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An exploration of the gateway math and science course relationships in the Los Angeles Community College District
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An exploration of the gateway math and science course relationships in the Los Angeles Community College District
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AN EXPLORATION OF THE GATEWAY MATH AND SCIENCE COURSE RELATIONSHIPS IN THE LOS ANGELES COMMUNITY COLLEGE DISTRICT by Donald G. Buchanan A Dissertation Presented to the FACULTY OF THE ROSSIER SCHOOL OF EDUCATION UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF EDUCATION August 2006 Copyright 2006 Donald G. Buchanan Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. UM I Number: 3236483 Copyright 2006 by Buchanan, Donald G. All rights reserved. INFORMATION TO USERS 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 bleed-through, substandard margins, and improper alignment can adversely affect reproduction. In the unlikely event that the author did not send 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. ® UMI UMI Microform 3236483 Copyright 2006 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. DEDICATION I dedicate this doctoral dissertation to my parents, John Francis and Maurine Buchanan, and to my loving wife, Martha J. Buchanan. Each has given me freedom to pursue my dreams in careers and education. Mom and dad not only gave me the foundational values and principles, but allowed me to seek my life goals as a geologist collecting rocks and pursuing a career totally foreign to their backgrounds which ranged from the red soils of Oklahoma to the limestone quarries of Missouri. Martha has shared my life, raised our children and encouraged me in my educational endeavors for decades as I have stretched our lives to reach that childhood goal of a doctorate degree, not in Physics as originally planned, but in Educational Leadership for Community College Instructors, where my heart found its niche in life teaching Earth Sciences. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. ACKNOWLEDGEMENTS Math and science have guided my life and dreams from my earliest childhood, and there are many who have helped focus me along the way so that I was able to excel in both subjects from early elementary school through multiple higher education institutions. I want to recognize two of my Grandview High School teachers though, who served not only as role models, but also helped to nurture my love for math and science that led to this particular study in the community colleges today. To Marcia M. Manning, who walked me through the mysteries of math in geometry, calculus and physics; who sponsored our Mu Alpha Theta math club; and who inspired my senior science project in math theory, I owe a great deal of gratitude. To Dale Endicott, who sponsored our science club in Grandview High School and guided me through college level preparation science courses of Botany, Zoology and Chemistry, I wish to thank for opening the doors into the maze of Earth Sciences that have become my life’s careers and pleasures. Mentors and colleagues who have especially inspired and provided valuable guidance, inspiration and support during college at the University of Missouri, Victor Valley College, San Bernardino Valley College and USC include Dr. Tom Freeman, Dr. Ray Ethington, Dr. Queen M. Hamilton, Dr. Kay Weiss, Dr. Greg Tanaka, Dr. James Stitt, Dr. Henry Yong, Dr. Melora Sundt, Dr. William McComas, Dr. James Smith, Dr. Ten Strong, Dr. Jaime Lester, Jerry Home, Celia Huston, Debbie Chang, Winny Chi, Nadine Singh, Lisa Galvan, Linda Pace, Shalita Cunningham, et al. iii Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Finally, I wish to thank Dr. Linda Serra Hagedom for her inexhaustible patience and for her endless faith as she guided me along the path in my journey from the beginning of my University of Southern California doctoral studies through her initial recruitment presentation for this Educational Leadership Cohort for Community College Instructors in May, 1997 to the final hours of accomplishment in the summer of 2006. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. TABLE OF CONTENTS Dedication................................................................................................................... ii Acknowledgements................................................................................................... iii Table of Contents....................................................................................................... v List of Tables........................................................................................................... viii Abstract...................................................................................................................... xi Chapter 1: The Problem and Its Underlying Framework............................................. 1 Introduction.............................................................................................................. 1 Background of the Problem.....................................................................................2 Purpose of the Study................................................................................................4 Research Questions..................................................................................................5 Significance of the Problem.....................................................................................6 Assumptions.............................................................................................................7 Limitations...............................................................................................................7 Delimitations............................................................................................................8 Definition of Terms..................................................................................................8 Academic success.................................................................................................8 Attrition................................................................................................................9 Attrition rate.........................................................................................................9 Course completion ratio.......................................................................................9 Gateway courses..................................................................................................9 Gateway science courses......................................................................................9 Grade point average.......................................................................................... 10 Los Angeles Community College District (LACCD).......................................... 10 Persistence......................................................................................................... 10 Retention............................................................................................................ 11 Retention Rate.................................................................................................... 11 Transfer.............................................................................................................. 11 Transfer and Retention o f Urban Community College Students (TRUCCS) 11 Organization of the Study...................................................................................... 11 Chapter 2: Review of the Literature........................................................................... 13 Introduction............................................................................................................ 13 Four-Year vs Two Year Literature Review........................................................... 15 Attrition, Persistence & Retention in Higher Education........................................ 17 Attrition or Dropout Rates................................................................................. 17 Persistence......................................................................................................... 18 Retention (Retention rate).................................................................................. 19 v Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Demographic Factors.............................................................................................20 Gender................................................................................................................20 Ethnicity.............................................................................................................23 Age......................................................................................................................25 Socioeconomic Status.........................................................................................26 Pre-enrollment Factors...........................................................................................27 High school and/or community college math course achievements..................28 High School senior GPA....................................................................................29 Undergraduate GPA..........................................................................................31 Student attitudes.................................................................................................31 Career aspirations.............................................................................................32 Gateway Science Courses......................................................................................33 Physics Course Research ............................................................................33 Chemistry Course Research...............................................................................34 Biology Course Research...................................................................................35 Economic Factors...................................................................................................37 Employment status (part-time vs. full-time).......................................................37 Conclusions............................................................................................................38 Chapter 3: Research Methodology.............................................................................40 Introduction............................................................................................................40 Research Questions................................................................................................40 Research Methodology..........................................................................................41 Research Design.................................................................................................41 Population and Sample......................................................................................42 Instrumentation..................................................................................................43 Data Collection................................................................................... 43 Validity and Reliability......................................................................................44 Data Analysis.....................................................................................................44 Chapter 4: Analysis of the Data and the Presentation of the Findings.......................49 Introduction............................................................................................................49 Research Questions................................................................................................49 Research Presentation............................................................................................50 Background Demographic & Economic Factors...................................................51 Findings..................................................................................................................56 Chapter 5: Summary, Conclusions and Recommendations.......................................91 Introduction............................................................................................................91 Purpose of the Study..............................................................................................91 Methodology......................................................................................................... 92 Importance of this Study........................................................................................92 Research Questions................................................................................................93 Summary of Findings.............................................................................................94 vi Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Conclusions & Implications................................................................................. I ll Recommendations................................................................................................ 119 References................................................................................................................ 122 Appendix.................................................................................................................. 131 vii Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. LIST OF TABLES Table 1: Life and Physical Science Courses at LACCD.......................................... 46 Table 2: Dichotomous Demographic Variable Population and M eans.................... 52 Table 3: Dichotomous Age Frequency Statistics...................................................... 53 Table 4: Dichotomous Gender Frequency Statistics................................................ 53 Table 5: Ethnicity Frequency Distribution Statistics................................................ 55 Table 6: Current Employment Status Statistics........................................................ 55 Table 7: Average “Self-Reported” High School Grades for TRUCCS Student Survey Group.............................................................................................. 56 Table 8: Highest Level of Math Taken in High School and College Means............ 58 Table 9: Highest Level of Math Taken in High School and College Frequency Statistics...................................................................................................... 59 Table 10: College Overall, Math and Science GPA Means...................................... 60 Table 11: Age Comparisons with College GPA....................................................... 60 Table 12: Group Statistics t-test of College GPA versus A ge.................................. 61 Table 13: Independent Samples t-test for Equality of Variances for College GPA versus A ge....................................................................................... 61 Table 14: Gender Comparisons with College GPA................................................. 62 Table 15: Group Statistics t-test of College GPA versus Gender............................ 62 Table 16: Independent Samples t-test for Equality of Variances for College GPA versus Gender.................................................................................. 63 Table 17: Ethnicity Comparisons with College GPA and the One-Way ANOVA Significance of Difference........................................................................ 64 Table 18: Current Employment Status Comparisons with College GPA and the One-Way ANOVA Significance of Difference........................................ 65 viii Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 19: Age Comparisons with College Course Completion Ratio...................... 66 Table 20: Group Statistics t-test of College Course Completion Ratio versus A ge................................................................................................ 66 Table 21: Independent Samples t-test for Equality of Variances for College Course Completion Ratio versus A ge...................................................... 67 Table 22: Gender Comparisons with College Course Completion Ratio................. 68 Table 23: Group Statistics t-test of College Course Completion Ratio versus Gender........................................................................................... 68 Table 24: Independent Samples t-test for Equality of Variances for College Course Completion Ratio versus Gender................................................. 69 Table 25: Ethnicity Comparisons with College Course Completion Ratio and the One-Way ANOVA Significance of Difference.................................. 69 Table 26: Current Employment Status Comparisons with College Course Completion Ratio and the One-Way ANOVA Significance of Difference................................................................................................. 70 Table 27: Highest Level of High School Math Course Comparisons with College GPAs and the One-Way ANOVA Significance of Difference................. 72 Table 28: Highest Level of High School Math Course Comparisons with College Course Completion Ratio and the One-Way ANOVA Significance of Difference........................................................................ 73 Table 29: Highest Level of College Math Course Comparisons with College GPAs and the One-Way ANOVA Significance of Difference................. 74 Table 30: Highest Level of College Math Course Comparisons with College Course Completion Ratio and the One-Way ANOVA Significance of Difference.......................................... 76 Table 31: Descriptive Statistics for College Biology, Chemistry and Physics Course Grades........................................................................................... 77 Table 32: Gateway Science Course Transfer Level Grade Statistics Comparisons with All Math and Science Course Transfer Level Grade Statistics 78 ix Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 33: Group Statistics t-test of College Biology, Chemistry and Physics Course Grades versus A ge........................................................................ 79 Table 34: Independent Samples t-test for Equality of Variances for College Biology, Chemistry and Physics Course Grades versus A ge.................. 80 Table 35: Group Statistics t-test of College Biology, Chemistry and Physics Course Grades versus Gender................................................................... 80 Table 36: Independent Samples t-test for Equality of Variances for College Biology, Chemistry and Physics Course Grades versus Gender................81 Table 37: Ethnicity Comparisons with College Biology, Chemistry and Physics Course Grades and the One-Way ANOVA Significance of Difference............................................................................................. 81 Table 38: Current Employment Status Comparisons with College Biology, Chemistry and Physics Course Grades and the One-Way ANOVA Significance of Difference........................................................................ 83 Table 39: Descriptive Statistics for College Biology, Chemistry and Physics Course Completion Ratio................................. 84 Table 40: Group Statistics t-test of College Biology, Chemistry and Physics Course Completion Ratio versus A ge....................................................... 85 Table 41: Independent Samples t-test for Equality of Variances for College Biology, Chemistry and Physics Course Completion Ratio versus A ge................................................................................................ 85 Table 42: Group Statistics t-test of College Biology, Chemistry and Physics Course Completion Ratio versus Gender................................................. 86 Table 43: Independent Samples t-test for Equality of Variances for College Biology, Chemistry and Physics Course Completion Ratio versus Gender........................................................................................... 86 Table 44: Ethnicity Comparisons with College Biology, Chemistry and Physics Course Completion Ratio and the One-Way ANOVA Significance of Difference........................................................................ 88 Table 45: Current Employment Status Comparisons with College Biology, Chemistry and Physics Course Completion Ratio and the One-Way ANOVA Significance of Difference..........................................................89 x Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. ABSTRACT This study evaluated selected demographic, pre-enrollment, and economic status variables in comparison to college-level performance factors of GPA and course completion ratios for gateway math and science courses. The Transfer and Retention of Urban Community College Students (TRUCCS) project team collected survey and enrollment data for this study in the Los Angeles Community College District (LACCD). The TRUCCS team surveyed over 5,000 students within the nine campus district beginning in the fall of 2000 and spring of 2001 with follow-up data for next several years. This study focused on the math and science courses; established background demographics; evaluated pre-enrollment high school self- reported grades; reviewed high school and college level math courses taken; investigated specific gateway courses of biology, chemistry and physics; and compared them to the overall GPAs and course completion ratios for 4,698 students. This involved the SPSS development of numerous statistical products including the data from frequency distributions, means, cross-tabulations, group statistics t-tests, independent samples t-tests, and one-way ANOVA. Findings revealed demographic and economic relationships of significance for students’ performance factors of GPA and course completion ratios. Furthermore, findings revealed significant differences between the gender, age, ethnicity and economic employment relationships. Conclusions and implications for institutions of higher education were documented. Recommendations for dissemination, intervention programs, and future research were also discussed. xi Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. CHAPTER 1 THE PROBLEM AND ITS UNDERLYING FRAMEWORK Introduction Student achievement in math and science educational programs is lagging across our nation, and especially in California, yet is necessary for the academic preparation of high-tech careers in our modem society (“Science, math teaching urged,” 2005). “If California is to be a leader in tomorrow’s economy, we need to put more emphasis on science and math instruction,” (“Science,” 2005). Governor Schwarzenegger shared this view at the Irvine campus of the University of California on June 1,2005 as he announced a program before a select group of executives from more than a dozen businesses, which have donated $4 million to startup an enhanced teacher’s training program in California to prepare students for the future. State officials stated that “less than 7 percent of all teaching credentials issued in 2002-03 were in math and science and the state has a critical shortage in those areas,” (“Science,” 2005). They indicated that recent National Science Foundation rankings placed California’s eighth-graders last in the country in science and seventh from the bottom in math. University of California President Robert Dynes noted “there are fewer and fewer students that are educated in the state of California that are completely trained in mathematics and science,” (“Science,” 2005). These are the students who are entering our community colleges ill prepared in the basic math and science skills necessary to develop high-tech careers. The efforts to keep these students in college for transfer and graduation through gateway science courses into 1 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. high-tech careers were of interest not only to the students themselves, but also to their parents, educators and government agencies. Background of the Problem Introductory college science courses have been considered to be ‘gateway’ courses into science related careers (Gainen, 1995), yet if students in the pipeline from secondary schools are ill prepared; the challenge becomes even more difficult. Students have been having difficulties not only with the basic reading, writing and math skill courses, but are significantly challenged when getting into the math and science prerequisite courses for science careers. Since student completion of transfer required science courses in our community colleges serves as vital ‘gateways’ into higher paying careers in engineering, advanced technologies, modem biology research, health sciences, and medical sciences, this has become a concern for not only students, but also educators, state governments as funding sources, and society in general (Coppola, 1999; Hart & Cottle, 1993). Extensive research exists for student persistence or retention in higher educational four-year colleges and universities, but there has been significantly less research for community colleges (Hoyt, 1999b). A literature review revealed there is even less research examining the community college student demographic and pre enrollment background student variables in respect to persistence and retention, and other factors influencing students to transfer and graduate (Coppola, 1999). When looking at the literature focusing on specific math and science program attrition, retention or achievement to meet requirements for transfer or graduation, the number 2 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. of relevant studies narrowed to an even significantly smaller segment of research (Vazquez-Abad, Winer & Derome, 1997). The traditional demographic factors found in most math and science research studies include gender, ethnicity, and age (Hagedom, Siadat, Fogel, Nora, & Pascarella, 1999). Other research efforts indicated there are several pre-enrollment and economic factor variables found to influence students’ decision to leave the college before completing their program or degree, including employment status (full-time, part-time), family obligations, and student’s financial concerns (Bonham & Luckie, 1993; Lewallen, 1993). Research also showed that parents’ education level and family origin were significant predictors of attrition (Coppola, 1999). Brawer (1996) noted that one major factor directly influencing retention and attrition of community college students in the nineties related to a student’s full-time or part- time attendance status. Many studies referred to economic status as socioeconomic status (SES), i.e. a composite indicator of not only the parents’ education, but also the parents’ occupations and income levels, which creates a “socioeconomic gap” relationship with academic achievement (Ma, 2000). What we don’t know is the effect SES has on math and science students in their progress into these gateway careers. An additional underlying problem has been the need for a reform in the undergraduate science curriculum and numerous studies have been pointing in this direction (Krockover, Shepardson, Eichinger, Nakhleh, & Adams, 2002). Krockover et al., (2002) pointed out that 3 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Shaping the Future (National Science Foundation, 1996), Science Teacher Preparation in an Era of Standards-Based Reform (National Research Council, 1997), and Reinventing Undergraduate Education: A Blueprint for American Universities (Boyer Commission on Education Undergraduates in the Research University, 1998) ... all recommend that college faculty should be moving away from lecture-based courses and moving toward integrative learning that incorporates laboratory and group work and discussion opportunities for all students, (p. 266) They also emphasized that to prepare a scientifically literate society, there has been a great need, as Governor Schwarzenegger and others perceive, to place significant efforts in providing a quality, relevant science education experience for students who will become the K-12 teachers of the future (Krockover et al., 2002). They pointed to College Pathways to the Science Education Standards by Siebert & McIntosh (2001) as an excellent guideline for this reform effort to link math and science curricula together to create gateways into higher paying careers (Krockover et al., 2002). Purpose of the Study The purpose of this study was to evaluate selected demographic, pre enrollment, and economic status variables in direct relationship to college-level performance. Literature revealed that these variables have influenced successful student retention, persistence and completion of math and science courses at four- year institutions, but that two-year community college research was limited or non existent, especially for the math and science fields of study. This study served to fill a missing gap of knowledge about math and science relationships of the diverse population of students seeking gateway careers in math and sciences in the Los Angeles Community College District (LACCD), the largest district in the United States. The study specifically produced meaningful data to enhance the effective 4 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. management of career preparations at this institution and similar community colleges around the nation. Research Questions The general research aimed to establish the relationship between students’ high school grades; their level of high school and college math courses; their math, science and overall grade point averages (GPAs); their demographics (age, gender, and ethnicity); and their economic employment status in respect to their successful course completion of gateway science classes in the LACCD for achievement of transfer and graduation requirements. Specific research questions were listed below: QUESTION 1: What were the high school GPAs of the students enrolled in the LACCD? QUESTION 2: What level of math courses did the science students take in high school and college? QUESTION 3: What was the overall, math, and science community college GPAs and course completion ratios of students enrolled in the LACCD? QUESTION 4: What were the student GPAs and course completion ratio relationships to the demographic variables of age, gender, and ethnicity, and to the economic variable of current employment status? QUESTION 5: What were the relationships between the highest level of high school and college math with overall, math, and science community college GPAs and course completion ratios of students in the LACCD? 