access to those software tools, such as mult imedia, web based, presentation, and video editing, that will allow them to be productive citizens in the 21 st century.
102 A third recommendation for policy is the development of a sustainable professional development plan for educators on the use of techn ology in the classroom. As the data reveals, while there is access to computers at the school it does necessarily ensure the computers are being used in the classroom for core curriculum instruction. Further analysis of the data could show the current meth ods of professional development being done with those schools that high level of teacher technology use as a tool. A final recommendation for policymakers is a common method for reporting the use of funds needs to be developed at the state and federal le vel. It is imperative that data gathered on this issue be as accurate as possible to ensure accountability for the expending of federal dollars for technology. The data elements need to reflect the use of technology in the classroom that promote higher ord er thinking skills. The data used from the Technology Resource Survey provide a framework for the elements that could be used at a federal level. Recommendations for Modifications to the Survey After using data from the Technology Resource Survey, it bec ame apparent there are gaps in the survey. A key limitation in the survey was the lack of a mechanism in the access to technology portion that ensured the accuracy of the data being collected. A mechanism needs to be developed within the user input that en sures that the reporting of computers in each area is accurate. Verification for each field in the location of modern and non modern computers needs to be added to the survey. Further analysis needs to be done on the items in the survey to ensure the dat a being provided represent the actual use of technology in the classroom. This could be
103 done by rewording the items to be consistent and specific through a common terms glossary. For example, the item that examines the types of software on the computers ne eds to be clearly defined so that there is no repetition among items, such as the delineation between "Integrated Learning System" and "Drill and Practice" software. A common set of terms needs to be clearly defined for the survey. Recommendations for Fut ure Research Data from this research revealed there is still a lack of information on the actual use of technology in the classroom with low SES students. Further research needs to be done on the actual work being done by students with technology. Is a le arning environment being created with high expectations for all students in the use of technology? Researchers need to be in the actual classroom to understand all the aspects of the classroom and the method in which technology is being integrated into the curriculum with the low SES students. Additional research needs to be done on those schools that are integrating technology in the curriculum as a tool with high and low SES students. It is important to understand all of the aspects of the school that ha s created a learning environment for technology to be integrated as tool in the curriculum. For example, the method in which technology is integrated into the school's improvement plan and t he philosophical beliefs of the principal and teachers about the u se of technology with low SES students. An observation tool which examines the levels of technology integration in the classroom. Further research also needs to be conducted on the teachers in high and low SES schools. This can be done by examining teac hing experience, philosophy on classroom
104 instruction and level of degree. In addition, examining the actual professional development the teacher has received on the use of technology in the classroom would be beneficial For example, was the focus of the training an actual software program or the creation of a student product, such as a digital book ? In addition, there should be interviews with educator on how the use of technology, concerns for high stakes testing, and curriculum goals for technoloby. Su mmary This purpose of this study was to examine the use of technology in low and high SES elementary schools in Florida. Through an iterative process with districts and schools, the Florida Department of Education conducts an annual survey in which they g ather data on the capacity of technology in the schools. Data from the 2005 06 Technology Resource Survey w ere used to examine the essential conditions for technology integration : access to technology, student centered learning environment, and technology in the curricular framework. The variables examined in this study were students' access to computers, the types of software available on the computer, the frequency of student use of software programs, and the teacher use of technology. Findings from thi s study expand our knowledge about the essential conditions for the integration of technology in high and low SES schools. The Technology Resource Survey data provided information on access to technology, student centered learning environment, and the use of technology in the curricular framework. However, additional research must be done on the actual use of technology in the classroom with high and low SES students. As education continue to have increased high stakes testing, we need to
105 ensure that techno logy is being used as a tool to develop high order thinking skills for students. This study should serve as the launching point for an in depth examination of the use of technology in low SES classrooms.
