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Examining the factors that predict the academic success of minority students in the remedial mathematics pipeline in an urban community college

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Examining the factors that predict the academic success of minority students in the remedial mathematics pipeline in an urban community college

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Content EXAMINING THE FACTORS THAT PREDICT THE ACADEMIC SUCCESS OF MINORITY STUDENTS IN THE REMEDIAL MATHEMATICS PIPELINE IN AN URBAN COMMUNITY COLLEGE. by Alice Rice 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 2004 Copyright 2004 Alice Rice Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. UMI Number: 3145271 Copyright 2004 by Rice, Alice 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 3145271 Copyright 2004 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. ACKNOWLEDGMENTS Thanks, to mom and dad, my shining light that has never been so close, yet never so far. My darling Will, whose encouragement to be patient and seek advisement has advanced my commitment to the quality of this paper. Rafael and Cortes, thanks for your support. Especial thanks to Dr. Linda Serra Hagedom for the mentoring, nurturing, and most importantly sharing the data that is the essence of this paper. I wish to thank Dr. William McComas and Dr. Melora Sundt for their valuable input. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. TABLE OF CONTENTS Acknowledgments............................................................................................................. ii List of Tables.................................................................................................................... vi List of Figures................................................................................................................. vii Abstract...........................................................................................................................viii CHAPTER 1. BACKGROUND OF THE PROBLEM..................................................1 Outreach Program Focuses on College-Prep............................................................1 Get Tough Policies Affect Minorities and Females.................................................1 Controversial Experiment Indicator for the Rest of the Country........................... 3 The End of Remediation is the End of Open Admission........................................ 3 State Legislatures Agree Higher Education Accountable for Remediation.......... 5 Community College must Monitor Student Course taking Patterns......................6 Statement and Significance of the Problem.................................................................... 7 Purpose of the Study....................................................................................................... 10 Research Questions......................................................................................................... 10 Assumptions..................................................................................................................... 11 Limitations....................................................................................................................... 11 Delimitations....................................................................................................................11 Definition of Terms......................................................................................................... 12 Organization of the Study...............................................................................................15 CHAPTER 2. REVIEW OF THE LITERATURE.......................................................17 Role of the Community College............................................................................. 18 Definition of Remedial/Developmental Coursework & Completion Rates 20 Assessing and Placing Students..............................................................................22 Enrollment Trends in Postsecondary Remedial Education...................................24 Nontraditional Student Attrition Conceptual Model.............................................28 Academic Success Rate in Remedial Math............................................................35 Factors and Variable Predicting Academic Success............................................. 36 Conclusions......................................................................................................................38 Implications......................................................................................................................39 CHAPTER 3. RESEARCH METHODOLOGY..........................................................41 Research Questions.........................................................................................................42 Research Design.............................................................................................................. 42 Population and Sample........................................................................................... 43 Instrumentation........................................................................................................44 Data Analysis........................................................................................................... 47 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. IV Where Minority Students Rank............................................................................ 47 Relationships between Gender, Ethnicity, and Age............................................48 Factors that Predict Academic Success................................................................49 Predictors of Course Completion......................................................................... 50 CHAPTER 4. FINDINGS..............................................................................................51 Initial Findings..........................................................................................................51 Where Minority Students Rank with respect to the Course Placement, Progression, Completion and Academic Success in the Community College Remedial Math Pipeline...........................................................................................52 African American and Latino Females, Age 30 and over more Prevalent Placement in Remedial Math Courses................................................................. 52 African American and Latino’s, Age 30 and over had Math Course Progression Drops Off from Remedial to Transfer Level....................................................... 55 Asian had more Prevalent Course Completion Percentages in Transfer Level Math Courses..........................................................................................................58 African American and Asian Females had more Prevalent Academic Success in Transfer Math Courses......................................................................................61 Relationships between Gender, Ethnicity, and Age with respect to the Course Placement, Progression, Completion and Academic Success in the Community College Remedial Math Pipeline............................................................................64 The African American and Latino Females, Age 30 and over Predominant Placement in Remedial Math were Significant................................................... 64 African American and Latino’s, Age 30 and over Decreasing Math Course Progression from Remedial to Transfer Levels was Significant........................66 The Asian Student’s Predominant Course Completion in Transfer Math Courses was Significant........................................................................................ 67 African American Females Academic Success Ranking Improved when they Reached Transfer Math Courses...........................................................................69 Factors that Predict Academic Success in the Remedial Math Pipeline..............71 Initial Analysis of the Independent Variables..................................................... 71 Description of the Population............................................................................... 71 Background variables: high school GPA, enrollment status and highest math placement were factors predicting academic success in the remedial math pipeline................................................................................................................... 75 Does Gender, Ethnicity, or Age Predict Course Completion in the Remedial Math Pipeline?......................................................................................................... 78 Does High School GPA, Placement exam, or Performance Predict Course Completion in Remedial Math Pipeline?............................................................... 81 CHAPTER 5. SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS 85 The Purpose of the Study........................................................................................ 86 Summary of Findings...................................................................................................... 