5 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. QUESTION 6: What were the gateway biology, chemistry, and physics course grades; the transfer potential of these grades versus math and science GPAs; and course completion ratios of the LACCD students and their demographic and economic relationships? Significance of the Problem The Los Angeles Community College District has been documented as the largest community college district in the United States (USC, May 2001). The LACCD has over 100,000 students, many designated as at-risk, most of whom have lofty aspirations and hopes of degrees and meaningful employment. In reality though, at the rates of retention and transfer at this institution at the beginning of the 21s t Century, the probability of completing a bachelor's degree was low, especially for the district's minority and lower socioeconomic students. The initial longitudinal project undertaken at LACCD with a $1.1 million dollar U.S. Department of Education grant was designed to study the factors, both organization and individual, that promote retention, persistence and transfer among this important group of students in a major metropolitan area (USC, 2000). The results of this study looking at math and science relationships are transferable to other metropolitan cities with diverse minority populations such as New York, Chicago, Miami, etc. These factors are also beneficial to many smaller community colleges across the nation where diverse populations have been increasingly evident and changing the character of their student populations and the teaching methodologies to meet changing times. 6 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. This study examined one of the most diverse groups of students in the nation. The study served to inform the faculty and the administrators in the LACCD of their classroom environment through identification of direct relationships of gender, age, employment status, ethnicities, levels of mathematical course preparations, etc., to the math, science, and overall GPAs and course completion ratios for transfer or graduation into gateway careers for higher income employment. It also evaluated specific gateway science courses of biology, chemistry and physics in relationship to grade performance for transfer, GPA and course completion ratios. Assumptions This study assumed that the following conditions have been met: a) the survey instrument used within the LACCD has both construct validity and reliability, b) the survey participant’s one class period timeframe to answer the questions was adequate for answering each question as accurately as possible, c) the proctors for the survey used consistent procedures in administering the surveys, d) the data were compiled and computerized by University of Southern California (USC) and University of California in Los Angeles (UCLA) using standard data coding procedures, and e) the questionnaire data for both faculty and students were gathered and analyzed using "good" research methods. Limitations This study limited itself to subjects who agreed to participate voluntarily. The scope of the study pertained only to urban community college students in the LACCD. This study used data collected from a structured survey instrument and 7 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. from the student transcripts obtained from LACCD. A quantitative design limited the results of this study. Delimitations This study included data from nearly 5000 students’ surveys and their corresponding enrollment records of students taking classes at the nine community colleges in the LACCD in the fall of 2000 and spring 2001. This study studied the selected demographic, pre-enrollment and economic variables in relationship to academic success of the students’ math, science and overall courses. This study focused on students’ academic success of their math and science courses, and correlations with levels of math classes taken in high school and college at LACCD. Definition of Terms For the purpose of this study, the following terms served as pertinent definitions from relevant literature to help readers understand the concepts contained within this research study. Academic success The proportion of successful course completions to total courses attempted has been the normal definition for this term, but was modified for this study to also imply the successful completion of the math and science courses for gateway career transfer and graduation. It was described in the form of course completion ratios for math, science and overall courses taken in the LACCD. 8 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Attrition In the context of at-risk minority college student studies attrition pertained to those students who a) voluntarily transferred to another campus or permanently dropped out of school, b) due to poor grades were dropped from the institution (Levin & Levin, 1991). Attrition rate According to State Postsecondary Review Entity (SPRE) regulations established by the 1992 Reauthorization of the Higher Education Act of 1965, “attrition rate was the percent of entering students not graduating nor persisting in their studies at an institution” (Wyman, 1997). Course completion ratio The percentage of students actually completing all assigned work, exercises, quizzes, and exams required in math and/or science courses resulting in a passing grade of “C” or higher was defined as a successful academic course completion ratio (CCR). Gateway courses In this study of science education processes the introductory college math and science courses were considered to be ‘gateway’ courses into science related careers (Gainen, 1995). Gateway science courses For this study, gateway science courses were defined as collections of course data from a specified list of selected physical and life science courses taken at the 9 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. LACCD. A table in Chapter 3 identified 20 some courses along with the rationale for exclusion of other courses not included in this study. Grade point average Grade point average (GPA) has been normally calculated as a product of student performance in an academic setting whether in K-12, community colleges or universities. GPA is felt by many researchers to be a good predictor of college academic performance (Smittle, 1995). Los Angeles Community College District (LACCD) The Los Angeles Community College District (LACCD) in 2000-01 was the largest community college district in the United States serving over 100,000 students in 36 cities from the greater Los Angeles area (LACCD, 2003). Persistence This term has various meanings in educational literature. Levin and Levin described this student characteristic as those who decided to continue to pursue their academic goals rather than dropping out of college (1991). Tinto (1975) defined persistence as the completion of a baccalaureate degree. This worked well for four- year institutions, but has been modified to indicate pursuit of transfer or graduation from a community college for two-year institutions. Later Tinto (1987, 1993) addressed persistence in the community college setting as being similar to the process of developing competent membership in a community. This required personal contacts with other students, faculty and staff, and integrating into the academic life of the community college setting (Tinto, 1987, 1993). 10 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Retention Traditionally retention has been defined as students who a) continued to reenroll for courses at the same academic institution, or b) eventually transferred or graduated from that college (Levin & Levin, 1991). Retention Rate According to State Postsecondary Review Entity (SPRE) regulations established by the 1992 Reauthorization of the Higher Education Act of 1965, “retention rate is the percent of entering students graduating or persisting in their studies at an institution” (Wyman, 1997). Transfer Transfer referred to students who leave a community college for academic transfer to another institution of higher education, either to another community college or to a four- year degree granting college or university to continue their studies toward a credential or degree to demonstrate goal achievement. Transfer and Retention o f Urban Community College Students (TRUCCS) The acronym for the multi-year federally funded Transfer and Retention of Urban Community College Students (TRUCCS) project conducted within the Los Angeles Community College District (Hagedom, 2001). Organization of the Study Chapter 1 of the study presented the introduction, the statement of the problem, the purpose of the study, the questions to be answered, the research 11 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. hypotheses, the significance of the study, and the definitions of terms, among other key topics. Chapter 2 was a review of relevant literature. It addressed the following major topics: attrition, persistence, retention, demographic and pre-enrollment factors, gateway science courses, and economic factors. Chapter 3 presented the methodology that used in the study, including the research design, population and sampling procedures, the instruments and their selection or development, together with information on validity and reliability. This chapter concluded with a description of the procedures for data collection and the plan for data analysis. Chapter 4 presented the analysis and findings of the study using tables of statistical data processed with SPSS, Windows version, from TRUCCS survey data and LACCD student transcripts. Chapter 5 restated the purpose of this study, its research questions and the importance of the study before summarizing the findings in respect to each of the six research questions. It also included conclusions, implications and recommendations for future efforts and studies. References and Appendix A with the original TRUCCS survey and release authorization letter concluded the study. 12 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. CHAPTER 2 REVIEW OF THE LITERATURE Introduction A literature review revealed there is an abundance of higher educational studies on the four-year institutional level into the topics of attrition, persistence and/or retention in higher education using various methods and approaches (Hoyt, 1999), but that there were few studies examining the corresponding relationships for community college students. Braxton (1999) noted that this problem of attrition, persistence and/or retention has been studied by hundreds of researchers since 1926. That is nearly 80 years of research, primarily on four-year institutions. These studies have concentrated on demographic and pre-enrollment background student variables in respect to persistence and retention, and other factors influencing students to transfer and graduate (Coppola, 1999). When seeking out literature focusing on specific math and science program attrition or achievement to meet requirements for transfer or graduation, the numbers of studies significantly dwindled, especially when narrowing the field from four-year institutions to community colleges, and were fairly recent in time frame (Vazquez-Abad, Winer & Derome, 1997). Introductory college science courses are considered ‘gateway’ courses into science related careers (Gainen, 1995), yet there has been very little research on these courses and their relationship with student academic success. Since student completion of math and science courses in our community colleges has served as vital gateways into higher paying careers in engineering, advanced technologies, 13 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. modem biology research, health sciences, and medical sciences, this has become a concern for not only students, but also educators, state governments as funding sources, and society in general (Coppola, 1999; Hart & Cottle, 1993). The traditional demographic factors found in most math and science research studies included gender, ethnicity, and age of students (Hagedom, Siadat, Fogel, Nora, & Pascarella, 1999). Other articles indicated pre-enrollment and economic status variables were found to influence students’ decision to leave the college before completing their program or degree to include employment status (full-time, part- time), family obligations, and student’s financial concerns (Bonham & Luckie, 1993; Lewallen, 1993). Research also revealed that the parent’s education level and family origin were significant predictors of attrition (Coppola, 1999). Brawer (1996) noted that one major factor directly influencing retention and attrition of community college students in the nineties related to student’s full-time or part-time attendance status. Some studies referred to economic status as socioeconomic status (SES), i.e. a composite indicator of not only the parents’ education, but also the parents’ occupations and income levels, which created a “socioeconomic gap” relationship with academic achievement (Ma, 2000). The purpose of this literature review was first to investigate the literature contributions discussing the traditional demographic variables (gender, ethnicity, age, SES) or other attributes that have influenced students’ decisions to persist to stay in school and complete their math and science courses in preparation for transfer or graduation to enter gateway careers. The review then investigated literature 14 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. describing the pre-enrollment factors of high school and/or community college math course achievements and GPA in relationship to community college retention and science course completion. The study also evaluated research literature that pertained to student attitudes towards math and science and their career aspirations as influencing factors to understand the breadth of the issues involved. Four-Year vs Two-Year Literature Review The development of community colleges during the 20th Century offered access to higher education that could not be realized in the selective four-year institutions in the United States (Cohen & Brawer, 1996). These two-year colleges with their flexible open admissions have provided opportunities for disadvantaged individuals in their local communities who might otherwise never have attended college (Cohen & Brawer, 1996; Bryant, 2001). The community college provided access to higher education and educational achievement for millions of underprepared, minority and lower socioeconomic students across the United States. The community colleges’ "open door" access policy in many states created a second chance for the academically underprepared students to realize their educational goals; as a result, nationally the underprepared are overrepresented in the community college (Cohen & Brawer, 1996; Hoyt, 1999). The National Center for Education Statistics (NCES) reported that approximately 41% needed remedial education in 1995 (U.S. Department of Education, 1996). The community college’s mission to accommodate these higher numbers of disadvantaged or underprepared students, however, created challenges 15 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. for students, faculty and administrative staff as the shear numbers of underprepared students have lowered retention rates on these campuses (Hoyt, 1999b). In 1962 the State of California legislature established a Commission on California State Government Organization and Economy, that has, over time, come to be known as the Little Hoover Commission (2000). The legislature defined the Little Hoover Commission’s purpose: ... to secure assistance for the Governor and itself in promoting economy, efficiency and improved services in the transaction of the public business in the various departments, agencies and instrumentalities of the executive branch of the state government, and in making the operation of all state departments, agencies and instrumentalities, and all expenditures of public funds, more directly responsive to the wishes of the people as expressed by their elected representatives.. .(inside front cover). The Little Hoover Commission (2000) reported that 75% to 80% of the students who enrolled in the community colleges to receive basic math and English general education requirements for transfer, did not progress beyond a single course. This formal report and the recent findings with dismal rankings for 8th grade preparation for science and math by the National Science Foundation (2000) indicated a major challenge for the state government, educators and parents in getting California students prepared for community colleges. The “open door” policy and these statistics yielded contradictory results when assessing community college student retention and success rates. Even though today’s ethnic minority students appeared better prepared than their predecessors of past generations for successful completion of college, their performance on access achievement tests and persistence in college created concerns 16 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (Cohen & Brawer, 1996; Jalomo, 2000). Underprepared, disadvantaged, ethnic minority and lower socioeconomic student retention and completion rates of undergraduate requirements for transfer drew the attention of external constituencies (parents, taxpayers, district boards, state and local legislators, and accreditation organizations) and internal constituencies (faculty, administrators, institutional researchers, academic advisors, counseling staff, student service staff, etc.) who function as concerned stakeholders in this era of higher education accountability (Cohen & Brawer, 1996; Cress, 1996; Terenzini, Springer, Yaeger, Pascarella, & Nora, 1996; Jalomo, 2000). Community colleges in the United States enrolled a more diverse population of students in terms of ethnicities, learning styles, and academic preparation than at any other time in the last century of their existence, and are finding themselves to be under the watchful eye of numerous stakeholder constituencies to ensure their efficiency and effectiveness in improving quality education while increasing retention and completion rates for these diverse underprepared students (Cohen & Brawer, 1996; Jalomo, 2000). Attrition, Persistence & Retention in Higher Education Attrition or Dropout Rates Levin and Levin (1991) noted that high rates of attrition and low levels of achievement were the norm for large numbers of Black, Hispanic and Native American students during the 1970-80s after the passage of the Higher Education Act of 1965, as they primarily qualified for financial aid based on need rather than merit. Their research found that the research literature indicated that college 17 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. administrators across the nation became convinced that some academic intervention measures were necessary to offset the high dropout rates and improve the attainment of student dreams through persistence or retention. Oakes (1990) noted that the highest period of attrition for females occurs in the transition from high school and in first years of college. Gainen (1995) indicated that the pre-college experiences in math and science courses taught in high school and the resultant loss of self esteem severely impacts these students’ success in the introductory “gateway” college science courses that lead to gateway science careers. Kuh (2001) shared that the dropout rates in introductory “gateway” mathematics courses ranges from 30-40% during the first years of college. A lot of this failure has been attributed to a culture of academia who stress mastery of challenging mathematical concepts over teaching for successful knowledge transfer of concepts (Kuh, 2001). Persistence Student persistence revolves around involvement, whether academic or social (Tinto, 1998). Numerous research efforts agreed that the more students interacted with each other and faculty, the higher the persistence (Astin, 1984; Mallette & Cabrera, 1991; Nora, 1987; Pascarella & Terenzini, 1980; Terenzini & Pascarella, 1977). Braxton (1999) noted that over 400 citations were made on Tinto’s interactionalist theory in respect to persistence and that more than 170 dissertations have referenced or used his theory. As this body of research on persistence and the related variables continues to grow, Braxton has increased the estimated number of 18 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. citations for Tinto’s work to 775 (Braxton & Hirschy, 2004). Braxton, Sullivan and Johnson (1997) pointed out that evidence showed academic and social integrations were more important in the four-year than in the two-year institutions to student persistence. In community colleges student time was much more limited to class time than it was for residential four-year institutions, so their interactions with peers and faculty were much more important to persistence (Tinto, 1997). Astin and Astin (1993) reported in their national study of factors influencing students’ interest in studying sciences, mathematics, and engineering (SME) for pursuit of careers in those fields, that the students’ final career choice was positively and significantly influenced by the numbers of peers with similar career goals. Other researchers went on to state that these SME students were more likely to persist and to earn bachelor degrees than their peers majoring in social sciences, humanities, and education (Pascarella & Terenzini, 2005). During the seventies, the terminology in literature shifted from “persistence” to “retention” as the needs of the institution came into play, and as the focus moved to program adaptations and techniques to retain students (Marchese, 1985). Retention (Retention rate) Wyman (1997) observed that a demand for greater accountability in higher education inspired academic institutions, government and society in general to develop measures of institutional effectiveness. He noted that the United States Congress ushered in new opportunities for states to evaluate their public colleges and universities using a broad range of measures through the passage of the 1992 19 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Reauthorization of the Higher Education Act of 1965. As a result retention rate was one measure that was being used widely as an indicator of effectiveness. Retention rate in this context was defined as the percentage of entering students persisting or graduating in their studies at an institution. Alternatively, the complement would be attrition rate for those who dropout, fail to persist or don’t graduate. The evaluation of persistence, retention or retention rate as a measure of institutional effectiveness has been a major concern for institution accreditation across the nation (Wyman, 1997). Demographic Factors Gender Phillippe and Patton (1999) noted that 58% of community college students across the nation are females, and that they experienced some unique difficulties and issues in comparison to their male counterparts. These adult college bound women have high levels of stress associated with financial challenges, parenting, and their health (Johnson, Schwartz & Bower, 2000). Studies by Goldsmith and Archambault (1997) indicated that the persistence of adult females in two-year community colleges was directly related to their ultimate goals, financial aid support, and GPA. Those who dropped out or fell by the wayside appeared to not have been able to successfully integrate themselves into the college life (i.e. student activities, student association, campus clubs, etc.), and/or have not committed themselves to accomplishing the requirements for transfer or graduation goals (Goldsmith and Archambault, 1997). 20 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Some studies on dropout rates (attrition rate) from science programs have observed significant gender effects; ex. Donaldson & Dixon (1995) discovered female students withdrawing from large first-year chemistry courses far more quickly than male students. Callas (1993) noted that for many decades educators and researchers observed gender differences in the mathematics and attributed this to biological causes. More recent studies have shown evidence of environmental influences associated with lack of support programs for females or biased textbooks that were a major factor in reduced numbers of females in the gateway science courses and career paths (Callas, 1993). There are many issues involved with under-representation of females in the high paying science and engineering professions (Tai, 2001). He noted in his article on gender differences in introductory undergraduate physical science course performance that in recent years, women have received degrees in most fields in numbers approaching or exceeding their 51% of the American population, but not in the physical sciences, mathematics, and engineering careers. Tai pointed out from Rosser’s studies of U.S. women in science using National Science Foundation statistics from 1986 to 1992 that only 16% of the scientists and engineers in the United States workforce are women, compared to 45% of the total workforce (Rosser, 1995). This lack of representation in science-related professions has created numerous problems for individuals and the U.S. society. There are some positive effects associated with women majoring in sciences, mathematics and engineering in respect to subsequent earnings, if they can persist 21 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. through the challenges of majoring in these fields. Pascarella and Terenzini (2005) reported that there is a large body of research on earnings potential for men and women in these careers. Women in general in engineering earned about 1.5 times as much in subsequent earnings as for men. Furthermore, the research showed that for women majoring in mathematics or physical sciences the net impact on subsequent earnings has been about 1.75 times that of earnings for males (Pascarella & Terenzini, 2005). Schoon (2001) commented that one of the primary factors influencing job or career development has been gender, especially in the sciences, and that this occurred earlier in life than community college age students. Science has been normally looked upon as a “masculine, hard, complex, difficult” field of study (Colley, 1995). Her research indicated that females normally selected “softer disciplines” of science, such as biology and medicine, whereas males have dominated the “hard disciplines” of physics and engineering. Colley described male dominated science careers as those that deal with things rather than people, using characteristics of forcefulness, analytical thinking, ambition, and individual competitiveness (1995). He further pointed out that females normally aspired for careers that are more people-oriented, with characteristics of affection, compassion and personal interaction (Colley, 1995). Developing an understanding of the gender role in math and science career development through passage of gateway science courses has been a factor of concern. 