106 REFERENCES Alaimo, K., Olson, C. M., & Frongillo, E A., Jr., (2001). Food insufficiency, family income, and health in US preschool and school aged children, American Journal of Public Health 91(5), 781 786. Ausubel, D. (1963). The psychology of meaningful verbal learning New York : Grune & Stratton. Av ery, P. G. (1999). Authentic instruction and assessment. Social Education 65(6), 368 373. Baker, E. L., M. Gerhart & J. L. Herman. (1994). Evaluating the Apple classrooms of tomorrow. In Technology Assessment in Education and Training, edited by E .L. Bak er, and H .F. ONeil, Jr. Hillsdale: Lawrence Erlbaum. Bandura, A. (2001). Social cognitive theory: An agentive perspective. Annual Review of Psychology, 54 (1), 1 26. Bausel, C. V., & Klemick, E. (May 2007). Tracking U.S. trends, Technology counts 2007: A digital decade. [Online] Available at: http://www.edweek.org/ew/toc/2007/03/29/index.html Becker, H., & Ravitz, J. (1998). The equity threat of promising innovations: Pioneering internet connected schools. Journal of Educational Computing Research 18 (4) Berkowitz, B., & Serim, F. (May / June 2002), Moving every child ahead: The Big6 success strategy, MultiMedia Schools [Online] Available: http://www.infotoday.com/MMSchools/may02/berk owitz.htm
107 Becker, H. J., Ravitz, J. L., & Wong, Y. T. (1999, November). Teacher and teacher directed student use of computers and software. Teaching, Learning, and Computing: 1998 National Survey Report #3. Benson, A. (1995). Review and analysis of Vygo tskys Thought and language Article from website for UHCL. http://220.127.116.11/inst5931/Vygotsky.html Biddle, B. J., & Berliner, D. C. (2002). Unequal school funding in the United States. Educational Leadership 59(8), 48. Borman, G.D. (2003). How can Ti tle I improve achievement? Educational Leadership,60, 4, 49 53. Bowman, B. (1994). The challenge of diversity. Phi Delta Kappan 76 234 38. Bozeman W. C., & Baumbach, D. J. (1995). Educational technology: Best practices from America's schools. New York: Eye on Education, Inc. pp.174 176. Brophy, J. (1998). Motivating students to learn New York: McGraw Hill Brookhart, S., & Rusnak, T. (1993). A pedagogy of enrichment, not poverty: Successful lessons of exemplary urban teachers. Journal of Teacher Educati on 44 (1), 17 26. Brown, J. S., Collins, A., & Duguid, P. (1989). Situated cognition and the culture of learning. Educational Research, 18 (1), 32 42. Chang, H., Henriquez, A., Honey, M., Light, D., Moeller, B., & Ross, N. (1998). The Union City story: Ed ucation reform and technology Students performance on standardized tests. Technical report. EDC/Center for Children and Technology.
108 Citizens' Commission on Civil Rights. (1998). Title I at midstream: The fight to improve schools for poor kids. Corrine M. Yu and William L. Taylor, eds. Washington, DC: Citizens' Commission on Civil Rights. Clements, D. H. (2002). Computers in early childhood mathematics Contemporary Issues in Early Childhood, Volume 3, Number 2, 2002, pg. 160 181 Coley, R., Crandler, J., & Engel, P. (1997). Computers and classrooms: The status of technology in U.S. schools Educational Technology Testing Service. Retrieved from ftp://ftp.ets. Org/pub/res/compclss.pdf Culp, K., Hawkins, J., & Honey, M. (1999). Review paper on educationa l technology research and development. New York: Education Development Center, Center for Children and Technology. Dalton, D. W., & Goodrum, D. A. (1991). The effects of computer programming on problem solving skills and attitudes. Journal of Educational C omputing Research, 7 (4), 483 506. Darling Hammond, L. (1997) The right to learn: A blueprint for creating schools that work Jossey Bass: San Francisco. Dickard, N. (2003) The sustainability challenge: taking ed tech to the next level. Benton Foundation. R etrieved from: http://www.benton.org/publibrary/sustainability/sus_challenge.html Dooley, K. E. (1999) Towards a holistic model for the diffusion of educational technologies: An integrative review of educational innovative studies. Educational Technology & Society 1999; 2(4):1236 4522.