86 Discussion........................................................................................................................92 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. V Assessing and Placing Students..............................................................................92 Enrollment Trends in Remedial Education............................................................93 Nontraditional Student Attrition Conceptual Model............................................. 94 Academic Success Rate in Remedial M ath...........................................................95 Factors and Variable Predicting Academic Success and Course Completion....96 Factors Predicting Academic Success................................................................. 96 Variables Predicting Course Completion............................................................97 Conclusions......................................................................................................................98 Recommendations......................................................................................................... 101 Implications.............................................................................................................101 Recommendation for Further Research............................................................... 102 References...................................................................................................................... 104 Appendixes..................................................................................................................... I l l A. Survey Instrument One.......................................................................................I l l Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. vi LIST OF TABLES Table Page Table 1 Ethnicity by Highest Math Placement Levels................................................. 53 Table 2 Gender by Highest Math Placement Levels.................................................... 53 Table 3 Age by Highest Math Placement Levels..........................................................55 Table 4 Math Pipeline Progression by Ethnicity.......................................................... 56 Table 5 Math Pipeline Progression by Gender...........................................................560 Table 6 Math Pipeline Progression by Age................................................................... 57 Table 7 Math Pipeline Averages Success Ratio by Ethnicity......................................59 Table 8 Math Pipeline Averages Success Ratio by Gender.........................................63 Table 9 Math Pipeline Averages Success Ratio by Age.............................................. 64 Table 10 Math Pipeline Progression by Ethnicity.........................................................62 Table 11 Math Pipeline Progression by Gender...........................................................63 Table 12 Math Pipeline Progression by Age................................................................. 64 Table 13 Conceptual Model Constructs........................................................................ 73 Table 14 Regression Weights........................................................................................ 77 Table 15 Pearson Correlation. R squared, F Statistics................................................. 80 Table 16 Pearson Correlation. R squared. F Statistics................................................. 83 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. LIST OF FIGURES Figure Page FIGURE 1. CONCEPTUAL MODEL OF NONTRADITIONAL STUDENT ATTRITION (BEAN AND METZNER 19851 ....................................................... 34 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. viii ABSTRACT The goal of this study was to examine the factors that predict the academic success of the minority students in remedial math courses at an urban community college. Data were from a five-year longitudinal and comprehensive study of goals, success and academic patterns of an urban community college. The theoretical framework relied on the Bean and Metzner conceptual model of nontraditional student attrition. It was concluded from the study that the factors that predict the academic success in the minority student math pipeline were identified as: highest math placement, enrollment status, attendance, hours of employment, opportunity to transfer, overall GPA, and quality of teaching. The predictors of course completion in remedial math courses were: ethnicity, gender, age, high school GPA, highest math placement, and academic advising, where age and average grade in high school are the best predictors. The predictors of course completion in basic math courses were; age, average grade in high school, highest math placement, overall GPA, and academic advising. The predictors of course completion in intermediate math courses were: age, high school GPA, highest math placement, and overall GPA. The predictors of course completion in transfer math courses were: ethnicity, average grade in high school, highest math placement, overall GPA, and academic advising, where ethnicity was the best predictor. Furthermore, overall GPA is the best predictor for course completion in the remedial math pipeline in the basic, intermediate, and transfer math courses. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. The implication for the community college administrator is the necessity for further study that examines the scheduling of remedial math courses that meet: high school performance prerequisites, enrollment status and opportunity to transfer, for future curriculum implementations. Further research is recommended using separate prediction equations for ethnicity and quality of teaching. Replication of this study is recommended throughout the country in order to implement a common perspective for evaluating the effectiveness of remedial math pipelines in community colleges throughout this country. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 1 CHAPTER 1 BACKGROUND OF THE PROBLEM Outreach Program Focuses on College-Prep. California State University (CSU) is trying to reduce the percentage of students who need remedial classes by helping to improve California’s public school system (Weiss, 2001). The CSU system has spent $9 million to work closely with 172 high schools that send the greatest number of students needing remedial work, to align their standards with university expectations (Clayton, 2002). This targeted outreach program does not focus on the lowest performing students. Instead it focuses on those following a college-prep track, helping them with first- and second- year algebra and English composition to prepare them for success at the university (Weiss, 2001). But, one major outcome has been to persuade students to take math as high school seniors. Otherwise, many of them find that they are lacking essential math skills necessary before they enter the university (Weiss, 2001). Get Tough Policies Affect Minorities and Females. Of the 31,187 freshmen who were enrolled in CSU in the fall of 1999, 19,741 needed help in English, math or both. A year later, only 4,236 of those students still needed remedial help. Of those students, 2,009 were not permitted to re-enroll, 1,604 left on their own and 623 were given a chance to re-enroll, on the condition that they Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 2 wrapped up all remedial work that fall (Weiss, 2001). According to CSU the failure rate of freshmen on the proficiency test is 46% for math and 45% for English (Weiss, 2001). Using Harvey’s (2002) classification of the five minority (ethnicity) groups, we found that a breakdown by ethnicity and gender of all CSU freshmen who needed remedial work in English or math showed that 73% of the African American, 63% Latino and 48% American Indian students needed remedial math, while 39% Asian and 37% White students needed remedial math. Additionally, 66% African American, 61% Asian, and 61% Latino students needed remedial English, while 38% American Indian and 28% White students needed remedial English (Weiss, 2001). Lastly, 53% female and 35% male students needed remedial math and 46% female and 45% male students needed remedial English (Weiss, 2001). In particular, more than one half (63%) of the 1999 freshman class needed remedial education. The failure rates were nearly one half for proficiency tests in math (46%) and English (45%). The most prevalent percentages of African American (73%) and Latino (63%) students needed remedial math, while more prevalent percentages of African American (66%), Latino (61%) and Asian (61%) students needed English remediation. The White students had the least prevalent percentage needing remedial math (37%) and English (28%). Furthermore, there were a predominant percentage of female (53%) students needing remedial math than male (35%) students. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 3 Controversial Experiment Indicator for the Rest o f the Country. With 388,000 students, CSU is the nation’s largest university system, and its “get-tough policy” is being closely watched (Clayton, 2002). According to a California State University Chancellor’s Office memo (1995) the policy to mandate the completion of remedial math prior to admission to public institutions such as the California State University, which admits only the upper one-third of the high school graduating class and students who have succeeded in community college, would not admit many students who are not fully prepared for college work.” Many support this admission policy while others argue that they will unfairly impact minorities and immigrants (Clayton, 2002, p. 13). Still, with state higher-education budgets getting tighter, many public institutions dearly want to slash the costs of remedial education (Clayton, 2002). Additionally, “what’s happening in ... California portends what will happen in the rest of country,” says Yolanda Moses, President of the American Association for Higher Education, a lobby group in Washington (cited in Clayton, 2002, p. 13). The End o f Remediation is the End o f Open Admission. The City University of New York (CUNY) is the third-largest university system in the country with a student population over 200,000. About 65 percent are from oppressed communities; African American, Latino and Asian. Most White students are recent immigrants from Eastern Europe or are from working class families (Dunkel, 1998). A vote by the City University of New York Board of Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 4 Trustees cutting remedial services for new students can deny thousand of working class students, most of them people of color, the right to a college education at a time when many jobs accept only college graduates (Dunkel, 1998). In 1999, CUNY trustees voted to bar students needing remedial work from its 11 bachelor’s degree programs (Arenson, 2002). Ending remediation will exclude 50 to 60 percent of students from oppressed communities, according to David Lavin, a sociologist at CUNY (cited in Dunkel, 1998). Some 35 percent of White students will also be affected (Dunkel, 1998). The proportion of African American and Latino students admitted to the City University of New York’s bachelor’s degree programs has not been significantly affected by new admissions standards, though they continue to be admitted at lower rates than White and Asian students, according to data released by the university (Arenson, 2002). The data are being submitted to the New York State Regents and the United States Education Department’s Office for Civil Rights, which are monitoring the policy’s impact in response to charges that African American and Latino students would be squeezed out (Arenson, 2002). Some CUNY critics say that CUNY’s data demonstrated that African American and Latino students were unfairly excluded by the new admissions policy since students of color have much lower passing rates on the standardized tests, have a significantly higher proportion that do not meet the cutoff scores, would have been admitted except for the additional test requirement, and that the university had not shown that the tests predicted students’ success there (Arenson, 2002). Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 5 State Legislatures Agree Higher Education Accountable for Remediation. According to Michelau’s (2000) research, four states Colorado, Florida, Texas and West Virginia have a state law that requires a placement exam for entering freshmen, but only Florida and Texas require a specific placement test to determine whether a student is prepared for college level work. Additionally, he found that California is considering a measure that would require the California State University to offer to all high school pupils who have completed the tenth grade the opportunity two times each year to take the Entry Level Mathematics Examination and English Placement Test which were developed for use by entering first year students of the California State University. Under this bill, the university would be required to report the results to the pupils so they might have an opportunity to seek remediation before they enter college, if necessary. According to a 1997 study by the State Higher Education Executive Officers (SHEEO), Illinois and Virginia have state policies that require postsecondary institutions to conduct assessments and determine placement, instead of the assessment and placement occurring at the state level (cited in Michelau, 2000). In Colorado, state law requires that students take basic skills remedial coursework no later than the end of their freshman year and only from specified schools. It also requires the State Board of Community Colleges and Occupational Education, local community colleges, Adams State College and Mesa State College to track all students who are enrolled in basic skills courses to determine whether those students successfully graduate, and requires that the Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 6 Colorado Commission on Higher Education report cost and student data to the legislature (Michelau, 2000). Furthermore, Michelau (2000) found that: New Mexico and South Carolina prohibits state funding for remediation at doctoral institutions, Massachusetts allows only a maximum percentage of university freshmen to be enrolled in remedial education classes, Arkansas places a statutory cap on the use of state funds for remediation at its public universities, Colorado, Florida and South Carolina offer only remedial education at two year college institutions and likewise funding for remediation is awarded to only these institutions. Community College must Monitor Student Course taking Patterns. The proposed policies hold secondary education and the community college directly accountable for the preparation of students to enter college level math. Community colleges are key players in linkages with the secondary and postsecondary education (Hodgkinson, 1999). Baccalaureate institutions in the State may offer some remedial coursework, but they must pay for it from their own budgets (Townsend & Twombly, 2001). This puts the community colleges in the primary role of providing the needed remediation and with limitations on how federal financial aid can be used to cover remedial coursework; community college administrators must monitor course-taking patterns of its students in a manner that baccalaureate institutions do not (Townsend & Twombly, 2001). Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 7 Gerda (1995) conducted a study of student course taking patterns for students enrolled in math courses at the California College of the Canyons and found a disturbing pattern emerging for about 3% of the students. According to the transcripts, these students had many course repetitions in math without success and were doing poorly in other areas as well. From this study Gerda (1995) recommended that these students probably needed more comprehensive guidance from more than an academic instructor. Additionally, he was instrumental in the modification of the policy to limit the number of repeat course enrollments for students to two unsatisfactory grades in order to allow for sufficient student success. This policy change led Glendale College, Cabrillo College, and several other colleges to limit repeat course enrollment for students. Statement and Significance of the Problem The explosion of students requiring remediation has created a channel for millions of dollars in funding for remedial education programs at the same time as the universities are accelerating the movement of remedial education to the community college. Kirst (1998) reported in Education Week, that the City University of New York planned to withdraw admission to its four-year schools if students failed placement exams, and redirect the students to community college. The high school graduates who could not at first show that they were ready for college-level work were able to do so after taking free immersion classes in math or English over the summer or after taking remedial classes offered by CUNY’s Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 8 community colleges (Arenson, 2002). These new entrance policies took effect the spring of 2000, ending remedial courses at the city university’s four- year colleges (Mazelis, 1999). Furthermore, Weiss (2001) reported in the Los Angeles Times, that the California State University (CSU) system expelled about 2,000 students, more than 6% of the previous year’s freshman class, in a “crack down” on unprepared students for failing to master English and math skills within their first year of classes. Additionally, the university notified 2,009 students system wide that they cannot re-enroll as sophomores. They were instructed to go to a community college to prepare them to pass CSU’s placement tests in math and English (Weiss, 2001). Subsequently, for the second year in a row, CSU expelled 2,277 students across its 22 campuses, or about 7% of its freshmen class (Clayton, 2002). The policies mandating the completion of remedial math courses prior to admission to the university establishes the need for the community college administrator to monitor the course placement, progression, completion, and the academic success of minority students in the remedial math pipeline. Though this dissertation, I sought to examine the factors that predict the academic success of minority students in the remedial mathematics pipeline for in urban community college. As I will discuss in Chapter 2, some researchers have recommended the study of the minority student in developmental mathematics courses and college algebra, since these students have been identified at a greater risk than others and require provisions for effective teaching strategies specifically Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 9 designed for these students (Penny & White, 1998). Other researchers have identified the inclusion of gender and ethnicity as appropriate variables to be utilized in developing