22 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Kennedy and Parks (2000) also discussed the under-representation of females in science, mathematics and engineering careers, and commented on interviews with women who have been in the classrooms. Hartman (1995) noted that one student in physics shared that she felt like being confined in a ‘shark tank’, and another recalled her experience by explaining, “that when she turned in an especially good work her professor suspected plagiarism” (p. 530). This chilly atmosphere was difficult to contend with creating an environment that decreases their self-confidence and self esteem, which in-tum relegated them to a minority status in the sciences (Kennedy & Parks, 2000). Ethnicity Recent studies of population growth and student ethnicity by Hagedom, Maxwell, Chen, Cypers, and Moon (2002) revealed a major shift in ethnicities within the state of California, and specifically within the LACCD. The history of California witnessed numerous ethnic groups migrating into this region over the last few centuries, yet the fastest growing recent segment has been of Latino descent, primarily due to immigrant population. The LACCD fall 2000 survey for campus wide enrollment by ethnicity shows that from 1972 to 2000 the Hispanic student population has increased from 16.1% to 47.4% validating these observations (LACCD, 2003). This demographic ethnicity increase across the nation coupled with achievement gaps between whites and minority students has created significant problems for U.S. educational institutions and their roles in developing future 23 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. scientific and careers (Hrabowski, 2003). Underrepresented minorities (African Americans, Native Americans, and Hispanics) comprised 24% of the working-age population in 1999, but they constituted only 7% of U.S. engineers and scientists according to the National Science Foundation (2000) research reports and comparative studies by Barlow and Villarejo (2004). The National Science Board (2000) predicted nearly a 50% growth in the number of new scientific and engineering jobs in the United States during the next decade. This is becoming a real challenge, as many minority students are simply not going to be ready for the opportunity. The American Council on Education (2001) statistics for 1998 indicated the minority student graduation rates from high schools and colleges were far behind their white counterparts, and their achievement gap in math and science was the highest (College Board, 1999). African American students often perceive themselves as different from their Caucasian counterparts through their lenses of added burdens of racism, discrimination and negative stereotypes. Moore (2001) stated that for African Americans success in college has had not so much to do with cognitive measures (e.g. high school GPAs, standardized achievement tests (SATs), and class rank), but more so with non-cognitive measures. These non-cognitive measures included self- efficacy, motivation, commitment, and persistence (Moore, 2001). Moore noted that literature documents non-cognitive feelings of “black inferiority” throughout society, and even in the college environment. He emphasized that African American students have been especially carrying this burden when entering the academic disciplines of 24 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. engineering, mathematics and science (2001). This feeling of “black inferiority” has been overcome when parents and other family members became involved in their educational process. Smith and Hausfaus (1998) noted from their studies that minority students did better in math and science when parents were supportive of their education and got involved. Hrabowski and Maton (1995) also found positive results in specific math and science courses when supplemental programs were offered to minority students. Age The age of community college students has varied both geographically in the United States and with time over this last century (Cohen & Brawer, 1996). The time of the traditional student entering at 18 is past as older students have shifted the median age higher each year. Community college students have become increasingly diverse and not only in ethnicity (Owen, 1996). Since 1970, there has been an increase in the number of students over the age of 25 entering American higher education institutions from 28% to 44% (U.S. Department of Education, 1996). The fastest growing segment entering colleges today are middle-aged females (National Center for Educational Statistics, 1995) and this changed the dynamics of learning (Gustentine & Keim, 1996). Gustentine and Keim found significant learning style variances between the ages of students (1996). Whereas traditional aged community college students processed information through reflective observation, older, non- traditional students used active experimentation to process information. This age 25 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. shift requires further research to evaluate needs for alternative teaching styles to create an effective learning environment (Gustentine & Keim, 1996) Age has other relationships with grades and course completion. A recent study of nearly 5000 Los Angeles Community College students revealed a positive correlation between older students in that their performance indicated higher grades and course completion than their younger counterparts (Hagedom, Maxwell, Chen, Cypers, & Moon, 2002). The need is for younger students to perform well in math and science courses, and the statistics are not always in their favor. Recent American Council of Education research reported that for 1998 only 60% of the Hispanic students and 73% of the African American students graduated from high school compared with 82% of the White students (2001). These minority students should be entering colleges today, but studies revealed that in the 18-24 year old age bracket only 34% of Hispanics and 41% of African Americans were registered in college in comparison with nearly 50% of their White counterparts (American Council of Education, 2001). Socioeconomic Status Ma (2000) described Socioeconomic status (SES) in his research across subject areas as “a composite indicator of parents’ education, parents’ occupation, and family income” that was related to student outcomes. He noted that few studies have evaluated the relationships of SES or “socioeconomic gaps” with academic achievement. Research studies have found that socioeconomic gaps cut across both mathematics and science (Crane, 1996; Young & Fraser 1993). Young and Fraser 26 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. further reported that even after adequate controls are applied there are significant socioeconomic gaps in science achievement (1993). Ma additionally stated that researchers have little understanding of the interrelationships between socioeconomic gaps in science performance and mathematics performance, and this has been compounded with the lack of studies on these relationships (2000). Pre-enrollment Factors The University of North Texas became concerned about student retention in the 1990s as the Texas legislature began efforts to tie funding to educational effectiveness, using graduation rates as indicators of academic quality. Coppola (1999) noted in his research that there was an abundance of studies at four-year institutions, but not for his concern with community colleges. Coppola initiated studies on the local North Lake College in Irving, Texas to see if there were any pre enrollment factors that could help universities identify at-risk students prior to their entry into the four-year institutions. His study specifically attempted to identify pre enrollment variables as predictors of retention and persistence. He selected four variables for evaluation that can be found within information that are normally available from the students before their entry into the institution, which would allow institutions to identify students who are at-risk before the problem with retention or persistence develops. The variables selected included (a) racial origin, (b) educational goals, (c) high school senior GPA, and (d) parent’s education. He found the latter three of these pre-enrollment factors as significant predictors of attrition at the p > .05 level (Coppola, 1999). This Pearson Chi-Square Test of Independence 27 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. study demonstrated that there were some important pre-enrollment information available to institutions that can be used to identify potential non-persisters for corrective actions (Coppola, 1999). High school and/or community college math course achievements Ellis (1993) reported his research with high school students supporting a relationship between improvement in performance of mathematics and improvement in attitudes towards science. He and his associates established a positive relationship between treatment variables, such as mentoring, and outcome variables such as high school completion, involvement in mathematics and science courses in high school, and attendance at a post-secondary institution. This was done through development of an integrated supplemental instruction program of mathematics, science and language arts activities with counseling (Ellis, 1993). Lenning (1982) commented on student academic factors that are important for predicting college success or attrition. He noted that those who took college- preparatory courses in mathematics and physical sciences tend to persist longer than those who didn’t. He also indicated that high school rank also played a role in prediction of success or failure in college (Lenning, 1982). Hoyt (1999a) evaluated results from a study of the level of math preparation in Utah’s high schools for transfer to local colleges finding some positive relationships. The results showed that the students’ level of math preparation contributed substantially to the students’ performance on the ACT test, math placement tests, and their ability to take college-level mathematics courses. Other 28 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. studies of school achievement demonstrated that student performance on math proficiency tests improved with the increasing number of math courses taken, the number of hours of math instruction, and/or the number of advanced math courses taken in high school (Jones, Davenport, Bryson, Bekhuis, & Zwick, 1986). Other studies indicated that students in a college preparatory track have a much better chance of success in terms of math preparation than do students in a normal general math program, and that supplemental transition courses may help improve the students’ math abilities for college level work (Lee, Croninger & Smith, 1997). Additional research in Florida with nearly 20,000 high school transcripts from graduating seniors demonstrated some positive relationships between course selections, grades and other demographic factors and a computerized placement test (CPT) given to the students upon entry into the community college in the fall of 1994 (Roth, Crans, & Carter, 2001). This research created a High School Performance (HSP) variable for math and English to account for the number of courses completed, the degree of difficulty, and the achieved course grade. The Math and English HSP coupled with the 4-year cumulative GPA, the Grade Ten Assessment Test (GTAT) in math and reading, plus race and gender all played significant roles on the probability of passing the CPT. The Math HSP had a greater positive effect on passing the Math CPT than GPA or GTAT. It was obvious to researchers, faculty and administrators that students could raise their math abilities and passage of the Math CPT by taking more difficult math courses in high school (Roth, Crans, & Carter, 2001). 29 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. High School senior GPA Smittle (1995) stated that some researchers have been confident about using high school grade point average (GPA) as a good predictor of college academic performance. Gillespie (1993) contended that “High School GPA has been a logical indicator for courses taken in a relevant subject area,” such as math would be for introductory science courses (p. 67). Fralick’s (1993) research with college attrition showed that high school GPA has been an indicator of not only academic college success, but also retention. Lenning (1982) indicated that high school GPA as a student academic factor for predicting attrition has a significantly higher relationship than any other single predictor. Others question the reliability of high school GPA when taking into consideration gaps between high school exit standards and college entrance requirements for traditional age students, especially when seeing that some high school competency exams only require ninth grade reading levels for graduation (Roueche, Baker, & Roueche, 1984). The influx of increasing numbers of returning middle aged students compounds the GPA usage as the high school records no longer accurately reflect learned knowledge (Smittle, 1995). Pascarella and Terenzini recently published a volume 2 review of the last three decades of research indicating that grade performance has been by far the single best predictor of student persistence or degree completion (2005). Even though grades are not a perfect measure of learning and intellectual development, since they reflect relative performance in comparison to other students rather than actual learned knowledge, they are the most consistent predictors of retention, 30 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. persistence, course completion, and degree accomplishment (Pascarella & Terenzini, 2005). Adelman (1999) reported that high school and college student survey and transcript data provided statistically significant, positive predictors of first year college grades and trends to degree completion, so the literature has established a good relationship between high school GPA and college performance factors. Undergraduate GPA Grade point average has been a widely accepted means of determining academic success and the degree to which students have learned what they are expected to learn, both in high school and college. Johnson (1997) noted that research literature is full of attrition and retention studies identifying factors that contribute to students persisting with their education at institutions of higher learning. Johnson pointed out that Astin (1972) discovered that the most closely related factor to persistence (retention) at an institution was the student’s undergraduate GPA. Likewise, Johnson noted that Bean (1985) later documented that within the first two years of college a low GPA was one of the major factors for students dropping out of school. More recently Zhai and Newcomb (2000) stated that of all the factors evaluated in their transfer student study, that GPA was the best indicator of expected academic performance. Student attitudes A recent study on nearly 5000 Los Angeles Community College students revealed a significant positive academic attitude in the Latino students for goal achievements (Hagedom, Maxwell, Chen, Cypers, & Moon, 2002). Earlier work by 31 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Nora and Rendon (1990) in the 1980s indicated that attitudes of female and Hispanic students were not quite that positive, and that these two groups were grossly under represented in math and science fields of study. They pointed out that at that time most comparative research studies on student attitudes, participation and achievement were descriptive studies on differences between male and female or whites versus minorities at the pre-college level or in four-year colleges, but not within the community colleges. Subsequently Ellis (1993) reported on his 1980’s work with high school minority students using integrated supplemental instruction in math, science, language arts and counseling revealed positive attitude changes toward science. Career aspirations Schoon (2001) reported that research into career aspirations of teenagers who wanted to become natural scientists, engineers, or health professionals demonstrated that interest in scientific careers develops quite early in life in comparison with other careers, and that once developed tended to persist. She indicated that her research showed scientific interests in boys are established between 10 to 14, whereas it took a little longer for adolescent girls. These career aspirations are quite unstable through the adolescent years and change many times before entering community colleges and attaining adulthood (2001). Other studies estimated that up to 75% of all students changed their major at least once before graduation from college (Foote, 1980; Kojaku, 1971; and Titley, Titley, & Wolff, 1976). Science majors tend to be more persistent than many other 32 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. majors (Astin, 1971), yet students with weak backgrounds in mathematics and science often found themselves having to change their career path when finding out that their abilities do not match the curricular requirements (Gordon, 1985). Another factor with career aspirations has been that an increasing number of students have found themselves frozen out of their desired career path in nursing, engineering or other sciences due to the fact of not having enough credit hours for admission or having too low of a grade point average in specified courses (Gordon, 1985). Gateway Science Courses Physics Course Research Research in various science programs at academic institutions has shown mixed results with persistence and retention. In Montreal, Canada researchers noted that the dropout rate has hovered around 60% in their physics programs for a number of years at the Universite de Montreal, which has been comparable to science programs at other institutions (Vazquez-Abad, Winer, & Derome, 1997; & Tobias, 1990). Previous studies on science program dropout (Drew, 1990; Halpin, 1990; Mallette & Cabrera, 1991; & Tinto, 1975) did not provide specific enough information to reveal with any reliability the root causes for students dropping out of the institution as they abandoned specific science programs, or why students persisted at the institution when dropping out of the program. A smaller number of studies have focused on dropout from specific programs, i.e. science programs (Donaldson & Dixon, 1995; Hudson & Rottmann, 1981; Rigden & Tobias, 1991; Seymour, 1992; Tobias, 1990; Wollman & Lawrenz, 1984). These studies also exhibited mixed results in respect to 33 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. academic performance, i.e. they indicated that past performance in mathematics and science isn’t always a significant factor in student dropout from science programs. Hart and Cottle (1993) on the other hand noted a strong correlation between proficiency in mathematics and success in passing physics courses in their research with Florida State University students. Their research using the most recent math course grade before taking College Physics A, showed a strong correlation between students with a higher mathematics proficiency being able to achieve a passing grade point in physics, whereas those students with lower mathematics proficiency were twice as likely to fail the course (Hart & Cottle, 1993). Another fundamental challenge to study of physics dealt with understanding the language of physics, which pertained to the rigorous and routine use of mathematical equations. Sherin (2003) addressed this topic often ignored by academics in their search for why students’ achievement or success in this discipline has been so low. Chemistry Course Research Donaldson and Dixon (1995) noted that in their research more females dropped introductory chemistry courses than their male counterparts. In science disciplines where women are in a minority environment, Williams (1990) noted that the intellectual and social climates have been “chilly” for females. Other factors contributing to the higher dropout rate in these chemistry courses include lack of social support from friends, pressures from low-income part-time work, and lower 34 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. grades associated with poor self-esteem (Gilligan, Lyons, & Hanmer, 1990; Williams, 1990). Rigden and Tobias (1991) found that “fewer than one out of fifty in beginning chemistry complete the major” (p. 16). A similar pattern was found in other introductory science classes at the college level around the nation. They further stated that in the early 90s there were only about 200,000 students completing a science degree out of nearly half a million who left high school planning to graduate in science or engineering. This huge exodus from the science disciplines has created a voting public that is uncomfortable or even hostile to science, as observed in public sectors with topics like chemical and biological evolution in Kansas this last decade. Biology Course Research In centuries past biology was more of a descriptive science, where Darwin and others drew sketches of the birds and creatures they observed in the field to document their research and develop their theories. Today mathematics, statistics, and other analytical tools are required for advanced procedural biological courses in universities and community colleges (Crow, 2004). When doing surveys of major universities Gross (1994) noted that many institutions did not have rigorous mathematical requirements for biology courses though, and further research revealed that many introductory biology texts avoided use of mathematics (Crow, 2004). Crow discovered that in many community colleges with “open door” admissions across the United States, that prerequisites rarely required students to be even college ready in mathematics for the introductory biology courses (2004).Jungck (1997) 35 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. earlier wrote that separating mathematics from biology “deskills many biology students, misrepresents contemporary biological research and under-prepares students to contribute to many areas of biology” (p. 11). This subject specific research revealed major barriers for students seeking high paying career jobs in medicine and health related careers dependent upon introductory community college biology courses. Baker (2000) noted in her recent Bioscience article on recruiting minorities into biological sciences that the National Science Foundation’s edition of the Women, Minorities, and Persons with Disabilities in Science and Engineering has documented poor representations of minorities in graduate science and engineering programs. Between 1985 and 1995 there was an increase in actual numbers of minorities in biological sciences, but the numbers are woefully small. African American students who excel in biology normally select medical, pharmacology, or microbiology careers over organismal biology as these health sciences offer high paying jobs. Many minorities interested in biology shy away from academic research and teaching as career choices due to their perception of these careers as not providing enough money or prestige in their cultural communities. Her research indicated that there was only one African American entomologist in the United States and no Hispanic ecologists, as minority cultural differences have hindered pursuit of these biological careers (Baker, 2000). Barlow and Villarejo (2004) found that minority student success was far below White counterparts. Even though minority students entering college are found 36 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. to be on an equal footing in their desire to pursue a science degree, the actual graduation results proved otherwise. They noted that of the minority students who entered college in 1989 intending to major in science or engineering, only 27% graduated in one of those fields by 1994, compared to 46% of their White and Asian counterparts who started at the same time (Barlow & Villarejo, 2004). Economic Factors Employment status (part-time vs. full-time) Work is a reality in the American post-secondary educational experience today. Researchers have found that a substantial number of students have been having to work while going to college (Luzzo, McWhirter, & Hutcheson, 1997). The National Center for Education Statistics (1996) data revealed that approximately 14.5% of all college students work 34 or more hours per week (full-time), while 10.9% do not work. Students working part-time with less than 15 hours per week made up 31.2% of the surveyed data, and those working from 15 to 33 hours completed the study results with 43.4% of the students. Some studies showed negative effects on academic effort-involvement and time allocated to studying (Pascarella, Edison, Nora, Hagedom, & Terenzini, 1998). Other studies have shown the possibility of positive impacts on critical thinking skills, and work related toward the student’s major or career can have positive relationships with early career success (Pascarella & Terenzini, 2005). Donaldson and Dixon (1995) indicated that part-time low wage work contributed to a higher attrition of female students taking science courses, 37 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. specifically large introductory first-year chemistry courses. The pressure of having to work more hours for the same pay as their male counterparts, and putting more time into study of this difficult subject, led many more female students to withdraw from the courses. They noted that this lower retention in turn has a detrimental effect on our rapidly changing society that needs more females in the sciences and technology (1995). The number of studies has been limited for community colleges in respect to employment relationships with student performance. Conclusions The literature review showed that educational research on students in four- year institutions has dominated the educational literature during the last century, and that there was a definite need for community college studies to complement these efforts (Braxton, 1999; Coppola, 1999; Hoyt, 1999). The review revealed not only a dearth of studies in the community college concerns of retention, transfer, and other well studied topics in the four-year institutions, but more so in math and science course research or factors relating academic success for students to enter gateway careers. Gainen (1995) identified introductory college science courses as ‘gateway’ courses into science related careers, and documented the lack of any significant amount of research on these courses and their relationship with student success. Since student completion of math and science courses in our community colleges served as vital gateways into higher paying careers in engineering, advanced technologies, modem biology research, health sciences, and medical sciences, this 38 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. has become a concern for not only students, but also educators, state governments funding sources, and society in general (Coppola, 1999; Hart & Cottle, 1993). Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. CHAPTER 3 RESEARCH METHODOLOGY Introduction This chapter included the research questions and an expanded description of the research methodology. The research methodology provided the research design, sampling procedures and population, instrumentation, data collection, validity and reliability, and final procedures for data analysis. Research Questions The general research aimed to establish the relationship between students’ high school grades; their level of high school and college math courses; their math, science and overall GPAs; their demographics (age, gender, and ethnicity); and their economic employment status in respect to their successful course completion of gateway science classes in the Los Angeles Community College District for achievement of transfer and graduation requirements. Specific research questions were listed below: QUESTION 1: What were the high school GPAs of the students enrolled in the LACCD? QUESTION 2: What level of math courses did the science students take in high school and college? QUESTION 3: What was the overall, math, and science community college GPAs and course completion ratios of students enrolled in the LACCD? 40 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. QUESTION 4: What were the student GPAs and course completion ratio relationships to the demographic variables of age, gender, and ethnicity, and to the economic variable of current employment status? QUESTION 5: What were the relationships between the highest level of high school and college math with overall, math, and science community college GPAs and course completion ratios of students in the LACCD? QUESTION 6: What were the gateway science biology, chemistry and physics course grades and course completion ratios of the LACCD students and their demographic and economic relationships? Research Methodology Research Design A leadership team of doctoral students and faculty from University of Southern California (USC) and the University of California, Los Angeles (UCLA) designed a Community College Student Survey in 2000 for use within a major multi year federally funded Transfer and Retention of Urban Community College Students (TRUCCS) project. Hagedom and Maxwell (2002) designed the TRUCCS project to emphasize explanation over description, so that neither random sampling nor random assignments would be feasible to meet conceptual objectives or the hypothesis about the effect of various college features on retention and transfer. The sampling design therefore included stratification for remedial and standard courses, vocational, learning communities, and gateway courses (Hagedom, 2001). 41 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Population and Sample Approximately 5,000 Los Angeles Community College District (LACCD) students in nine colleges participated in the multi-page survey consisting of 47 questions. This was a small, but representative sample of the nearly 120,000 students served by the LACCD (Hagedom & Maxwell, 2002). The nine colleges within the LACCD included Los Angeles City, East Los Angeles, Los Angeles Harbor, Los Angeles Mission, Los Angeles Pierce, Los Angeles Southwest, Los Angeles Trade- Tech, Los Angeles Valley, and West Los Angeles. The LACCD served a diverse population of students primarily in 36 cities throughout the greater Los Angeles area (Los Angeles Community College District, 2003). The fall 2000 LACCD enrollment data reflected a definite minority based population within the 110,000 plus students. Whereas in 1972 Whites dominated the campuses with 56.2%, their numbers dropped to only 20.0% of the student population in the fall of 2000 as this study began. Hispanics have increased from 16.1% in 1972 to 47.4% leading the transition movement to minority student dominance. Black student population has fluctuated up and down during this period and was recorded at 16.6% in 2000, while Asian students have more than doubled from 5.5% to 13.7%. All of the other ethnicities represented on the campuses are less than 3% of the population and of insignificance to this study. Gender wise, the females made up the majority of students at 64, 324 compared to the males at 46,486 in the fall of 2000 (LACCD, 2003). 