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A quantitative and qualitative study of computer technology and student achievement in mathematics and reading at the second- and third-grade levels: A comparison of high versus limited technolo...
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A quantitative and qualitative study of computer technology and student achievement in mathematics and reading at the second- and third-grade levels: A comparison of high versus limited technolo...
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INFORMATION TO USERS This manuscript has been reproduced from the microfilm master. UMI films the text directly from the original or copy submitted. Thus, some thesis and dissertation copies are in typewriter face, while others may be from any type of computer printer. The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleedthrough, substandard margins, and improper alignment can adversely affect reproduction. In the unlikely event that the author did not send UMI a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion. Oversize materials (e.g., maps, drawings, charts) are reproduced by sectioning the original, beginning at the upper left-hand comer and continuing from left to right in equal sections with small overlaps. Photographs included in the original manuscript have been reproduced xerographically in this copy. Higher quality 6” x 9” black and white photographic prints are available for any photographs or illustrations appearing in this copy for an additional charge. Contact UMI directly to order. ProQuest Information and Learning 300 North Zeeb Road, Ann Arbor, Ml 48106-1346 USA 800-521-0600 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. A QUANTITATIVE AND QUALITATIVE STUDY OF COMPUTER TECHNOLOGY AND STUDENT ACHIEVEMENT IN MATHEMATICS AND READING AT THE SECOND AND THIRD GRADE LEVELS: A COMPARISON OF HIGH VERSUS LIMITED TECHNOLOGY INTEGRATION by Renae Knutson Dreier A Dissertation Presented to the FACULTY OF THE ROSSIER SCHOOL OF EDUCATION UNIVERSITY OF SOUTHERN CALIFORNIA in Partial Fulfillment o f the Requirements for the Degree DOCTOR OF EDUCATION August 2000 Copyright 2000 Renae Knutson Dreier Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. UMI Number: 3054865 ___ ® UMI UMI Microform 3054865 Copyright 2002 by ProQuest Information and Learning Company. All rights reserved. This microform edition is protected against unauthorized copying under Title 17, United States Code. ProQuest Information and Learning Company 300 North Zeeb Road P.O. Box 1346 Ann Arbor, Ml 48106-1346 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. University of Southern California School of Education Los Angeles, California 90089-0031 This dissertation, written by Renae Knutson Dreier under the direction o f her Dissertation Committe, and approved by all members o f the Committee, has been presented to and accepted by the Faculty o f the School o f Education in partial fulfillment o f the requirements for the degree o f DOCTOR OF EDUCATION Dean Dissertation Committee .Chairperson Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. DEDICATION This dissertation is dedicated to my husband, Dr. Frederick Garon Dreier, for his patience, support, and inspiration throughout my doctoral program. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. ACKNOWLEDGEMENTS A very special thanks to my committee chairperson, Dr. Dennis Hocevar, for his encouragement and guidance throughout the writing of this dissertation. I would also like to thank Dr. Robert Ferris for sharing his years of experience in leadership and his support to me throughout this doctoral program. Finally, a special thanks to Dr. Mike McLaughlin for the vision and commitment that brought USC to the Redding area. Mike’s encouragement, and role as mentor, have been inspirational to all of us in the Redding cohort. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. iv Table of Contents Dedication................................................................................................................ ii Acknowledgements.................................................................................................. iii List o f Tables............................................................................................................ vi List o f Figures......................................................................................................... ix Abstract.................................................................................................................... xii Chapter 1 Introduction......................................................................................... 1 Background of the Problem, 1; Statement of the Problem, 2; Purpose o f the Study, 3; Research Questions, 3; Importance of the Study, 4; Limitations and Delimitations, 4; Methodology, 5. Chapter 2 Literature Review.............................................................................. 8 Introduction, 8; The Demand for Computers in American Classrooms, 8; Financial Implications, 10; The Critics, 13; Positive Effects o f Technology on Student Achievement, 22; Summary, 30. Chapter 3 Research Methods.............................................................................. 32 Purpose o f the Study, 32; Statistical Inference, 32; Sampling Technique, 33; Research Design, 37. Chapter 4 Research Results................................................................................ 42 Purpose of the Study, 42; Second Grade Math Achievement, 42; Second Grade Reading Achievement, 54; Third Grade Math Achievement, 60; Third Grade Reading Achievement, 67; Growth in Scaled Math Scores, 76; Growth in Scaled Reading Scores, 80; Qualitative Results of Teacher Survey Regarding Computer Technology and Student Achievement, 83. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. V Chapter 5 Summary and Discussion................................................................... 92 Summary, 92; Discussion, 94. References Cited.................................................................................................. 99 Appendix A: Data Collection Sheets............................................................... 108 Appendix B: Questionnaire of Teacher Attitudes Regarding Computer Technology and Student Achievement................................. I l l Appendix C: Histograms of Samples Used in this Study........................... 113 Appendix D: ANOVA and ANCOVA Summary Tables........................... 124 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. List of Tables Table Page 1. Second and Third Grade Sample Sizes by School, Grade, and Variable.......................................................................................... 38 2. One-Way ANOVA and Descriptive Statistics Comparing Second Grade Raw Math SAT-9 Scores by Level of Technology................ 43 3. One-Way ANCOVA Comparing Second Grade Raw Math SAT-9 Scores by High Versus Limited-Technology Samples with Gender as a Covariate....................................................................................... 46 4. One-Way ANCOVA Comparing Second Grade Raw Math SAT-9 Scores by High versus Limited-Technology Samples With Gender and Free and Reduced Lunch (SES) as Covariates......................... 49 5. One-Way ANCOVA Comparing Second Grade Raw Math SAT-9 Scores by Free and Reduced Lunch (SES) Samples with Teaching Method and Gender as Covariates.................................................... 50 6. One-Way ANCOVA and Descriptive Statistics Comparing Second Grade Math SAT-9 Scores by Gender with SES and Level of Technology as Covariates................................................................. 52 7. One-Way ANOVA Comparing Second Grade Raw Reading SAT-9 Scores by High versus Limited-Technology Samples.................. 55 8. One-Way ANCOVA Comparing Second Grade Raw Reading SAT-9 Scores by Free and Reduced Lunch (SES) with Gender and Level of Technology as Covariates................................................. 58 9. Descriptive Statistics and One-Way ANOVA Comparing Third Grade Raw Math Scores by High versus Limited-Technology Samples.. 61 10. Descriptive Statistics and One-Way ANOVA Comparing Third Grade Math SAT-9 Scores by Gender........................................... 63 11. Descriptive Statistics and One-Way ANOVA Comparing Third Grade Raw Math SAT-9 Scores by Free and Reduced Status 65 permission of the copyright owner. Further reproduction prohibited without permission. vii 12. Descriptive Statistics and One-Way ANOVA Comparing Third Grade Raw Reading SAT-9 Scores by Level of Technology 69 13. Descriptive Statistics and One-Way ANOVA Comparing Third Grade Raw Reading SAT-9 Scores by Gender........................... 71 14. Descriptive Statistics and One-Way ANOVA Comparing Third Grade Reading SAT-9 Scores by Free and Reduced Lunch Status................................................................................ 73 15. Descriptive Statistics and One-Way ANCOVA Comparing Third Grade Raw Reading SAT-9 Scores by Free and Reduced Lunch Status with Gender and Level of Technology as Covariates 75 16. Descriptive Statistics and One-Way ANCOVA Comparing Third Grade 1998 and 1999 Scaled Math SAT-9 Scores with Level of Technology, Gender, and Free and Reduced Lunch Status as Covariates...................................................................................... 78 17. Descriptive Statistics and One-Way ANCOVA Comparing Third Grade 1998 and 1999 Scaled Reading SAT-9 Scores with Level of Technology, Gender, and Free and Reduced Status as Covariates.................................................................................... 81 18. Results of Teacher Surveys on Attitudes Regarding Computer Technology and Student Achievement...................................... 84 19a. Data Collection Sheet-Second Grade................................................... 109 19b. Data Collection Sheet-Third Grade.................................................... 110 20. One-Way ANOVA Summary Table Comparing Second Grade Raw Math SAT-9 Scores by High versus Limited-Technology Samples....................................................................................... 125 21. One-Way ANCOVA Summary Table Comparing Second Grade Raw Math SAT-9 Scores by High versus Limited-Technology Samples with Gender as a Covariate......................................... 126 22. One-Way ANCOVA Summary Table Comparing Second Grade Raw Math SAT-9 Scores by High versus Limited Technology Samples with Gender and Free and Reduced Lunch as Covariates................................................................................... 127 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. viii 23. One-Way ANCOVA Comparing Second Grade Raw Math SAT-9 Scores by Free and Reduced Lunch (SES) Samples With Gender and Teaching Method as Covariates.... :.......................... 128 24. One-Way ANOVA Summary Table Comparing Second Grade Raw Reading SAT-9 Scores by High versus Limited Technology Samples.......................................................................................... 129 25. One-Way ANCOVA Summary Table Comparing Second Grade Raw Reading SAT-9 Scores by Free and Reduced Lunch (SES) with Gender and Teaching Method as Covariates......................... 130 26. One-Way ANOVA Summary Table Comparing Third Grade Raw Math SAT-9 Scores by High versus Limited-Technology Samples............................................................................................ 131 27. One-Way ANOVA Summary Table Comparing Third Grade Raw Math SAT-9 Scores by Gender..................................................... 132 28. One-Way ANOVA Summary Table Comparing Third Grade Math SAT-9 Scores by Free and Reduced Lunch Status...................... 133 29. One-Way ANOVA Summary Table Comparing Third Grade Reading SAT-9 Scores by Level of Technology.......................... 134 30. One-Way ANOVA Summary Table Comparing Third Grade Reading SAT-9 Scores by Gender................................................ 135 31. One-Way ANOVA Summary Table Comparing Third Grade Reading SAT-9 Scores by SES..................................................... 136 32. One-Way ANCOVA Summary Table Comparing Third Grade Reading SAT-9 Scores by SES Samples with Gender and Technology as Covariates............................................................ 13 7 33. One-Way ANCOVA Summary Table Comparing Scaled Math SAT-9 Scores from 1998 to 1999 with Gender, Level of Technology, and SES as Covariates............................................ 138 34. One-Way ANCOVA Summary Table Comparing Scaled Reading SAT-9 Scores from 1998 to 1999 with Gender, Level of Technology, and SES as Covariates...................................... 139 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. List of Figures Figure Page 1. Boxplot Comparing Second Grade Raw Math SAT-9 Scores by Level of Technology...................................................................................... 45 2. Line Graph Comparing Second Grade Raw Math SAT-9 Scores by Level of Technology and Gender.................................................................. 47 3. Line Graph Comparing Second Grade Raw Math SAT-9 Scores by SES, Level of Technology, and Gender....................................................... 51 4. Boxplot Showing Second Grade Math SAT-9 Scores by Gender and Level of Technology...................................................................................... 53 5. Boxplot Comparing Second Grade Raw Reading SAT-9 Scores by High versus Limited-Technology Samples................................................. 56 6. Line Graph Comparing Second Grade Raw Reading SAT-9 Scores by Free and Reduced Lunch Status (SES), Gender, and Level of Technology.......................................................................................... 59 7. Boxplot Comparing Third Grade Raw Math SAT-9 Scores by High versus Limited-Technology Samples............................................................ 62 8. Boxplot Comparing Third Grade Raw Reading SAT-9 Scores by Gender.... 64 9. Boxplot Comparing Third Grade Raw Math SAT-9 Scores by Free and Reduced Lunch Status.......................................................................... 66 10. Line Graph Comparing Third Grade Raw Math SAT-9 Scores by Level of Technology, SES, and Gender............................................................ 68 11. Boxplot Comparing Third Grade Raw Reading SAT-9 Scores by Levi of Technology.......................................................................................... 70 12. Boxplot Comparing Third Grade Raw Reading SAT-9 Scores by Gender... 72 13. Boxplots Comparing Third Grade SAT-9 Reading Scores by Free and Reduced Lunch Status......................................................................... 74 permission of the copyright owner. Further reproduction prohibited without permission. X 14. Line Graph Comparing Third Grade Raw Reading SAT-9 Scores by Level of Technology, SES, and Gender............................................ 77 15. Boxplot of Scaled Math SAT-9 Scores Comparing Students’ 1998 with 1999 Score by High versus Limited-Technology Sample.................................................................................................. 79 16. Boxplot of Scaled Reading SAT-9 Scores by Comparing Students’ 1999 Score by Level of Technology............................................................ 82 17. Results of Teacher Response to Question 1: “Student Achievement is Increased when I Use Technology in my Teaching.”........................ 84 18. Results of Teacher Response to Question 2: “I Think That Using Computer Technology for Instruction Improves my Students’ Performance.”...................................................................................... 86 19. Results of Teacher Response to Question 3: “Computer Technology Helps Students Learn Basic Skills.”................................................. 87 20. Results of Teacher Response to Question 4: “Computer Technology Helps Students Learn Problem-Solving Skills.”.............................. 88 21. Results of Teacher Response to Question 5: “Having Computer Technology in my Classroom has Increased my Ability to Accommodate Different Learning Styles.”................................... 89 22. Results of Teacher Response to Question 6: “Computer Technology Makes Students Like School More.”............................................... 90 23. Histogram of Math SAT-9 Scores for Second Grade Students by High versus Limited Technology Samples................................................. 114 24. Histogram of Math SAT-9 Scores for Second Grade Students by Gender.... 115 25. Histogram o f Math SAT-9 Scores for Second Grade Students by Free and Reduced Lunch Status................................................................. 116 26. Histogram o f Reading SAT-9 Scores for Second Grade Students by Free and Reduced Lunch Status................................................................. 117 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. xi 27. Histogram Comparing Third Grade Raw Math SAT-9 Scores by Level of Technology.......................................................................................... 118 28. Histograms of Third Grade Raw Math SAT-9 Scores by Gender............... 119 29. Histograms of Third Grade Raw Math SAT-9 Scores by Free and Reduced Lunch Status......................................................................................... 120 30. Histograms of Third Grade Raw Reading SAT-9 Scores by Level of Technology........................................................................................... 121 31. Histograms of Third Grade Raw Reading SAT-9 Scores by Gender 122 32. Histograms of Third Grade Raw Reading SAT-9 Scores by Free and Reduced Lunch Status......................................................................... 