and implementing retention studies because they are associated with college performance, persistence and completion (Andreu, 2002). Additionally, the U.S. Department of Education (1996) suggested the study of remedial courses in order to evaluate the current availability against demand for these courses in higher education. The epidemic for remedial education is emerging at a time when university access cannot be guaranteed for all students. The “get tough” policies are requiring students to first show that they are ready for college work and pass placement exams for math and English before they can enroll in college. At the same time, state budget cuts in higher education will require community colleges to accommodate additional demands for lower division courses to prepare students for transfer as a priority when determining course selections being offered. This puts the community colleges in the primary role of providing the remedial education necessary for university access. Furthermore, researchers have called for retention efforts to quickly integrate high- risk students into the university environment, help students balance educational commitment with work responsibilities, examine the impact of developmental courses on graduation rates and determine the impact of student services on academic success. Therefore, it is important for the community college administrator Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 10 to examine the factors that predict the academic success of minority students in the remedial math pipeline. Purpose of the Study The purpose of this study was to examine course enrollment and completion in the remedial mathematics pipeline for minority students in an urban community college to determine which variables (examples: high school grade point average (GPA), ethnicity, gender, age, placement exam score or performance level (overall GPA)) are predictors of academic success in mathematics courses. Research Questions 1. Where do minority students rank with respect to the course placement, progression, completion and academic success in the community college remedial mathematics pipeline? What are the relationships between gender, ethnicity, and age with respect to course placement, progression, completion and academic success in the remedial mathematics pipeline? 2. What factors predict the academic success of minority students in the remedial mathematics pipeline? Does gender, ethnicity, or age predict course completion in the remedial mathematics pipeline? Does high school GPA, placement exam score, or performance levels (overall GPA) predict the course completion in remedial math pipeline? Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 11 Assumptions 1. The data were accurately recorded and analyzed. 2. The participants responded to the best of their abilities. 3. The Los Angeles Community College District is similar to other urban community colleges. So, the findings of the study can be generalized to other urban community colleges. Limitations 1. This study is limited to subjects who agree to participate voluntarily. 2. This study is limited to the number of subjects surveyed and the amount of time available to conduct the study. 3. Validity of this study is limited to the reliability of the instruments used. Delimitations This study confined itself to a secondary analysis of data previously collected from the project on Transfer and Retention of Urban Community College Students (TRUCCS), a five-year longitudinal and comprehensive study of goals, success and academic patterns of Community College students in the urban Los Angeles Community College District (LACCD). The present study focused on comparing the relationships between the age, gender, ethnicity, high school GPA, math courses enrollment and completion, academic success, placement exam score, and performance levels (overall GPA) of remedial math students at the community Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 12 college level. Only Asian, African American, Latino, and White ethnicities and age population between 19 and 54 years were included in the study. Definition of Terms ACT. A standardized external battery of tests administered by the American College Testing Program covering English, mathematics, reading and science reasoning. The tests are designed to assess the student's educational development and readiness for college-level study. College entrance examinations. A series of standardized examinations required by colleges and universities as part of an admissions application process. The examinations are designed to provide information on the level of academic preparedness of applicants for successful collegiate study at either the undergraduate or graduate levels. Developmental level courses. Those classes offered to college students who score below a pre-determined cutoff score on a standardized skills assessment. The classes are often considered remedial and generally count towards units needed to graduate but, do not transfer to other colleges (Mercer, 1995). ELM. The entry-level mathematics examination is designed to assess the skill levels of entering CSU students in the areas of mathematics typically covered in three years of rigorous college preparatory mathematics courses in high school. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 13 Minority Group Classifications. A classification indicating general racial or ethnic heritage based on self-identification, as data collected by the U.S. Census Bureau, or based on observer identification, as in data collected by the Office for Civil Rights. These categories are in accordance with the Office of Management and Budget’s standard classification scheme presented below (Harvey, 2002): • White/Caucasian A person having origins in any of the original peoples of Europe, North Africa, or the Middle East. Normally excludes persons of Hispanic origin except for tabulations produced by the U.S. Census Bureau. • Black/African American A person having origins in any of the black racial groups in Africa. Normally excludes persons of Hispanic origin except for tabulations by the U.S. Census Bureau. • Hispanic/Latino A person of Mexican, Puerto Rican, Cuban, Central or South American, or other Spanish culture of origin, regardless of race. • Asian or Pacific Islander/Asian Americans A person having origins in any of the original peoples of the Far East, Southeast Asia, the Indian subcontinent, or the Pacific Islands. This area includes, for example, China, India, Japan, Korea, the Philippine Islands, and Samoa. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 14 • American Indian or Alaskan Native A person having origins in any of the original peoples of North America and maintaining cultural identification through tribal affiliation or communication recognition. Placement examinations. A series of standardized examinations required by colleges and universities as part of an admissions application process. The examinations are designed to provide information on the level of academic preparedness of applicants for successful collegiate study at either the undergraduate or graduate levels. Remedial Math Courses. According to LACCD, examples of math courses identified as university level include College Algebra, Statistics, Trigonometry, Pre- Calculus, and Calculus. Therefore, Introduction to Elementary Algebra, Elementary Algebra, and Basic Elementary Algebra are categorized as remedial. Remedial Math Pipeline. A description of the problems and hierarchical nature of the progress in remedial mathematics used to understand the path community college students follow in pursuing a college education. Remedial Student. A remedial college student would be a college student that has had instruction in college preparatory math and needs to receive the instruction again in order to be prepared for college courses. SAT I Reasoning. A standardized test offered by the College Board through a contract with the Educational Testing Service (ETS). Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 15 SAT II Subject Matter Tests. A series of standardized tests offered by the College Board through a contract with the Educational Testing Service (ETS) to assess the proficiency of prospective students in specific subject areas (e.g., chemistry, history, calculus, Spanish, etc.). Organization of the Study Chapter 1 presents the problem and the underlying framework of the study, the background of the problem, the statement and significance of the problem, the purpose of the study, the research questions to be answered, the assumptions, limitations, delimitations, and the definitions of terms. Chapter 2 is a review of relevant literature. It addresses the following topics: The Role of the Community College, Definitions of Remedial/Developmental Coursework & Completion Rates, Assessing and Placing Students, Enrollment Trends in Postsecondary Remedial Education, Academic Success Rates in Remedial Math, and Factors and Variables Predicting Academic Success. Chapter 3 presents the methodology used in the study, including the research design; population and sampling procedure; and the instruments and their selection or development, together with information on validity and reliability. Each of these sections concludes with a rationale, including strengths and limitations of the design elements. The chapter goes on to describe the procedures for data collection and the plan for data analysis. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 16 Chapter 4 presents the results of the study. Chapter 5 discusses and analyzes the results, culminating in conclusions and recommendations. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 17 CHAPTER 2 REVIEW OF THE LITERATURE The problem of underprepared college students is pervasive (Frankenstein, 1990). The 1995 Digest of Educational Statistics (U.S. Department of Education, 1996) reported that in 1978 16.8% of the African-American and 57.6% of the Anglo 17 year-old students could perform reasoning and problem solving involving fractions, decimals, percents, elementary geometry, and simple algebra (Walker & Plata, 2000). In 1990, the Digest of Educational Statistics (U.S. Department of Education, 1996) reported that 32.8% of the African-American and 63.2% of the Anglo 17 year-old students could perform these skills (Walker & Plata, 2000). Although the performance of these reasoning and problem solving skills are slowly increasing, the progression is towards achieving the goals for the seventh grade math standards of California public schools (California Department of Education, 2000). Ultimately, at the collegiate level, nearly one in four college freshmen is taking high school or remedial math (Education Trust, 2002). Even at highly selective institutions, about 20% of total student enrollments in math are at the remedial or pre-calculus level (Education Trust, 2002). The National Center for Education Statistics (1996), Remedial Education at Higher Education Institution in fall 1995 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 18 reported that 41 percent of first-time freshmen at community colleges enrolled in at least one developmental education reading, writing, or math course. Currently there are estimates that 40% of first-time students entering the average community colleges are underprepared for college level work (National Center for Educational Statistics, 1996). The recommendation reported in the Education Trust (2000) suggests reporting the data on the state of remedial education for each state or community. Role o f the Community College Many four-year institutions were eager to transfer their academic preparatory programs to community colleges in the early half of the twentieth century due to the rapid increase in the number of postsecondary institutions (Arendale, 2002). To this end, the use of standardized college admissions tests made it easier for colleges to sort students based on test scores to different types of institutions according to the varying levels of admission selectivity (Arendale, 2002). As a result, when four-year institutions began to receive more state and federal appropriations, there was a lessening need to admit the high numbers of academically underprepared students needed to help pay tuition to meet institutional expenses (Richardson, Martens, & Fisk, 1981). Some people assert that community colleges are best equipped to teach developmental courses (Kozeracki, 2002). In a number of states, pressure is being exerted on the public higher education system to restrict developmental courses to Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 19 the community college (Kozeracki, 2002). A well publicized battle took place in New York, with the mayor and the Board of Trustees of the City University of New York requiring that all developmental coursework be moved to the community colleges (Healy, 1998; The Mayor’s Advisory Task Force, 1999). Colorado, Missouri, Florida, and South Carolina prohibit developmental education at four-year institutions, and Georgia, Maryland, Minnesota, Nevada, Ohio, and Virginia are considering similar statutes (Kentucky Council on Postsecondary Education cited in Michigan Department of Education, 1999; Roueche & Roueche, 1999). In 1995, the California State University Board of Trustees received recommendations from the sub-committee on Remedial Education to change their policy to read: “ Effective with the Fall term in the year 2001, it will be a condition o f admission to the California State University that entering undergraduate students must demonstrate readiness to undertake college level instruction. ” (Precollegiate Instruction in the CSU, July, 1995, pp 4-5). A Legislative Analyst’s Office (2001) report stated that the California State University standards for demonstrating college preparedness in math require students demonstrate that they were prepared in one of three ways. First, students can demonstrate preparedness by scoring a minimal level on the SAT, American College Testing Assessment (ACT), or Advanced Placement (AP) exams. Second, students who do not score sufficiently high on the standardized exams can demonstrate they are prepared by performing satisfactorily on placement Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 2 0 exams developed and administered by the universities. Finally, students who do not score sufficiently high on college admissions or placement exams must pass precollegiate courses. The proposed policy and surrounding discussion explicitly hold K-12 and the community college accountable for the availability of students prepared to enter college level English and Math (Weissman, Bulakowski, & Jumisko, 1997). Recommendations offered suggest that California State University and the California Community College work with high schools to develop methods to diagnose students’ readiness for college-level studies and consider aligning skills assessment with existing exams (Legislative Analyst’s Office, 2001). Definition o f Remedial/Developmental Coursework & Completion Rates The purpose of developmental education is to enable college students to gain the skills necessary to complete college-level courses and academic programs successfully (Weissman, Bulakowski, & Jumisko, 1997). Success for developmental college students is often defined as completion of the developmental course. More than 90% of responding community colleges in one study have utilized this method, as opposed to less than 30% that use standardized exit exams or have students retake college wide assessment tests (Schults, 2000). California State University (CSU) categorizes precollegiate skills instruction as either remedial or developmental. The distinction is based on whether a student who arrives at the CSU has ever been fully exposed to instruction in preparatory college mathematics. A remedial college student would be one who has had Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 21 instruction in college preparatory math, but for whatever reason, needs to receive the instruction again in order to be prepared for college courses. A developmental college student would be one who has not ever been exposed to college preparatory instruction (Precollegiate Instruction in the CSU, 1995, pp. 4-5). Recommendations suggest that the California Framework for Mathematics and English Language Arts should be collaboratively reviewed and modified as determined appropriate by the K-12 and CSU faculty (Academic Senate for California Community Colleges, 1995). According to the Remediation Taxonomy developed by the 1987 Master Plan Review Commission (Academic Senate for California Community Colleges, 1995) examples of Math courses identified as university level include Calculus, Pre- Calculus, and Analytical Geometry. While college credit is sometimes offered for the courses in College Level 1, courses in this level of instruction are sometimes categorized as remedial. Examples include Advanced Algebra, Intermediate Algebra and Trigonometry (Precollegiate Instruction in the CSU, 1995, Appendix A). Therefore, a gross generalization could be that students who are not prepared at the Calculus or Analytical Geometry level are placed in remedial math courses. Recommendations suggest a greater coordination between the content of the curriculum, form and style, which determine success on the ELM assessment used in the CSU, should be consistent with the California Community College curriculum (Academic Senate for California Community Colleges, 1995). Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 2 2 Assessing and Placing Students Beginning in the late 1970s, the nation’s community colleges moved toward a system of placement testing, restricted admissions to many courses and programs, and integrated remedial programs (Cohen & Brawer, 1996). The placement process of determining where students belong within a sequence of courses is the pillar on which a developmental education program rests, particularly in mathematics where the course contents are carefully delineated and are hierarchical (Akst & Hirsh, 1991). Students, who did not possess essential algebra skills, as determined by scores on the basic algebra test published by the Mathematical Association of America (MAA) (1996), were placed in developmental courses (Walker & Plata, 2000). Entering California State University (CSU) students who have met CSU determined cut-off scores on the Scholastic Aptitude Test (SAT) or the American College Testing Examination (ACT) are eligible to enroll in college level math. Students may be exempt from testing if they have taken appropriate courses at other institutions of higher education. Students not eligible by the SAT, the ACT, or previous course work, are tested with the Entry Level Mathematics Test (ELM) (Precollegiate Instruction in the CSU, 1995, pp. 4-5). The statewide Academic Senate for California Community Colleges recommends that CSU should review existing assessment practices for validity, cultural and gender bias and appropriateness before further consideration is given to implementing the proposed policy and the CSU faculty should collaborate with the California Community Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 23 College (CCC) faculty on the determination of test cut-off scores and curriculum implications for the CCC preparing the students to meet the CSU determined levels of preparation (Academic Senate for California Community Colleges, 1995). Beginning in the fall (Andreu, 2002), 1996, Florida law requires that students who test into college preparatory instruction to subsequently enroll in college preparatory instruction coursework. These students must successfully complete the required college preparatory studies by the time they have accumulated twelve (12) hours of college credit coursework. Otherwise, they must maintain continuous enrollment in college preparatory coursework each semester until the requirements are completed while performing satisfactorily in the degree earning coursework (Florida Law Rule 6A-10.003 Section 240.177(4) p. 173). Southwest Virginia Community College offers three developmental courses for students who arrive with the need for preparatory course work in mathematics including Basic Arithmetic-Math 02, Basic Algebra I-Math 03, and Basic Algebra II- Math 04 (Waycaster, 1998). Upon enrollment, full-time students are given the appropriate level of the ASSET placement test that assesses their developmental needs in mathematics as well as other courses (Waycaster, 1998). Based on their scores, the students who need work in developmental mathematics are then advised to take one of the three basic courses (Waycaster, 1998). Following this initial placement, students are given an in-house readiness test on the first or second day of Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 24 class in each of these developmental mathematics courses to determine if they do indeed have the necessary math skills to succeed in this specific course. Results of these readiness tests are returned to the students during the first week of classes so that necessary course changes can be made early in the semester during the drop/add period. Waycaster (1998) recommended increasing all sections of remedial math to five credit hours each and that scheduling of these courses be varied in such a way as to meet the needs of students. Enrollment Trends in Postsecondary Remedial Education. The vast majority of students enrolled in remedial courses are White (Townsend & Twombly, 2001). According to the American Council on Education (1996), 65 percent of those participating in remediation are White. However, ethnic and racial minorities are disproportionately represented in remedial courses. For example, while African Americans make up only 10 percent of the college student population, they comprise 15 percent of those enrolled in remedial courses (Townsend & Twombly, 2001). In total, though, minorities account for only 35 percent of remedial students (U.S. Department of Education, 1996). At the collegiate level, nearly one in four college freshmen are taking high school or remedial math (Education Trust, 2002). Bonsangue (1999) studied the transcripts analyses of 813 students currently enrolled as seniors at one of six of the 22 campuses of the California State University (CSU). About three-fourths (74%) of these were transfer students from a community college who entered as third-year Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 25 students. According to the findings nearly half (45%) of the minority students (enrolled in mathematics-based majors) had not completed their mathematics requirement (first and/or second year calculus) even though the students were seniors. The U.S. Department of Education (1996) statistics indicate that in 1995, freshmen were more likely to enroll in a remedial mathematics course than in a remedial reading or writing course. In fact, from 1989 to 1995, the percentage of freshmen who enrolled in a remedial mathematics course increased, while the percentage who enrolled in a remedial reading or writing course remained the same. In 1995, freshmen in public 2-year colleges were far more likely to enroll in remedial courses than freshmen in public 4-year institutions (41 compared to 22 percent, respectively). The authors suggest using the percentage of institutions offering remedial courses and the percentage of freshmen who enroll in these courses to provide a snapshot of the current availability and the demand for these courses in higher education. According to statistics gathered from the National Center for Education Statistics (Foote, 1997), approximately 42% of the first-time freshmen in the U.S. enroll in a community college; the average age of the student is approximately 32; the most frequent age of the student is 19; women make up 57.8% of community college enrollments. In terms of ethnicity, 69.8% of community college students are Caucasian, 11.1% are African American, 10.5% are Latino, 4.6% are Asian, and 1% Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 26 is Native American, and approximately 1% is a nonresident alien. Approximately 46.4% of all minorities enrolled in higher education are attending two-year colleges. Today’s undergraduate population is different than it was a generation ago. In addition to being 72 percent larger in 1999 than in 1970 (with fall enrollment growing from 7.4 to 12.7 million), proportionately more students are enrolled part- time (39 versus 28 percent) and at 2-year colleges (44 versus 31 percent), and women have replaced men as the majority (representing 56 percent of the total instead of 42 percent) (U.S. Department of Education, 2002b). Additionally, there were proportionately more, older students on campus as well: 39 percent of all postsecondary students were 25 years or older in 1999, compared with 28 percent in 1970 (U.S. Department of Education, 2002b). The term nontraditional student is not a precise one, although age and part- time status (which often go together) are common defining characteristics (Bean and Metzner, 1985). An NCES study examining the relationship between nontraditional status and persistence in postsecondary education identified nontraditional students using information on their enrollment patterns, financial dependency status, family situation, and high school graduation status (Horn, 1996). Almost three-quarters of undergraduates are in some way nontraditional, 73 percent of all undergraduates in 1999-2000 had one or more characteristic to define them as such (Horn, 1996). More specifically, the percentage of undergraduates with nontraditional characteristics for Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 27 1992-93 (e.g., attended part time 54%, delayed enrollment 43%) and 1999- 2000 (e.g., attended part time 48%, delayed enrollment 46%) are shown in Figure 1. According to Horn (1996), the percentage of undergraduates with each nontraditional characteristic and in 1999-2000 show, financial independence was the most common nontraditional characteristic (51 percent), followed by part-time attendance (48 percent), and then delayed enrollment (46 percent). Between 1992-93 and 1999-2000, the percentages of students who delayed enrollment, worked full time, had dependents, and were single parents all increased (Figure 1). The percentage of undergraduates attending part time decreased (Horn, 1996). There were no measurable changes between the two years in the percentages who were financially independent or did not have a high school diploma (Horn, 1996). Figure 1. —P ercen ta g e of u nd ergrad uates with nontraditional ch aracteristics: 1 9 9 2 -9 3 and 1 9 9 9 -2 0 0 0 Any nonindWontl characteristic Rnartoiifly tndepandant Attended part time Delayed enrollment Worked full time Had dependent* Single parant No high school diploma m 1992-93 1999-2000 2 7 1 a H 6 ? 40 60 Percent SOURCE: U.S. Department of Education, NCES. National Postsecondary Student Aid Study (NPSAS:2000). Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 28 Nontraditional Student Attrition Conceptual Model Some of the most influential theoretical contributions to understanding the student attrition process, those of Spady (1970), Tinto (1975), and Pascarella (1980), relied heavily on socialization or similar social processes (e.g. shared values and friendship support) to explain the attrition process (Bean & Metzner, 1985). The causes of dropping out and the models and methodologies used to explain these causes (Astin 1995; Bean & Metzner 1985; Cabrera, et al. 1992; McGrath & Braunstein 1997; Murtaugh, Bums & Schuster 1999; Pascarella & Terenzini 1991; Tinto 1975; 1987; 1993) including, for example, students’ background variables (e.g., age), pre-entry skills and abilities, initial intentions, motivations and goal commitments, institutional experiences and satisfaction, and academic/social integration vary and the strategies designed to reduce it produced different results at institutions of higher education (cited in Rautopuro & Vaisanen, 2001). One defining characteristic of the nontraditional student is the lack of social integration into the institution (Bean & Metzner, 1985). Yet, no research was located to explain the unimportance of socialization in the college environment (Bean & Metzner, 1985). The underlying process of attrition proposed here excludes the social integration variables. The theories of traditional student attrition contain elements other than socialization, which should not be ignored (Bean & Metzner, 1985). Each contains a set of background variables expected to affect how a student will interact with the institution, each indicates that dropout is a longitudinal process, and each identifies a Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 29 set of academic variables that are expected to affect attrition decisions (Bean & Metzner, 1985). The Bean and Metzner (1985) conceptual model of nontraditional student attrition is presented in Figure 1. The model indicates that dropout decisions will be based primarily on four sets of variables (Bean & Metzner, 1985). Students with poor academic performance are expected to drop out at higher rates than students who perform well, and GPA is expected to be based primarily on past (high school) academic performance. The second major factor is intent to leave, which is expected to be influenced primarily by the psychological outcomes but also by the academic performance (Bean & Metzner, 1985). The third group of variables expected to affect attrition are the background and defining variables, primarily high school performance and educational goals (Bean & Metzner, 1985). Finally, the environmental variables are expected to have substantial direct effects on dropout decisions (Bean & Metzner, 1985). Bean and Metzner’s (1985) recommended to researchers, that if a particular factor, such as an extended orientation program, that was not included in the model due to insufficient empirical study, but that is assumed to be of importance at a particular institution, such a factor can be added to the model in its appropriate place (in this case, as an academic variable). Furthermore, they recommended that if the researcher chose to concentrate their efforts on part of the model and to use it as a guide to the study of variables such as: GPA, satisfaction, stress, goal commitment, or other intervening variables, then each of these variables can be treated as a Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 30 dependent variable. Key to their recommendations were the collected works from researchers in the 1990’s and 2000’s that have studied modified versions of the Bean and Metzner (1985) model in an attempt to better understand student attrition and retention in higher education. Farabaugh-Dorkins (1991) used a modified version of the Bean and Metzner (1985) model of nontraditional student attrition in an effort to understand why older students frequently dropped out of a large public university in the Midwest. The results revealed (1) the intent to leave, followed by grade point average (GPA) and goal commitment were the most important variables in explaining attrition; and (2) the number of children, weekly study hours, and number of hours enrolled in school failed to contribute directly/indirectly to explaining attrition variance. Stahl and Pavel (1992) conducted a study to determine how well the Bean and Metzner Model fit with their community college student data. The Bean and Metzner model was found to be a weak fit with Stahl and Pavel’s (1992) community college student data and exploratory factor analysis was employed to identify a new model that was plausible for their community college student data. Cabrera, Nora & Castaneda (1993) conducted a longitudinal study with a student population drawn from the fall 1988 entering freshmen class at a large southern urban institution of 2,459 traditional, first time freshman, under twenty-four years of age, and not married. They examined the extent to which Tinto’s (1975, 1982, 1987) theoretical framework of Student Integration Model and Bean and Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 31 Metzner’s (1985) Student Attrition Model could be merged in explaining students’ persistence decisions by simultaneously testing all non-overlapping propositions underlying both conceptual frameworks. Cabrera et al. (1993) found a direct effect of finance attitudes on persistence behavior, academic experiences on institutional commitments and social integration on goal commitment to be statistically significant. Additionally, their results indicated that encouragement from friends and family does exert a positive effect on social integration. They also found that the effect of social integration on goal commitment and the effect of academic experiences on institutional commitment were statistically significant in contrast to the parameters found for the integrated model, while there was support for freeing encouragement from friends and family to goal commitment. They found the largest total effect on persistence was accounted by intent to persist, followed by GPA, institutional commitment, encouragement from friends and family, goal commitment, academic integration, finance attitudes, and social integration. Finally, the study offered an integrative framework in understanding interplay among individual, institutional, and environmental variables in the college persistence process. The results stressed the need for college administrators to focus on variables that are highly predictive of students’ intent to re-enroll as the target of variables to address intervention strategies. Summers (2000) used the Bean and Metzner (1985) model of nontraditional student attrition to investigate whether student enrollment and registration behaviors Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 32 predict student academic outcomes in a rural community college. The study concluded that a combination of four enrollment and registration behaviors: when student made changes to the schedule, number of course drops, number of course adds, and when students initially enrolled could predict 37.6% of the variation of the fall semester GPA and 48.6% of the variation in course completion. Additionally, Summers (2000) found that a combination of three enrollment and registration behaviors: number of course drops, when students initially enrolled, and number of course adds could best predict the odds of attrition. Rovai (2003) used a composite persistence model that synthesized Tinto (1975,1987,1993) and Bean and Metzner (1985) together with relevant research in online skills to explain student persistence in online distance education programs. His model was divided into student characteristics and skills prior to admission and external and internal factors affecting students after admissions. The study resulted in the development of a model that can better explain persistence and attrition among largely nontraditional students that enroll in online courses. Several researchers have used the Bean and Metzner or Tinto models to assess the impact of various factors on student retention at community colleges (Hoyt, 1999). According to Hoyt (1999) conflicting findings existed among many of these studies as to whether gender, student goals, need for remedial education, student grade point averages, contact with faculty, or hours studied could be related to student persistence. Furthermore, Hoyt (1999) contends that these studies have Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 33 consistently revealed that older students, part-time students, minority students, and working adults have higher drop-out rates. These findings support the need for further study to account for the extent to which remedial math education at community colleges significantly increases a student’s risk of dropping out of college and the factors that predict student retention. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 34 FIGURE 1. CONCEPTUAL MODEL OF NONTRADITIONAL STUDENT ATTRITION (BEAN AND METZNER 1985). BACKGROUND AND DEFINING VARIABLES Age Enrollment Status Residence Educational Goal High School Performance Ethnicity Gender ENVIRONMENTAL VARIABLES Finances Hours of Employment Outside Encouragement Family Responsibilities Opportunity to Transfer V i SOCIAL INTERGRATION 1 VARIABLES Dropout Intent to leave ACADEMIC OUTCOME GPA PSYCHOLOGICAL OUTCOMES Utility Goal Commitment Satisfaction Stress Academic Advising Absenteeism Major Certainty Courses Availability ACADEMIC VARIABLES Study Habits Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 35 Academic Success Rate in Remedial Math. Stage and Kloosterman (1995) found a relationship between exposure to high-school mathematics and final grades in remedial mathematics for males but not for females. A study conducted at Antelope Valley College in California to explore the use of multiple measures in placement recommendations found that for math courses, success was strongly associated with high school grade point averages (Lewallen, 1994). According to Farmer (1992) many studies report that in using GPA as a criterion for program effectiveness both negative and positive relationships existed between GPA gains and participation in developmental education programs. He recommends a common perspective for assessing the effectiveness of Developmental Education throughout this country. Several studies have found that remedial math helps the remedied student pass rate (Penny & White, 1998; Short, 1996). Penny & White (1998) recommended that individuals especially at risk in developmental mathematics and college algebra, such as, African Americans, Latinos, and part-time students, will need to be recognized by developmental program personnel as being at greater risk then other students, and require provisions