42 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. This study established a sample set of students taking math and science classes from the TRUCCS database who were representative of the diverse population who needed to achieve successful transfer and graduation into gateway careers. The TRUCCS sample mirrored the diversity and the primary language groups of the LACCD. The TRUCCS project team deliberately over sampled the traditional age community college students (under 20 years old) representing approximately 34% of the sample. This was done to obtain an adequate number of transfer level classrooms that would allow the research team to follow the transfer students longitudinally for several years (Hagedom & Maxwell, 2002). Instrumentation The TRUCCS project team developed a six-page Community College Student Survey questionnaire consisting of 47 questions, available in Appendix A. Team members consisted of students and faculty from both USC and UCLA. Hagedom (2001) focused the research with internal validity of the design, so that the sampling was characteristic of quasi-experimental research. The approach was to maximize the variation in the independent variables in the sample relative to the hypotheses in order to have made internally valid comparisons of subgroups. Data Collection The initial surveys took place within the nine separate colleges in the LACCD starting in the fall of 2000. The leadership team recruited additional doctoral students in the spring of 2001 to administer a second phase of surveys. The survey administrators read an approved script, and distributed information sheets and 43 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. surveys to each student. Participating students signed permission releases (Sample in Appendix A) for research use of the survey forms, their college transcripts, and other district records. The transcript data included each student’s history within LACCD back to 1974. These surveys were collected at each of the nine campuses and hand carried back to USC for data processing into a master database. The TRUCCS team then tabulated, coded, and computerized the data taken from these surveys and processed them using SPSS software for data analysis. Validity and Reliability This study assumed that the following conditions had been met: a) the survey instrument usage within the LACCD had both construct validity and reliability, b) the survey participant’s one class period timeframe to answer the questions was adequate for answering each question as accurately as possible, c) the proctors for the survey used consistent procedures in administering the surveys, d) the data were compiled and computerized by USC and UCLA using standard data coding procedures, and e) the questionnaire data for both faculty and students were gathered and analyzed using "good" research methods. Data Analysis Initial analysis included descriptive tables describing the sample by age, gender, ethnicity and employment status. All research and analyses for this study used SPSS for Windows. The first steps in the pre-analysis involved identification of the math and science courses available to the students and determining student sample sizes. Science students need high levels of math, so identification of how 44 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. many students have taken various courses at specific levels in both high school and college including Algebra I, Geometry, Algebra II, Trigonometry, and the typography of college level math for AA or AS degree and transfer level math was necessary. The TRUCCS database and this research study used the following math coding for groupings of math courses taken in high school or college: • 0 - Remedial: No prerequisites are required to enter the course and the course was designed to teach the students the necessary skills to be successful in basic level courses and beyond. • 1 - Basic: There may be a prerequisite to join the course and the course was designed at a basic skills level aiding the students to master the basic skills needed to be successful in the advanced level courses. • 2 - Intermediate: There exists a prerequisite to enroll in the course and the course is beyond the basic understanding of the core concepts. Usually the course itself is indicated with the title of intermediate. However, the course does not provide transfer credit to either the University of California (UC) or the California State University (CSU) systems so is not at the advanced transfer level. • 3 - Advanced/Transfer: There exists a prerequisite to enroll in this class and the class is designed to teach concepts at the advanced level. Because of its nature, classes at this level are deemed transferable to the UC or the CSU systems. 45 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Selecting the appropriate science courses for study from the transcript file was necessary to categorize the natural sciences into two major categories for further analyses: life and physical sciences. Psychology, sociology, anthropology and other social sciences were not evaluated in this study that focuses on what is sometimes considered “hard sciences” for medical, health, engineering and high-technological gateway science careers. Life science included courses like anatomy, animal science, biology, plant science, etc., while physical sciences included astronomy, chemistry, computer science, geology, physics, etc. Table 1 identified the two primary categories of science courses used for the research statistics in this study. Table 1 Life and Physical Science Courses at LACCD Life Sciences Physical Sciences Anatomy Astronomy Animal Science Chemistry Biology Civil Engineering Botany Computer Science Nursing Earth Science Plant Science Economic Engineering Electrical Engineering Engineering Technology Environmental Science General Engineering Geography Geology Mechanical Engineering Meteorology Oceanography Physical Science Physics 46 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Most technical science courses used for technology work as opposed to gateway science careers were not used, such as computer technology, fire science, wastewater, etc. Once the science syntax used in this study was established as a subset of all the available science courses at LACCD through a manual evaluation of course titles, then the students’ overall sample size was established for those students taking these math and science courses versus all other courses to develop an overall GPA for comparison. The next phases of pre-analysis necessitated establishing course grades, calculating grade point averages (GPAs) and determining the course completion ratios (CCRs). This required calculating college GPAs and CCRs for math courses, science courses, and overall GPA for the students in this sample population from transcript data. The following step required merging the transcript file data into the TRUCCS survey file for analysis. Initial frequency statistics provided basic means and standard deviations for each of the variables. Then a second set of analyses entailed running group statistics t-test frequencies and independent samples t-tests on the transcript file of students comparing college math, science and overall GPA as well as CCRs versus dichotomous demographic variables of age and gender. In addition, a series of one way ANOVA statistical analyses provided information relating multiple category variables of ethnicity and employment status. Analyses also included an evaluation of average grade (GPA) in high school and identification of math courses taken by these students in high school and college to determine any correlation relationships. 47 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. These analyses then provided additional segments of a profile of the math and science course students within this TRUCCS database for further research. This study also entailed evaluating dichotomous variables of a high GPA of science gateway course grades with “B and above” versus just passing the course with a “C and above” to distinguish those students who have good chances of succeeding in gateway science courses versus just passing the course to meet transfer or graduation requirements. A matrix table with the three major gateway course grade thresholds of “B and above” and with a “C and above” plus corresponding math and science GPA frequencies for these levels of accomplishment was developed to evaluate transfer potential into gateway careers. 48 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. CHAPTER 4 ANALYSIS OF THE DATA AND PRESENTATION OF THE FINDINGS Introduction This research study evaluated nearly 5,000 students from the overall TRUCCS database to address specific research questions. This study included frequency distributions, means, standard deviations, group statistics t-tests, and one way analysis of variance (ANOVA) tests of selected variables. The independent demographic variables of age, gender, and ethnicity, plus the economic employment status of the students from the TRUCCS survey (Appendix A) were described at the beginning of this chapter in Tables 2 - 6 for comparison purposes throughout the study. Literature review provided a documented need for community college research to collect and analyze data with emphasis on the dependent and independent variables used in this study for evaluations of retention, transfer and graduation studies (Coppola, 1999). Research Questions The general research objectives were restated here in the form of research questions to establish the relationship between students’ high school grades; their level of high school and college math courses; their math, science and overall GPAs; their demographics (age, gender, and ethnicity); and their economic employment status in respect to their successful course completion of gateway science classes in the Los Angeles Community College District for achievement of transfer and 49 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. graduation requirements. These six research questions established the layout of the remainder of this chapter: QUESTION 1: What were the high school GPAs of the students enrolled in the LACCD? QUESTION 2: What level of math courses did the science students take in high school and college? QUESTION 3: What was the overall, math, and science community college GPAs and course completion ratios of students enrolled in the LACCD? QUESTION 4: What were the student GPAs and course completion ratio relationships to the demographic variables of age, gender, and ethnicity, and to the economic variable of current employment status? QUESTION 5: What were the relationships between the highest level of high school and college math with overall, math, and science community college GPAs and course completion ratios of students in the LACCD? QUESTION 6: What were the gateway biology, chemistry, and physics course grades; the transfer potential of these grades versus math and science GPAs; and course completion ratios of the LACCD students and their demographic and economic relationships? Research Presentation Tables 7 through 9 presented “self-reported” high school grades and the evaluation of the highest level of math courses taken in high school and college for means and their frequency distributions to include significance of variances to 50 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. address the first two research questions. Questions 3 and 4 were addressed simultaneously to investigate the overall college, math and science GPAs, and overall college, math and science course completion ratio (CCR) and to determine their relationships with the independent variables. This grouped each set of variables together for both frequency and variance of difference testing together. Tables 10 through 26 presented the data for these analyses as the primary dependent variables in this study to serve as a database for future research. Furthermore this study showed in Tables 27 to 30 the relationship between the highest level of high school and college math taken with the overall, math, and science community college GPAs and CCRs of these students to explore answers to research question 5. The final analysis dealt with a review of the three primary gateway science biology, chemistry and physics courses. Tables 31-45 provided the specific gateway course’s average grades and CCRs to include their central tendency characteristics, descriptive statistics, group statistics t-tests and one-way ANOVA tests. Table 32 additionally provided a matrix comparison of gateway science course transfer grade thresholds to math and science GPA. Background Demographic & Economic Factors The demographic variables shown in Tables 2 through 5 presented an initial profile of the math and science students at the LACCD for comparison purposes with selected dependent variables. Table 6 added the economic factor of employment status for additional breadth of analysis for comparison with literature. These data were extracted from the TRUCCS Survey (Appendix A) questions as follows: (a) 51 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. “Question 28: Your gender,” (b) “Question 29: How old will you be on December 31 of this year?”, (c) “Question 30: What is your ethnic group(s)?”, and (d) “Question 35: Which one of the following best describes your employment status at this time.” Table 2 established the sample population (N) at 4719 out of the larger study group of nearly 5,000 students and the means for the two dichotomous variables of age and gender. The overall large size of the sample just below 5000 students lent itself well to credible significance of difference testing by t-tests or one-way ANOVA. Table 3 established the dichotomous age groups for study as students under 30 years old, coded as 1, and as students 30 and older, coded as 2. The mean of 1.29 shown in Table 2 indicated a much younger sample population, but remember that the TRUCCS project team deliberately over sampled the traditional age community college students (under 20 years old) representing approximately 34% of the sample. This was done to obtain an adequate number of transfer level classrooms that would allow the research team to follow the transfer students longitudinally for several years (Hagedom & Maxwell, 2002). The males were Table 2 Dichotomous Demographic Variable Population and Means Age Gender Sample Size 4719 4719 Mean 1.29 1.61 coded as 1 and females as 2. Table 2’s higher mean of 1.61 for gender indicated a higher proportion of females (57.4% as seen in Table 4), which agreed exceedingly 52 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. well with the overall population of the LACCD at 58% for females when this study was initiated in the fall of 2000 (LACCD, 2003). The distribution statistics shown in Table 3 indicated that 67.7% of the students were under age 30 in this sample population for valid comparisons before removal of the 249 missing students, leaving only 27.3% of the students in the age 30 or older category. Table 3 Dichotomous Age Frequency Statistics Frequency Percent Age < 30 3364 67.7 Age > 30 1355 27.3 Sample Size 4719 95.0 Missing Cases 249 5.0 Total 4968 100.0 Table 4 depicted a higher number of females taking courses in this sample than males. The females made up 57.4% of the sample with a 5% unknown factor associated with 249 missing students, compared to 37.5% males. In other words, Table 4 Dichotomous Gender Frequency Statistics Frequency Percent Male 1865 37.5 Female 2854 57.4 Sample Size 4917 95.0 Missing Cases 249 5.0 Total 4968 100.0 53 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. there were a significantly higher number of young females in this LACCD student sample population, which was close to the overall LACCD female population value of 58% (LACCD, 2003). Those of Hispanic ethnicity represented the largest group in the TRUCCS sample population as demonstrated in Table 5 with a 46.4% frequency in comparison with the other three major subgroups. This sample frequency for Hispanic students compared favorably with the 47.4% for the overall LACCD population as determined in a separate LACCD study conducted in the fall of 2000 at nearly the same time as when this TRUCCS study database was established (LACCD, 2003). African American, Asian and White ethnicities made up the balance of the four distinct population groups normally recorded by LACCD surveys (LACCD, 2003). The other three smaller ethnic groups made up a combined 36.1%. The African American students made up the second highest percentage of this TRUCCS sample population at 13.5% with Asian and White students nearly matched at just above 11%. The data also contained another 12.4% of students of mixed ethnicities plus those who “refused to state” their ethnicity, and other ethnicities that were of such small numbers as to be insignificant for study comparisons. The two minor subgroups - American Indian/Alaskan Native and Pacific Islander, which only consisted of 25 students total, were of no significant value for inclusion in this study. All data other than the primary four ethnicities were combined into one category entitled “Other” for reference and ease of display. 54 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 5 Ethnicity Frequency Distribution Statistics Frequency Percent White 553 11.1 Asian 572 11.5 Hispanic 2303 46.4 African American 673 13.5 Other 619 12.4 Sample Size 4720 95.0 Missing Cases 248 5.0 Total 4968 100.0 The corresponding independent variable for economic factor relationship analyses shown in Table 6 pertained to current employment status with a valid 36.5% of the students indicating part-time employment. The part-time students accounted for the largest group of students working followed by 32.2% working full time. After accounting for 155 students missing, which was the lowest number of missing student data of the four demographic variables, over two thirds (68.7%) of the students were working part or full-time while going to school. The frequency Table 6 Current Employment Status Statistics Frequency Percent not employed/not looking 586 11.8 not employed/looking 818 16.5 employed part-time 1811 36.5 employed full-time 1598 32.2 Sample Size 4813 96.9 Missing Cases 155 3.1 Total 4968 100.0 55 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. data revealed that the smallest group of students was 11.8% for those ‘not employed and not looking,’ probably from more economically secure family environments or with sufficient financial aid or scholarship support to avoid having to work. The last group of 16.5% stated they were not employed, but looking for employment. Findings Findings Related to Research Question 1: What were the high school GPAs o f the students enrolled in the LACCD? Table 7 depicted the range of “self-reported” high school grades taken from the TRUCCS survey question 24 (Appendix A), their frequency and percentages. Survey Question 24 specifically asked, “What was your average grade in high school?” The mean high school grades from “self-reported” data were 5.47 or just above a B- using the range from D or lower up to an A or a nine-point scale (D or lower = 1, C- Table 7 Average “Self-Reported” High School Grades for TRUCCS Student Survey Group Frequency Percent A 249 5.0 A- 425 8.6 B+ 839 16.9 B 890 17.9 B- 836 16.8 C+ 779 15.7 c 575 11.6 c- 137 2.8 D or lower 66 1.3 Sample Size 4796 96.5 Missing Cases 172 3.5 Total 4968 100.0 56 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. = 2, C = 3, etc., respectively). The median and mode as calculated with SPSS Windows were both a 6.00 (B) on this nine-point scale. Findings Related to Research Question 2: What level o f math courses did the science students take in high school and college? The data in Tables 8 and 9 recorded the highest level of math courses taken in high school from “self-reported” information for the high school pre-enrollment data using answers to TRUCCS survey Question 25 (Appendix A). This compound question was as follows: “Before this semester, what mathematics courses have you taken? Include courses in high school or previous college work.” Actual TRUCCS transcript records were also used to verify college math courses taken for these two tables. The TRUCCS database and this research study used the following math coding for groupings of math courses taken in high school or college: • 0 - Remedial: No prerequisites are required to enter the course and the course was designed to teach the students the necessary skills to be successful in basic level courses and beyond. • 1 - Basic: There may be a prerequisite to join the course and the course was designed at a basic skills level aiding the students to master the basic skills needed to be successful in the advanced level courses. • 2 - Intermediate: There exists a prerequisite to enroll in the course and the course is beyond the basic understanding of the core concepts. Usually the course itself is indicated with the title of intermediate. However, the course does not provide transfer credit to either the UC or the CSU systems so is not at the advanced transfer level. 57 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. • 3 - Advanced/Transfer: There exists a prerequisite to enroll in this class and the class is designed to teach concepts at the advanced level. Because of its nature, classes at this level are deemed transferable to the UC or the CSU system. In Table 8 the mean of the highest level of math courses taken at the college level was higher at 2.0 than the 1.5 in high school math courses. What this revealed, when using the math course level coding of zero to 3 as defined above, was that the mean high school math course frequency was highest between the basic and intermediate course levels, and in college the mean shifted closer to the advanced/transfer level of courses, which agreed with data found in Table 9. Table 8 Highest Level of Math Taken in High School and College Means The frequency data in Table 9 indicated that the highest frequency of student achievement was at the intermediate math level with 42.2 % in high school. In college the highest frequency of achievement of math courses shifted numerically down to 37.2 % and moved population-wise from the intermediate level up to the advance/transfer level math courses. This left 27.3 cumulative percent of the students in the remedial or basic, not qualifying for transfer or graduation. The data also revealed a significant drop in the number of remedial students from high school High School College Sample Size Missing Cases Mean 4689 3971 279 997 1.5 2.0 58 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. to college or from 21% to 10%, while the number of students or percentage for basic level math students stayed relatively unchanged. Another observation pertained to the amount of missing information, which significantly increased from 5.6% in high school to 20.1% in college level or from 279 missing data to 997 using the high school TRUCCS survey data and college TRUCCS transcript data. Table 9 Table 9Highest Level of Math Taken in High School and College Frequency Statistics High School Frequency Percent Remedial 1041 21.0 Basic 807 16.2 Intermediate 2096 42.2 Advanced/Transfer 745 15.0 Sample Size 4689 94.4 Missing Cases 279 5.6 Total 4968 100.0 College Frequency Percent Remedial 495 10.0 Basic 859 17.3 Intermediate 771 15.5 Advanced/T ransfer 1846 37.2 Sample Size 3971 79.9 Missing Cases 997 20.1 Total 4968 100.0 Findings Related to Research Question 3 & 4: What were science community college GPA and course completion ratios o f students enrolled in the LACCD? What were the student GPA and course completion ratio relationships to the demographic variables o f age, gender, ethnicity, and current employment status? The SPSS data in Table 10 through 26 addressed the answers to research questions 3 and 4. Table 10 initialized this analysis sequence with the mean values 59 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. for college overall, math and science GPAs for the student population reviewed in this research study. The overall college GPA was noticeably higher than both math and science GPAs in Table 10 and throughout the review of research question 3 and 4 data in Tables 11 through 18. Likewise the science GPAs were always higher than math GPAs in comparison with the demographic and economic status variables. Table 10 College Overall, Math and Science GPA Means College GPA Mean Overall 2.49 Math 2.17 Science 2.36 Table 11 provided the first look at the dichotomous age demographic variable in respect to these dependent college GPA variables. It was apparent that those 30 years old and above had mean GPAs that were higher in all three comparisons, with overall, math, and science GPAs. The college overall GPA means (for both age groups) were by far the highest with science mid-level and the math GPA as the Table 11 Age Comparisons with College GPA Dichotomous Age College Overall College Math College Science Variable GPA GPA GPA Age < 30 Sample Size 3292 2577 2411 Missing Cases 72 787 953 Mean 2.39 2.07 2.27 Age > 30 Sample Size 1334 912 846 Missing Cases 21 443 509 Mean 2.74 2.44 2.59 60 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. lowest. The data also revealed a much larger missing portion of student grades for math and science as compared to the college overall GPA, which lowers the sample size. The independent samples t-test in Table 13 indicated that a significant difference existed between the two age groups for the overall GPA, but differences between age groups for math and science were not significant at the p < .05 level. Table 12 Group Statistics t-test of College GPA versus Age College GPA Dichotomous Age Variable Sample Size Mean Standard Deviation Overall Age < 30 3292 2.39 .81 Age > 30 1334 2.74 .75 Math Age < 30 2577 2.07 1.08 Age > 30 912 2.44 1.07 Science Age < 30 2411 2.27 1.02 Age > 30 846 2.59 1.05 Table 13 Independent Samples t-test for Equality of Variances for College GPA versus Age College GPA Levine’s Test for Equality of Variances F Sig. Overall Equal Variances Assumed 9.91* .00 Math Equal Variances Assumed .31* .56 Science Equal Variances Assumed 1.43* .23 * p < .05 The frequency data for the second demographic dichotomous variable of gender when evaluated in respect to college GPAs provided very similar observations in Tables 14 and 15. It was apparent that the female students’ mean GPAs were higher than males in all three comparisons with overall, math, and 61 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. science GPAs. These data also revealed a larger number of missing student grades for math and science as compared to the college overall GPA, as shown likewise in the age variable data in Table 11. The college overall GPA means for both genders were by far the highest with science next highest and math GPA as the lowest. Table 14 Gender Comparisons with College GPA Dichotomous Gender College Overall College Math College Science Variable GPA GPA GPA Male Sample Size 1827 1337 1297 Missing Cases 38 528 658 Mean 2.39 2.08 2.27 Female Sample Size 2799 2152 1960 Missing Cases 55 702 894 Mean 2.56 2.22 2.41 Table 15 Group Statistics t-test of College GPA versus Gender College GPA Gender Sample Size Mean Standard Deviation Overall Male 1827 2.39 .85 Female 2799 2.56 .78 Math Male 1337 2.08 1.11 Female 2152 2.22 1.08 Science Male 1297 2.27 1.07 Female 1960 2.41 1.00 In Table 16 the independent samples t-test for equality of variances revealed that a significant difference existed between the two genders for the overall GPAs 62 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. and science GPAs, but math GPAs were not significant at the p < .05 level for the differences between the two genders. Table 16 Independent Samples t-test for Equality of Variances for College GPA versus Gender College GPA Levine’s Test for Equality of Variances F Sig. Overall Equal Variances Assumed 9.67* .00 Math Equal Variances Assumed .65* .42 Science Equal Variances Assumed 6.09* .01 * p < .05 The third demographic variable (ethnicity) in Table 17 compared college GPAs. The college overall GPA means indicated that the White and Asian students achieved the highest GPAs at 2.82 and 2.71 respectively with the Hispanic and African American nearly equal in class standings at 2.39 and 2.31. In the math GPAs there was a noticeable drop in the means for all four categories. The Asian students demonstrated a reversal with the White students, as their mean GPAs were highest at 2.63 compared to 2.52 for the White students. The Hispanic and African American students maintained the same relationship with Hispanic students faring slightly higher at 2.03 mean GPAs compared to 1.97 for African Americans. The science GPA data ranked the Asian students again as highest with a mean of 2.66 followed by the White students at 2.59, and the Hispanic and African American were lowest, but much closer statistically together this time at 2.23 and 2.20, respectively. The one-way ANOVA between ethnicity groups demonstrated a significant difference for the three GPA categories (overall, math and science) at p < .05. 