109 Dixon Krauss, L. (1996). Vygotsky in the classroom: Mediated literacy instruction and assessment. New York: Longman Publishers. Ferguson, R. ( 1998). Can schools narrow the black white test score gap? In C. Jencks and M. Phillips (Eds.). The black white test score gap. Washington, DC: Brookings Institution Press. Fisher, C., Dwyer, D, & Yocam, K. (1996). Education & technology: Reflections on com puting in classrooms. San Francisco: Josey Bass Publishers. Fosnot, C. T. (1996). Constructivism: A psychological theory of learning. In C. T. Fosnot (Ed.) Constructivism: theory, perspectives, and practice (pp. 8 33). New York: Teachers College Press. Fou ts, J. (2000). Research on computers and education: Past, present, and future. Seattle, WA: Bill & Melinda Gates Foundation. Fouts, J. T. & Stuen, C. (1997). Copernicus Project: Learning with laptops: Year 1 evaluation report. ERIC Document ED 416947. Gag ne, R.M. (1985). The conditions of learning and theory of instruction. Fort Worth, TX: Holt, Rinehart, and Winston, Inc. Glennan, T. K., & Melmed, A. (1996 ). Fostering the use of educational technology: Elements of a national strategy (MR 682 OSTP/ED). Sa nta Monica, CA: RAND. Retrieved from http://www.rand.org/publications/MR/MR682/ Good, T.L., & Brophy, J.E. (1996). Looking in classrooms. (7 th ed.) New York: Longman. Haberman, M. (1991). Pedagogy of poverty versus good teaching. Phi Delta Kappan, 73 290 294.
110 Hall, G. E., & Hord, S. M. (2001). Implementing change: Patterns, principles, and potholes Boston: Ally & Bacon. Haycock, K. (2001). Closing the achievement gap. Educational Leadership (March ), 6 11. Hoover, H. D., Hieronymus, A. N., Frisbie, D. A., & Dunbar, S. B. (1996). Iowa tests of basic skills, Form M Itasca, IL: Riverside Publishing. Hopson, M. H., Simms, R. L., & Knezek, G. A. (2002, Winter). Using a technologically enriched enviro nment to improve higher order thinking skills. Journal of Research on Technology in education 34 (2), 109 119. Huang, S. Y. L., & Waxman, H. C. (1996). Classroom observations of middle school students technology use in mathematics School Science and Ma thematics 96 (10),28 34. International Society for Education. (2000). National Educational Technology Standards for students: Connecting curriculum and technology Oregon: ISTE. International Society for Education. (2004). National Educational Technology Standards for students: Connecting curriculum and technology Oregon: ISTE. Jonassen, D. H., Wilson, B. G., Wang, S., & Grabinger, R. S. (1993). Constructivist uses of expert systems to support learning. Journal of Computer Based Instruction ,20 (3), 86 94 Jonassen, D. H., & Reeves, T. C. (1996). Learning with technology: Using computers as cognitive tools. In D. H. Jonassen, (Ed.), Handbook of research on educational communications and technology (pp. 693 719). New York: Macmillan.
111 Kelley, L., & Ringstaf f, C. (2002). The learning return on our educational technology investment: A review of findings from research. San Francisco: WestEd. Available: http://www.WestEd.org/online_pubs/learning_return.pdf. Knapp, M. (1995). Teaching for meaning in high poverty classrooms New York: Teachers College Press. Knapp, M., & Shields, P. (1990, June). Reconceiving academic instruction for the children of poverty Phi Delta Kappan, 71 753 58. Knapp, M., Shields, P., & Turnbull, B. (1995). Academic challenge in high pove rty classrooms, Phi Delta Kappan 76, no. 10: 770 776. Knapp, L. R., & Glenn, A. D. (1996). Restructuring schools with technology. Boston: Allyn and Bacon. Kritt, D. (1993). Authenticity, reflection, and self evaluation in alternative assessment. Middle Sc hool Journal, 25 (2), 43 45. Lehr, J. B., & Harris, H. W. (1988 ). At risk, low achieving students in the classroom Washington, D.C.: National Educational Association Professional Library. Leroy, C. & Symes, B. (2001). Teachers' perspectives on the family backgrounds of children at risk. McGill Journal of Education, 36, 1 45 60. Wilson Web July 9, 2001. Livingston, A., & Wirt, J. (2005). The condition of education 2005 in brief (NCES 2005 095). U.S. Department of Education, National Center for Education St atistics. Washington, DC: U.S. Government Printing Office.