123 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. ABSTRACT The purpose of this study is to 1) examine the effects of classroom computers on student achievement; and 2) examine the attitudes of teachers regarding the effects of computers on student achievement. The student sample consisted of 142 second and third grade students selected from the Redding School District, Redding, California. Half of the students were selected from high-technology classrooms and half were selected from limited-technology classrooms. The researcher attempted to select an equal number of subjects by gender and socioeconomic status. Raw and scaled scores from the selected students’ 1999 Stanford-9 tests were collected. Descriptive statistics and a One-Way Analysis of Variance were used to compare and analyze the differences in second and third grade student scores for the high and limited-technology samples. A One-Way Analysis of Covariance was used to determine the effects of additional independent variables such as gender and socioeconomic status on student achievement. Both quantitative and qualitative data from this study suggested that computer technology does have a positive effect on student achievement. The results of the analysis showed no statistically significant differences in student achievement between students in high versus limited-technology classrooms. Analysis of Covariance indicated other findings that provide significant information about the effects of gender and socioeconomic status on student achievement. There was a significant difference in Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. xiii second grade math scores when comparing SES groups with the higher SES students scoring higher. There was a significant difference in second grade math scores with boys scoring at a significantly higher level than girls. When examining second grade reading scores, high SES students again scored significantly higher than low SES students. Third grade reading scores also showed high SES students scoring significantly higher than low SES students. Despite the lack of statistically significant differences between high and limited- technology samples, there were mean differences throughout the study that favored high- technology classrooms. The data consistently revealed lower SES students having higher mean scores in high-technology classrooms than in limited-technology classrooms in both grade levels and in both mathematics and reading. This was especially noticeable among lower SES males. In addition, the study revealed that teachers in this technology-rich school district report that they believe that computers have a positive effect on student achievement and student satisfaction with school. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 1 CHAPTER 1 Introduction School leaders are increasingly faced with demands to provide additional computer technology in public schools. Meanwhile, in this era of accountability, the demand for improved student technology intensifies while financial resources remain relatively constant. Decisions are being made to increase financial allocations to create and maintain technology-rich schools without ample empirical evidence to show that there is a significant relationship between computer use in the classroom and student achievement. This study examines whether extensive integration of computer technology into the curriculum results in increased student achievement in reading and mathematics during the primary years. The researcher examines both quantitative student achievement as well as qualitative teacher attitudes regarding the role of computer technology in student achievement. Background of the Problem Significant public school resource allocation has been directed at improving access to technology in recent years. Over the past decade, billions of tax dollars have been spent on computer hardware, software, and Internet access for classrooms ('Technology purchasing forecast. 1996: Technology in education. 1997). In 1988, there was one computer per 37 students in K-12 schools; by 1998, the ratio had been reduced to one computer for every seven students (Ravitch, 1998). The number of schools with Internet service in K-12 schools doubled during the 1996-1997 school year (Technology in education. 1997). Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. The pressure to expand and improve technology in America’s schools comes from a variety of sources including private business, the federal government, state and local governments, communities, parents, educators, students, and school administration (Pisapia, 1994; Squadron & Birenbaum, 1995; Topp, 1996; November, 1998; Report to the president. 1997; Houghton, 1997; Hope, 1997; Eastman & Hollingsworth, 1997; Sava, 1997; Beggs, 1998). The federal government has been very aggressive in its pressure to equip the nation’s schools with the latest in technology, with the National Task Force on Educational Technology calling for one computer per student in every school by the year 2000 (Pisapia, 1994; Investing in school technology. 1997). Furthermore, the American public is so convinced that computers will improve education that 61 percent of voters in one survey indicated that they would support increased taxes to place computers in classrooms (Sava, 1997). A controversy has developed in recent years over this aggressive push to equip the nation’s schools with the latest in technology. Critics cite the lack of reliable research to support the relationship between computers in the classroom and student achievement (Noble, 1996; Baines, 1997; Oppenheimer, 1997; Cuban, 1998; Healy, 1998). This study examines this controversy in the context of public school education. Statement of the Problem In order for school leaders to justify the continued extensive investment in technology for schools, empirical research is needed to show a positive relationship between technology and student achievement. A review of the literature reveals a paucity of information on the success of technology-rich classrooms and student achievement at Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 3 the primary (kindergarten-third grade) levels. Most studies targeting elementary schools focus on grades four through eight. This study examines the relationship between student achievement and technology-rich instruction in primary schools. Purpose of the Study The purpose of this study is to examine the question of computer use in public school education. Empirical research is needed to guide school leaders as they make decisions about the allocation of scarce financial resources. Data on student achievement as well as teacher attitudes toward computers in technology-rich schools is needed to help sort through the facts in the controversy over the investment of extensive funds for technology in schools. Research Questions The following research questions are addressed: (1) In the primary school years, does extensive integration of computer technology into the curriculum result in increased student achievement in reading? (2) In the primary school years, does extensive integration of computer technology into the curriculum result in increased student achievement in mathematics? (3) Does computer integrated curriculum during the primary years have a greater positive effect on the achievement scores of boys or girls? (4) Does computer integration have a greater positive effect on the achievement scores among students of a lower or higher socioeconomic group? (5) Do teachers in technology-rich schools believe that computers have a positive effect on student achievement? Reproduced with permission of the copyright owner. Further reproduction prohibited without permission 4 Importance of the Study This study will add to the body of knowledge on the subject of technology in our schools. A review of the literature indicates a significant lack of empirical research on the issue of technology and student achievement. The controversy among technology's advocates and detractors described above calls for more research and less prosletyzing on both sides of the issue. This study contributes new information to the debate over the effectiveness of computers in our schools. Furthermore, this study provides information for school leaders, particularly superintendents and their school boards, to assist in decision making about financial allocations for their schools. School leaders, with mandates from the state legislatures and pressure from the public to increase student achievement scores, need empirical data when making decisions about the allocation of limited school financial resources. Whether these leaders choose to invest heavily in technology or not, they need empirical research to support their decisions. Limitations and Delimitations The limitations and delimitations of this study include: (1) The limitations of using norm-referenced testing to evaluate student achievement; in this case, the Standard Achievement Test, Ninth Edition (SAT-9); (2) The lack of other measures of assessment in determining student achievement; (3) This study is limited to grades two and three; (4) This study is limited to one district, the Redding School District. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Methodology Subjects. The student sample consists of approximately 150 second and third grade students from the Redding School District in Redding, California. This district has invested heavily in technology over the past four years. Half of the students were selected from high-technology classrooms and half were selected from limited-technology classrooms. In the sampling process, this researcher attempted to select an equal number of subjects by gender and socioeconomic status. Free and reduced lunch status was used as a criteria for determining socioeconomic status. Approximately 90% of the students in the Redding School District are Caucasian and English speaking. The teacher sample includes 36 primary school teachers in the Redding School District who responded to the questionnaire of teacher attitudes regarding computer technology and student achievement (see Appendix E). Measurements. The goal of this study is to determine whether there are differences in student achievement in high-technology versus limited-technology classrooms by comparing the results of norm-referenced test scores in the areas of reading and mathematics. Student results from the SAT-9 are used. Additional statistical investigations are made by grade level, gender, and socioeconomic status. In this study, the high-technology sample is composed of students in classrooms with a minimum of a 2:1 computer-to-student ratio who spend a minimum of 10 hours per week in technology-integrated instruction in the areas of language arts and mathematics. The limited-technology sample is composed o f students in classrooms with Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 6 limited access to computers who spend less than four hours per week in technology- integrated instruction in language arts and mathematics. The issue of teacher training is not a criteria in selection because the Redding School District has provided extensive computer training for teachers over the past four years. Qualitative data on teacher attitudes toward computer technology and student achievement is presented as percentages of responses by Lickert category ranging from strongly agree to strongly disagree. Procedure. Raw and scaled scores from the selected students’ 1999 Stanford-9 tests were gathered from the Redding School District. Descriptive statistics and a One- Way Analysis of Variance are used to compare and analyze the differences in second and third grade student scores for high and limited-technology samples. A One-Way Analysis of Covariance is used to determine the effects of additional extraneous independent variables such as gender and socioeconomic status on student achievement. In addition, for the third grade sample, a pre-post test design examines the differences in reading and mathematics growth between students in the two test samples. The pre-test is the students’ 1998 scaled scores on the SAT-9 test and the post-test is their scaled scores on the 1999 SAT-9 test. Again, an Analysis of Covariance model is used to determine the effects of gender, socioeconomic status, and technology on the 1998-1999 scaled score differences. The following Null Hypotheses are tested: (1) The extensive integration of computer technology into the curriculum does not result in increased student achievement in reading and mathematics at Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. the second and third grade levels as measured by the Stanford-9 test scores. (2) During the early elementary years (second and third grade), gender is not a factor in increased student achievement among students in computer- integrated classes; and (3) Computer integration into the curriculum has no significant effect by socioeconomic status. Results. The results of this study are descriptively and statistically presented in Chapter 4 using tables, graphs, and charts. The alpha level for all statistical analyses is set at .05. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 8 CHAPTER 2 Literature Review Introduction This chapter presents a review of the relevant literature related to the controversy over public school expenditures on computer technology, focusing on the issue of the effects of computer technology on student achievement. The chapter first reviews the recent demand for technology in the classroom and the related dramatic increase in public school spending in this area. This will be followed by a discussion of the research critical of the significant public school spending on computer technology. Finally, the research that indicates a positive relationship between computer technology and student achievement, including student motivation to learn, will be discussed. The Demand for Computers in American Classrooms The literature is rich with research reporting significant public school resource commitment to computer technology in recent years. The pressure to expand and improve technology in America’s schools comes from a variety of sources including private business, the federal government, state and local governments, communities, parents, teachers, students, and school administration (Pisapia, 1994; Squadron & Birenbaum, 1995; Topp, 1996; Report to the president. 1997; Eastman & Hollingsworth, 1997; Hope, 1997; Houghton, 1997; Sava, 1997; Beggs, 1998; Crafiton, 1998; November, 1998). For example, Squadron and Birenbaum (1995) argue that society ought to provide all its school children with access to computers. The National Task Force on Educational Technology strongly encourages the use of technology in schools. The Task Force called Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. for the placement of one high-quality computer per student in every classroom in the United States by 2000 (Pisapia, 1994; Investing in school technology. 1997). The report submitted by the President’s Committee of Advisors on Science and Technology indicates that states and school districts are in a major push to put technology in the hands of America’s teachers and students (Houghton, 1997; Report to the president. 1997). Their efforts seek to “equip schools and classrooms with computers, educational software, school-based computer networks, and links to both statewide and global information networks” (p. 1). In addition, the report recommends that schools focus on learning with technology, emphasize content and pedagogy, give special attention to professional development, engage in realistic budgeting, and ensure equitable, universal access to computers. The American public is so convinced that computers will improve education that 90 percent support computers in schools and 61 percent of voters have indicated that they would support a federal excise tax increase to support the installation of computer technology in all schools (Sava, 1997). Parents are eager to support and invest in computers for home use and expect that schools will provide ample technology for their children. Sava (cited in Healy, 1998) states that 80 percent of personal computer buyers cite their children’s education as the main reason for their purchase. Educators pressuring colleagues and administrators has been a powerful motivating factor in the trend to increase technology in schools. Many educators, clamoring to get the latest hardware into every school, argue that teachers who steadfastly refuse to include computers in their curriculum need to be persuaded of the computer’s Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 10 usefulness in the classroom (Hope, 1997). Pro-technology teacher advocates (Eastman & Hollingsworth, 1997) argue that in some communities more computer technology is present in the homes of students and teachers than in the schools, inferring a lower quality learning experience in low-technology schools. Pro-technology educators have lobbied extensively to outfit our nation’s schools with the newest in technology. Financial Implications Expansion and improvement of computer technology in most schools is increasing with significant financial resource allocation directed at improving school technology being reported during the past five years (Technology purchasing forecast, 1996; Technology in education. 1997; Fouts & Stuen, 1997; Bare & Meek, 1998; Stegall, 1998; “Computers fast becoming literacy,” 1999; Salpeter, 1999). A report through Quality Education Data (QED) reveals a significant increase in hardware, software, and network purchasing in K-12 schools for 1996-1997 as compared to 1995-1996 (Technology purchasing forecast. 1996). A similar report by the Market Data Retrieval (MDR) service showed that the Internet service in K-12 schools doubled during the 1996-1997 school year. The MDR also reports that there were 6.3 million instructional-use computers in United States’ public schools in 1997 (Technology in education. 1997). According to Ravitch (1998), ten years ago there was one computer per 37 students; now there is one computer for every seven students. Salpeter (1999) predicted that a ratio of five students per multimedia computer would be achieved by the year 2000. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. To illustrate the significant financial increases being directed at technology in the schools, QED, a Denver-based consulting firm estimated that public schools would spend $5.4 billion on technology in 1999, nearly double the amount spent five years earlier (“Johnny’s computer doesn’t boot,” 1999). In addition to increased allocations for computer hardware, there has been subsequent increased spending on educational software. Industiy experts report the sale of computer programs has taken off in schools and homes with combined sales doubling over the past five years to $ 1.2 billion in 1998 (“Computers fast becoming literacy,” 1999). Technology-aggressive school districts such as Hacienda La Puente Unified and San Gabriel Valley in California are spending significantly on technology. In 1999, San Gabriel Valley spent $3 million on a new computer-based curriculum to supplement reading instruction in all kindergarten, first, and second grade classrooms (“Computers fast becoming literacy,” 1999). The literature provides many examples of school districts investing heavily in the latest in technology. Some school districts report creative programs to increase instructional computer use in their schools without the usual initial heavy investment by forming partnerships with private businesses. An example is the Copernicus Project, a multi-district effort designed to bring laptop computers into public schools. Participants of this project included six school districts in Washington state, the Toshiba and Microsoft Corporations, and parents. The project called for a 1:1 student-to-laptop computer ratio with computers owned or leased by students and taken home each night (Fouts & Stuen, 1997). permission of the copyright owner. Further reproduction prohibited without permission. 12 More recently, access to the Internet has been advertised as the vehicle to connect students to the world and it has been viewed as an excellent source of information. Published by the National Center for Educational Statistics, Bare and Meek (1998) report on the increase of Internet access in public schools. The survey included a nationally representative sample of public schools in 1994 with subsequent surveys in 1995, 1996, and 1997. The percentage of school Internet access increased from 35 percent in 1994 to 78 percent in 1997. A report by the Market Data Retrieval (MDR) service showed that the Internet service in K-12 schools doubled during the 1996-1997 school year (Technology in education. 1997). Data from 1996 indicated that 87 percent of those schools lacking Internet capabilities planned to obtain Internet access by the year 2000 (Bare & Meek, 1998). The trend to increase computer technology has also been noted in private schools in the United States. Stegall (1998) reported that 31 percent of fifty-four private Catholic elementary schools in south Texas had Internet access. In addition, 85 percent of these schools had a computer curriculum. An innovative funding plan to promote private school technology is reported by Connelly (1997) who describes a “Driving for Education” program where computers were awarded to Catholic schools in return for generating no-obligation test drives at local Chevrolet dealerships. Private schools have responded to parental expectation that computers are an integral part of a quality education. The trend to create technology-rich schools is well on its way. However, has the educational community placed the “cart before the horse” and given way to internal and Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 13 external pressures without asking a basic question— will increased technology in schools result in better student learning environments and increased student achievement? The Critics The pro-technology movement in public schools has come under attack in the past few years. Beginning in 1996, critics have cited the lack of reliable research to support the relationship between computers in the classroom and student achievement; they also criticize the academic trade-offs made by school districts to purchase computers (Noble, 1996; Baines, 1997; Dertouzos, 1997; Oppenheimer, 1997; Sava, 1997; Cuban, 1998; Healy, 1998). Oppenheimer’s scathing critique of the current era of educational technology cited a number of examples of questionable choices by school districts favoring computer purchases over spending for traditional programs. For instance, at the same time New Jersey cut state aid to many school districts, it spent $10 million on computers for classrooms. Other examples of educational trade-offs for computers were the elimination of a music program in a Los Angeles school to pay for a technology coordinator and the elimination of art, music, and physical education teaching positions to pay for $330,000 for computers. Oppenheimer further criticized Kulik’s meta-analysis of 245 studies finding positive effects of computer-aided instruction on student achievement as lacking “the necessary scientific controls to make solid conclusions possible.” He also criticized Apple Computer’s “Classrooms of Tomorrow” with “...after a decade of effort and the donation of equipment worth more than $25 million to thirteen schools, there is scant evidence of greater student achievement” (Oppenheimer, 1997). In his critique of educational technology, Oppenheimer quoted Apple Computer co-founder Steven Jobs Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 14 who “spearheaded giving away more computer equipment to schools than anybody else on the planet” as saying “What’s wrong with education cannot be fixed with technology. No amount of technology will make a dent...” (Oppenheimer, 1997). Baines (1997) also criticizes the soaring school expenditures on technology in an era of school funding shortages. “Over the course of five years in the school district in which I lived and taught, the number of students per classroom ballooned, budgets for textbooks and supplies were slashed, and plans were shelved for capital improvements to deteriorating buildings and for the construction of a new school. Yet computers were purchased for every classroom in eveiy school in the district.” Baines concludes that “Technology can make learning more fun, easier, and cleaner. But no data support the conclusion that technology causes gain in student achievement” (Baines, 1997). Healy (1998) also argues that despite the significant public school spending on computers for classrooms, there is very little research that indicates a relationship between these educational computers and student achievement. She further cautions that parents and educators should be concerned with potential social, emotional, and cognitive damage to young minds when exposed to extensive computer technology. The high cost of technology has been a concern of other researchers. The research indicates significant resource allocations directed at school technology (Schrage, 1994; Technology purchasing forecast. 1996; Technology in education, 1997; Fouts & Stuen, 1997; Bare & Meek, 1998; Hamilton, 1998; Healy, 1998; Hunt & Lockard, 1998; MacNeil & Delafield, 1998; Price, 1998; Stegall, 1998; “Computers fast becoming literacy,” 1999; “Johnny’s computer doesn’t boot,” 1999; Salpeter, 1999). Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 15 Schrage (1994) argues that acquisition of computer technology by public schools will make entrepreneurs and bankers rich without improving educational quality. According to Schrage, this is made evident by the commercial interest in promoting technology and educator interest in acquiring it. In a similar vane, Noble (1996) states that, ...educators need to understand that high tech corporate moguls and marketers, despite their government supporters, are scrambling to predict the future, are seduced by their own high-tech fantasies, and are locked in treacherous, high-stakes gambles...their state-of-the-art technologies have not in the past been products with direct or immediate applications for education, nor will they be in the future (p.22). The decision to commit resources to educational technology brings with it other financial issues. The high cost of maintaining the fleet of recently purchased hardware in public schools is often cited by critics. Ravitch (1998) states, ...the $5 billion spent this year on computers in schools is only the tip of the iceberg, because almost all of the money goes for hardware. Given the pace of technological change, the tab will rise steadily, as equipment purchased ten even five years ago becomes obsolete. Only 15 percent of teachers have appropriate training, so taxpayers will have to pay for that too (p. 134). In a study of elementary school district superintendents’ perceptions of challenges related to the implementation of networked educational technology and Internet access, Price (1998) reported that a ranking of concerns identified costs associated with maintenance and replacement to be the greatest concern. Hamilton (1998) reports that in one district, after a bond issue failed three times, the district adopted leasing as an alternative to computer purchase. Their three-year contract supplies used computers and a maintenance contract for less than the district’s computer maintenance budget. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 16 School districts have not budgeted adequately for computer maintenance and replacement. In “Johnny’s computer doesn’t boot” (1999), the author maintains that schools have neglected to fund for maintenance. The annual cost of maintaining and replacing a computer system in the business world is estimated to be between 33% and 80% of the original purchase price (Rukeyser, 1998). According to the author, businesses know they have to spend as much on training, maintenance, and troubleshooting as they do on hardware, but school districts have not learned this yet. In a study of principal leadership and successful school technology implementation, MacNeil (1998) indicates that principals and assistant principals view technology as very important in their schools and that it is important for teachers to learn technology as a curriculum tool. However, the study shows that the main inhibitors to successful technology implementation are lack of financial resources and time for professional development and planning. Creating a technology-based learning environment requires that schools allocate resources for on-going teacher training and staff development (Topp, 1996; Jurema et al., 1997; Ehrich et al., 1998). In the rush to place computer hardware in every classroom in America, there has been little attention given to this support such as maintenance and teacher training which would be necessary to bring about real changes in classroom instruction. Until recently, there has been little research directed at the question of the effects of computer technology on learning. Some of the research indicates that computers may not produce the student outcomes desired by pro-technology supporters (Heidenreich, Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 17 1992; Taylor, 1992; Waxman & Huang, 1995; Sewall, 1996; Baines, 1997; Howley & Barker, 1997; Meghabghab & Price, 1997; Nikiforuk, 1997; Oppenheimer, 1997; Cuban, 1998; Edgell, 1998; Grimm, 1998; Ravitch, 1998; Saunders, 1998; Sherman, 1998; Westermeier, 1998; “Computers fast becoming literacy,” 1999; Latham, 1999). In the area of curriculum, a study was conducted to determine the effects of HyperStudio on the achievement of seventh grade social studies students. In this study, Heidenreich (1992) noted that after three weeks the students who were instructed using HyperStudio did not achieve statistically significant higher scores on the post-test than did students whose instruction did not include HyperStudio. However, according to Heidenreich (1992), students in the experimental group had a more positive attitude toward learning. In a study of 200 urban elementary and middle school classrooms, Waxman and Huang (1995) attempted to determine the extent to which computer technology was integrated into content area classes. They found no technology integration in the elementary classrooms and middle school students were observed working with computers in content areas only two percent of the time. Similarly, Edgell (1998) reported the results of a study conducted to determine whether Computer Assisted Instruction (CAI) makes a significant difference in test scores comparing two fifth grade classes, one which used CAI and the other which did not. Edgell reported no significant differences between the test scores of these two groups. Teacher Attitudes. According to other studies, teachers who are knowledgeable about computers and their effective integration into the curriculum do make a significant Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 18 difference in whether the use of computers in the classroom will have a positive impact on learning gains (Ely, 1995; Sewall, 1996; Cuban, 1998; Westermeier, 1998; Sherman, 1998). According to Sewall (1996), educators who do not learn to use computers and the information highway in the classroom may be depriving themselves and their students of opportunities to access important information and develop skills. Similarly, Cuban (1998) found that both college faculty and public school teachers make limited, unimaginative uses of new technologies, despite having equipment available. This is partially the result o f teachers’ attitudes towards computers in the classroom, conflicting beliefs about the purposes of schools, and teachers’ feelings about rapidly changing technology. Rosen and Weil (1995) estimate that between one-third and two-thirds of all teachers do not take full advantage of the computers available to them for instruction because they lack self-efficacy in technology. Cuban (1998) suggests that if the new technologies available in public schools are to be used effectively, teachers will need to resolve their conflicts about the role of technology in education. In a study o f teachers’ computer literacy and students’ uses of computers, Westermeier (1998) and Ely (1995) found a significant correlation between teachers’ levels o f computer literacy and the amount of time their students used computers in the classroom. Access to technology in school can motivate students to become more independent learners; however, according to Sherman (1998), technology will have little effect if teachers are technologically illiterate. Sherman (1998) continues by stating that “to be effective, technology must be integrated into the curriculum, and teachers need Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 19 support and training in the use of technology and related new pedagogical methods. They also need time to leam and experiment with computers” (p. 2). Issues of Gender and Socioeconomic Status. An expanded review of the literature indicates that the use of technology in schools may not provide the same benefits for all groups of students. Gender, socioeconomic status, and rural versus urban schools, appear to be major factors affecting positive outcomes for students (Collis, Kass, & Kieren, 1989; Koontz, 1991; Newman, 1995; Haug & Waxman, 1996; Chapman, 1997; Hanson, 1997; Levin & Matthews, 1997; Steele, 1997; Brosman, 1998; Gelpi, & Young, 1998; Hanor, 1998; Matthews, 1998; Nicholson, Latham, 1999). Newman (1995) found that female attitude toward computer use for 5-to-9 year olds would be less positive if females had been exposed to rich gender stereotypes. Several studies, for example, have found that female students report using technology significantly less than males in science and mathematics classes (Collis, et al., 1989). Furthermore, there are studies that have found sex inequities favoring males during classroom instruction related to technology (Koonz, 1991). Research has also suggested that computers can increase inequalities for students of color and poor students. Hanson (1997) reports on a study which explores how some of the dimensions of the language of computers and technology, computer culture, and computer-based activities are linked to the language and culture of mathematics. In this study, she explains how the use of computers often creates inequities for students of color and poor students rather than act as an educational panacea. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 2 0 In a six month study of first graders using Open-Ended Software, Nicholson et al. (1998) found that, females in mixed-gender groups were more likely to have their competence or work laughed at or criticized when working alone or in all-female groups. In addition, Brosnan (1998) found that boys hold more favorable attitudes toward computers than girls. Problems of gender and technology are not limited to students. Matthews (1998) found that male teachers perceive themselves as having higher ability in technology use than female teachers. He also found that the fewer years a teacher has been in the classroom, the higher the perceived ability of the teacher to use technology. This indicates that veteran teachers are more reluctant to integrate computers into the curriculum and that they have a greater need for staff development and training than newly hired teachers. Studies show that socioeconomic status can also hinder a student’s success with technology (Taylor, 1992; Rosen & Weil, 1995; Coley & Engel, 1997; Howley & Barker, 1997; Meghabghab & Price, 1997; Wenglinsky, 1998). According to Coley & Engel (1997), technology can also help reduce performance gaps among subgroups of students. They noted that technology has the potential to decrease opportunity gaps by granting students from different backgrounds equal access to the wealth of information available on the Internet. However, at the same time, these gaps widen if access to technology is inequitably distributed. To support this, Taylor and Budin (1992) found that poorer schools tend to use computers mainly for drill work, while richer ones often used them for other purposes which support creativity and research. * Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 21 School funding methods can also affect student access to computers. Meghabghab (1997) reports that the impact of technology integration on teaching and learning is evident in many of Georgia’s schools. However, the discrepancies in the allocation of state funding across school levels and among regions have widened the gap between the “information rich” and the “information poor.” Howley and Barker (1997), in a study of technology and rural schools, reports that smaller and more rural schools have fewer computers and less telecommunications service. They further state that, “despite decades of expectation, computers have not yet ‘revolutionized’ schooling” (p. 1). In a more recent study of student technology uses and student outcomes, Wenglinsky (1998), drawing from the 1996 National Assessment of Educational Progress (NAEP) in mathematics, collected data from 6,227 fourth graders and 7,146 eighth graders. The findings revealed that the greatest inequities in computer use are not in how often they are used, but in the ways in which they are used. Poor, urban, and rural students are less likely to be exposed to higher level uses of computers than non-poor and suburban students. Wenglinsky reported that for both fourth and eighth grades, suburban teachers had more professional development in technology than either rural or urban teachers. His study also reported that for both grades, black students were less likely to have a computer in the home than white students. Critics of the recent dramatic increase in public school spending on technology cite the lack of research to support its effectiveness on student achievement. Public school leaders have also been criticized for poor planning, with billions being spent on Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 22 computer hardware and software while little is budgeted for maintenance, replacement, and teacher training. Rukeyser (1998) reports that the year 1997 was a “major turning point: when people began to question some of the claims that computers and the Internet are the cure-all for our education system.” During 1997, the executive director of the National Association o f Elementary School Principals was quoted in a speech, “If computers make a difference [in school], it has yet to show up in achievement” (Rukeyser, p. 1). The same month, Oppenheimer published a cover story in The Atlantic Monthly titled “The Computer Delusion” (Oppenheimer, 1997), harshly questioning the selling of technology to public schools in America. Critics question whether $100 billion to put computers in classrooms is a solid investment in our children’s education. Positive Effects of Technology on Student Achievement Of the hundreds of journal articles written about educational technology, relatively few have tackled the difficult task of analyzing the impact of technology on student achievement. The following is a review of the literature that examines the impact of technology on student learning (Riel, 1985; Riel, 1989; Newman, 1989; Kulik, 1994; Winther, 1994; Baenen, 1995; Laub, 1995; Reininger, 1995; Templin, 1995; Tillman, 1995; Allen, 1996; Paul et al., 1996; Scardamalia, 1996; Terrell & Rendulic, 1996; Casey, 1997; Ediger, 1997; Hurley & Vosburg, 1997; Larose-Kuzenko, 1997; Mann & Shafer, 1997; Seng & Choo, 1997; Owston & Wideman, 1997; Van Horn, 1997; Amos, 1998; Bohannon, 1998; Bronner, 1998; Chirwa, 1998; Christie, 1998; Defrieze, 1998; Milone, 1998; Sivin-Kachala, 1998; Johnson, 1999; Mann, 1999). Most of studies cited in this review have been published in the past five years, concentrating on the past three Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 23 years, to provide the most current research on this topic. Since the anti-computer backlash began in 1997, there has been a research trend to respond to critics such as Oppenheimer (1997) and Baines (1997) in defense of technology in the classroom. Several doctoral dissertations were written in 1998 (Amos, 1998; Bohannon, 1998; Defrieze, 1998) which report finding a positive relationship between computer use and student achievement. Furthermore, in the past two years, two large scale studies have reported substantive research that also support the positive relationship between computer use and student achievement (Wenlinsky, 1998; Mann, 1999). Recent substantive research. Wenglinsky (1998) reports the results of a study conducted by the Educational Testing Service in Princeton, New Jersey, which provided the scores of thousands of fourth and eighth graders on the 1996 math portion of the National Assessment of Educational Progress. Wenglinsky, an associate researcher with the Educational Testing Service, reported that technology can have positive benefits for student achievement, depending on how the technology is used. Among eighth graders, the study found that when students used computers for simulations and higher level thinking software, gains in math scores of more than a third of an academic year (15 weeks) above grade level were reported. In addition, eighth grade students whose teachers received professional development on the use of computers scored up to 13 weeks above grade level in math scores. On the other hand, both fourth and eighth grade students in classrooms where computers were used primarily for “drill and practice” activities, which are usually associated with lower level thinking skills, performed worse than students who did not use the computer for drill and practice activities. When Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 24 computers were used in fourth grade classrooms, students using computers for “math learning games” showed higher math gains than students who did not use computer games; however, these gains were minimal with these students only 3 to 5 weeks ahead of students who did not use computer technology in the classroom. Frequency of home computer use was also positively related to academic achievement in this study. The size of this study’s nationally representative sample, with test results of 6,600 fourth graders and 7,100 eighth graders analyzed, gives considerable support to Wenglinsky’s findings that, under the right circumstances, computers can have a positive effect on student achievement. Mann’s (1999) recent study of 950 fifth grade students and 290 teachers in West Virginia found a positive relationship between the use of West Virginia’s Basic Skills/Computer Education (BS/CE) program and student achievement on Stanford-9 test scores. The program is an Integrated Learning System focusing on spelling, vocabulary, reading, and mathematics. Mann found that increased participation in BS/CE resulted in increased test scores on the Stanford-9 test. All participating student test scores rose, but lower achieving students showed the greatest increase. Furthermore, Mann found no gender differences in achievement, access, or use in his study. Mann’s study also measured teacher attitude toward technology. Half of the teachers in the sample reported positive attitudes toward the BS/CE program, believing that technology helped “a lot” in increasing student achievement. The results of Mann’s study compare to Kulik’s (1994) meta-analysis of more than 500 individual research studies of computer-based instruction. Kulik found a Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. positive relationship between computer-based instruction and student achievement, with students in the technology sample scoring at the 64th percentile on student achievement tests compared to the 50th percentile for students without computers. Kulik found that students learned more rapidly using computer-based instruction and that they had more positive attitudes toward classes using the technology. In a study examining the effects of two different curricula to teach chemistry, Winther (1994) found that African-American students enrolled in an inner-city high school who took ChemCom, a Science-Technology-Society-based science curriculum scored significantly higher on a standardized chemistry achievement test than similar students enrolled in a traditional chemistry course in the same school. Reininger (1995) reported the results of a longitudinal study of third through fifth grade student achievement in six academic areas in classrooms using the Teaching and Learning with Computers (TLC) method. Using third grade test scores, the researcher used statistical analysis to predict fourth and fifth grade test scores. The results of the study indicated that composite scores of vocabulary, language, and reading tests showed positive differences over predicted test scores for these students. Reininger attributes the student success in these three areas to the use of technology in the curriculum. Determining the effectiveness of educational courseware was the purpose of a study by Laub (1995) who investigated the relationship between student achievement in mathematics and the use of an integrated learning system using Computer Curriculum Corporation Math Concepts and Skills. In this study, mathematics achievement of 314 suburban, computer-literate fourth and fifth grade students measured in scaled scores permission of the copyright owner. Further reproduction prohibited without permission. 26 using the Stanford-9 test was compared to the mathematics achievement o f two previous fourth and fifth grade classes in the same school. The results were statistically significant with gains for those students using computer technology. In addition, the results showed that the greater the students’ overall level of achievement, the greater impact the computer program had on test scores. In average and high achieving groups, males outscored females, while females outscored males in low-achieving groups. Laub recommends that the integrated learning system studied be used as an instructional tool in other settings. However, caution may be indicated when interpreting the results of this study in which students supplemented their normal math lesson with twelve minutes daily of computerized math instruction. The extra instructional time for students studied may account for increased student achievement rather that the use of computers. Defrieze (1998), in a study of the influence of computer use on student achievement comparing unstructured and structured computer lab environments, reported mixed results. In a structured environment, computers influenced student achievement on the Iowa Test of Basic Skills for Math Total only. In an unstructured environment, Deffieze found that computers influenced student achievement in Reading, Advanced Skills, and Reading Total. The study indicated that the use of computers for instruction influenced student achievement more in reading than in mathematics and Defrieze suggests that teacher control may be the key to differences in student achievement when using computers. Amos (1998) reported the results of a study measuring the effects of the use of computers in the instruction of 53 second and third grade students in eastern Washington Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. State. Using the Iowa Test of Basic Skills as a pre and post-test instrument to measure academic growth, the study indicated mixed results with a moderate treatment effect favoring the third grade treatment group using computers over the non-computer using students. Bohannon (1998), in a study that examined the relationship between frequency-of- use of school computers among fifth graders and five dependent variables-language achievement, mathematics achievement, reading achievement, computer skill, and attitudes toward learning with computers- revealed significant differences in 1 )language achievement by gender and by home computer use, but not by frequency of school computer use; 2) in mathematics use; and 3) in reading achievement by frequency-of-use of school computers. In this study, student who had more frequent access to computers at school developed higher levels of computer competence, and almost all students displayed favorable attitudes toward learning with computers. O ther Recent Studies. Baenen (1995), in a status report on the Magnet Schools Assistance Program (MSAP), found that MSAP elementary schools and Ligon Middle School had made progress towards a two-year goal of reducing the gap between majority and minority performance on end-of-grade achievements by increasing technology in instruction. A study (Paul, et al., 1996), involving 2,500 elementary, middle, and high schools, demonstrated the positive impact of school ownership of the Accelerated Reader (AR) technology-based literacy program on attendance and standardized test scores. These schools were compared with approximately 3,500 schools of similar geographic and permission of the copyright owner. Further reproduction prohibited without permission. 28 demographic characteristics that did not own the software. The data revealed statistically significant evidence on every subject test (including reading, writing, math, science, and social science), a majority of schools that owned AR software performed better than socioeconomically comparable non-AR schools. Mann and Shafer (1997) reported the results of a three-year study examining increased student access to computers in 55 New York school districts. The study revealed that increased technology supported and facilitated student achievement. The gains reached across schools and districts with different educational policies and socioeconomic backgrounds. VanHom (1997) suggests that standardized test scores can be elevated if the classroom computers are used for more than one hour per day. He suggests that learning experiences and software should accomplish more than one purpose and recommends that teachers use software that allows students to practice a number of skills at once. Like VanHom (1997), Ediger (1997) reports the value of new and efficient software in reading instruction. With appropriate software, Ediger (1997) recognizes numerous advantages in classroom computer use. He suggests that 1) problem solving rather than old simulation programs be used for reading instruction; 2) quality phonics instruction programs can assist students to progress in sequential steps; and 3) software programs that stress critical thinking be used. In addition, Ediger (1997) predicts the Internet will revolutionize reading instruction. Student Achievement and Motivation. Student attitude toward learning either enhances or detracts from achievement. In studies of computers and student Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 29 achievement, it is relevant to examine the role of computers on student motivation and attitude toward learning. Even though computer availability in schools was in its infancy in 1985, Reil (1985) found that third and fourth grade students wrote more material as a result of using computers. In addition, informal observations suggest that students’ attitude toward reading and writing, and toward their reading and writing performance, improved when using computers. Similarly, Newman (1989) reported that seventh and eighth grade students begem to write longer texts as they researched and shared information with their “computer pals” in other locations. Newman also found that students’ attitudes toward writing and toward their language development improved, exceeding teacher expectations, with the use of the computer in the classroom. Several years later, in a three year study o f two groups of elementary school students (grades three through five), Owston and Wideman (1997) reported that writing quality improved in a high-computer access school. In-class observation supported the argument that word processing computers contributed to writing improvement. Increased motivation to leam was analyzed in a study by Terrell and Rendulic (1996) who found that the use of computer-managed instructional feedback can have a positive effect on student motivation and achievement. Similar results were reported by Tillman (1995) who found that fifth grade low-socioeconomic boys and girls who used the computer one day per week to read and answer questions had a more positive attitude toward learning. Similar to Tillman (1995), Chirwa (1998) found that elementary students in a computer-based learning environment had increased attention spans and Reproduced with permission of the copyright owner. Further reproduction prohibited without permission 30 elevated levels of awareness during learning sessions. In that study, Chirwa (1998) observed that computers stimulated learning motivation when motivational strategies were built into the program. In addition to the above findings, Larose-Kuzenko (1997) reported that technology has positive effects on student attitudes toward learning and gives students more control over their learning. In a study of primary schools in Singapore, Seng and Choo (197) found that students had strong positive feelings for computer-related activities. Over one-third o f the students in the study expressed a strong liking for learning using software and about one-half enjoyed repairing computers. Positive student attitude was also observed by Hurley and Vosburg (1997) who examined the attitude of students toward technology, their attitudes toward learning using modem technology in an academic setting, and whether there is a correlation between the two attitude variables. There was a high positive correlation between students’ attitudes toward technology and toward learning. In addition, the study showed that there were neither gender nor grade level effects on students’ attitudes toward learning. Many studies report the relationship between computer use and enthusiasm for learning. This enthusiasm, translated to motivation to learn, may have a positive effect on student achievement. Summary A review of the literature reveals a significant body of research examining the effects of computer technology in the classroom. The great demand for educational technology from the various stakeholders as well as the financial commitment made to outfitting America’s schools with the latest in technology has been well documented. In Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 31 recent years, critics such as Oppenheimer, Baines, and Healy have called into question the effectiveness of educational technology in our schools. Beginning in 1997, a potential backlash against this financial commitment has led to a number of studies examining the effects of technology on student achievement. Several of these studies have reported results similar to Kulik’s (1994) earlier meta-analysis of 500 studies which showed that computer technology can have a positive effect on student achievement in certain circumstances. The results from this dissertation will provide important empirical data to add to the body o f knowledge regarding the effects of computer technology on student achievement. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 32 CHAPTER 3 Research Methods Purpose of the Study The purpose of this study is to examine the effect of computer technology on student achievement in public school education. Empirical research is needed to guide school leaders as they make decisions about the allocation of scarce financial resources. Data on student achievement in technology-rich schools are needed to help sort through the facts in the controversy over the investment of extensive funds for technology in schools. This study addresses the following questions: (1) In the primary years, does extensive integration of computer technology into the curriculum result in increased student achievement in reading and mathematics? (2) Does computer integrated curriculum during the primary years have a greater positive effect on the achievement scores of boys or girls? (3) Does computer integration have a greater positive effect on the achievement scores among students of a lower/higher socioeconomic (SES) group (with SES group defined by a student’s eligibility for Free and Reduced Lunch)? (4) Do teachers in technology-rich schools believe that computer technology has a positive effect on student achievement? Statistical Inference The goal of this study is to determine if there are differences in student achievement in high-technology versus limited-technology classrooms by comparing the results of norm-referenced test scores in the area of reading and mathematics at the second and third grade levels. Student results from the Stanford-9 achievement test Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 33 (SAT-9) will be used. Furthermore, the study will analyze third grade achievement by using these students’ second grade SAT-9 scores as a pre-test for comparison. Additional statistical investigations are made by grade level, gender, and socioeconomic status. The following Null Hypotheses are tested: (1) The extensive integration of computer technology into the curriculum does not result in increased student achievement in reading and mathematics at the second and third grade levels as measured by the Stanford-9 test scores; (2) During the elementary years (second and third grades), gender is not a factor in increased student achievement among students in computer-integrated classes; and, (3) Computer integration in the curriculum has no significant effect by socioeconomic status. Sampling Technique Students selected for this study were enrolled in the Redding School District, Redding, California, during the spring of 1999. They were randomly selected from second and third grade classrooms by school and by teacher as defined by the degree of computer integration in the classroom (the criteria used to place students in high and . limited-technology groups is defined below). The student samples were selected from intact classrooms from the following schools in the Redding School District: Bonnyview, Cypress, Juniper Academy, Manzanita, and Turtle Bay Schools. All teachers and administrators within the Redding School district have had extensive opportunities for computer technology staff development within the past four Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 34 years. Those teachers with a strong interest in integrating computers into their curriculum have had their classrooms outfitted with the desired amount of hardware and software. The student/instructional computer ratio within the elementary schools as of 1999 was 2 :1. The selection of high versus limited-computer integrated classrooms was made by each school’s principal. Within this school district there is no district-wide curriculum director; principals serve as curriculum and instructional leaders for each school and have been responsible for the development of computer technology at their sites. All of the principals consulted in this study have extensive administrative experience. All are very familiar with the instructional programs within their schools and were able to identify the teacher at each grade level who integrates computer technology most extensively in the curriculum as per the criteria which will be explained below. They were also able to identify a comparable teacher at each grade level whose integration of computer technology is limited. This researcher’s observation of these high-technology classrooms found teachers with an intense commitment to educational technology and a strong belief that computers were having a positive effect on their students’ learning. A ratio of one computer per student is typical of these classrooms. Students were observed working independently on a variety of software programs including Accelerated Reader, Type to Learn, Math Blaster, Kid Pix, and other elementary level programs. In addition to these drill and practice type programs, students used computers for writing and age-appropriate Internet research. Throughout the day students spent up to 30 minutes at a time on-task, actively Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 35 engaged in learning activities, with few behavior problems. Teachers report using computer-aided instruction in approximately 30 minute blocks throughout the day to reinforce instruction in mathematics and reading. One teacher reported that her second grade students arrive at school early in order to work on computer programs before school and often have to be asked to leave the classroom for recess. The intense interest of elementary age students in computer instruction and on-task learning behavior is easily observed in these high-technology classrooms. These teachers have learned to integrate computer instruction into the curriculum throughout the day and their students are motivated to leam with computers. Teachers selected for the qualitative portion of the study were those who responded to a questionnaire on teacher attitudes toward computer technology and student achievement. Thirty-six primary school (K-3) teachers responded to the anonymous questionnaire which was presented to each school staff by that school’s principal. Participation was voluntary. Student Selection Criteria. The following were criteria used in identifying classrooms from which students were selected for the study. Teachers in high-technology classrooms used computers for reading and mathematics instruction a minimum of ten hours per week. Teachers in limited-technology classrooms used computers for reading and mathematics instruction from zero to four hours per week. Principals identified high- technology teachers by the number of hours of computer integration per week. Next, principals identified matching limited-technology teachers with comparable years of experience and comparable curriculum and teaching styles. The principals at Bonnyview Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 36 School and Cypress School were unable to provide a matching limited-technology teacher for the second grade; therefore, only third grade results from these schools are reported in this study. Although parent requests are allowed in the Redding School District, each principal is responsible for the creation of heterogeneously balanced classes. All teachers were identified by their principals as strong, experienced teachers. Students were chosen from 1998-99 class lists from each of the five schools chosen for this study. A data collection sheet was constructed to record all appropriate data (see Appendix A). Two samples were randomly selected, one sample comprised of students from identified high-technology classrooms and a second with students from identified limited-technology classrooms. The initial sampling process required that twelve students from each classroom be selected, half male and half female; half from free/reduced lunch eligibility and non-ffee/reduced eligibility lists which were supplied by the district Food Services Department. To acquire this sample, as far as possible, every even-numbered student was selected from class lists. When the even-numbered selection cycle did not produce a balanced number of students by gender or socioeconomic status, the researcher returned to the top of the list and selected odd-numbered students until the balanced number of twelve students was achieved. Every effort was made to select equal numbers by gender and socioeconomic status. As the selection process proceeded, it became apparent that it was impossible with some classrooms or schools to acquire an equal number of free/reduced status students and non free/reduced status students. Furthermore, at Cypress School the poverty level is Reproduced with permission of the copyright owner. Further reproduction prohibited without permission 37 high enough for 100% of the students to qualify for free/reduced lunch status. As a result, there are not equal numbers of free/reduced status students represented in the samples. Furthermore, the student selection process was complicated by the researcher’s decision to eliminate students from the sampling process whose SAT-9 scores on either the Total Reading or Total Mathematics were not reported. In addition, students were eliminated from the selection process if one of their scores fell below the 10I h percentile. Elimination of these extreme outliers is appropriate at the second and third grade level when reading ability is still in question for some students, making their scores invalid measures of student achievement. As a result of the above limitations, fewer than twelve students per class were selected from Cypress and Juniper Academy (see Table 1 for a review of the sampling sizes by school, technology level, gender, and socioeconomic status). There were ten third grade teachers identified for this study. Each teacher had a class size of no more than 20 students. Of the approximately 200 students in this population, 84 (42 percent) were selected by the sampling process as described above. There were seven second grade teachers identified for this study, each with no more than 20 students per class. Of the approximately 140 students in this population, 58 (41 percent) were selected by the sampling process. Research Design The quasi-experimental design explained above identifies the dependent variable as the Stanford-9 test score, and the independent variable as the degree of computer- technology integration in the classroom. The experimental group is the sample ofstudents Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Reproduced w ith permission o f th e copyright owner. Further reproduction prohibited without permission. Table 1. Second and Third Grade Sample Sizes by School, Grade, and Variable School/ Sex Level of Technology Free and Reduced Lunch Grade Male Female High Technology Limited Technology Yes No Turtle Bay (n=24) Second Grade 14 10 12 12 6 18 Third Grade 1 1 13 12 12 8 16 Manzanita School (n=24) Second Grade 10 14 12 12 12 12 Third Grade 1 1 10 12 9 5 16 Cypress School (n=13) Second Grade NA NA NA NA NA NA Third Grade 8 5 7 6 1 3 0 Juniper Academy (n-10) Second Grade 5 5 5 5 9 1 Third Grade 4 6 6 4 5 5 Bonnyview (n=16) Second Grade NA NA NA NA NA NA Third Grade 7 9 8 8 3 13 Total Number 2n d Grade (n=58) 29 29 29 29 27 31 3r d Grade (n=84) 41 43 45 39 34 50 NA: Data Not Available L k ) 00 39 identified and recorded as high-technology students; the control group as the limited- technology students. For the purpose of this study, it is assumed that increased exposure of students in the high-technology classroom to computer technology is the main treatment criteria which distinguishes the two samples from one another. The purpose of this study is to determine if there is a statistical difference in the SAT-9 scores when comparing the means of the high-technology sample with the limited- technology sample. In addition, for the third grade sample, a pre-post test design examines the differences in reading and mathematics growth between students in the two test samples. The pre-test is the students’ score on the SAT-9 test during their second grade year, and the post-test is the SAT-9 scores from their third grade year. Two problems may complicate these results. First, the selection of students was not purely random. A cluster sampling of students from identified classrooms was accomplished by selecting students in the numerical order that they appeared on the class roll list. Secondly, even though the two samples (high and limited technology groups) were matched according to certain criteria described above, there still is the chance that the two samples may differ on certain important characteristics. To overcome these limitations, this researcher used the one-way analysis of covariance (ANCOVA) to assess the mean differences between the two samples. According to Wildt and Ahtola (1978) analysis of covariance provides a method to adjust for preexisting differences among the intact groups employed in the experiment, and that analysis of covariance may remove bias attributable to intact groups which are not similar, or matched, on certain test unit characteristics. This increases the precision of the experiment by reducing the error Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 40 variance. The strength of ANCOVA is that it controls for initial differences between groups with respect to one or more control variables (covariates). In this study, the variables (covariates) of gender and socioeconomic status can be analyzed by ANCOVA with respect to their effects on the difference between the means of the dependent and independent variable. Algebraically, the analysis of covariance model for the one-way layout with one covariate is represented as: YjpU+Xj+pCXy- x)+e0; i=l,...,k; To use the analysis of covariance technique in a valid manner, the following assumptions are made (Wildt and Ahtola, 1978): {1} The scores on the dependent variable are a linear combination of four independent components: an overall mean, a treatment effect, a linear covariate effect, and an error term. According to Wildt and Ahtola (1978) most authors conclude that “violation of this additivity assumption should not be a prime concern for researchers. In certain cases, severe departure from an additive model may be corrected by variable transformation.”(p. 90) {2} The error is normally and independently distributed with mean zero and variance c2 z. Wildt and Ahtola (1978) conclude that analysis of covariance is “robust with respect to violations of the assumptions of normality and homogeneity of variance, as is the analysis of variance model.”(p. 90) {3} The (weighted) sum over all groups of the treatment/group effect is zero. This is a restriction placed on the model rather than an assumption. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 41 {4} The coefficient of the covariate (slope of the regression line) is the same for each treatment/group. This is not a problem as explained by Pechman (1969) who argues that “...as the situation departs from homogeneous slopes, the analysis becomes more conservative with respect to making a type I error (p. 91).” He further states, “...that the robustness continues to hold in quazi-experimental settings in which the distributions of the covariate (or covariate means) differ by treatment group.”(p. 91) {5} The analysis of covariance procedure is based on the assumption that the covariate is fixed and measured without error. This researcher is very confident that this assumption has not been violated. In this study, all statistical data was analyzed by SPSS statistic software, version 9.0. In addition, all confidence levels are set at .05. Qualitative Research Design. Qualitative data regarding teacher attitudes toward the effectiveness of computer technology on student achievement was gathered through an anonymous questionnaire created by the researcher (see Appendix B). A Leikert scale rating teacher attitudes from one to five (strongly agree to strongly disagree) measured responses to questions on the role of computers on student achievement, student performance, the ability of students to leam basic skills, the ability of students to learn problem-solving skills, the ability of teachers to accommodate different student learning styles, and student attitudes toward school. Descriptive statistics are used to report the results of the qualitative portion of this study in Chapter 4. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 42 CHAPTER 4 Research Results Purpose of Study The data presented in Chapter 4 analyzes the following questions: (1) In the primary years, does extensive integration of computer technology into the curriculum result in increased student achievement in reading and mathematics? (2) Does a computer-integrated curriculum during the primary years have a greater positive effect on the achievement scores of boys or girls? (3) Does computer integration have a greater positive effect on the achievement scores among students of a lower/higher socioeconomic (SES) group (defined by a student’s eligibility for Free and Reduced Lunch status)? (4) Do teachers in technology-rich schools believe that computers have a positive effect on student achievement? The research data is analyzed visually, descriptively, and statistically for differences in student achievement in high versus limited-technology classrooms by comparing the results o f SAT-9 scores in the area o f mathematics and reading at the second and third grade levels. Second Grade SAT-9 Mathematics and Reading Results Second Grade Math Achievement The descriptive statistics for second grade raw math SAT-9 scores by high and limited-technology samples is presented in Table 2. The data show a difference in mean SAT-9 scores with the high-technology sample having a higher score than the limited- technology sample. The two distributions also show differences in sample variances. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 2. One-Way ANOVA and Descriptive Statistics Comparing Second Grade Raw Math SA T-9 Scores by Level of Technology Level of Technology N Mean S.D. Skewness/Kurtosis F-ratio P High Technology 29 53.93 10.299 -.233/-.799 4.088 •048. Limited Technology 29 48.93 8.443 .647/.248 a Significant at the .05 level 44 When visually and statistically analyzing the histograms for each sample (see Figure 23, Appendix C), noticeable differences can be seen. For example, both distributions have unique characteristics with the high-technology sample showing a negatively skewed distribution of scores while the limited-technology distribution is positively skewed. The boxplot of the two samples in Figure 1 visually shows these differences. By comparison, the high-technology sample shows a higher mean score than the limited-technology sample. When testing the null hypothesis that there is no difference between the two samples, a One-Way Analysis of Variance (ANOVA) test o f the sample means show that the high-technology sample has a significantly higher mean score than the limited- technology sample (F-ratio=4.088; p=.048; see Table 2 for the ANOVA results, and Table 20, Appendix D, for the ANOVA summary table). To avoid violating the assumption of addivity, additional independent variables (gender and SES) were added to the ANOVA model to determine what effect they may have on the difference between the two samples. First, the covariate, gender, was added. A One-Way Analysis of Covariance (ANCOVA) did not change the results (see Table 3 for the ANCOVA results, Table 21, Appendix D, for the ANCOVA summary table and Figure 24, Appendix C, for the gender histograms). There still existed a significant difference between the two samples (F-ratio=4.021; p=.05) supporting the alternative hypothesis that there was a significant difference between the samples when gender is considered (see the line graph in Figure 2, visually showing the difference between high and limited-technology samples by gender). Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. n^hT) W i. Figure 1. Boxplot Comparing Second Grade Raw Math SA T-9 Scores by Level of Technology Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Reproduced w ith permission o f th e copyright owner. Further reproduction prohibited without permission. Table 3. One-Way ANCOVA Comparing Second Grade Raw Math SA T-9 Scores by High Versus Limited Technology Samples With Gender as a Covariate Level of Technology/ Covariate N Mean S.D. F-ratio P High Technology 29 53.93 10.299 4.021 <9 t o © Limited Technology 29 48.93 8.443 Covariate: Gender 4.021 •05a a Significant at the .05 level - b . Q \ 47 5 6. 00. < 0 s 0 0 <0 54.004 £ 1 1 £ Sift,. s o (0 * 8.004 n ^ T o cti. F Level of t “ -Her < * * Figure 2. Line Graph Comparing Second Grade Raw Math SAT-9 Scores by Level of Technology and Gender Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 48 However, when adding an additional covariate, SES, to the ANCOVA model, the results change. With gender and SES controlled, there is no longer a significant difference (F-ratio=1.101; p=.299) between high and limited-technology samples in math SAT-9 scores (see Table 4, for the ANCOVA results, and Table 22, Appendix C, for the ANCOVA summary table), supporting the null hypothesis that there is no difference in student achievement when comparing high and limited-technology samples. To determine the effect of SES on raw math SAT-9 scores, the mean raw math scores were compared by Free and Reduced Lunch eligibility with gender and level of technology as covariates. The results show a significant difference (F-ratio= 5.045; p=.029) between the mean math scores with the non-Free and Reduced Lunch sample showing a significantly higher mean score (see Table 5, for the statistical data, Table 23, Appendix D, for the ANCOVA summary table, Figure 3 for the line graph comparing SES samples by level of technology and gender, and Figure 25, Appendix C, for the histograms of the SES samples). When controlling for both SES and level of technology, the results show that there is a significant difference in math SAT-9 scores by gender, with boys scoring significantly higher than girls in math (see Table 6 for the ANCOVA results, and Figure 3 and Figure 4 for the line graphs and boxplots visually showing this difference). When examining the line graph in Figure 3, there is an obvious visual difference in the distributions of high and limited-technology samples by gender within the SES groups. The high-technology sample has greater variation and a higher mean score. Among the lower SES sample, students (especially males) in the high-technology Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Reproduced w ith permission o f th e copyright owner. Further reproduction prohibited without permission. Table 4. One-Way ANCOVA Comparing Second Grade Raw Math SA T-9 Scores by High versus Limited Technology Samples With Gender and Free and Reduced Lunch (SES) as Covariates Level of Technology/ Covariate N Mean S.D. F-ratio P High Technology 29 53.93 10.299 1.101 .299 Limited Technology 29 48.93 8.443 Covariates: Gender 5.5 •023a Free and Reduced Lunch 5.045 •029a a Significant at the .05 Level • p * v O Reproduced w ith permission o f th e copyright owner. Further reproduction prohibited without permission. Table 5. One-Way ANCOVA Comparing Second Grade Raw Math SA T-9 Scores by Free and Reduced Lunch (SES) Samples With Teaching Method and Gender as Covariates Free and Reduced Lunch (SES)/ Covariates N Mean S.D. F-ratio P Power Yes (SES) 27 48.15 8.6 5.045 •029a .579 No (SES) 31 54.29 9.8 Covariates: Gender 5.499 •023a .634 Teaching Method 1.101 .299 .178 a Significant at the .05 Level K J \ o Students Not On Free And Reduced Lunch No 0 a 5 § * ? o o . ■ 8 8 •o § 4 .0 0 , I 4 4 * . Female a m . S 4 » , < 4 0 0 , Students On Free And Reduced Lunch Yes Male Female * 0 1 1 * T *® hnoiogy G e m * * Level ^ T e c h i i n ° * O B y G « iid * Figure 3. Line Graph Comparing Second Grade Raw Math SAT-9 Scores by SES, Level of Technology, and Gender Reproduced w ith permission o f th e copyright owner. Further reproduction prohibited without permission. Table 6. One-Way ANCOVA and Descriptive Statistics Comparing Second Grade Math SAT-9 Scores by Gender With SES and Level of Technology as Covariates Gender N Mean S.D. F-ratio P Male 29 53.93 9.38 5.499 •023, Female 29 48.93 9.45 Covariates: SES 5.045 •023a Level of Tech. 1.101 .299 a Significant at the .05 level N J 30 Lim ited Tech. N = 14 15 15 14 M Gender Figure 4. Boxplot Showing Second Grade Math SA T-9 Scores by Gender and Level of Technology Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 54 classrooms appear to be achieving at a higher level than those in limited-technology classrooms. In summary, the statistical results show that level of technology does not account for the statistical difference seen in second grade math scores. When controlling for level of technology and gender, SES does play a significant role in math student achievement. In addition, when level of technology and SES are controlled, there is a significant difference between the math achievement of boys and girls with boys achieving at a significantly higher level. Finally, the results show lower SES students, especially males, achieving at a higher level in high-technology classrooms than in limited-technology classrooms. Second Grade SAT-9 Reading Achievement Table 7 presents the descriptive statistics for second grade raw reading SAT-9 scores by level of technology. A One-Way ANOVA was used to test the null hypothesis that there is no difference between reading scores by level of technology. Even though there was a difference in mean reading scores when comparing high and limited- technology sample, with the high-technology sample showing the greatest mean score, the difference was not statistically significant (F-ratio=2.404; p=.127; see Table 7 for the descriptive statistics and ANOVA results; Table 24, Appendix D, for the ANOVA summary table; and Figure 5 for the boxplot comparing the two samples). Since SES was a significant factor in differences in math scores for the second grade, an ANCOVA model was constructed to determine if SES was a signficant factor in differences in raw reading scores. The mean reading scores from the two SES samples Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Reproduced w ith permission o f th e copyright owner. Further reproduction prohibited without permission. Table 7 . One-Way ANOVA Comparing Second Grade Raw Reading SA T-9 Scores by High versus Limited Technology Samples Level of Technology N Mean S.D. Skcwness/Kurtosis F-ratio P High Technology 29 80.38 ' 22.38 -.160/-1.406 2.404 .127 Limited Technology 29 72.14 . 17.85 .519/.508 Figure 5. Boxplot Comparing Second Grade Raw Reading SA T-9 Scores by High versus Limited Technology Samples Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 57 (Free and Reduced Lunch eligible and non-Free and Reduced Lunch eligible) were compared with gender and level of technology as covariates. Upon examining the histograms o f the two SES samples, differences were noted (see Figure 26, Appendix C, for the histograms). Both distributions are skewed, but in different directions. The Free and Reduced Lunch sample is positively skewed while the non-Free and Reduced Lunch sample is negatively skewed. It is apparent from this data that a greater number of higher SES students are doing better in reading achievement than are lower SES students. After controlling for gender and level of technology, there was a statistically significant difference between second grade reading scores comparing SES samples with higher SES students achieving at a significantly higher level (F-ratio=8.257; p=.006; see Table 8 for the descriptive statistics, Table 25, Appendix D, for the ANCOVA summary table, and Figure 6 for the line graph comparing SES samples by gender and level of technology). In summary, the data show that there is no difference in second grade raw SAT-9 reading scores by level of technology. When controlling for gender and level of technology, there was a significant difference in mean reading scores by SES samples with the higher SES sample showing a significantly higher mean score. In addition, the results show that technology does have an effect on second grade reading scores by gender within SES groups. Figure 6 visually shows that males in high-technology classrooms from both SES groups scored better in reading than in limited-technology classrooms. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Reproduced w ith permission o f th e copyright owner. Further reproduction prohibited without permission. Table 8. One-Way ANCOVA Comparing Second Grade Raw Reading SA T-9 Scores by Free and Reduced Lunch Eligibility (SES) with Gender and Level of Technology as Covariates Free and Reduced Lunch (SES)/ Covariates N Mean S.D. Skewness/Kurtosis F-ratio P Yes (SES) 27 67.67 17.88 .481/-.847 8.257 •006a No (SES) 31 83.74 19.91 -.097/-1.378 Covariates: Gender Level of Technology .910 .147 .344 .703 a Signficant at the .05 level o o Reproduced w ith permission o f th e copyright owner. Further reproduction prohibited without permission. ft I L 9 C 1 # 7 0 J » 1 (9 L jje o * o ) Students Not Free and Reduced Lunch Fem ale Students Free and Reduced Lunch Yes a o o o * j o o o * » < # • Male Female G e n d « r V V s Gen<J®r Figure 6. Line Graph Comparing Second Grade Raw Reading SAT-9 Scores by Free and Reduced Lunch Eligible (SES), Gender, and Level of Technology 60 Third Grade SAT-9 Mathematics and Reading Results Third Grade Math Achievement The descriptive statistics for third grade raw math SAT-9 scores by high and limited-technology samples are presented in Table 9. The data show very little difference between the two samples. The results of One-Way ANOVA show no significant statistical difference in third grade raw SAT-9 math scores by level of technology (F- ratio=.145; p=.704; see Table 9 for the ANOVA results, Table 26, Appendix D, for the ANOVA summary table, Figure 7 for the boxplot comparing the two technology samples, and Figure 27, Appendix C, for the histograms). Similar results were recorded when analyzing third grade math scores by gender and SES (see Table 10, and Figure 8, for the statistical data by gender; Table 11, and Figure 9 for SES). When comparing math scores by gender, there was no statistical difference (F-ratio=.018; p=.894; see Table 27, Appendix D, for the ANOVA summary table). Futhermore, when comparing math scores by SES, there was no statistical difference (F-ratio=3.008; p=.087; see Table 28, Appendix D, for the ANOVA summary table). See Figures 28 and 29, Appendix C, for the histograms of gender and SES distributions. In summary, the data show no statistical difference in third grade math SAT-9 scores by level of technology, gender, or SES. Although there was no statistical difference between math scores by level of technology within each SES group, the results do show that lower SES males are achieving at a higher level in high-technology classrooms than in limited-technology classrooms. In contrast, high SES males are achieving at a higher level in limited-technology classrooms (see Figure 10 for the line Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Reproduced w ith permission o f th e copyright owner. Further reproduction prohibited without permission. Table 9. Descriptive Statistics and One-Way ANOVA Comparing Third Grade Raw Math SA T-9 Scores by High versus Limited Technology Samples Level of Technology N Mean S.D. Skewness/Kurtosis F-ratio P High Technology 45 54.02 10.44 -.352/-.311 .145 .704 Limited Technology 39 54.90 10.59 -.327/-.969 R a w Mathe Scores 1999 62 80- 70- 60- 50- 40- 30- 20_______________ N = 45 39 H igh T echnology Limited T echnology Level of Technology Figure 7. Boxplot Comparing Third Grade Raw Math SA T-9 Scores by High versus Limited Technology Samples M M M M N msssGBiBSSSSm siSb iS s s i S i &Si Si SiS i S ^ Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Reproduced w ith permission o f th e copyright owner. Further reproduction prohibited without permission. Table 10. Descriptive Statistics and One-Way ANOVA Comparing Third Grade Raw Math SAT-9 Scores by Gender Gender N Mean S.D. Skewness/Ku rtosis F-ratio P Female 43 54.28 10.37 -.174/-.647 .018 .894 Male 41 54.58 10.66 -.499/-.572 O n U > Figure 8. Boxplot Comparing Third Grade Raw Reading SAT-9 Scores by Gender Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Reproduced w ith permission o f th e copyright owner. Further reproduction prohibited without permission. Table 11. Descriptive Statistics and One-Way ANOVA Comparing Third Grade Raw Math SAT-9 Scores by Free and Reduced Eligible Status Free & Reduced Lunch N Mean S.D. Skewness/Kurtosis F-ratio P Yes 34 52.06 10.77 -.3931-373 3.008 .087 No 50 56.04 10.02 -.217/-.953 ON U i 66 Figure 9. Boxplot Comparing Third Grade Raw Math SAT-9 Scores by Free and Reduced Status Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. achieving at a higher level in limited-technology classrooms (see Figure 10 for the line graph that visually shows these differences). Third Grade Reading Results The descriptive and visual statistics for third grade raw reading SAT-9 scores are presented in Table 12, and Figure 11. When testing the null hypothesis that there is no difference between third grade reading scores comparing high versus limited-technology samples, the data show no statistical difference (F-ratio=.341; p=.561; see Table 29, Appendix D, for the ANOVA summary table, and Figure 30, Appendix C, for the histograms). Using ANOVA to analyze the difference in third grade reading scores by gender, no statistical difference was noted (F-ratio=.850; p=.359). Table 13 presents the descriptive statistics and ANOVA results, while Figure 12 displays boxplots showing each gender distribution (see Table 30, Appendix D, for the ANOVA summary table, and Figure 31, Appendix C, for gender histograms). The data did show a noticeable difference in mean third grade reading scores when comparing SES groups, with the non-Free and Reduced Lunch sample having the higher mean score (see Table 14 for the descriptive statistics and Figure 13 for the boxplots comparing SES groups). A One-Way ANOVA was used to statistically test the null hypothesis that there was no difference between the SES samples. The results show a significant difference between samples (F-ratio=4.205; p=.043; see Table 31, Appendix D, for the ANOVA summary table, and Figure 32, Appendix C, for the SES histograms). Even with gender and level of technology controlled in a ANCOVA model, there was still a significant difference between SES sample means (see Table 15 for the descriptive permission of the copyright owner. Further reproduction prohibited without permission. Reproduced w ith permission o f th e copyright owner. Further reproduction prohibited without permission. Students Not On Free And Reduced Lunch a® o u ( 0 Ssm (8 s H No Female Male so® swot 5 M o t 5 4 ® j S2.0M s o o o ^ Students On Free And Reduced Lunch Yes Male Female Uwe,0,T*chnology Gender U v e ,o fT e c h r W te w F it* Gender Figure 10. Line Graph Comparing Third Grade Raw Math SA T-9 Scores by Level of Technology, SES, and Gender O s 00 Table 12. Descriptive Statistics and One-Way ANOVA Comparing Third Grade Raw Reading SAT-9 Scores by Level of Technology Samples Level of Technology N Mean S.D. F-ratio P Power High Technology 45 61.93 12.44 .341 .561 .089 Limited Technology 39 63.46 11.38 Figure 11. Boxplot Comparing Third Grade Raw Reading SAT-9 Scores by Level of Technology Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Reproduced w ith permission o f th e copyright owner. Further reproduction prohibited without permission. Table 13. Descriptive Statistics and One-Way ANOVA Comparing Third Grade Raw Reading SA T-9 Scores by Gender Gender N Mean S.D. Skewness/Kurtosis F-ratio P Female 43 63.81 11.36 -.623/-.248 .850 .359 Male 41 61.41 12.49 -.466/-.674 R aw Reading Scores 1999 72 Figure 12. Boxplot Comparing Third Grade Raw Reading SAT-9 Scores by Gender Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Reproduced w ith permission o f th e copyright owner. Further reproduction prohibited without permission. Table 14. Descriptive Statistics and One-Way ANOVA Comparing Third Grade Raw Reading SA T-9 Scores by Free and Reduced Lunch Eligible Status Free and Reduced Lunch N Mean S.D. Skewness/Kurtosis F-ratio P Yes 34 59.47 11.25 -.009/-.480 4.205 •043a No 50 64.80 11.98 .388/.448 a Significant at the .05 level U > pree aNRi etJuced r ^nch Figure 13. Boxplots Comparing Third Grade SAT-9 Reading Scores by Free and Reduced Luch Eligible Status. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Reproduced w ith permission o f th e copyright owner. Further reproduction prohibited without permission. Table 15. Descriptive Statistics and One-Way ANCOVA Comparing Third Grade Raw Reading SAT-9 Scores by Free and Reduced Lunch Eligible Status with Gender and Level of Technology as Covariates Free & Reduced Status/Covariates N Mean S.D. F-ratio P Free and Reduced Lunch Yes 34 59.47 11.25 3.978 •05a No Covariates: Gender Level of Technology 50 64.80 11.98 1.057 .171 .307 .681 a Significant at the .05 level 'j L /i 76 statistics and ANCOVA results; and Table 32, Appendix D, for the ANCOVA summary table). In summary, there was no significant difference when analyzing third grade reading SAT-9 scores by level of technology or gender. However, there is a significant difference when comparing SES groups when gender and level of technology are controlled. Consistent with the findings in the above areas, lower SES third grade males are achieving at a higher level in reading in high-technology classrooms than are lower SES students in limited-technology classrooms (see Figure 14 for the line graph that visually shows this difference). Comparing 1998 and 1999 SAT-9 Scores Growth in Scaled Math Scores The 1998 and 1999 scaled math SAT-9 scores were compared to examine if there was significant growth from the second to third grade. A student’s second grade scaled math scores were compared with their third grade scaled math scores (see Table 16 for the descriptive statistics). The results show significant growth in scaled math scores from second to third grade. When controlling for level of technology, gender, and SES, there was still a significant difference between 1998 and 1999 scaled math scores (F- ratio=2.74; p=.001; see Table 33, Appendix D, for the ANCOVA summary table). Figure 15 visually shows the boxplots of scaled math growth from 1998 to 1999 by level of technology. At both technology levels there is significant math growth. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Reproduced w ith permission o f th e copyright owner. Further reproduction prohibited without permission. Students Not On Free And Reduced Lunch U v , No Female Male ° » T i % l o o , ! Students On Free And Reduced Lunch Yes Female •"h ^ B y Figure 14. Line Graph Comparing Third Grade Raw Reading SAT-9 Scores by Level of Technology, SES, and Gender VI Reproduced w ith permission o f th e copyright owner. Further reproduction prohibited without permission. Table 16. Descriptive Statistics and One-Way ANCOVA Comparing Third Grade 1998 and 1999 Scaled Math SAT-9 Scores With Level of Technology, Gender, and Free and Reduced Lunch Eligibility as Covariates Independent Variables N Mean S.D F-ratio P 1999 Scaled Math Scores 84 614.80 33.53 2.74 .001, 1998 Scaled Math Scores 84 580.51 35.12 Covariates: Level of Technology .610 .439 Gender 2.514 .120 Free and Reduced Status 5.736 •02 lb a Significant at the .001 level b Significant at the .05 level 00 Reproduced w ith permission o f th e copyright owner. Further reproduction prohibited without permission. 800 r fi o o C O O ) I - 1 - < C O _ c ra 2 T3 0 ) (0 O C O 700 600 500 400 061 0 62 049 N = 45 45 High Technology 017 39 39 Limited Technology SCMATH98 SCMATH99 Technology Figure 15. Boxplot of Scaled Math SAT-9 Scores Comparing Students' 1998 with 1999 Score by High versus Limited Technology Sample. v O 8 0 Growth in Scaled Reading Scores The 1998 and 1999 scaled reading SAT-9 scores were compared to examine if there was growth from the second to third grade. The student’s second grade scaled reading scores were compared with their third grade reading scores (see Table 17 for the descriptive statistics and ANCOVA results). The results show significant growth in scaled reading scores from second to third grade. When controlling for gender, level of technology, and SES, the growth in scores was still significant (F-ratio=2.578; p=.003; see Table 34, Appendix D, for the ANCOVA summary table). Figure 16 visually shows the growth of scaled reading scores from 1998 to 1999 by level of technology. As can be seen, there was very noticeable growth in reading scores from the second to third grade, much like the results for math. In summary, when comparing the growth of scaled math and reading SAT-9 scores from the second through third grade, there was significant growth in each curricular area even with gender, level of technology, and SES controlled. The results suggest that the growth from second to third grade is not associated with level of technology, gender, or SES, and is probably associated with other factors related to the growing sense of awareness about the importance of test preparation and increased demand for higher test results as measured by the SAT-9 test. In addition, the significant increase in SAT-9 scores from 1998 to 1999 in both curricular areas might be associated with the normal academic/cognitive growth associated with neurological maturation during the primary years. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Reproduced w ith permission o f th e copyright owner. Further reproduction prohibited without permission. Table 17 . Descriptive Statistics and One-Way ANCOVA Comparing Third Grade 1998 and 1999 Scaled Reading SA T-9 Scores with Level of Technology, Gender, and Free and Reduced Lunch Eligibility as Covariates Variables N Mean S.D. F-ratio P 1999 Scaled Reading Scores 84 635.16 34.87 2.578 •003a 1998 Scaled Reading Scores 84 594.52 37.53 Covariates: Level of Technology .010 .921 Gender 1.89 .180 Free and Reduced Status 1.65 .209 a Significant at the .01 level 00 io n o f th e copyright owner. Further reproduction prohibited without permission. 800 < /) 0 L . 8 700- (0 O ) ■ * < C O u> 600- c T > (O 0 O H 0 500- 0 O C O 400 065 1998 1999 N = 45 45 High Technology 39 39 Limited Technology Level of Technology Figure 16. Boxplot of Scaled Reading SAT-9 Scores Comparing Students' 1998 with 1999 Score by Level of Technology 83 Qualitative Results of Teacher Survey Regarding Computer Technology and Student Achievement Table 18 presents the results of the teacher questionnaire of attitudes regarding computer technology and student achievement. Only 38% of the teachers responded positively to Question #1 that student achievement is increased when technology is used; 44% responded as “neutral” while 18% disagreed that student achievement is increased when technology is used in the classroom (see Figure 17). On the other hand, when the question shifts from one of student achievement to student performance, 56% of the teachers responded that computer technology increases student performance while the neutral responses dropped to 34% and those disagreeing were relatively constant at 16% (see Figure 18) . Teachers responded very positively to Question #3, with 74% answering that they either agreed or strongly agreed to the statement that computers help students learn basic skills. Only 17% were neutral and 9% disagreed with the effectiveness of computers on basic skill attainment (see Figure 19). Responses to Question #4 regarding the effectiveness of computers in helping students learn problem-solving skills was also positive with 62% of teachers either agreeing or strongly agreeing that computers help with problem-solving skills, 25% reporting a neutral response, and 13% in disagreement (see Figure 20). Teachers likewise responded veiy positively to Question #5 that computers increase a teacher’s ability to accommodate different learning styles with 66% responding either very strongly agree or agree; 34% were neutral and 6% disagreed with the role of Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 84 Table 18. Results o f Teacher Survey on Attitudes Regarding Computer Technology and Student Achievement Category Questions Strongly Agree Agree Neutral Disagree Strongly Disagree Question #1 Student achievement is increased when I use technology.... Number Percent 0 13 15 6 0 0 38 44 18 0 Question #2 I think that using computer technology for instruction improves my student’s performance. Number Percent 1 17 11 5 0 3 53 34 16 0 Question #3 Computer technology helps students learn basic skills. Number Percent 5 21 6 3 0 14 60 17 9 0 Question #4 Computer technology helps students learn problem-solving skills. Number Percent 2 18 8 4 0 6 56 25 13 0 Question #5 Having computer technology in my classroom has increased my ability to accommodate different learning styles. Number Percent 7 14 11 2 1 22 44 34 6 3 Question #6 Computer technology makes students like school more. Number Percent 6 25 4 1 1 17 67 11 2 2 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 85 H Agree D I O Disagree ^ Neutral g Strongly Agree | Strongly Disagree Figure 17. Results o f Teacher Response to Question 1 (percentage): " Student Achievement is Increased When I Use Technology in My Teaching." Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 86 Strongly Agree p e r c e n t H Agree B U I D isagree ^ Neutral S Strongly Agree B Strongly Disagree Figure 18. Results o f Teacher Response to Question 2 (percentage): "I Think That Using Computer Technology for Instruction Improves My Students' Performance" Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 87 p e r c e n t Ml Agree Bill D isagree Om Neutral gO Strongly Agree B Strongly D isagree Figure 19. Results o f Teacher Response to Question 3: "Computer Technology Helps Students Learn Basic Skills." Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 8 8 Strongly Agree 6, p e r c e n t ■ Agree Q[| D isagree ^ Neutral £ | Strongly Agree ■ Strongly Disagree Figure 20. Results o f Teacher Response to Question 4: "Computer Technology Helps Students Learn Problem-Solving Skills." Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 89 computers in increasing teachers’ ability to accommodate different learning styles (see Figure 21). Question #6, that computer technology “makes students like school more” had the highest percentage of positive responses, with 84% of the teachers either strongly agreeing or agreeing. Only 11% of teachers responded neutrally to this question and only 4% disagreed that computers “make students like school more” (see Figure 22). In summary, Question #1 which states that “student achievement is increased when I use technology in my teaching” had the least positive responses and the most negative responses. The term student achievement is often directly related to student achievement scores as measured by norm referenced tests like the SAT-9. When teachers are asked whether they agree that computers increase student achievement, they may well be interpreting this as student achievement scores. When asked about other indicators of achievement such as student performance, basic skill attainment, and problem-solving skills, teachers responded very positively. When asked about the effect of computers on their ability to accommodate different student learning styles, teachers again responded very positively. Furthermore, when asked whether or not computers “make students like school more,” the response was overwhelmingly positive. Overall, the teacher responses from this questionnaire indicate a high satisfaction with computer technology in their teaching as well as a general feeling that computer technology does help students perform in the classroom, even if they are unsure whether or not this directly translates to student achievement as measured by test scores. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 90 Strongly Disagree p e r c e n t H A gree Q Q I D isagree E 8 Neutral S Strongly A gree H Strongly D isagree ; Disagree 5.50% Figure 21. Results o f Teacher Response to Question 5: "Having Computer Technology in my Classroom Has Increased my Ability to Accommodate Different Learning Styles." Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 91 Strongly Dlsagi p e r c e n t B Agree HQ Disagree ^ Neutral § | Strongly A gree | Strongly D isagree Disagree 2.02% Figure 22. Results o f Teacher Response to Question 6: "Computer Technology Makes Students Like School More." Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 92 CHAPTER 5 Summary and Discussion Summary The analysis of the data presented in Chapter 4 supports the null hypothesis that there is no difference in student achievement between student scores in high-technology and limited-technology classrooms. Although mean differences favoring the high- technology classrooms were consistent throughout the study, no statistically significant differences were found in mean student achievement in mathematics and reading between the high-technology and limited-technology samples. A One-Way Analysis of Variance (ANOVA) was the basic statistical tool used to analyze the mean differences between samples. To acquire additional information about differences between samples, a One- Way Analysis of Covariance (ANCOVA) was used to determine the effects of relevant variables on observed mean differences. Analysis o f Covariance has proven to be a useful tool in this study by its ability to determine the effects of single or multiple independent variables (covariates) on observed mean differences. The following are the results of the quantitative portion of the study: O Second Grade Mathematics Results. The statistical results show that level of technology does not account for the statistical difference seen in second grade math scores. When controlling for level of technology and gender, SES does play a significant role in math student achievement. In addition, when level of technology and SES are controlled, there is a significant difference between the math achievement of boys and girls, with boys achieving at a significantly higher level. Although there is no significant Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 93 difference in math student achievement by level of technology, the results show lower SES male students achieving at a higher mean level in high-technology classrooms than in limited-technology classrooms. 21 Second Grade Reading Results. The data show that there is no difference in second grade raw SAT-9 reading scores by level of technology. When controlling for gender and level of technology, there was a significant difference in mean reading scores by SES samples, with the higher SES sample showing a significantly higher mean score. The results also show that technology does not have a significant effect on second grade reading scores in either SES group. However, males in the lower SES group had noticeably higher mean reading scores in the high-technology sample than in the limited- technology group. 31 Third Grade Mathematics Results. The data show no statistical difference in third grade math SAT-9 scores by level of technology, gender, or SES. Although there was no statistical difference between math scores by level of technology within each SES group, the results do show that lower SES students are achieving at a higher level in high- technology classrooms than in limited-technology classrooms. Conversely, third grade high SES students in math seem to achieve at a lower mean level in high-technology classrooms than in limited-technology classrooms. 41 Third Grade Reading Results. There was no significant difference when analyzing third grade reading SAT-9 scores by level of technology or gender. However, there was a significant difference when comparing SES groups when gender and level of technology are controlled. The higher SES sample scored significantly higher in mean Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 94 reading scores than the lower SES group. Although not significantly different, lower SES third grade male students are achieving at a higher level in reading in high-technology classrooms than in limited-technology classrooms. The opposite trend is observed for the higher SES group. Males scored lower in the high-technology sample than in limited- technology classrooms. 5) SAT-9 Test Score Growth from 1998-1999. When comparing the growth of scaled math and reading SAT-9 scores from the second through third grade, there was significant growth in each curricular area even with gender, level of technology, and SES controlled. The results suggest that the growth from second to third grade is not associated with level of technology, gender, or SES, and is probably associated with other factors related to the growing sense of awareness about the importance of test preparation and increased demand for higher test results as measured by the SAT-9 test. The pressure for students to succeed academically increases for both parents and teachers by the end of the third grade when testing occurs. In addition, the significant increase in SAT-9 scores from 1998-1999 in both curricular areas might be associated with the normal academic/cognitive growth associated with neurological maturation during the primary years. Discussion Despite the lack of statistically significant differences between high and limited- technology samples, the data consistently reveals mean differences in SAT-9 scores with lower SES students having a higher mean score in high-technology classrooms than in limited-technology classrooms in both grade levels and in both mathematics and reading. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 95 This was especially noticeable among lower SES males. This is consistent with the recent findings of Wenglinsky (1998) and Mann (1999). Wenglinsky’s analysis of more than 13,000 math test scores from the 1996 National Assessment of Educational Progress revealed that technology can have positive benefits for student achievement, depending on how technology is used. Mann’s study of 950 students’ SAT-9 scores revealed increased test scores among students participating in West Virginia’s Basic Skills/Computer Education (BS/CE) program. These studies indicate that computer technology, under the right circumstances, can have a positive effect on student achievement. Further studies of student achievement and computer technology are needed at this time. Similar to the limitations of the other research on the relationship between computer technology and student achievement, this study has limited the analysis to one dependent variable, SAT-9 scores. Multiple measures of achievement would have augmented the data reported here by expanding the research scope beyond a single normed-reference test measure, exploring the relationship of computer technology to other forms of student achievement. Noting this limitation, this researcher provided qualitative data that supports the positive relationship between computer technology and student achievement. This study found significant gender differences among second grade math students in high versus limited-technology classrooms. Boys performed significantly higher than girls in both high and limited-technology classrooms. Also, this study does report notable differences in the effects of computer technology on the achievement of Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 96 low SES students. In both mathematics and reading tests, low SES males in high- technology classrooms scored higher than low SES students in limited-technology classrooms. Among higher SES students in the third grade, the opposite was noted. High-technology classrooms scored lower in math and reading than limited-technology classrooms. This was expecially noticeable among male students. High levels of integration of computer technology into the curriculum appears to have a positive effect on student achievement among lower SES students. These findings could have significant implications for school districts seeking to improve student achievement, particularly among schools with large concentrations of lower SES students. Further study is indicated to examine the role of computer technology in closing the achievement gap between the rich and poor in schools. The qualitative data presented through a survey of teachers in the Redding School District report positive teacher attitudes toward computer technology and student achievement. When teachers were asked whether or not they believed that classroom computer use resulted in increased student achievement, mixed results were reported. Teachers are reluctant to claim a direct relationship between computers and student achievement. As discussed in Chapter 4, this may well be due to the association of the term “student achievement” with student achievement scores; however, they report very positive responses to other measures of student achievement that may not be linked to student achievement scores such as “student performance,” “basic skill attainment,” and “problem-solving skills.” Teachers also reported the very positive effect of computers on their ability to accommodate different student learning styles. Furthermore, when asked Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 97 whether or not computers “make students like school more,” the response was overwhelmingly positive. Although teachers are reluctant to conclude that computers increase student achievement, they report very positive outcomes for students as the result of having computers in the classroom. It is critical that teachers using new technology have confidence in the ability of computers to have a positive effect on student achievement. Both quantitative and qualitative data from this study suggest that computer technology does have a positive effect on student achievement. Despite a lack of statistically significant differences between scores of students in high and limited- technology classrooms, there were mean differences throughout the study that favored high-technology classrooms. Furthermore, this study revealed that lower SES students achieve at a higher level in high-technology classrooms. In addition, teachers in this technology-rich school district report that they believe that computers have a positive effect on student achievement and student satisfaction with school. In response to critics of spending on technology in schools, this study provides both quantitative and qualitative data to support the continued resource allocation for computers in classrooms. As access to technology widens the gap between the haves and have-nots in America, it is imperative that all students be provided with access to technology and that all teachers be provided with the technical and instructional support to provide a technology-rich curriculum to their students. This study shows that poor students in high-technology classrooms achieve at a higher level than poor students in limited-technology classrooms. Further investigation is needed to determine how Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 98 technology may raise student achievement in schools with large concentrations of low- SES students. This issue has significant implications for compensatory education funding. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. References Cited 99 Allen, D. (1996). Teaching with technology: Problem-solving strategies. Teaching Pre-K. 27(31 14-16. 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Dissertation Abstracts International. 59. 08A. Wildt, A.R. & Ahtola, O.T. (1998). Analysis of Covariance. Newbury Park, London: Sage Publications. Winther, A. A., & Volk, T.L. (1994). Comparing achievement of inner-city high school students in traditional versus STS-Based chemistry courses. Journal of Chemical Education. 71(6), 501-505. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. APPENDIX A Table 19a: Data Collection Sheet Second Grade Table 19b: Data Collection Sheet Third Grade Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. T a b le 19a. D a ta Collection Sheet for Second Grade 109 Free and Reduced No Yes S A T 9 Score Read M ath - "O o ■ C * - > O £ Lim ited-Tech u> _c IE o a o > I— HighTech Sex U- s Student School CM CO I D CO h - 0 0 o > o T — T — T ” C V J CO « D CD h - CO 0 3 O CM CM CM CM CO CM M - CM Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. T able 19b. D a ta Collection Sheet for T h ird Grade 1 1 0 Free & Reduced N o | Yes Read " 9 8 "9 9 M ath R a w S c a le d N C E " 9 8 "99 S A T 9 Score Read " 9 8 "99 M ath " 9 8 "99 Reading "99 M ath "99 Teaching Method Non-Tech Tech Sex u . 5 Student School T “ C\l CO L O CO h - c o 0 5 o T ~ CM CO M - lO T— CO X - h- o o 0 5 o CM CM CM CM CO C M C M Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. I l l APPENDIX B Questionnaire of Teacher Attitudes Regarding Computer Technology and Student Achievement Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 112 Questionnaire of Teacher Attitudes Regarding Computer Technology and Student Achievement Place the numbered answer o f your choice next to each question. Use the scale below to answer the questions on this page. Please feel free to write comments on the bottom of this page or on the back. 1 - strongly agree 2 - agree 3 - neutral 4 - disagree 5 - strongly disagree Student achievement is increased when I use technology in my teaching. I think that using computer technology for instruction improves my students’ performance. Computer technology helps students learn basic skills. Computer technology helps students learn problem-solving skills. Having computer technology in my classroom has increased my my ability to accommodate different learning styles. Computer technology makes students like school more. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 113 APPENDIX C Histograms of Samples Used In this Study Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Frequency 114 Figure 23. Histogram o f Math SAT-9 Scores fo r Second Grade Students by High versus Limited Technology Samples Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Frequency 115 Gender Figure 24. Histogram of Math SAT-9 Scores for Second Grade Students by Gender Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Frequency 116 6- 4- 2 “ 0- I % Figure 25. Histogram o f Math SA T-9 Scores for Second Grade Students by Free and Reduced Lunch Status Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Fraquancy 117 Not Free and Reduced Lunch Free and Reduce Lunch Figure 26. Histogram o f Reading SA T-9 Scores for Second Grade Students by and Reduced Lunch Status Free Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Frequency 118 T e c h i Figure 27. Histogram Comparing Third Grade Raw Math SAT-9 Scores by Level of Technology Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 120 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Eligible Status Reproduced with permission o f th e copyright owner. Further reproduction prohibited without permission. Technology = High Technology Technology = Lotech Figure 30. Histograms of Third Grade Raw Reading SA T-9 Scores by Level of Technology Samples to 122 A oum nbm jj Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Figure 31. Histograms o f T h ird G rade R a w Reading S A T - 9 Scores b y Gender Reproduced with permission o f th e copyright owner. Further reproduction prohibited without permission. Figure 32. Histograms of Third Grade Raw Reading SAT-9 Scores by Free and Reduced Lunch Eligible Status to 124 APPENDIX D ANOVA and ANCOVA Summary Tables Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Reproduced w ith permission o f th e copyright owner. Further reproduction prohibited without permission. Table 20. One- Way ANOVA Summary Table Comparing Second Grade Raw Math SA T-9 Scores by High versus Limited Technology Samples Math Sum of Squares df Mean Square F-ratio P Between Groups 362.50 1 362.50 4.088 •048a Within Groups 4965.724 56 88.67 Total 5328.224 57 a Significant at the .05 Level 125 Table 21. One-Way ANCOVA Summary Table Comparing Second Grade Raw Math SA T-9 Scores by High versus Limited Technology Samples With Gender as a Covariate Math Type III Sum of Squares df Mean Square F-ratio P Corrected Model 700.833b 2 350.417 4.165 .021 Intercept 69628.394 1 69628.394 827.586 .000 Gender 338.333 1 338.333 4.021 •05a Teaching Method 338.333 1 338.333 4.021 •05a Error 4627.391 55 84.134 Total 158747.000 58 Corrected Total 5328.224 57 a Significant at the .05 level b R Squared = .132 (adjusted R Squared= .100) Reproduced with permission o f th e copyright owner. Further reproduction prohibited without permission. Table 22. One-Way ANCOVA Summary Table Comparing Second Grade Raw Math SAT-9 Scores by High versus Limited Technology Samples With Gender and Free and Reduced Lunch as Covariates Math Type III Sum of Squares df Mean Square F-ratio P Corrected Model 1096.227b 3 365.409 4.663 .006 Intercept 54733.729 1 54733.729 698.399 .000 Gender 430.941 1 430.941 5.499 •023a SES 395.394 1 395.394 5.045 •029a Teaching Method 86.306 1 86.306 1.101 .299 Error 4231.997 54 78.370 Total 158747.00 58 Corrected Total 5328.224 57 a Significant at the .05 level b R Squared= .206 (adjusted R Squared= .162) K > -0 Reproduced with permission o f th e copyright owner. Further reproduction prohibited without permission. Table 23. One-Way ANCOVA Comparing Second Grade Raw Math SAT-9 Scores by Free and Reduced Lunch (SES) Samples With Gender and Teaching Method as Covariates Math Type III Sum of Squares df Mean Square F-ratio P Corrected Model 1096.227b 3 365.409 4.663 .006 Intercept 43290.408 1 43290.408 552.383 .000 Gender 430.941 1 430.941 5.490 •023a Teaching Method 86.306 1 86.306 1.101 .299 SES 395.394 1 395.394 5.045 •029a Error 4231.997 54 78.370 Total 158747.000 58 Corrected Total 5328.224 57 a Significant at the .05 level b R Squared= .206 (adjusted R Squared= .162) to oo Table 24. One-Way ANOVA Summary Table Comparing Second Grade Raw Reading SAT-9 Scores by High versus Limited Technology Samples Reading Sum of Squares df Mean Square F-ratio P Between Groups 984.845 1 984.845 2.404 .127 Within Groups 22942.276 56 409.683 Total 23927.121 57 Reproduced with permission o f th e copyright owner. Further reproduction prohibited without permission. Table 25. One-Way ANCOVA Summary Table Comparing Second Grade Raw Reading SA T-9 Scores by Free and Reduced Lunch (SES) With Gender and Teaching Method as Covariates Reading Type III Sum of Squares df Mean Square F-ratio P Corrected Model 4140.456s 3 1380.152 3.767 .016 Intercept 101604.640 1 101604.640 277.290 .000 Male 333.365 1 333.365 .910 .344 Teaching Method 53.744 1 53.744 .147 .703 SES 3025.680 1 3025.680 8.257 .006 Error 19786.664 54 366.420 Total 361219.000 58 Corrected Total 23927.121 57 a R Squared= .173 (adjusted R Squared= .127) u > o Reproduced with permission o f th e copyright owner. Further reproduction prohibited without permission. Table 26. One-Way ANOVA Summary Table Comparing Third Grade Raw Math SAT-9 Scores by High versus Limited Technology Samples Level of Technology Type III Sum of Squares df Mean Square F-ratio P Total Corrected Model 16.004a 1 16.004 .145 .704 Intercept 247862.242 1 247862.242 2245.683 .000 Technology 16.004 1 16.004 .145 .704 Error 9050.568 82 110.373 Total 257914.000 84 Corrected 9066.571 83 aR squared=.002 (adjusted R Square=-.010) U > Table 27. One-Way ANOVA Summary Table Comparing Third Grade Raw Math SAT-9 Scores by Gender Gender Type III Sum of Squares df Mean Square F-ratio P Corrected Model 1.969, 1 1.969 .018 .894 Intercept 248739.683 1 248739.683 2250.143 .000 Gender 1.969 1 1.969 .018 .894 Error 9064.602 82 110.544 Total 247914.000 84 Corrected Total 9066.571 83 a R Squared=.000 (adjusted R Square=-.012) Reproduced with permission o f th e copyright owner. Further reproduction prohibited without permission. Table 28. One-Way ANOVA Summary Table Comparing Third Grade Math SAT-9 Scores by Free and Reduced Lunch Status SES Type III Sum of Squares df Mean Square F-ratio P Corrected Model 320.769a 1 320.769 3.008 .087 Intercept 236489.341 1 236489.341 2217.307 .000 SES 320.769 1 320.769 3.008 .087 Error 8745.802 82 106.656 Total 257914.000 84 Corrected Total 9066.571 83 aR SquarecK035 (adjusted R Square=.024) U > u > Reproduced with permission o f th e copyright owner. Further reproduction prohibited without permission. Table 29. One- Way ANOVA Summary Table Comparing Third Grade Reading SA T-9 Scores by Level of Technology Level of Technology Type III Sum of Squares df Mean Square F-ratio P Corrected Model 48.793a 1 48.793 .341 .561 Intercept 328516.651 1 328516.651 2295.657 .000 Technology 48.793 1 48.793 .341 .561 Error 11734.492 82 143.104 Total 341410.000 84 Corrected Total 11783.286 83 aR Squared=004 (adjusted R Square=-.008) u > Reproduced with permission o f th e copyright owner. Further reproduction prohibited without permission Table 30. One-Way ANOVA Summary Table Comparing Third Grade Reading SAT-9 Scores by Gender Gender Type III Sum of Squares df Mean Square F-ratio P Corrected Model 120.823a 1 120.823 .850 .359 Intercept 329139.490 1 329139.490 2314.214 .000 Gender 120.823 1 120.823 .850 .359 Error 11662.463 82 142.225 Total 341410.000 84 Corrected Total 11783.286 83 aR Squared=. 010 (adjusted R Square=-.002) U > K J\ Table 31. One-Way ANOVA Summary Table Comparing Third Grade Reading SA T-9 Scores by SES Eligible Group SES Eligible Type III Sum of Squares df Mean Square F-ratio P Corrected Model 574.815a 1 574.815 4.205 .043 Intercept 312540.529 1 312540.529 2286.514 .000 SES 574.815 1 574.815 4.205 .043 Error 11208.471 82 136.689 Total 341410.000 84 Corrected Total 11783.286 83 aR Squared=.049 (adjusted R Square=.037) Reproduced with permission o f th e copyright owner. Further reproduction prohibited without permission. Table 32. One-Way ANCOVA Summary Table Comparing Third Grade Reading SAT-9 Scores by SES Eligible Samples With Gender and Technology as Covariates SES Eligible/ Covariates: Gender & Technology Type III Sum of Squares df Mean Square F-ratio P Corrected Model 735.006a 3 245.002 1.774 .159 Intercept 100340.317 1 100340.317 726.559 .000 Technology 23.552 1 23.552 .171 .681 Gender 145.963 1 145.963 1.057 .307 SES 549.351 1 549.351 3.978 .05 Error 11048.280 80 138.104 Total 341410.000 84 Corrected Total 11783.286 83 aR Squared=.062 (adjusted R Square=.027) U ) Reproduced with permission o f th e copyright owner. Further reproduction prohibited without permission. Table 33. One-Way ANCOVA Summary Table Comparing Scaled Math SAT-9 Scores from 1998 to 1999 With Gender, Level of Technology, and SES as Covariates Math Scaled Scores by Year/ Gender, Technology, SES as Covariates Type III Sum of Squares df Mean Square F-ratio P Corrected Model 82366.488a 40 2059.162 4.425 .000 Intercept 4011083.949 1 4011083.949 8619.444 .000 Gender 4746.649 1 4746.649 10.200 .003 Level of Technology 69.068 1 69.068 .148 .702 SES 1401.465 1 1401.465 3.012 .090 Scaled Math 1999 74095.332 37 2002.577 4.303 •000b Error 20010.179 43 465.353 Total 28409298.170 84 Corrected Total 102376.667 83 aR Squared=.805 (adjusted R Square= .623) b Significant at the .001 level £ o o Reproduced with permission o f th e copyright owner. Further reproduction prohibited without permission. Table 34. One-Way ANCOVA Summary Table Comparing Scaled Reading SAT-9 Scores from 1998 to 1999 With Gender, Level of Technology, and SES as Covariates Reading Scaled Scores by Year/ Gender, Technology, SES as Covariates Type III Sum of Squares df Mean Square F-ratio P Corrected Model 91462.268a 38 2406.902 4.261 .000 Intercept 4180587.717 1 4180587.717 7401.691 .000 Gender 685.578 1 685.578 1.214 .276 Level of Technology 29.536 1 29.536 .052 .820 SES 903.818 1 903.818 1.600 .212 Scaled Reading 1999 88652.328 35 2532.924 4.485 •000b Error 25416.684 45 564.815 Total 29807398.000 84 Total Corrected 116878.952 83 aR Squared =.783 (adjusted R Square =.599) b Significant at the .001 level U> VO
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A quantitative and qualitative study of computer technology and student achievement in mathematics and reading at the second- and third-grade levels: A comparison of high versus limited technolo...
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