63 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 17 Ethnicity Comparisons with College GPA and the One-Way ANOVA Significance of Difference Ethnicity Variable College Overall GPA College Math GPA College Science GPA White Sample Size 537 354 378 Missing Cases 16 199 175 Mean 2.82 2.52 2.59 Asian Sample Size 553 414 438 Missing Cases 19 158 134 Mean 2.71 2.63 2.66 Hispanic Sample Size 2264 1761 1555 Missing Cases 39 542 748 Mean 2.39 2.03 2.23 African Sample Size 667 500 447 American Missing Cases 6 173 226 Mean 2.31 1.97 2.20 df Between Groups 4 4 4 F Between Groups 53.82* 40.01* 24.79* Sig. Between Groups .00 .00 .00 * p < .05 The current employment status variables shown in Table 18 presented several interesting relationships that stood out immediately. Those students who were “not employed and are not looking” held the highest GPA means across all three categories of overall college, math and science GPAs. It was also apparent that the overall GPA means were far better than math and science GPAs alone. In addition, both the part-time and full-time student GPA means were higher than the group of students who were “seeking to find jobs and/or need employment.” The data also showed that the science GPA means were higher than math GPA for all employment status categories. The one-way ANOVA between groups in Table 18 indicated a Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 18 Current Employment Status Comparisons with College GPA and the One-Way ANOVA Significance of Difference College Overall College Math College Science Current Employment Status GPA GPA GPA Not employed/ Sample Size 535 398 396 not looking Missing Cases 51 188 190 Mean 2.68 2.41 2.55 Not employed/ Sample Size 760 588 522 looking Missing Cases 58 230 296 Mean 2.40 1.99 2.26 employed Sample Size 1707 1360 1273 part-time Missing Cases 104 451 538 Mean 2.49 2.17 2.34 employed Sample Size 1508 1062 990 full-time Missing Cases 90 536 608 Mean 2.47 2.18 2.36 df Between Groups 3 3 3 F Between Groups 13.52* 11.37* 6.19* Sig. Between Groups .00 .00 .00 * p < .05 significant difference between the employment status groups for the overall, math, and science GPA categories using p < .05 level. The next series of tables established relationships between the independent demographic variables and the college course completion ratio (CCR) variable. Table 19 provides the comparison of age to CCR for the overall courses in comparison with the math and science. This table revealed that the older student’s completion ratios were higher across the board with overall, math and science respectively. An interesting observation was that the science CCR of 84% for older students is actually better than the math (80%) or overall (74%) respectively. In addition, it is noted that the younger students also reflected a higher CCR for science 65 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (78%) than math (70%) or overall (67%), with overall completion ratio being lowest in both age groups. In other words, science CCR was higher for both age groups in comparison with math and overall CCRs within this sample. Table 19 Age Comparisons with College Course Completion Ratio Dichotomous Age College Overall College Math College Science Variable CCR CCR CCR Age < 30 Sample Size 3309 2577 2411 Missing Cases 55 787 953 Mean 67% 70% 78% Age > 30 Sample Size 1343 912 846 Missing Cases 12 443 509 Mean 74% 80% 84% Tables 20 and 21 similarly provided group statistics t-test of college course completion versus two age groups, age < 30 and age > 30, and independent samples t-test for equality of variances for college course completion ratio versus age. The data shown in Table 20 demonstrated that the older students achieved higher means for course completion ratios than for both age groups. Table 20 Group Statistics t-test of College Course Completion Ratio versus Age Dichotomous Mean Standard College CCR Age Variable Sample Size Percent Deviation Overall Age < 30 3309 67 .24 Age > 30 1343 74 .22 Math Age < 30 2577 70 .36 Age > 30 912 80 .31 Science Age < 30 2411 78 .33 Age > 30 846 84 .30 66 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. The independent samples t-test for equality of variances in Table 21 indicated that a significant difference existed between the two age groups for all three CCR categories (overall, math and science) at the p < .05 level. Table 21 Independent Samples t-test for Equality of Variances for College Course Completion Ratio versus Age College CCR Levine’s Test for Equality of Variances F Sig. Overall Equal Variances Assumed 21.16* .00 Math Equal Variances Assumed 66.84* .00 Science Equal Variances Assumed 26.62* .00 * p < .05 The demographic gender variable comparison to CCR in Table 22 presented an interesting observation where the female student data reflected higher mean completion ratios than males in all CCR categories for this study group. The females at 82% completion ratio for science courses not only exceeded the male’s 77% ratio, but also were higher than all math and overall CCRs. Both the males and females appeared to have a higher CCR for science than math or overall. Correspondingly, the math CCRs were higher than the overall student course performance, unlike GPA in Tables 14 and 15 where the overall GPA was higher across the board. Table 23 provided group statistics for college CCRs versus gender. These data showed a higher college CCR for females for all three categories of overall, math and science with science being the highest at 82%, followed by math (74%) and finally the overall CCR at 70%. The males performed less well in CCR ranging from a high of 77% for science to a low of 66% for overall CCR in the same sequential order. No 67 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. significant difference existed between the genders for all three categories of CCR (overall, math, and science). Table 22 Gender Comparisons with College Course Completion Ratio Dichotomous Gender College Overall College Math College Science Variable CCR CCR CCR Male Sample Size 1839 1337 1297 Missing Cases 26 528 568 Mean 66% 70% 77% Female Sample Size 2813 2152 1960 Missing Cases 41 702 894 Mean 70% 74% 82% Table 23 Group Statistics t-test of College Course Completion Ratio versus Gender College CCR Gender Sample Size Mean Percent Standard Deviation Overall Male 1839 66 .25 Female 2813 70 .23 Math Male 1337 70 .37 Female 2152 74 .34 Science Male 1297 77 .34 Female 1960 82 .31 Table 24 presented the independent samples t-test for equality of variances for college CCR versus gender. Table 24 exhibited a significant difference for gender versus all three categories at the p < .05 level, although there was a considerable amount of missing cases noted in Table 22 for math and science CCRs. 68 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 24 Independent Samples t-test for Equality of Variances for College Course Completion Ratio versus Gender College CCR Levine’s Test for Equality of Variances F Sig. Overall Equal Variances Assumed 21.11* .00 Math Equal Variances Assumed 18.89* .00 Science Equal Variances Assumed 25.84* .00 * p < .05 In Table 25 the science completion ratio ranked highest in all four ethnicities above the math and overall CCR. As noted with age and gender, the science CCR was highest followed by math with overall as lowest. When comparing the four Table 25 Ethnicity Comparisons with College Course Completion Ratio and the One-Way ANOVA Significance of Difference College Overall College Math College Science Ethnicity Variable CCR CCR CCR White Sample Size 544 354 378 Missing Cases 9 199 175 Mean 74% 81% 83% Asian Sample Size 555 414 438 Missing Cases 17 158 134 Mean 75% 82% 85% Hispanic Sample Size 2277 1761 1555 Missing Cases 26 542 748 Mean 67% 70% 79% African Sample Size 668 500 447 Americani Missing Cases 5 173 226 Mean 63% 66% 75% df Between Groups 4 4 4 F Between Groups 29.02* 19.64* 6.35* Sig. Between Groups .00 .00 .00 * p < .05 69 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. major ethnicity groups, the Asian students set the pace for highest science completion ratios at 85%, with Whites second at 83%, and then Hispanics at 79% and finally African Americans at 75%, which was the same order of ranking as noted for the science GPAs comparisons in Table 17. The math and overall completion ratios demonstrated the same rank order from Asian to African American. The one way ANOVA identified a significant difference between ethnicities for all three CCR categories (overall, math and science). Table 26 comparison of current employment status to college CCR reflected the “not employed and not looking” for a job subgroup achieving the highest CCR for all three CCR categories. Science CCR ranked highest for all four-employment Table 26 Current Employment Status Comparisons with College Course Completion Ratio and the One-Way ANOVA Significance of Difference College Overall College Math College Science Current Employment Status CCR CCR CCR not employed/ Sample Size 537 398 396 not looking Missing Cases 49 188 190 Mean 74% 77% 83% not employed/ Sample Size 767 588 522 looking Missing Cases 51 230 296 Mean 67% 67% 77% employed Sample Size 1715 1360 1273 part-time Missing Cases 96 451 538 Mean 69% 73% 80% employed Sample Size 1517 1062 990 full-time Missing Cases 81 536 608 Mean 67% 73% 80% df Between Groups 3 3 3 F Between Groups 11.01* 6.58* 1.99* Sig. Between Groups .00 .00 .11 * p < .05 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. status categories with math in second place and overall last. The not employed and actually looking for employment again recorded the lowest CCR in overall, science and math as were the case for lowest GPA. The one-way ANOVA showed a significant difference for overall and math CCR categories versus the employment status of these students, but not for science CCR when using the p < .05 level. Findings Related to Research Question 5: What were the relationships between the highest level o f high school and college math with overall, math, and science community college GPA and course completion ratios of students in the LACCD courses? The next set of Tables 27 through 30 address research Question 5, which evaluated the relationships between the highest level of high school and college math courses with college overall, math, and science GPAs and CCRs. Table 27 presented the means for the four levels of math course accomplishment of remedial, basic, intermediate and advanced/transfer and provided the one-way ANOVA for significance. These data indicated that the intermediate level high school math courses had the highest numbers of students across all three college overall, math and science GPA categories. They also reflected that the highest mean GPAs were observed in the advanced/transfer high school level math courses across all three college overall, math and science GPA categories. Furthermore, the college overall GPA means were higher for all high school levels of comparison, with college science GPA means mid-level, and college math GPA means were the lowest. In addition, Table 27 depicted that with the exception of the remedial means being equal to or higher than the basic level across all three GPA categories, the GPAs 71 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 27 Highest Level of High School Math Course Comparisons with College GPAs and the One-Way ANOVA Significance of Difference Highest Level of College Overall College Math College Science High School Math GPA GPA GPA Remedial Sample Size 982 676 583 Mean 2.39 1.97 2.21 Basic Sample Size 769 611 534 Mean 2.32 1.97 2.21 Intermediate Sample Size 1957 1545 1451 Mean 2.49 2.18 2.35 Advanced/ Sample Size 687 544 564 Transfer Mean 2.86 2.64 2.73 df Between Groups 3 3 3 F Between Groups 67.96* 49.75* 32.75* Sig. Between Groups .00 .00 .00 * p < .05 increased in value from one math level to the next. The one-way ANOVA between the highest levels of high school math when compared to college GPAs reflected a statistically significant difference between the high school math levels taken for the GPAs of overall, math and science categories. Table 28 revealed that the intermediate level high school math courses again had the highest numbers of students across all three-course CCR categories. Furthermore, the highest CCR percentages were observed in the advanced/transfer high school level math courses across all three college level CCR categories. Note that the college overall CCR percentages were lowest for all high school levels of comparison this time, with college science CCR as highest, and college math CCR 72 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. percentages as mid-level values. In addition, the data indicated that with the exception of the remedial ratios being slightly higher than the basic level across all three CCR categories, the CCRs increased in value from one math level to the next. The one-way ANOVA between the highest levels of high school math when compared to college CCR reflected a significant difference between all math levels for the three CCR categories (overall, math and science) at the p < .05 levels. Table 28 Highest Level of High School Math Course Comparisons with College Course Completion Ratio and the One-Way ANOVA Significance of Difference Highest Level of High School Math College Overall CCR College Math CCR College Science CCR Remedial Sample Size 989 676 583 Mean 66% 68% 77% Basic Sample Size 771 611 534 Mean 65% 67% 76% Intermediate Sample Size 1964 1545 1451 Mean 69% 73% 80% Advanced/ Sample Size 693 544 564 Transfer Mean 78% 84% 87% df Between Groups 3 3 3 F Between Groups 50.44* 29.80* 13.69* Sig. Between Groups .00 .00 .00 * p < .05 The comparisons between the highest level of college math courses with college overall, math and science GPA means in Table 29 provided a shift in student populations. The data demonstrated that the advanced/transfer college level math courses had the highest numbers of students across all three GPA categories instead 73 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. of intermediate level math as observed with high school students in Table 27. The highest mean GPAs were also found in the advanced/transfer college level math courses across all three college level GPA categories, as noted in Table 27. The college overall GPA means were again higher for all college levels of math course comparison, with college science GPA means second highest, and college math GPA means the lowest. Table 29 Highest Level of College Math Course Comparison with College GPAs and the One-Way ANOVA Significance of Difference Highest Level of College Overall College Math College Science College Math Course GPA GPA GPA Remedial Sample Size 492 292 212 Mean 2.03 1.43 1.83 Basic Sample Size 858 705 513 Mean 2.30 1.93 2.12 Intermediate Sample Size 771 715 578 Mean 2.41 1.99 2.15 Advanced/ Sample Size 1844 1777 1701 Transfer Mean 2.75 2.46 2.60 df Between Groups 3 3 3 F Between Groups 169.46* 114.97* 72.77* Sig. Between Groups .00 .00 .00 * p < .05 Furthermore, these data revealed that the GPAs increased in value from one math level to the next from remedial to advanced/transfer across all three GPA categories. The one-way ANOVA between the highest levels of college math when compared to college GPAs reflected a significant difference between the highest Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. levels of college math taken for all three GPA categories of overall, math and science. Table 30 revealed that the advanced/transfer level college math courses had the highest numbers of students across all three college overall, math, and science course CCR categories, which shifted from the high school intermediate level in Table 28. The highest level of CCR percentages were also found in the advanced/transfer college level math courses across all three college overall, math and science CCR categories. The lowest CCRs were found with the remedial math courses in Table 30, whereas in Table 28 the lowest CCRs were in the basic math level courses. The column of college overall CCR percentages reflected the lowest values for the top three levels of college math courses taken, but when it came to remedial math level, the math CCR was lowest at 46%. The column of college science CCRs data indicated the science CCRs were highest, and the college math CCR percentages were mid-level values, except for the remedial level courses as noted above. The data also showed that the CCRs increased in value from one math level to the next from remedial to advanced/transfer across all three college overall, math and science GPA categories, with the exception that the science basic and intermediate level math course CCRs are equal in value at 75%. The one-way ANOVA indicated a significant difference between the four distinct highest levels of college math taken groups for all three CCR categories of college science, math and overall to demonstrate validity in these data at the p < .05 level. 75 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 30 Highest Level of College Math Course Comparisons with College Course Completion Ratio and die One-Way ANOVA Significance of Difference Highest Level of College Math Course College Overall CCR College Math CCR College Science CCR Remedial Sample Size 495 292 212 Mean 55% 46% 62% Basic Sample Size 859 705 513 Mean 62% 64% 75% Intermediate Sample Size 771 715 578 Mean 67% 68% 75% Advanced/ Sample Size 1846 1777 1701 Transfer Mean 77% 82% 87% df Between Groups 3 3 3 F Between Groups 193.03* 129.16* 62.41* Sig. Between Groups .00 .00 .00 * p < .05 Findings Related to Research Question 6: What were the gateway biology, chemistry, and physics course grades; the transfer potential o f these grades versus math and science GPAs; and course completion ratios o f the LACCD students and their demographic and economic relationships? The next area of study dealt with evaluating three individual science courses (biology, chemistry and physics), the three primary gateway science courses for transfer and graduation requirements in relationship to research Question 6. The following tables provided means, descriptive statistics, t-tests and one-way ANOVA for these three primary science courses. In addition, this research addressed the comparison of these three gateway science course transfer level grades of C and above, and B and above with the math and science GPAs. 76 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 31 specifically addressed the average grades for these three courses with central tendency descriptive statistics. The smaller populations of these three specific gateway science courses were noted as 1407,639 and 234 (respectively) for the biology, chemistry and physics subjects. The corresponding higher numbers of students not taking these gateway science courses were labeled as missing cases for this particular evaluation of TRUCCS sample database in relationship to question 6. Table 31 Descriptive Statistics for College Biology, Chemistry and Physics Course Grades Biology Chemistry Physics Sample Size 1407 639 234 Missing Cases 3561 4329 4734 Mean 2.31 2.60 2.90 Median 2.00 3.00 3.00 Mode 2.00 2.00 4.00 Standard Deviation 1.07 1.07 .97 The physics students with only 234 students had by far the highest-grade averages with a mean of 2.90, a median of 3.00 and a mode of 4.00, and the smallest standard deviation at .97. The subpopulation of the physics group was smallest when compared with the biology student group being the largest and chemistry mid-level in size. The chemistry student subpopulation was intermediate not only in size, but also with an intermediate mean grade average of 2.60, a median of 3.00 and mode of 2.00. The biology student group, the largest subpopulation, had the lowest grade mean at 2.32, with also the lowest median and mode. The biology grade’s standard deviation was the same as for the chemistry group at 1.07. Note that the standard 77 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. deviation for physics is significantly smaller indicating a narrow variance for the physics student’s mean grades, probably due to the smaller population of 234 students. Table 32 provided an analysis of these three gateway science course grades from descending frequency statistics and compared them to the math and science GPA means. The data indicated that the percentage of physics students receiving C or better (92.3%) and B or better (62.4%) was much higher than for the other two gateway science courses. The gateway science course statistics also demonstrated significantly higher numbers of students achieving transfer level grades than the corresponding numbers of students for the grouped college level math and science courses. Only 64.1% of the students taking math courses were achieving a passing grade with a C or better. Only 73.4% of the students taking general life and physical science courses were achieving transfer level grades. The percentages of students achieving B or better in the gateway courses appeared much higher than found in a normal bell curve distribution. Table 32 Gateway Science Course Transfer Level Grade Statistics Comparisons with All Math and Science Course Transfer Level Grade Statistics Courses >C > B Biology 78.4% 39.9% Chemistry 83.9% 52.6% Physics 92.3% 62.4% Math 64.1% 29.6% Science 73.4% 34.1% 78 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Group statistics and independent samples t-tests for equality of variances of college biology, chemistry and physics course grades versus demographic age group variables in Tables 33 and 34 complemented the initial central tendency data in Table 31. Table 33 data indicated that the older students in all three-subject areas achieved higher-grade means than their counterpart younger students under the age of 30. Physics students demonstrated higher grades than either chemistry or biology, with biology being lowest for both age groups. The Table 34 independent samples t- test for equality of variances data indicated that no significant difference existed between the two age groups for these three gateway courses of biology, chemistry and physics when using p < .05 level. Table 33 Group Statistics t-test of College Biology, Chemistry and Physics Course Grades versus Age College Courses Dichotomous Age Variable Sample Size Mean Standard Deviation Biology Age < 30 1092 2.25 1.06 Age > 30 315 2.55 1.08 Chemistry Age < 30 464 2.51 1.05 Age > 30 175 2.84 1.07 Physics Age < 30 181 2.89 .96 Age > 30 53 2.91 1.02 Whereas Table 34 showed age data relationships for these three gateway courses, Tables 35 and 36 provided group statistics t-test and independent samples t- test for equality of variances of college biology, chemistry and physics course grades versus the gender variable for comparison. 79 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 34 Independent Samples t-test for Equality of Variances for College Biology, Chemistry and Physics Course Grades versus Age College Courses Levine’s Test for Equality of Variances F Sig. Biology Equal Variances Assumed 1.16* .28 Chemistry Equal Variances Assumed .01* .94 Physics Equal Variances Assumed .81* .37 * p < .05 Table 35 revealed that the females achieved higher-grade means than the males in all three subjects. The grade distribution again showed that physics grades were highest, with intermediate chemistry grades and biology students were performing the lowest. The chemistry and biology class populations clearly showed a higher participation of females that males, but there was a major departure in the physics class, with a gender population reversal where the numbers of females were drastically lower. The independent samples t-test for equality of variances data in Table 36 data for biology and chemistry course average grades did not demonstrate a significant difference between genders, while the physics course t-test did. Table 35 Group Statistics t-test of College Biology, Chemistry and Physics Course Grades versus Gender College Courses Gender Sample Size Mean Standard Deviation Biology Male 482 2.27 1.09 Female 925 2.34 1.06 Chemistry Male 220 2.54 1.11 Female 419 2.63 1.07 Physics Male 148 2.83 .95 Female 86 3.01 1.01 80 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 36 Independent Samples t-test for Equality of Variances for college Biology, Chemistry and Physics Course Grades versus Gender College Courses Levine’s Test for Equality of Variances F Sig. Biology Equal Variances Assumed .24* .63 Chemistry Equal Variances Assumed .13* .72 Physics Equal Variances Assumed .02* .90 * p < .05 The ethnicity analyses shown in Table 37 demonstrated continuing low sample sizes, but surprisingly, the one-way ANOVA showed significant differences between ethnicity groups. The grades reflected an interesting inverse relationship with the student population sizes. Table 37 Ethnicity Comparisons with College Biology, Chemistry and Physics Course Grades and the One-Way ANOVA Significance of Difference Ethnicity Variable Biology Chemistry Physics White Sample Size 130 65 14 Mean 2.56 2.77 3.19 Std. Dev. 1.12 1.02 .85 Asian Sample Size 204 150 55 Mean 2.70 2.88 2.99 Std. Dev. 1.04 1.05 .95 Hispanic Sample Size 712 242 87 Mean 2.19 2.37 2.48 Std. Dev. 1.02 1.00 .94 African Sample Size 192 89 44 American Mean 2.06 2.41 3.41 Std. Dev. 1.10 1.16 .83 df Between Groups 4 4 4 F Between Groups 15.33* 7.74* 8.75* Sig. Between Groups .00 .00 .00 * p < .05 81 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 37 indicated that the physics grades with extremely low sample sizes were the highest grades and that biology grades were lowest across all ethnicities. The biology student populations were consistently larger corresponding with the lowest grades for all ethnicities. The chemistry grades and population sample sizes were consistently in the middle for this study. The Asian students achieved higher grades in both biology and chemistry than their counterparts, but dropped down to third position in physics as the African American achieved the highest grades. Whites were second highest in physics and Hispanics were lowest. The African Americans had the lowest grades only in biology, so were doing much better than with overall, math and science GPAs where their performance was always lowest previously in this study as noted in Table 17. The current employment status comparison data shown in Table 38 for the biology, chemistry and physics course grades differed from the comparison Table 18 for college GPA in almost every category. It was most noticeable that the “not employed/not looking” students who had the highest GPAs previously for overall, math and science GPA, only excelled in biology and chemistry course grades. The physics part-time and full-time employed students actually achieved higher grades than the “not employed/not looking” students. The chemistry mean grades were nearly the same in three work status categories except for the “not working/not looking” category where the mean was significantly higher than both other subject grades as well as other categories. The one-way ANOVA exhibited significant differences between these employment groups in all three-subject areas. 