112 Mann, D. (1999). Documenting the effects of instructional technology: A fly over of policy questions (Secretarys Conference on Educational Technology). Retrieved from http://www.ed.gov/Technology/ TechConf/1999/whitepapers/paper6.html Marlowe, B. & Page, M. (1999). Making the most of the classroom mosaic: A constructivist perspective. Multicultural Education, 6, (4),19 21. McMillan Culp, K., Honey, M., & Mandinach, E. (2003). A retrospective on twen ty years of education technology policy Washington, DC, U.S. Department of Education, Office of Educational Technology. Retrieved from http://www.nationaledtechplan.org/participate/20years.pdf. McCombs, B. (2000, September). Assessing the role of educati onal technology in the teaching and learning process: A learner centered perspective. Paper presented at the Secretary's Conference on Edu cational Technology: Measuring the Impacts and Shaping the Future, Washington, DC. (a) Retrieved from http://www.ed.gov/Technology/techconf/2000/mccombs_paper.html Means, B., Blando, J., Olson, K., & Middleton, T. (1993). Using technology to support educa tion reform Washington, DC: Office of Educational Research and Improvement. Means, B., & Olson, K. (1995). Technology and education reform: Technical research report. Volume 1: Findings and conclusions Menlo Park, CA: SRI International. Melendez, M. (19 93). Bilingual education in California: A status report. Thrust for Educational Leadership, 22 (6), 35 38. Miller, D. E. (1993). The literature project: Using literature to improve the self concept of at risk adolescent females. Journal of Reading, 36 (6), 442 448.
113 National Center for Education Statistics. (1999). Teacher quality: A report on the preparation and qualifications of public school teachers. NCES 1999 080. Washington, DC: U.S. Department of Education. National Center for Education Statistics. (2 002). The condition of education 2002. NCES 2002 025. Washington, DC: U.S. Department of Education. National Center for Education Statistics. (2003). The condition of education 2003. NCES 2003 067. Washington, DC: U.S. Department of Education. Newmann, F.M (1996). Authentic achievement: Restructuring schools for intellectual quality. San Francisco, CA: Jossey Bass Newmann, F. M., Bryk, A. S. & Nagaoka, J. K. (2001, January). Authentic intellectual work and standardized tests: conflict or coexistence? Chica go: Consortium on Chicago School Research. Newmann, F. M., Secada, W. G., & Wehlage, G. G. (1995). A guide to authentic instruction and assessment: Vision, standards and scoring. Madison, WI: Wisconsin Center for Education Research. Newmann, F. M., Marks, H. M., & Gamoran, A. (1996). Authentic pedagogy and student performance. American Journal of Education 104 (8), 280 312. Newmann, F. M., & Wehlage, G. G. (1995). Successful school restructuring: a report to the public and educators. Madison, WI: Universit y of Wisconsin, Education Center. Newmann, F. M., Wehlage, G.G., & Lamborn. S.D. (1992). The significance and sources of student engagement, Student engagement and achievement in American
114 secondary schools edited by F.M. Newmann, pp. 11 30. New York: Teac hers College Press. Niemiec, R. P., & Walberg, H. J. (1992). The effects of computers on learning. International Journal of Educational Research 17, 99 108. Office of Technology Assessment, U.S. Congress. (1995). Teachers and technology: Making the connect ion Washington, DC: U.S. Government Printing Office. [On line] Available: http://www.wws.princeton.edu/~ota/disk1/1995/9541.html Padron, Y. N., & Waxman, H. C. (1999). Classroom observ ations of the five standards of effective teaching in urban classrooms with English language learners. Teaching and change, 7(1), 79 100. Page, M. (2002). Technology enriched classrooms: Effects on students of low socioeconomic status. In D. Ayersman & W. M. Reed (Eds.), Journal of Research on Technology in Education (389 409). Oregon:ISTE, Publishers. Painter, S. R. (2001). Issues in the observation and evaluation of technology integration in K 12 classrooms. Journal of Computing in Teacher Education, 17( 4 ), 21 25. Partnership for 21st Century Skills. (2002). Learning for the 21st century. Retrieved from http://www.21stcentruyskills.org Payne, R. K. (1998). A framework for understanding poverty (Revised Ed ). RFT Publishing Co: Baytown, TX. Payne, R. K. (2001). A framework for understanding poverty (Revised Ed). RFT Publishing Co: Baytown, TX. Penuel, W., & Means, B. (1999). Observing classroom practices in project based learning using multimedia: A tool for evaluators In The Secretary's Conference