82 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 38 Current Employment Status Comparisons with College Biology, Chemistry and Physics Course Grades and the One-Way ANOVA Significance of Difference Current Employment Status Biology Chemistry Physics not employed/ Sample Size 174 108 30 not looking Mean 2.59 2.83 2.77 Std. Dev. 1.08 .98 1.11 not employed/ Sample Size 213 91 44 looking Mean 2.19 2.58 2.63 Std. Dev. 1.13 1.01 1.12 employed Sample Size 592 246 98 part-time Mean 2.25 2.55 3.10 Std. Dev. 1.07 1.08 .85 employed Sample Size 390 180 55 full-time Mean 2.37 2.57 2.79 Std. Dev. 1.01 1.11 .94 df Between Groups 3 3 3 F Between Groups 6.06* 1.87* 3.03* Sig. Between Groups .00 .13 .03 * p < .05 Table 39 presented CCR for these three primary gateway science courses. The smaller populations in these three specific gateway science courses were noted as 1407, 639 and 234 for the biology, chemistry and physics subjects respectively. The corresponding higher numbers of students not taking these gateway science courses were labeled as missing for this particular evaluation of TRUCCS sample database. The CCR in physics was the highest at 92% with the smallest standard deviation. The biology students with a larger subpopulation at 1407 demonstrated the lowest CCR at 81%, while the chemistry group was mid-level CCR. The 100% CCR median and mode demonstrated a significant number of students actually completing these courses. This observation became even more meaningful when noting that the Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. range of mean values of 81% to 92% for these three gateway science courses was much higher than the corresponding range of 70% to 84% for grouped math and science CCRs found in Table 19, and these CCRs were significantly higher than the 67% to 74% for the overall college CCRs. Table 39 Descriptive Statistics for College Biology, Chemistry and Physics Course Completion Ratio Biology CCR Chemistry CCR Physics CCR Sample Size 1407 639 234 Missing Cases 3561 4329 4734 Mean 81% 86% 92% Median 100% 100% 100% Mode 100% 100% 100% Standard Deviation .36 .32 .25 Tables 40 and 41 provided group statistics t-test and independent samples t- test for equality of variances of college biology, chemistry and physics CCRs versus demographic age group variables to complement the central tendency data in Table 39. Table 40 data showed that the older students in biology and chemistry had higher CCRs than their younger students under the age of 30, but a reversal occurred in the physics courses where the younger students out performed the older. Physics students demonstrated higher CCRs than either chemistry or biology with chemistry intermediate and biology lowest for both age groups. Even though the physics student mean percent CCRs were higher that the chemistry and biology means, the independent samples t-test for equality of variances for these three college course average CCRs demonstrated that between the two age groups there was no 84 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. significant difference for the physics courses using the p < .05 level of evaluation. The biology and chemistry t-tests showed a significant difference between the two age groups, those under 30 years, and those 30 years or older. Table 40 Group Statistics t-test of College Biology, Chemistry and Physics College Course Completion Ratio versus Age Dichotomous Mean Standard College Courses Age Variable Sample Size Percent Deviation Biology Age < 30 1092 80 .37 Age > 30 315 87 .32 Chemistry Age < 30 464 85 .33 Age > 30 175 89 .29 Physics Age < 30 181 92 .24 Age > 30 53 90 .27 Table 41 Independent Samples t-test for Equality of Variances for College Biology, Chemistry and Physics College Course Completion Ratio versus Age College Courses Levine’s Test for Equality of Variances F Sig. Biology Equal Variances Assumed 33.18* .00 Chemistry Equal Variances Assumed 6.89* .01 Physics Equal Variances Assumed 1.85* .18 * p < .05 Group statistics t-tests and independent samples t-tests for equality of variances of college biology, chemistry and physics CCRs versus the gender variable shown in Tables 42 and 43 indicated some interesting observations. Table 42 revealed that females have slightly higher CCRs in biology and chemistry and equaled the male CCRs in physics. As noted in the group statistics Tables 40 and 41 85 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. for grades the chemistry and biology class populations for CCRs clearly showed a higher participation of females than males. In physics there is a significant reversal. Whereas the females outnumbered the males at nearly a 200% level in the other two classes they dropped to less than 60% in the male-dominated physics courses. Table 42 Group Statistics t-test of College Biology, Chemistry and Physics College Course Completion Ratio versus Gender College Courses Gender Sample Size Mean Percent Standard Deviation Biology Male 482 80 .38 Female 925 82 .35 Chemistry Male 220 85 .33 Female 419 86 .31 Physics Male 148 92 .25 Female 86 92 .25 Table 43 Independent Samples t-test for Equality of Variances for College Biology, Chemistry and Physics College Course Completion Ratio versus Gender College Courses Levine’s Test for Equality of Variances F Sig. Biology Equal Variances Assumed 8.25* .00 Chemistry Equal Variances Assumed 1.51* .22 Physics Equal Variances Assumed .18* .67 * p < .05 The independent samples t-test for equality of variances for these college CCRs in Table 43 indicated no significant difference between genders for chemistry and physics, but achieved a significant difference for biology at the p < .05 level. 86 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. The ethnicity comparisons with college biology, chemistry and physics CCRs and one-way ANOVA significance of difference in Table 44 presented some commonality with previous observations, but also demonstrated some major variations in performance differing from previous research results in this study. The physics students achieved the highest CCRs across all ethnicities in Table 44 similar to their performance with grades in Table 36. The African American student’s CCRs were exceptionally high at 97% with White students only one percentage point lower at 96%. The Asian students followed the descending sequence at 93% and Hispanic students were lowest at 87%, which matched the same sequential order noted previously for grade comparisons. The biology courses again dominated the higher population of students enrolled in these three courses and recorded the lowest CCRs as well as with grades. Table 44 differed with the White students moving ahead of the Asian students with CCRs to achieve a slightly higher CCR for chemistry at 90% versus 89%, but the Asian students maintained their dominance in biology with a 89% CCR, much higher that the other ethnicities. The Hispanic’s CCRs tied the White student’s performance at 81% in biology, and were higher than African American students in chemistry at 83% compared to 80%. The African American students demonstrated the lowest CCRs in both biology and chemistry, which differed slightly from Table 37 with grades, but agreed with their position with overall CCR in Table 25. Unlike the one-way ANOVA between groups for all three CCR categories (overall, math and science) testing for differences between ethnicities in 87 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 25, the comparisons for chemistry and physics courses did not show a significant difference using p < .05. The biology one-way ANOVA between ethnicities demonstrated a significant difference. Table 44 Ethnicity Comparisons with College Biology, Chemistry and Physics Course Completion Ratio and the One-Way ANOVA Significance of Difference Ethnicity Variable College Biology CCR College Chemistry CCR College Physics CCR White Sample Size 130 65 14 Mean 81% 90% 96% Std. Dev. .36 .28 .13 Asian Sample Size 204 150 55 Mean 89% 89% 93% Std. Dev. .28 .29 .22 Hispanic Sample Size 712 242 87 Mean 81% 83% 87% Std. Dev. .36 .33 .31 African Sample Size 192 89 44 American Mean 76% 80% 97% Std. Dev. .39 .38 .17 df Between Groups 4 4 4 F Between Groups 3.47* 2.07* 1.46* Sig. Between Groups .01 .08 .21 * p < .05 Table 45 presented the current employment status comparisons with biology, chemistry and physics CCRs, and the corresponding one-way ANOVA significance of difference data reported some similarity and contrasts with previous tables. In Table 26’s college overall, math and science CCR comparisons, the “not employed/not looking” employment status achieved the highest CCRs across the board for overall, math and science CCRs, but in Table 45 it was just for biology and chemistry CCRs, as found also in Table 38. The physics part-time and full-time Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. employed students actually matched or achieved higher CCRs at 93% and 95% respectively than the “not employed/not looking” status students. The chemistry CCRs were nearly the same with mid-80 percentages in three work status categories, but rose sharply to a 93% for the “not working/not looking” category. Table 45 Current Employment Status Comparisons with College Biology, Chemistry and Physics Course Completion Ratio and the One-Way ANOVA Significance of Difference Current Employment College College College Status Biology CCR Chemistry CCR Physics CCR not employed Sample Size 174 108 30 not looking Mean 86% 93% 90% Std. Dev. .32 .22 .28 not employed Sample Size 213 91 44 looking Mean 75% 87% 85% Std. Dev. .39 .29 .35 employed Sample Size 592 246 98 part-time Mean 80% 84% 95% Std. Dev. .37 .34 .19 employed Sample Size 390 180 55 full-time Mean 85% 84% 93% Std. Dev. .33 .33 .23 df Between Groups 3 3 3 F Between Groups 4.46* 2.64* 1.74* Sig. Between Groups .00 .05 .16 * p < .05 The lowest CCR performances noted in Table 45 were for the “not working/looking” employment status for biology and physics, but shifted to both part-time and full-time workers for chemistry at an 84% CCR value. While the biology course CCR indicated significant difference between the employment status groups using the one-way ANOVA, the chemistry CCR was borderline not 89 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. significant using the p < .05 level, and physics course CCRs definitely failed to demonstrate significant difference between employment groups. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. CHAPTER 5 SUMMARY, CONCLUSIONS & RECOMMENDATIONS Introduction The objectives of this chapter were as follows: (a) to review the purpose of the study, (b) to comment on the methodology for conducting this study, (c) to discuss its importance, (d) to summarize the findings of this in relationship to the research questions, (e) to share conclusions and implications, and (f) to provide recommendations. Purpose of the Study The purpose of this study was to evaluate selected demographic, pre enrollment, and economic status variables in direct relationship to college-level performance. Literature revealed that these variables have influenced successful student retention, persistence and completion of math and science courses at four- year institutions, but that two-year community college research was limited or non existent, especially for the math and science fields of study. This study served to fill a missing gap of knowledge about math and science relationships of the diverse population of students seeking gateway careers in math and sciences in the Los Angeles Community College District, the largest district in the United States. The study specifically produced meaningful data to enhance the effective management of gateway math and science career preparations at this institution and similar community colleges around the nation. 91 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Methodology Initial analyses included descriptive tables showing the sample data set of nearly 5,000 students by age, gender, ethnicity and employment status derived from use of SPSS for Windows, on the TRUCCS database, described in Chapter 3 entitled Research Methodology. High school grades and math levels of achievement were acquired and extensive descriptive frequencies were produced to explore the GPA and course completion ratio relationships. Group statistics and independent statistics t-tests were developed to evaluate the GPA and CCR versus demographic variables. One-way ANOVA were used to evaluate ethnicity and employment status variables for validity at the p < .05 levels likewise. All of these independent and dependent variables were analyzed through the lenses of six research questions. Importance of this Study Since student completion of math and science courses in our community colleges has served as vital gateways into higher paying careers in engineering, advanced technologies, modem biology research, health sciences, and medical sciences, this has become a concern for not only students, but also educators, state governments as funding sources, and society in general (Coppola, 1999; Hart & Cottle, 1993). Student achievement in math and science in education has been lagging across our nation, and especially in California, yet has become necessary for the academic preparation of high-tech careers in our modem society (“Science, math teaching urged,” 2005). “If California is to be a leader in tomorrow’s economy, we need to put more emphasis on science and math instruction,” (“Science,” 2005). The 92 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. literature review indicated a significant lack of research in community college student retention and basic GPA and course completion ratio data relationships to make valid decisions. The results of this study should be not only useful to the faculty and leadership of the LACCD, but also transferable to other metropolitan cities with diverse minority populations such as New York, Chicago, Miami, and others. These factors are also beneficial to many smaller community colleges across the nation where diverse populations are increasingly evident and changing the character of their student populations and the teaching methodologies to meet changing times. Research Questions The general research aimed to establish the relationship between students’ high school grades; their level of high school and college math courses; their math, science and overall GPAs; their demographics (age, gender, and ethnicity); and their economic employment status in respect to their successful course completion of gateway science classes in the Los Angeles Community College District for achievement of transfer and graduation requirements. Specific research questions used in developing the results in Chapter 4 were listed below: QUESTION 1: What were the high school GPAs of the students enrolled in the LACCD? QUESTION 2: What level of math courses did the science students take in high school and college? 93 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. QUESTION 3: What was the overall, math, and science community college GPAs and course completion ratios of students enrolled in the LACCD? QUESTION 4: What were the student GPAs and course completion ratio relationships to the demographic variables of age, gender, and ethnicity, and to the economic variable of current employment status? QUESTION 5: What were the relationships between the highest level of high school and college math with overall, math, and science community college GPAs and course completion ratios of students in the LACCD? QUESTION 6: What were the gateway biology, chemistry, and physics course grades; the transfer potential of these grades versus math and science GPAs; and course completion ratios of the LACCD students and their demographic and economic relationships? Summary of Findings The findings in Chapter 4 were reported sequentially in relationship to the six research questions after establishing initial background demographics and economic status, so the same approach were used in this summary. The means and frequency tables for age, gender, ethnicity and employment status revealed a young female student population dominating these campuses. Over two thirds of the students were under the age of 30 years old and females made up 57.4% of the sample population compared to 58% for the overall LACCD student population (LACCD, 2003). This 58% value lent even more importance for validity and reliability comparison purposes when noting that Phillippe and Patton (1999) observed the same 58% level 94 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. of female presence in community colleges nationwide. The Hispanic minority students recorded the highest percentage of the student sample at 46.4% comparable to 47.4% for the LACCD nine campus wide population statistics, with smaller percentages for White, Asian and African American students for use in this study compared to a separate LACCD fall 2000 survey data set (LACCD, 2003). The majority of these students at LACCD were working part or full-time (68.7%) in this sample, compared to a much higher percentage for nationwide student employment rate of 89.1% (National Center for Educational Statistics, 1996). Findings Related to Research Questions 1 and 2: Findings related to research questions 1 and 2 established a sample group of students with central tendency and frequency characteristics from survey answered “self reported” grades and math course pre-enrollment factors. The student mean grade in this study at a 5.47 in a nine-point scale with median and mode of a B showed a well-prepared student body for college coursework. The highest level of math courses taken in high school and college were categorized into four levels of remedial, basic, intermediate and advanced/transfer math courses. The frequency statistics for these levels when compared to college GPAs and CCRs showed a definite shift from highest student population numbers in the intermediate level math courses (42.2%) in high school to highest student population numbers in the advanced or transfer levels (37.2%) in college. In other words, the number of students in the advanced/transfer courses shifted from 15% in high school to 37.2% 95 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. in college or more than doubled student participation demonstrating academic success with advanced math courses. Findings Related to Research Questions 3 and 4: Findings related to research questions 3 and 4 to address the overall, math and science GPA and course completion ratio (CCR) of students enrolled in LACCD in relationships to the demographic variables (age, gender, ethnicity) and employment status are found in Tables 10 through 26. The overall student sample mean GPA (2.49) was higher than for the student math GPA (2.17) and science GPA (2.36). It was apparent that the overall GPA means were higher than corresponding math and science GPAs throughout all of the comparison tables with demographic variables and economic factors, indicating that math and science course grades were lowering the overall GPA for some students at this institution, and especially math. The age demographic variable analyses in Tables 11 through 13 showed that the overall GPAs were the highest for both younger and older student groups, with science mid-level and the math GPA as the lowest. Math and science coursework were challenging to both age groups resulting in lower GPAs. Students 30 and older had higher GPA means in all three categories of study for overall, math and science GPAs, but independent sample t-tests indicated that a significant difference only existed between the two age groups for the overall GPA, while differences for math and science age groups were not significant. Results indicated that even though older students outperformed their younger counterparts with overall GPAs, when it comes to math and science, the older students lost their competitive edge. 96 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. The gender and ethnicity relationships were shown in Tables 14 through 17. The female students outperformed their male counterparts in overall, science and math GPAs, but did not exhibit statistically higher math GPAs when applying the independent samples t-test. Overall GPAs for both genders were highest with science GPAs in mid-level, and math GPAs lowest, which was the same ranking when comparing age. The college overall GPA means for the four major ethnicity groups indicated that White and Asian students achieved the highest GPAs at 2.82 and 2.71 respectively with Hispanic and African American students nearly equal in class standing at 2.39 and 2.31. In math and science, the Asian students achieved the highest means with White students shifting to second position. The Hispanic students had the third position with mean GPAs across all three categories of overall, math and science and African American students were lowest in all three categories. The one-way ANOVA demonstrated a significant difference between ethnicity groups for all mean GPA categories providing validity for these observations. Even though these ethnicity findings agreed with Barlow and Villarejo’s (2004) research findings where minority student success was found to be far below White and Asian counterparts, this local and nationwide observation has become a concern for recruiting minorities into high paying careers. The economic factor evaluation of GPA means comparing employment status revealed some results with concerns. Those students who were “not employed and not looking” held the highest GPA means across all three categories of overall, math 97 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. and science GPAs, suggesting that these students were economically secure enough to spend more time to direct study efforts. The data in Table 18 reflected that the overall GPA means again were far better than math and science GPAs for all employment groupings, and science GPA means were higher than math again, a trend observed throughout all GPA tables. The one-way ANOVA significance of difference testing was valid between all employment status groups. The problem noted in these employment status data centered on the subject of math GPAs being extremely low for all employment categories other than those not employed and not looking. The students who needed employment and were looking had the lowest GPAs across all three categories, and were especially lowest for math GPAs. Those student math GPAs were below the passing grade level with a mean of 1.99 and the two employed groups were only slightly better at 2.17 and 2.18. The part-time and full-time employed student GPA means were higher for all three GPA categories than the group of students who were seeking to find jobs and/or needed employment, indicating more stability for those employed and more time to improve their educational efforts. These students were significantly challenged to exist in this college environment and need intervention efforts to increase college retention and transfer rates. The CCR tables revealed some relationships changes from GPA data observations. While the older students’ CCRs were higher than younger students’ performances across the board with overall, math and science respectively as with GPAs, the science CCR for older students of 84% was higher than for math (80%) or 98 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. overall (74%). This was a revealing change from the GPA relationship sequence where overall GPA was highest. In addition, the younger students’ CCR data reflected the same shift to higher ratios for science followed by math and lowest for overall. These changes were with a significant difference for both age groups for all three categories of CCRs, unlike the GPA comparison that only had significant difference between age groups for overall GPA. The older students demonstrated a seriousness about passing these courses, and especially the science and math requirements for transfer, even though their science and math grades are lower. The gender variable comparison with CCR showed female student data reflecting higher mean completion ratios than for males in all categories (overall, math and science). The females 82% CCR for science courses not only exceeded the males’ 77% ratio, but also was higher than all math and overall CCRs. Both males and females appeared to have a higher CCR for science than math or overall, matching the same relationship as for age CCR comparisons. Correspondingly, the math CCRs were higher than the overall student course performance, unlike GPA where overall GPA was higher for both genders. The independent samples t-test demonstrated that a significant difference existed between the genders for all three categories of CCR. Whereas the females had the highest overall GPA for both genders at a 2.56 mean, they experienced the lowest overall CCR 70% in comparison as their performance shifted to 82% for science and 74% for math CCRs. The males on the other hand, who were lower in achievement of overall GPA means at 2.39, also 99 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. showed the lowest academic success or CCR of 66% for both genders and all three categories. The interpretation of these data was complex and will require further review. What has been disappointing pertains to high female CCR for transfer or graduation coupled with a moderate mid-level grade, but then literature has shown that few females enter into the science and math careers. Tobias (1990) indicated that this was partially due to how science is taught with a resultant culture of science that in effect alienates females. Donaldson and Dixon (1995) additionally commented in their studies that the issue of females dropping out of math and science courses and ultimately from the pursuit of science careers has to do with poor perceptions of their abilities, part-time work stresses, family obligations, and peer relationships. Whatever the causes of high attrition from math and science career paths, intervention efforts are needed to address this problem and to create pathways for success for these females into gateway science careers. When comparing the four major ethnicities, the Asian students had highest science completion ratios (85%), with White students second (83%), then Hispanic students (79%) and finally, the African American students (75%). This was the same order as observed with science GPAs. The math and overall CCRs demonstrated the same rank order, with math CCRs being lower than science, but exceeding overall CCRs for all four ethnicities as found with age and gender demographics. The one way ANOVA significance of difference was valid between ethnicity groups across all three CCR categories. Barlow and Villarejo’s (2004) research with minority student success found minorities to be far below White and Asian student 100 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. counterpart, which were mirrored in this sample of LACCD students. This has been a common problem that the entire nation needs to address, not just at LACCD. The economic factor data depicted the “not employed and not looking” for a job group achieving the highest CCR for all three categories of overall, math and science CCRs, as was noted for the GPA relationships. Science CCR ranked highest for all four-employment groups with math CCR in second place and overall CCR lowest. This indicated that all four independent variables shifted rank positions with CCR in relationship to GPA in the same manner. The one-way ANOVA demonstrated a significant difference between employment status groups for validity for only overall and math CCRs, but not for science. The sample population of science students was lowest of the three categories, possibly causing the loss of significance. The “not employed and actually looking” for employment had the lowest CCRs for all categories, as well as for lowest GPAs noted previously. This aspect of the data revealed an area of concern for administrators and educators, which warrants discussion in conclusions, implications and recommendations. Findings Related to Research Question 5: These findings were derived from the analysis of the relationships between the highest level of high school and college math courses with the college overall, math and science GPAs and CCRs. Tables 27 through 30 in Chapter 4 presented these data representing “self-reported” survey information for high school math course levels and enrollment transcript data for college math classes. The four math course levels were remedial, basic, intermediate, and advanced/transfer for both high 101 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. school and college data as defined in Chapters 3 and 4. These data indicated that the intermediate level high school math courses had the highest numbers of students for comparisons with college level math, science and overall GPAs and CCRs. This highest student population shifted to the advanced/transfer level of college level math courses taken when compared with college level math, science and overall GPAs and CCRs. The one-way ANOVA tests for all four tables of data showed a significant difference existed between all high school and college level math course levels versus college overall, math and science GPAs and CCRs. The highest mean GPAs were observed in the advanced/transfer level for both high school level and college level math courses across all college overall, math and science GPAs. This is a significant observation worthy of discussion below. The college overall GPA means were higher for all highest high school levels of math comparison, with college science GPA means mid-level, and college math GPA means lowest. It also was noted that with the exception of remedial means being equal to or higher than the basic level across all three GPA categories, the GPAs increased in value from one math level to the next. In other words, the more math courses taken in high school the higher the resultant GPA, as the students who took more high school math were doing better in all three categories of college GPA demonstrating transfer level grades. This finding agreed well with literature that showed student performance improved with the increasing number of math courses taken, the number of hours of math instruction, and/or the number of advanced math courses taken in high school (Jones, Davenport, Bryson, Bekhuis, & Zwick, 1986). 102 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Since both of the college math GPA means for high school math class levels of basic and remedial courses at 1.97 were not satisfactory for transfer requirements, and the college science GPAs at 2.21 were not much better, these findings validated the need for increased math and science course preparation in K -12 (“Science,” 2005). Each of the Tables 27 through 30 provided some similarities, but also revealed some unique observations. For instance, whereas the highest CCR percentages were observed in the advanced/transfer high school level math courses across all three college level CCR categories as with GPAs, the college overall CCR percentages were lowest for all high school math course levels of comparison this time. Science CCRs were highest and math CCRs were mid-level, quite different from the GPA relationships. The data also indicated with the exception of the remedial ratios being slightly higher than the basic level across all three CCR categories that the CCRs increased in value from one high school math course level to the next. This last observation of CCR value increase relationship agreed with literature again as noted from studies by Jones, Davenport, Bryson, Bekhuis, & Zwick (1986). Tables 29 and 30 for highest college level math courses taken provided nearly identical observations with the following exceptions: (a) the college overall CCR was lowest for the top three levels of highest college math courses taken, but when it came to the remedial math level, the math CCR was lowest 46%; (b) the college science CCRs were highest, and the college math CCRs were mid-level, 103 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. except for the remedial level courses as noted above. It was apparent that data had some variability with CCRs when compared to the highest levels of math courses taken. Findings Related to Research Question 6: Tables 31 through 45 presented data for the gateway science courses of biology, chemistry and physics to establish the average grades for these college courses and their CCRs in their relationships with the demographic and economic variables. The descriptive frequencies of these three courses provided central tendency means, modes and medians for comparisons, plus descending thresholds for course grades. The physics course means were highest (2.90) for a small student sample size of 234 students. The biology student population was highest at 1407 students and had the lowest mean grades (2.31). The chemistry course data were both mid-level for sample size and mean (2.6). There appeared to be an inverse relationship here with highest grades (2.90) related with smallest number of students (234) and lowest grades (2.31) associated with the largest sample population of biology students (1407). This may be related to smaller class sizes, improved student-peer relationships, increased time for student to faculty interactions, etc., as noted in literature (Pascarella & Terenzini, 2005; Tinto, 1998). An additional aspect of investigation within question 6 addressed the issue of transfer grade potential in this sample. When comparing transfer level grade statistics of these three courses in preparation for future research, a comparison was also made for the math and science GPAs previously reviewed. Table 32 in Chapter 4 provided 104 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. a unique look at student performances for these gateway courses, and is discussed here once more due to its unique character and the information contained. The physics students had much higher percentages of both transfer level C and above grades (92.3%) than the other two gateway science courses and were significantly higher than the 73.4% for all college level science courses or the 64.1% for math GPAs. All three of these gateway courses had much higher percentages of students achieving B or above than did the grouped math and science course statistics. These three specific courses are reflecting quite favorable preparation of these students to meet transfer and graduation requirements, but the overall math and science transfer grade thresholds don’t provide the same impression. Only 64.1% of the students taking math courses are achieving a passing grade with a C or better, and only 73.4% of the students taking general life and physical science courses were achieving transfer level grades. In other words, more than one third of the students taking math were not achieving a transfer level grade. This demonstrated a significant problem if the major portion of students taking these math and science courses are minorities, as they are being frozen out of the math, science and engineering careers that offer higher paying employment upon graduation. The group statistics t-test for age comparison with these three gateway science courses revealed that the older students achieved higher-grade means than the younger students in all three courses, probably related to maturity of study habits and desire to do better when returning to school after years of life experiences. 105 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Physics students achieved the highest grades with chemistry mid-level, and biology students had the lowest. Tables 35 and 36 presented gender variable data with similar observations where the females achieved the higher grades than males in all three subjects, with a decreasing sequence from physics grades as highest, to chemistry as mid-level and biology as lowest. The chemistry and biology student populations clearly showed a higher participation of females than males, but there was a major departure in the physics class, where a reversal of population occurred. The 86 females were outnumbered in physics nearly two to one, which agreed with research literature. Schoon (2001) noted in her studies that females normally select “softer disciplines” of science, such as biology, whereas males dominate the “hard disciplines” of physics. Kennedy & Parks (2000) commented further that the chilly atmosphere in physics was difficult to contend with for females creating an environment that decreased their self-confidence and self-esteem, which relegated them to a minority status in the sciences as also noted in this TRUCCS math and science study. The ethnicity data in Table 37 revealed a consistent similarity with age and gender demographic relationships with physics grades being highest, chemistry mid level and biology lowest, but with some interesting changes in ethnicity rank positioning. As with the previous age and gender data sets, the biology student population was highest, chemistry mid-level and physics lowest. The Asian students achieved the higher grades in biology and chemistry, but ranked third in physics where the African American students excelled. Whites ranked second, and Hispanic 106 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. students were lowest in physics. This observation was abnormal compared with overall GPA, math GPA, and science GPA, where the African American students consistently had the lowest grades and likewise the lowest CCRs. The African American students had the lowest grades only in biology, so were doing much better within these individual gateway science courses than found in overall, math and science GPA data tables. The African American students were few in numbers in the physics courses, but their improved performance (highest grades of 3.41) and persistence (highest CCRs of 97% in Table 44) agrees with Tinto’s observations, such that closer personal contact with other students, faculty and staff increases students’ integration into competent membership in community (1993). The current employment status comparison data shown in Table 38 for the biology, chemistry and physics course grades differed from the comparison Table 18 for college GPA in almost every category. The data reflected that the “not employed/not looking” students who had the highest GPAs previously for overall, math and science GPA, only excelled in biology and chemistry grades. The physics part-time and full-time employed students actually achieved higher grades than the “not employed/not looking” students. The chemistry means were nearly the same in three work status categories except for the “not working/not looking” category where it was significantly higher than both other subject grades as well as other categories. The one-way ANOVA revealed a significant difference between these employment groups for all three-gateway courses. The not employed and looking for employment group had lowest grades in biology and physics, but edged out both part-time and 107 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. full-time working students in the chemistry courses by only 1-3 hundreds, so this group again demonstrated a need for intervention to provide support for grade improvements. Table 39 presented CCR for these three primary gateway science courses. The smaller populations in these three specific gateway science courses were noted as 1407, 639, and 234 for the biology, chemistry and physics subjects respectively. The higher numbers of students not taking these gateway science courses were labeled as missing cases for this particular evaluation of TRUCCS sample database. The CCR in physics was the highest at 92%. The biology students with a larger sample population at 1407 demonstrated the lowest CCR at 81%, while the chemistry group was mid-level with CCR at 86%. The 100% CCR median and mode exhibited a significant number of students actually completing these courses. Table 40 data showed age demographic comparisons. The younger students in biology and chemistry had lower CCRs than the older students who were 30 years and older, which compared favorably with science CCR values observed in Table 19. The independent samples t-test data revealed a significant difference between age groups for these two subjects. In the physics courses the younger students (92%) outperformed the older students (90%), a complete reversal of roles, but the t-test did not show a significant difference between age groups for the physics courses. The physics students recorded the highest CCRs, chemistry middle level and biology lowest for both age groups. This high CCR observation may have to do with the lower number of students (234) taking physics on the nine campuses, and possibly 108 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. increased student involvement through a higher faculty to student relationship, which literature has shown to help improve persistence, course completion and transfer (Astin, 1984; Mallette & Cabrera, 1991; Nora, 1987; Pascarella & Terrenzini, 2005). Gender relationships shown in Table 42 revealed that the females had higher CCRs in biology and chemistry and equaled the male CCRs in physics. In physics there was a significant reversal whereas the females outnumbered the males at nearly a 200% level in biology and chemistry, they dropped to less than 60% in the male- dominated physics courses. These data were in agreement with literature as Schoon’s observations noted that females normally select “softer disciplines” of science, such as biology, whereas males dominated the “hard disciplines” of physics (2001). The ethnicity comparisons with college biology, chemistry and physics CCRs and one-way ANOVA significance of difference in Table 44 presented some commonality with previous observations, but also demonstrated some major variations in performance differing from previous research results in this study. The physics students achieved the highest CCRs across all ethnicities similar to their performance with grades in Table 37. The African American student’s CCRs were exceptionally high at 97% with White students only one percentage point lower at 96% followed by Asian at 93% and Hispanic students lowest at 87%. This matched the same sequential order noted previously for grade comparisons. The biology courses recorded the lowest CCRs as was observed with grades. Table 44 differed with the White students having slightly higher CCRs than the Asians for chemistry at 90% versus 89%, but the Asian students maintained their 109 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. dominance in biology with an 89% CCR, much higher than the other ethnicities. The Hispanic’s CCRs tied the White’s performance at 81% in biology, and were higher than African Americans in chemistry at 83% compared to 80%. Unlike the one-way ANOVA between ethnicity groups for all three CCR categories (overall, math and science), which showed a significant difference between ethnicities in Table 25, the comparisons for chemistry and physics courses did not show a significant difference using p < .05 for chemistry and physics courses. The biology one-way ANOVA between ethnicities did demonstrate a significant difference. The African Americans ranked with the lowest CCRs in both biology and chemistry, which differed slightly from Table 37 with grades, but agreed with their ranking with overall CCR in Table 25. Considering the fact that for overall, math and science CCRs and now two out of the three gateway science courses the African Americans have shown poor achievement in these data, intervention is warranted to help improve retention and transfer potentials at this institution. The last table presented the current employment status comparison with biology, chemistry and physics CCRs, and the corresponding one-way ANOVA significance of difference data. Table 45 reported some similarity and contrasts with previous tables. In Table 26’s college overall, math and science CCR comparisons, the “not employed/not looking” employment status achieved the highest CCRs across the board for overall, math and science CCRs, but in Table 45 it was just for biology and chemistry CCRs, as found also in Table 38 for grade comparisons. The physics part-time and full-time employed students actually matched or achieved 110 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. higher CCRs at 93% and 95% respectively than the “not employed/not looking” status students, similar to their performance with grades in Table 38. The chemistry CCRs were nearly the same with mid-80 percentages in three work status categories, but rose sharply to a 93% for the “not working/not looking” category. The lowest CCR performances were for the “not working/looking” employment status for biology and physics, but shifted to both part-time and full-time workers for chemistry at an 84% CCR value. While the biology course CCR indicated a significant difference between employment status groups for the one-way ANOVA tests of variance, the chemistry test was marginal just below or if rounded at the p < .05 level, and physics courses failed to demonstrate a significant difference. Pascarella and Terenzini (2005) indicated that many studies on work relationships produce varying results, and this data tables fits their description of mixed outcomes. Conclusions & Implications This study found the Los Angeles Community College District entering the 21st Century with a young, female dominated campus. Likewise the LACCD recorded a growing Hispanic influence and indicated that only 68.7% of the students are working either part or full-time. As student demographics and economic factors have changed over the last few decades, the need for an educated diverse population has risen, while our state’s performance has faltered (“Science,” 2005). The Asian students, a small minority at 11.5% of the sample student population for this study, outperformed the other students in collective math and science courses, while the White students at 11.1% achieved the highest overall GPAs. The Hispanic and 111 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. African American student’s performance as shown in this study with the Asian and White students identified the four major ethnicities coupled with other demographic and economic factors to document relationships for actions and future research. The summary of findings have given us a wealth of information for sifting through and applying to not only this institution, but others around the state of California and the nation. Some results in these findings have significant implications for actions that are stated here, while other implications will be derived by faculty, educator, and governance evaluations through follow-on review and analyses of the study’s results. The findings verified a common observation by many when discussing the relationship between math and science courses across the nation. These data documented definite positive relationships within this study of community college students between the highest levels of math classes taken and corresponding performance factors of GPAs and CCRs. The data also indicated several areas of weakness that need to be evaluated for potential intervention efforts to correct problem areas. In addition, some of the results created a few observations that create questions about the relationships established here in these analyses that need further discussion and possible follow-on research. The findings furthermore exposed a positive observation with physics courses that needs to be explored for potential sharing with other disciplines. The math relationships presented in Tables 27 through 30 definitely showed positive predictors of academic success with significant differences between levels 112 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. of math taken. The students who had the highest advanced/transfer level of math in high school through “self-reported” survey data were achieving the highest GPAs and CCRs in college. Correspondingly in Tables 27 and 28 it was noted that with the exception of remedial means being equal or higher than basic math levels, the GPAs and CCRs increased in value from lowest to highest level of math taken. Likewise, the data in Tables 29 and 30 from LACCD enrollment data showed these same relationships between highest levels of college math courses taken and the performance factors of GPA and CCR. The students with the highest GPAs and CCRs were the students who had taken the highest levels of advanced/transfer math in college. Furthermore, in the college level math tables the GPAs and CCRs increased in direct relationship to the math course levels from remedial to advanced/transfer. These data in Tables 27 through 30 demonstrated a direct positive correlation between levels of math taken in high school and college to the student performance in college math, science and overall GPAs and CCRs. In simple words, the data showed that the more math courses a student took the higher the overall, math, and science GPAs and CCRs achieved. These data tables validated the relationship between math levels taken and the performance in our LACCD student sample. This has a definite linkage to the published need for increased emphasis and funding for improving our math programs and the teachers who provide these courses in the K- 12 program in the state of California as most recently stated by Governor Schwarzenegger (“Science,” 2005). The math courses are the foundation to 113 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. improved science GPAs and CCRs necessary for entry into gateway careers (Gainen, 1995). In addition to this major finding, areas of weakness were revealed in the data affecting various student groups. A major problem area that the data indicated pertained to the “not working/looking” employment student group. This group demonstrated an exceptionally poor performance in both GPAs and CCRs across the board for overall, math and science, and in several gateway science course grade and CCR evaluations. This group undoubtedly needs intervention to increase retention and possible transfer or graduation. Pascarella and Terenzini (2005) shared that literature abounds with examples of intervention producing improvement in grades, persistence, course completion, retention and transfer when planned out to reach academic underachievers. Developmental activities by LACCD, with state program support or partnerships with industry could link these students needing financial stability into success programs targeted at their students’ interests. If faculty and policy makers could develop pro-active methods to identify these students and guide them into a Workforce Development Program associated with CalWORKs or other alternative programs, there are possibilities of college retention improvements for these disadvantaged students. Success programs tied to work-study programs, career potential internships, childcare, community service, job placement and educational goal attainment have shown positive results elsewhere. Their exceptionally poor performance in GPAs and CCRs across the board for overall, math and science 114 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. merits activities for faculty and administrators to reach down to them, as they are the at-risk students, probably of ethnic challenged family environments, who seek to change their lives. Positive outreach efforts to these students would not only give them income and self esteem, but some hope for the future. A second major area of concern with serious implications dealt with the African American and Hispanic students’ low math and science GPAs, CCRs, and gateway science course grades and CCRs. These study data indicated a low probability of their being able to get into gateway science careers with C- or De grades in these subjects, or minimal CCR performance. The research data showed that these two minority student groups are performing well below the other two ethnic groups with college overall, math or science GPAs. In fact, the African American mean GPA in all college math courses at 1.97 is below transfer level passing requirements, and the Hispanic 2.03 mean is only slightly better. The low GPA means for both of these ethnic groups with science and overall college GPAs less than 2.5 indicate a need for academic success support measures. These two ethnic groups also demonstrate the lowest CCRs from this study of academic enrollment records. Targeting these two ethnic groups for academic success programs such as Extended Opportunity Programs and Services (EOPS), STAR or other outreach programs should help them improve their potential for not only passing at a C or better level, but potentially excelling in math and science courses for entry into gateway science careers. These programs would include specialized academic, 115 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. career, and personal counseling, book service, tutoring, transfer assistance, special activities, skills workshops, financial aid counseling, and cultural enrichment programs. A third area of concern comes from the highest level of math taken in high school and college in comparison with the transfer Table 32 and related tables with math GPAs and CCRs. In Table 9 one notes that there was slightly more than a doubling of the number of actual students from high school to college in the area of advanced/transfer classes or from 15% to 37.2%, and that is good, as it indicated a significant increase in persistence and the basis for advanced/transfer levels of math achievement for potential transfer and graduation of the student in this sample population. Table 32 reflects that only 64.1% of the students are passing the math classes with a grade of C or better though. When combined with the observations that math was generally lowest for GPA and CCR data throughout the study, the achievement of advanced/transfer math as a preparation for gateway science course completions and transfer or graduation appeared to be a major stumbling block at LACCD. This math deficiency has been and still is a major area of concern for corrective actions to help LACCD prepare its students for higher paying gateway science course careers in medicine, engineering, health sciences, and other science disciplines. Since nearly 80% of the incoming high school students as shown in Table 9 are not well prepared in the math subject areas, this will require increased cooperation with the K-12 systems in the Los Angeles geographic region, state level 116 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. agencies, and the institution to improve the level of math achievements and the numbers of students passing advanced/transfer level math courses with a “C” or better. This has been a complex problem that has no easy solution, but remains as one of the major stumbling blocks to increased numbers of students being able to rise out of poverty and stagnation into higher paying science careers. Governor Schwarzenegger’s cooperative efforts in the state of California with the educators, legislative bodies, and private industry to pump millions of dollars into the state’s educational system for improvements in math and science instruction is needed to correct this deficiency in our educational system (“Science,” 2005). One area of observation presented lingering questions that require more research. The data throughout this study revealed consistent outstanding performance of the female students. Tables 14 through 17 revealed the females outperforming the males in overall, math, and science GPAs, and Tables 22 through 24 likewise for CCRs in the initial evaluations of questions 3 and 4. Further research with question 6 demonstrated the females additionally outperforming the males in the gateway science courses of biology, chemistry and physics in Tables 35-36 for grades and Tables 42-43 for CCRs. Now that the study showed the females doing exceedingly well in these math and science career-preparing courses, what happens to these females? Research by Tai (2001) and Rosser (1995) indicated that there has been a significant under-representation of females in high paying science and engineering professions nationwide for decades. How many females actually transfer and graduate from LACCD to pursue gateway science careers? 117 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. The flip side of these questions related to the success of male students in getting into gateway science careers, those male students whose performance was below the levels of the females throughout this study. What happened to these students? Did they transfer and graduate? How many actually pursued gateway science careers? These are lingering questions left in the researcher’s mind unanswered. Alternatively to significant positive correlations, problems and lingering questions, there was one area of research that was a shining success or at least appeared to be from this limited data analysis perspective. The gateway course data tables showed African American performance in physics courses with higher values for both GPAs and CCRs than their counterpart ethnic groups. When other overall, math and science GPA and CCR data indicated that the African Americans were only doing marginal or failing to pass courses, this stood out boldly in comparison. Lessons need to be learned as to what success factors were being used in these physics courses that resulted in significant performance changes with the physics courses, so as to share with other math and science disciplines. Were these high grades and CCR in physics associated with small class sizes as a direct result of low enrollment (234 students across nine campuses) which improved the faculty to student ratio and increased the opportunity for direct faculty relationships, or was there some industry based relationships established with these courses helping to provide African American role models? Whatever the answer, faculty and educators 118 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. need to learn what is causing this aberration and to potentially model it for students in other disciplines. The summary of findings established a rich data resource for meaningful assessment and use by the LACCD and other interested faculty, educators and leadership for potential developmental activities within their community college systems. My conclusions and implications focused only on the tip of the iceberg. Recommendations The findings from this study should be summarized and forwarded to the LACCD faculty and administrative leadership. In addition, the results of this research should be published in higher education for broader distribution within the educational community for awareness, actions and future research efforts to answer lingering questions and concerns. Potential future research after establishing this dissertation database on LACCD gateway math and science GPA and CCR relationship statistics could evolve in several directions. One logical pursuit would be to perform regression analyses beyond the initial analyses to explore the science course completion ratio as the dependent variable versus the independent variables of age, gender, ethnicity, employment status, highest level of math taken in high school and college, high school grades, math GPA, science GPA, etc. These analyses would lead to useful information for retention, transfer and graduation analyses and policy decisions by the LACCD institution, state educators, and other large diverse populated 119 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. community college districts around the country that serve ever-growing multi-ethnic student populations. Another direction of future research would be to investigate the complex TRUCCS survey question 26 which addressed not only “Which science courses have you taken?” but also asked students to “Include courses taken in high school or previous college work.” Research could include developing a similar sequence of descriptive frequency statistics, t-tests and one-way ANOVA analyses for comparison with this original dissertation study, then follow-up with regression analyses to complement the original foundational research as with math course levels taken in high school and college. A third research model could entail doing cross correlations of the various independent variables not frilly explored in the initial study, and then performing two-way ANOVAs and multivariate ANOVAs. First it would be interesting to see the results of demographic variables of age, gender, ethnicity and employment status comparisons in joint relationships. In other words, evaluate gender versus ethnicity to see what relationships are found in the performance data with multiple demographic factors cross-correlated. Then analyze gender and age, or gender and employment status to see what relationships are revealed, and of particular interest would be ethnicity and employment, or age and employment to see what trends might appear in these data. Once these were prepared then pursue two-way ANOVAs of two independent variables such as ethnicity and employment versus GPA or CCR to see 120 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. the impact of employment on performance and persistence. Two-way ANOVAs would provide a more in-depth analysis of these independent variables in relationships with the dependent variables of GPA and CCR. This research should include a full set of two-way ANOVAs for all possible independent variable relationships. A richer and more complex evaluation could then be accomplished doing a multivariate ANOVA of all four independent variables versus GPA and CCR. This additional research project should provide an excellent tool for faculty, educators, and administrators to derive a much better understanding of the complex relationships between students at LACCD and the gateway math and science courses being taught at LACCD to prepare these students for transfer and graduation into meaningful math, science and engineering careers. A fourth area of research might pursue the lingering questions in respect to the academic success of the female and male LACCD students into gateway science careers through a follow-on study into transfer and graduation data. Did they transfer and/or graduate? Did they transfer into colleges offering gateway science degrees? Were they able to successfully find employment in gateway science careers? Those and other questions are left for the curious of mind to pursue. 