115 on Educational Technology Web site [Online]. Available: http://www.ed.gov/Technology/TechConf/1999/whitepapers/paper3.html Penuel W. R., Means B., Golan S., & Korbak C. (2000). Challenge 2000 year 4 evalua tion report. Menlo Park: SRI International Ravitz, J. L., Becker, H. J., & Wong, Y. T. (2000). Constructivist compatible beliefs and practices among U.S. teachers. Teaching, Learning, and Computing: 1998 National Survey, Report 4. Center for Research on In formation Technology and Organizations, University of California, Irvine. Reeves, T. (1998, February). The impact of media and technology in schools. A research report prepared for the Berteslemann Foundation. Retrieved from http://www.athensacademy.org/in struct/media_tech/reeves0.html Reid, K. S. (2001). Technology counts 2001: Racial disparities. Education Week, XX (35), 16 17 Reilly, B. (1992) The negotiations of group authorship among second graders using multimedia composing software. Apple Classroom s of Tomorrow Report No. 14. Cupertino, CA: Apple Computer. [On line] Available: http://www.apple.com/education/k12/leadership/acot/library.html. Renchler, R. (2000). Grade span. Research Roundup, 16 (3), 1 4. Ringstaff, C., Sandholtz, J. H. & Dwyer, D. C. (1991). Trading places: when teachers utilize student expertise in technology intensive classrooms. ACOT Report 15 Cupertino, CA: Apple Computer. [On line] Available: http://www.apple.com/education/k12/leadership/acot/library.html
116 Ringstaff, C., Yocam, K., & Marsh, J. (1996). Integrating Technology into Classroom Instruction: An Assessment of the Impact of the ACOT Teacher Development Center Project. ACOT Report #22 Apple Computer, Inc [On line] Available: http://www.apple.com/education/k12/leadership /acot/library.html. Ringstaff, C. & Kelley, L. (2002). The learning return on our educational technology investment: A review of findings from research. WestEd Regional Technology in Education Consortium in the Southwest. Rivera, C., & Zehler, A. M. (1991) Assuring the academic success of language minority students: Collaboration in teaching and learning. Journal of Education, 173, 52 77. Rockman, et. al. (1998). Powerful tools for schooling: Second year study of the laptop program, Rockman, et. al: San Fr ancisco, CA. [Online]. Available: http://rockman.com/projects/laptop/ Rockman, S. (2000). A more complex picture: Laptop use and impact in the context of changing home and school access. San Francisco: R ockman et al. [Online] Available: http://rockman.com/projects/laptop/ Rockman, S. (2003). Learning from Laptops, Threshold Retrieved from: www.ciconline.org. Roschelle, J., Pea, R., Hoadley, C., Gordin, D., & Means, B. (2000). Changing how and what childr en learn in school with computer based technologies. The Future of Children 10 (2), 76 101. [On line] Available: http://ctl.sri.com/publications/displayPublication.jsp?ID=114
117 Sandholtz, J., Ringstaff, C., & Dwyer, D. (1990 ). Teaching in high tech environme nts: classroom management revisited, first fourth year findings. [On line] Available: http://www.apple.com/education/k12/leadership/acot/library.html Sandholtz, J. H., Ringstaff, C., & Dwyer, D. C. (1994a). Student engagement revisited: Views from techno logy rich classrooms. [On line] Available: http://www.apple.com/education/k12/leadership/acot/library.html Sandholtz, J., Ringstaff, C., & Dwyer, D. (1997). Teaching with technology: Creating student centered classrooms. New York: Teachers College Press. Silverstein, G., Frechtling, J., & Miyoaka, A. (2000). Evaluation of the use of technology in Illinois public schools: Final report, prepared for Research Division, Illinois State Board of Education. Rockville, MD: Westat. Slavin, R. (1998). Can education reduce social inequity? Educational Leadership, 55 6 10. Stephen, V. P., Varble, M. E., & Taitt, H. (1993). Instructional and organizational change to meet minority and at risk student needs. Journal of Staff Development 14(4), 40 43. Stevenson, K. R. (1998). Evaluation report year 2: Schoolbook laptop project (No. 2). Beaufort: University of South Carolina Department of Educational Leadership and Policies. [Online] Available: http://www.beaufort.k12.sc.us/district/ltopeval.html Strahan, D. (2003). Ge neral patterns and particular pictures: Lessons learned from reports from "beating the odds" schools. Journal of Curriculum and Supervision, 18 296 305.