121 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. REFERENCES Adelman, C. (1999). Answers in the toolbox: Academic intensity, attendance patterns, and bachelor’ s degree attainment. Washington, DC: U.S. Department of Education, Office of Educational Research and Improvement. American Council on Education. (2001). Minorities in higher education 2000-2001: Eighteenth annual status report. Washington, DC: Author. Astin, A. W. (1971). Predicting academic performance in college. New York: Free Press. Astin, A. W. (1972). College dropouts: A national profile. American Council on Education Research Reports. Washington, DC: American Council on Education. Astin, A. W. (1984). Student involvement: A developmental theory for higher education. Journal o f College Student Personnel, 25(3), 297-308. Astin, A., & Astin, H. (1993). 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Socioeconomic and gender effects on science achievement: An Australian perspective. School Effectiveness and School Improvement, 4, 265-289. Zhai, L., & Newcomb, L. H. (2000). Factors that influence transfer student academic performance and retention. (ERIC Document Reproduction Service No. ED 474 482). 130 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. APPENDIX Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. APPENDIX A The Transfer and Retention of Urban Community College Student (TRUCCS) Questionnaire Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Dear Student: This information is being collected by researchers from the University of Southern California and the University of California at Los Angeles in conjunction with the Los Angeles Community College District as part of a large study of community college students in Los Angeles. You have been selected as a participant in a multi-year project. Your cooperation will assist researchers to help Los Angeles Community College students to be successful in their educational pursuits. Your assistance is crucial to the project; we thank you for your participation in this important research. Social Security Number Name . Your primary email address:. Your phone number:______ © ® ® ® C P <® ® © C D <D O D © C D © □> O D © < X > ® <£ C D C D ® ® ffl 3 > ir ® > 'X G C C l a > Ij)® ® a > ® c i)® (3 > ® C 3 > © © tX ® ® ®® ® (Z > ® ©ft®®®®®®® ® a© aj® cD® cD® ©®©®®®®®® © © © © © © © O D ® We went to follow your progreaa for the next two y es re; yet we realize that many students will move from time to tim e. P lease provide the namea of two people who are likely to know your address even If you move. W erequaet the name, addres s , and phone number of two persona. Contact 1: A reialhw or friend who does not live with you and who is iikety to know your address at a# times: Name: Address: City, State. Zip: - Phone Number:. Email address: _ Contact 2: Another relative or friend who does not live with you and who Is Iikety to know your address at a t times: Name:_________________________________________ Address;. City, State, Zip: . Phone Number:. Email address: _ 11699 133 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. i mat might have tnOuanced your dedeton to (Mark ops for each statement) M y parents wanted me to come here My spouse, partner or other fam ffy member wanted me Id come here ... This college has a good reputation .. I wanted to go to a Cite rent oolege •tan many of my friends ........ This coliege has good social activities . I ooutirrt find a Job ...................... This college is affordable..................... A high school or otier counselor advised m e....................................... This college is dose to my hom e___ This college's graduates get good Jobe. This college's students transfer to good 4-year schools. ....................... I coukhrt find anything bettor to do___ I went to get a belter job...................... frly friends are attendng here............. This college is close to where I work .. This college offers educational profpams of ■ pedal Interest to me that other cofregee do M O T have........ I went to get a cdege degree............. lb leern English lor work.................... M y employer encouraged me to enroll This college offers ffte program or csrtitcato I need tor work ....... 0 3 0 0 3 0 0 3 0 or: Or O: - Or boor ooor b o o On O f 3 0 30 0 3 0 0 3 0 a p b t O'. OOO O • o o o o G O O o n o O D O r O r;> O OO O r. 30 O r > o On O' 2. How many of your ctoeect personal frtend* are also currently sttandfrig thto coffagaT (Mart one.) None of my dosaet friends . One of my ctoeaet friends....... A tow of my dosest friends — About half of my doeeet friends Most of my closest friends ... A H of my otoeest trends........... S. In general, e te t do the luffuwing people idnh about thto particular ootfeoeT (Mvk one for each statement.} You............................................................ tour cioeest friends ...................... tour spouse or partner.............................. tour parents or guardians........ . . . . ......... tour other relatives . .......................... tour high school teachers.......................... Others ........................ .......................... 4. Whtoh of the toff owing statements beet d eacftea your college p A a n e for next sem ester? (Mark one) i w» attend only thts oolege........................................................ i I wil attend this cdege and i ether college.............................. I wil attend tha coflege and 2 or more other coleges f m i not attend here, but I w ilt attend 1 other college................. I wil not attend here, but I M il attend 2 or more other ccfieges . " I *41 not attend any coSege..................................... ............... 5. Where did you ettend school? Unbed Another (Mark ae that apply in each ooiwnn.} Stalse Country Elementary school or eqwvntenf (Ages 4 to 11)... O ____ . O Junior high school (Ages 12 to 14)........................ ............. High school (Ages 15to 18).................................r . . .................C CoSege............................... — .......................... 8. Not Indudtag thto coltopo. how many other eofcgaa or univoreHee have you ever attended? (Markgne.) None (I have attended onty tiis colsge) .............. o 1 other ..................................... v _ . 2-3 others......................... . .. 4 or more others.................................................. O 7. Hew many eredlts have you earned at (Me o olege to n rav to Ma aamartaw ? ( M a r k ore.) None ................................... ■ " 1 -3 ...................................................................... 4* 9 ....................................................................... 10-18 . . . . . . . . 19-27...................................................................' 28*36 ................................................................... 37-60 .................................................... More than 60 ..................... 6. Since leaving high school, have you ever taken coureee el any other fcrettuteon? For Not for (M a r k §gthet apply.) CredN Credfc toe. at another communffy or junior coftegs.......... o ........ Q tos. at a 4-year coftege or university . . ; .. .. O toe. at some offter pasteeoondary echool (tor example, technical vocational, business)........O .................O 9. In addition 1o tMs college, are you taking coureee at another school or college this semester'? {M ark alt that apply. I tos. at another communty coVege........................ tos. at a four-year cotege or unw rtfy................. tos, at a high school , _ tos, at a vocational or trade school...................... tos. st an adult echo* ................................ 134 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 10. As Brings aland today, do you Brink you wil...? (Mark on* breach statement.) Change your oareer choice Graduate with honors Ploy verartyrintorcell agists adUedes OOOO Q«i a Bachelor's degree................ 3 0 Parmanently slop attemlng college . Leora IM S coeege temporarily and return laler . ......................... Transfer to another community college Transfer 1 0 a 4-yaer oolege or university. .. Develop does new relationships with students at this college — IS* regidarty with the instructors at the coSege.................................... Change your oolege maior............. p o o o o oo o o o o o o ooo o o o o o o o o ooo o o o o o o o 11. trxhcete all ootiega degrees earned (Many). (Mark M l that apply.) Associate degree (AA. or equhrelenl) . Bachelors degree (BA. B S., etc.) ... Graduate degree (MA.. M.S.. PhD., Ed.D.,J.D..M.D,elc.) ..................... Cerkfioale........................................... U ftM ABOtfwr SWM Country . . a . . . . . . o .... O o , . o r> .. . o 12 H there were no oheleclee, what la the Mgheel academic da y e a you would Ilka lo ettafe In your M a U ma? (Mark one.) W W H take Oeseas But do not Intend k) earn u degree O Vocational certificate ............. O Associate (A A or equivalent)...........................................O Bachelor's deyee (BA. B£.. etc I ...................................O At least a Bachelor's maybe m ore.......................... O Master's degree (MA. M.S~ etc.) ......................O Doctoral degree (Ph.tl, EdlX, J.D.. etc.)...................... O Medcal degree (M O.. D.0S., D.V .M ., etc.)................... O 13. A pprnelmats ty hew many tttaae In the peel 7 days. dM you: (Mark one lor each statement ! B M P a class.......... Talk with an instructor before or alter O O O T b S t wkh an instructor during office hours................................................. Use email or tie Internet tor homework Help anoBier student tmderttand homework......................................... Study In smal groups outside cl d e s s . Speak with an academic counselor . .. O O O o o o o o o o o o o o o 14. For this course only, approrlstately how ■neny IS -.. rtMyeec (Mark gng lor each statement.) Work in small groupe during dass time Telephone or emal another student to ask a question about your studies Ask the instructor questions Speak up during class ttacuassm 15. In the past 7 days, spprmlmataty how many hours dM you: (Mark one lor each statement) Work a! a jab ............................ Do housework or childcare............. Watch T V ................................... Spend on this campus (Induing time in class)................................. Spend Mting w W i students about things not reused lo a cou ra e_ _ Study alone a( hom e.................... Study alone In die colege IBrery.. Study win students Irom due course......................................... OO OOO o o p QOQt Study vridi students mom other coureee (not Ms course)................tO jO |D O O O O O O o o o o o o 0 0 0 0 0.0 o o o o o o O O O O o o o O D o o O O o: p o D O o o o o o o o 16. How largo a piubiem do you expect each of the toftowtng to be while getting your edeceUon at M e eolaga? (Mark one for each statement) Paridng...................................................... Transportation (access to pubic transportation. sharing oars, etc.)............. Family rssponsfeHitiee (e g. child care, parent cere)............................................ Job-related reaponaMiMs........................ Paying far colego.................................. Schodulng desses tor nevt semester .. Understanctng die Entfiah language ........ Otiecuffy of classes............... 17. How otfen do you uee (ngBah ttrittt (fee meoadng people? (Mark one tor each statement) O O OO o o o o oo o o otooo o o o o o o otoloo o o o o o o o With my parents......................................... w tei M e n d s ....................................................... will teachers or professore at die college - 3 - Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 1ft. How often tfo you u h a 10090090 other thawEnalleh with the following people? {Mark one tor each statement ) With my parents With friends With teachers or protoosofs et this oolege 19. How w sl art you sbte to wo the toftowfno In Enofloft? (Mark jog for each item.) Read Write Understand a cdege lecture Read a college text book Write en essay exam . Write a term paper Psriicipete in dees Communicate wflh instructors 29. Is EnftHsh your nethre language? l b s O Go to question 22 No O Continue to question 21 21. How weft are you able to 60 the toWowbia In sour w th e tewpuaas? ( M a r k one for each item.) — Read. OOO Understand a cofiege lecture. . . Read a college tew book .......... Write an essay exam ............... Writs a term paper................... Partfclpato in class discussions . Communicate sriti instructors .. O O o oc* QCf OO 2 t How long daw * tato you to Ira n i Id I M i col tag*? (Mark one.) Lees than 15 minutes............................................. O 15 to 30 minutes ........................................... O 31 to 45 minutes.....................................................O 46 to 60 minutes.....................................................o Between 1 and 2 hours.......................................... O More than 2 hours............. ....................................■ - J 25. Do you have a dteafctety? (Mark s i tool apply) Hearing. ___ . . . .. . . . .. . Speech........................................................ ............... Mobity impaired.......................................................... Attention deficit tseonter .................................. Psychological cftsorder............................................... Learning dteabilty.................................... Vision problem that cannot be corrected by glasses or contact lenses ................................................ Other.................................................................................. No dteabUitiee................................................................. 24. What was your average grade in high echoot? (Mark one.) Aor A* (ExtfBOrcinory} . .................... . . O A * (Superior Qualfty}............................................................ O (ExceBant)............................................................................... O B (Very Good)..............................................................................O B- (Good)................................................. C C+ (Above Average) , , - ..................... O C (Amrage).................................................................................C _ > C- (Below Average)..................................................................... O D oc lower (Poor). . . . . ................................ .................... 29* before this semester, whet mateemeftee courses have you taken? Inducte courses In high school or previous ceftege work. (Mark afi that apply.) Basic math, Business math, or Pre-aigebra Algebra! ........................................................................... . . • ' > Geometry.................... O Atgebra I I ..................... O Trigonometry .. O Pre-calculus............. ................................................................ Calculus...................................................................................... O 2ft- Before thte semester, whet ectenee courses have you taken? include coureee in high eehool or previous coftsgs work. (Mark g H that apply.) General Biofogy................................................................ ......... Chemistry.................................................................................... O Phyelce.................................... O Biofogy spedaky (i.e., microbiology, genetics, botany, oeH biofogy, marine biology, etc.) . . . > Other Earth science (i.e.. geology, meteorology etc.) ............... 27. Wfth whom do you ftvs while ettendfog id s college? (Mark ailfoat apply.) Wfih my spouee or partner. ...................................................... o Wlh my parents or guardians ........................................... O Wih my children/stepchildren................................. O Wkh stoftngs (brother(s) arndtor slster(s)) ....... O Wfihotherretedves , . . .., O Wih e roommste(8) or a trlend(s) ................................... O I Ive ato n e............... O 29. Vour gander: M ale.................O Fem ale O 2S. How old wM you be on December 31 of this year? 16 years or younger.................................. .......... 18 .......... 1 9 ........... 2 0 21-24 25-29 30 30 40-54 .. . 55 O r Older 136 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 30. What it your ethnic group**)? (Mark Hi that apply.) Chinee*....................................................................... • 'J FHpfoo........................................................................r J Japanese................ f -> Korean ............. ..................................... Thai............................................................................ Laotian ......................................,,. ... G Cambodian........................................ ................... O Vietnamese................................................................O South Asian (Indian Subcontinent ) .............................. 3 Arab..........................................................................D Atrfcan-Mienean/Btecfc.............................................3 Mexican . . . .......... ................................................. - ' Mexiearv-Ameiican/Chicano........................................v South American .............................. G Central American.................................................. . 3 Other LatnoMispanJc .............................................. G Alaskan Native............................................. . . G American Indian .... ............................................... t - Pacific islander/Samoan. rtawasan, or Guamanian . .. C'' Other Pacific Wander ....................................C ^ Caucasian/White .......................................................O Other .................................................................. C 31. Are you currently married? ^s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .G N o ............................................................................. G 32- W HO la (are) the primary wage aam erf r ) In your household? (Mark H |* st apply) **14*. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G PartnerfBpouse.........................................................c 3 ParenteiGuarttene ......................................O ChiflerVStepcMdran .. ............................ .......C Other .....................................................O 33. How many of your chlldrerU stopchlkfcsri are Iving In your household? (Mark on#.) N one............................... ............... G 1-2 ... .........................................................................................................g 3 -4 ...................................................................................G S or more................................................................ C 34. Excluding yourself, how many people (cMldren, grandchildren, brothers, •latere, parent*, ale.) are you •nantiaty ai^portlng? (Mark one for each Hem.) Under Syaareoi age. 5toi8yaareolage . Over 18 years of age 003 0 36. Which owe of the W owing beat describee your iwployw in t afatua at this time? (Mark one.) Employed lulMime (indudng se&emptoyad) Employed part-time (todutfng aaff-employed) — Not employed but looking tor work ........ ... Not employed and not presently foowng lor work.. 38. How do you think of yourself? (Mark one.) Primahty as a student who « employed............. Primarily as an employee aho Is going to oolege Primarily as a parent who a going to collage . . Solely as a student........................................... 37. For die toSowtng Hama, pteeee Indicate the extant to u d M o f c you agree or dteagres wfth Hie loHowing statements. (Mark one tor each statement.) My teachers here give me a lot of encouragement in my studies............... I enjoy doing challenging class assignments.............................................. What other people toink of me is very impoftont . ..................................... I start to study at least 2 or 3 days prior toteets................................................ I expect to do weft and earn good grades w cottage................................................. Urtderstanckng whet is taught is important to m e ...................................................... I always complete homework assignments . I keep trying even when l am tfus&sted by a task..................................... ........ Learning can be judged beet by too grade one gets................................................... It is important lor me to finish the courses In my program at studto c ............. ........... Things are harder for me because of my race or etvSctty....................................... I frequently have dttcufty masting deadlines .......................................... I am very determined to reach my goals ... I was inttaiiy vary nervous about stteneftng college ................................................... I feel moat satisfied when I work hard to achieve something .................................. My family « more importer* than my career.................................... Success In college is largely due to effort (has to do wito how hard you try) . I feel l belong at ttks cotage....................... I wait tmtu the day before an assig nment Is due before starting I I knew I can teem ai the iWte taught in ppp I wart to become vwoived in programs to dean up the environment..........................J 3 | 3 | 0 l 0 | 0 I have declared a college major. DODD > O D )D 3 D OO O O O o o o D O O O O o ■ 5 - Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 111111111111111111111111111111111111111111111111111111111111111 34. f hav« attended m orientation aiMion at tWa cottage. 'ifes .................................. O N o ........................................ O W. Ar» you raerivtafl M (Mowing ty » * of flnancM oMManot? (Mark mat app*y> Loon.....................................O Schotershj) or grant...............O 40. Do you own yew m m . . . ? (Mark one In each column.) r — M o Homo (not renting)................................................O ..... O Computer (wlh Internet access) ________ O ..O Computer (wthout Internet access) ...................O ........Q C a r ......................................... O O 41. What la toe h I0i oat low ! o f ionwei education obtained by your poranto etther bi tea 0 6 . or In another oounlry? ( M a r k one i n each column.) Ah grade or leaa .......................... Junior high or middle school........... Soma high school............................ Finished high school or QED........... Some community oolege .. Completed community cotege------- Some tour-year college ............. Completed four'yoarcoOage degree. Some graduate school..................... Graduate tep ee .................. O . . . .O o o o o I do not know, . ,....... O 42. W hM o you were graving op, nark the )ob that beet describee your parent's ma|or occupatioa- (Mark one In each oolwnn.) Mother Retired.................................................... O .............O Day laborer (cleaning, construction. term, tactory, etc.)................................. O ____ O Worker or hourly employee (serteoe. hotel, hospital, agriculture, buck driver, clerical, retail sales and service, tetaidry or maintenanoe, etc.).. O ......O factory worker (manufacturing. warahoustag. shipping, opamimte. telephone oparetor.ate.).. . Q ...... O Sklled tradesman (mechintet, pkntoer, Me safer, eleoblcian, aUo mechanic, nurse. secretary, chef, technician) .. ..............................................O .., . O Supervisor or manager (professorial)..................O ........O Ebnaff business owner (relaR, construction, service, s ic.)......................................................O ........O Profesalonal, white collar (sales, finance, teaching, consulting. engineer, accounting, doctor, lawyer, etc.).............................................O ........O Housework (tateng cere of children or home) O ......O Unampicyed or on w afers ...................... O .. .. O Do not know .......................................................O . ., O 4k Write In your father's main )ob (or, R not working now, Me moot recent job). 44. Wrte In your mothers main job (or. If not working now, her moat re a m job). 4S. Describe your present work/oarear. 44. Describe the type of work/career you pfen to be I nvolved In 7 o re years (rare now. 47. How much education do you think te needed lor tee above type of work you are planning? (Mark one.) High school (fiptoma or GH> ...................... O Some community college..........................................................O Completion of Associate degree (A . A. or equivteont)..................O Some tour-year ooftege work.....................................................O Completion of a tour-yeer college degree (BA. &S ) .......... O Completion of more than a four-year ccttege degree..................O Completion of a professional degree or credential O Completion of a graduate degree (Master's Dope#)..................O Completion of an advanced proieeeionai degree (Doctorate, Ph.D.. M .O., ate.)............................ .... ......O 138 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. use ROSSIER C o d e. R EC O R D S R ELEA SE AUTHORIZA TION SCHOOL OF EDUCATION Dear Student, We request your participation in an important study. The information we are gathering from this project will he used to improve college teaching and learning and improve the student experience in community colleges It would be helpful if we could examine records pertaining to educational preparation, demographic characteristics and course enrollment information along with your responses to this survey. The Family Educational Rights and Privacy Act of 1974 (FERPA) provides that an educational institution may not release confidential information about a student without the student's consent. Please provide us with permission to access these portions of your records with the Los Angeles Community Colleges. Your consent will also allow us to contact you for follow-up research. Thank you. Linda Serra Hagedttrn Ph.D. Associate Professor & Chair, Community College Leadership 213-740-7218 I hereby authorise the research team headed hy Dr. Linda Serra Hagedom to obtain from (Ik Los Angeles Community Colleges the records of course registration, the final course grades I receive, information from my college application, scores from my assessment tests, and other records directly pertaining to my academic experience at the Los Angeles Community Colleges. This permission is valid only for the purposes of the research described herein. I understand that my name and other information that may identify me individually will not be released by the researchers. I provide my permission freely without coercion or threat Student's Signature Date Your full name (please prim) USC ISPIRB 400-0S-I81 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
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Asset Metadata
Creator
Buchanan, Donald G.
(author)
Core Title
An exploration of the gateway math and science course relationships in the Los Angeles Community College District
School
Rossier School of Education
Degree
Doctor of Education
Degree Program
Education
Publisher
University of Southern California
(original),
University of Southern California. Libraries
(digital)
Tag
education, community college,Education, Mathematics,education, sciences,OAI-PMH Harvest
Language
English
Contributor
Digitized by ProQuest
(provenance)
Advisor
Hagedorn, Linda Serra (
committee member
)
Permanent Link (DOI)
https://doi.org/10.25549/usctheses-c16-577131
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UC11341893
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3236483.pdf (filename),usctheses-c16-577131 (legacy record id)
Legacy Identifier
3236483.pdf
Dmrecord
577131
Document Type
Dissertation
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Buchanan, Donald G.
Type
texts
Source
University of Southern California
(contributing entity),
University of Southern California Dissertations and Theses
(collection)
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The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the au...
Repository Name
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Repository Location
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Tags
education, community college
Education, Mathematics
education, sciences