118 Statham, D. S., & Torrell, C. R. (1999). Technology in public education in the United States. Retriev ed from http://www.tea.state.tx.us/Textbooks/archives/litrevie.htm Thomas, J. (2000). Executive summary. A review of research on project based learning. San Rafael, CA: Autodesk Foundation. Tiene, D., & Luft, P. (2002). The technology rich classroom. Ameri can School Board Journal, 189(8), 37 39. Trilling, B., & Hood, P. (1999). Learning, technology, and education reform in the knowledge age or Were wired, webbed, and windowed, now what? Educational Technology, 39 (3), 5 18. Trotter, A. (May 2005). Federa l Role Seen Shifting, Technology Counts 2005: Electronic Transfer: Moving Technology Dollars in New Directions, [Online] Available at: http://www.edweek.org/ew/toc/2005/05/05/index.html U.S. Department of Education. (January 2001). High standards for all s tudents: A report from the National Assessment of Title I on progress and challenges since the 1994 reauthorization. Prepared for the Planning and Evaluation Service, Office of the Under Secretary, U.S. Department of Education. U.S. Department of Education (2002). No Child Left Behind Act Washington, D.C.: U.S. Government Printing Office. U.S. Department of Education (2003, March 12). Press Release of remarks made by Secretary Rod Paige to Commonwealth Club of California, Palo Alto, CA. Retrieved from htt p://www.ed.gov/PressRelease/03 2003/03122003.html
119 Waxman, H. C., & Huang, S. L. (1996). Classroom instruction differences by level of technology use in middle school mathematics. Journal of Educational Computing Research 14(2), 157 169. Waxman, H. C., Hu ang, S. L., & Padron, Y. N. (1995). Investigating the pedagogy of poverty in inner city middle level school. Research in Middle Level Education, 18 (2), 1 22. Waxman, H. C., & Padron, Y. N. (1995). Improving the quality of classroom instruction for studen ts at risk of failure in urban schools. Peabody Journal of Education, 70 (2), 44 65. Waxman, H. C., Padron, Y. N., & Knight, S. L. (1991). Risks associated with students' limited cognitive mastery. In M. C. Wang, M. c. Reynolds, and H. J. Walberg (Eds.), H andbook of special education: Research and practice New York: Pergamon. Waxman, H. C., Padron, Y.N. & Arnold, K. M. (2001). Effective instructional practices for students placed at risk of academic failure. In G.D.Borman, S.C.Stringfield & R.E.Slavin (Eds .), Title 1: Compensatory education at the crossroads (pp.137 170). Mahwah: NJ: Lawerence Erlbaum Associates, Inc. Wenglinsky, H. (1998). Does it compute? The relationship between educational technology and student achievement in mathematics Princeton, N J: Educational Testing Service. Retrieved from ftp://ftp.ets.org/pub/res/technolog.pdf Wenglinsky, H. (2005) Using Technology Wisely: The Keys to Success in Schools. Teachers College Press, New York
120 Wiggins, G. (1998). Educative assessment: Designing assessments to inform and improve student performance San Francisco, CA: Jossey Bass Publishers. Wiggins, G. (1989). Teaching to the (authentic) test. Educational Leadership ,46 41 47. Wilhelm, T., Ca rmen, D., & Reynolds, M. (2002, June). Connecting Kids to Technology: Challenges and Opportunities. Baltimore: The Annie E. Casey Foundation. (ED 467 133) Retrieved November 1, 2002, http://www.kidscount.org Zucker, A. A. (2004). Developing a research agen da for ubiquitous computing in schools. Journal of Educational Computing Research 30(4), 371 386.
ABOUT THE AUTHOR Kate Kemker is a life long educator who views technology as a tool that can level the learning field and enrich all students lives. Kate recently completed her doctorate at the University of South Florida in the area of Curriculum and Instruction, in which she examined Florida's elementary students access to technology in high and low socioeconomic schools. She also received from USF her M asters in Education Degree for Instructional Technology and Bachelors in Science Degree for Music Education. She has presented and conducted workshops at various state and national conferences, such as the Florida Educational Technology Conference (FETC), the Annual Conference of the Association for Supervision and Curriculum Development (ASCD), and the National Educational Computing Conference (NECC). In addition, she has published articles, chapters, and websites on the utilization of technology as tool for learning.