for effective teaching strategies specifically designed for these students. Levine (1990) at Nassua Community College New York found that remediated students’ pass rates in three of five higher level math courses were comparable to students who did not need remediation. Armstrong (1991) at Eastern New Mexico University found that 54% of the new students needed Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 36 developmental math and that three years later 88% of them passed the math course series. Generally speaking, if students complete their remedial coursework successfully, they go on to succeed in the regular college program (Cohen & Brawer, 1996). However, less than half the students entering the remedial courses completed them (Cohen & Brawer, 1996). Petrowsky (1994) found that completing a college level math course had a small but significant effect on the final term average in a college economics course. At Arapahone Community College Colorado (1994), students who successfully completed pre-algebra subsequently performed at about the same level in algebra as students who tested directly into algebra. Factors and Variable Predicting Academic Success. Hagedom, Siadat, Fogel, Pascarella and Nora (1999) found background variables played a major role in determining success in college mathematics for first- year college students enrolled in remedial-type mathematics courses. They found significant differences in the levels of gains in mathematical achievement between males and females, minority and non-minority students during the first year of college; a direct effect for taking lower level courses that resulted in lower achievement and for math enrollment the remedial student with poor high school grades generally led to enrollment in lower-level courses; and that women tended to enroll in the lowest of these courses. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 37 Britt & Kim (1996) cited that the academic variables that played a major role in predicting academic success indicated by researchers have focused mainly on groups of related predictors such as SAT scores or study time (Corley, Goodjoin, & York, 1991; Dickerson & O’Connell, 1990; Dreher & Singer, 1985). Additionally, they cited that the factors that were found to be predictive of aptitude scores include age bias (Zeidner, 1987), time resources (Bee & Ronaghy, 1990), GPA (Young, 1991), and gender (McComack & McLeod, 1988). Snyder, Hackett, Stewart and Smith (2002) found that GPA at the end of the first year is associated with gender, high school GPA, and SAT scores; however, remedial courses were not predictive of academic achievement or retention. The authors recommend further examination to determine if the number or type of developmental course taken has any impact on graduation rates and whether the levels of participation in the university’s academic support services will predict academic success as well as graduation rates. Seginer (1983) suggested that the environmental variables such as parents’ expectations of their children might be both a cause and an effect of academic achievement. Jay and D’Augelli (1991) found a significant difference between Africans Americans and Whites with regard to perceived support from friends and family, however, when family income was controlled there was no difference (cited in Britt & Kim, 1996). Crandall, Dewey, Katkovsky, and Preston (1964) found a cross-sex influence of parental behaviors on student performance. Green and Jaquess Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 38 (1987) found that academic performance was not significantly affected by part-time employment (cited in Britt & Kim, 1996). Ganz and Ganz (1988) also found that the number of hours that a student worked each week had no effect on his or her final grade average (cited in Britt & Kim, 1996). Gadzella and Williamson (1984) found that psychological outcomes such a positive self-concept was related to school success and self-concept correlated highly with a measure of study skills. They found that the skills most related to good self- concept and achievement appear to be oral communication and interpersonal relation skills. In another study, Gadzella, Williamson, and Ginther (1985) looked at the relationship between self-concept, locus of control, and academic performance and found that most self-concept subscales had significant positive correlations with the Internal Locus of Control scale (cited in Britt & Kim, 1996). Data from a study by Uguroglu and Walberg (1986) suggested that motivation by itself predicts achievement (Britt & Kim, 1996). Conclusions There is an abundance of literature presenting the community college as the future recipient for remedial math education. Additionally, some researchers suggested reporting community college data on remedial education for each state or community. Many researchers have found that, in higher education, ethnic and racial minorities are disproportionately represented in remedial math courses and suggest using the percentage of students who enrolled in remedial math courses to provide a Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 39 snapshot of the current availability and the demand for these courses in higher education. In fact, the percent of students, at community colleges, enrolled in at least one developmental education math course have been reported in the literature review. The placement process of determining where students belong within a sequence of courses, as recommended by researchers, should be reviewed for cultural and gender bias and appropriateness for the community colleges preparing the students to meet determined levels of preparation. Other researchers have identified the need for gender and ethnicity as appropriate variables for further study because they are associated with college performance, persistence and completion. Additionally, the researchers suggested the study of remedial courses to provide the current availability and demand for these courses in higher education. Furthermore, researchers have suggested a call for retention efforts to quickly integrate high-risk students, especially at risk in developmental mathematics and college algebra; African Americans, Latinos, and part-time students, into the university environment. The researchers stress the focus on variables that are highly predictive of students’ intent to re-enroll as the target of variables to address for the intervention strategies of the high-risk students. Implications The literature review established the community college as the recipient for remedial math education. Therefore, the community college must monitor the course-taking patterns of the students enrolled in remedial math courses. The data on Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 40 remedial math education should include appropriate variables so they can be used in developing and implementing retention studies. These retention efforts will help students balance education commitment with work responsibilities, aid in the impact of developmental courses on graduation rates and the determination of the impact of student services on academic success. Accordingly, the scheduling of remedial math courses to be varied in such a way as to meet the needs of students is a curriculum implication for community colleges. Additionally, future study for high-risk students will allow for the required provisions for effective teaching strategies designed specifically for these students. Lastly, the development and implementation of a common perspective for assessing the effectiveness of Developmental Education throughout this country with retention studies and reporting their results at future meetings of community college researchers is imperative. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 41 CHAPTER 3 RESEARCH METHODOLOGY The California State University (CSU) system has spent $9 million working closely with the high schools that send the greatest number of students needing remedial work (Weiss, 2001). But CSU isn’t the only college or university wrestling with remedial education. Nationally, 29% of all freshmen take at least one remedial class in reading, writing or math (Weiss, 2001). While some critics are suggesting this represents a double billing of taxpayers for the failure of the public high schools to properly prepare students, others argue the unfair impact of remedial education on minorities and immigrants. At the same time the community college has been placed in the primary role of providing remedial education. Therefore, it is important for the community college administrator to monitor the course enrollment and completion of the students. The purpose of this study was to examine course enrollment and completion in the remedial mathematics pipeline for minority students in an urban community college to determine which variables (examples: high school grade point average (GPA), ethnicity, gender, age, placement exam score or performance level (overall GPA)) are predictors of academic success in mathematics courses. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 42 This chapter includes the research questions, the research design, and a description of the research methodology. The latter includes the sampling procedure and population, instrumentation, and procedures for data collection and analysis. Research Questions 1. Where do minority students rank with respect to the course placement, progression, completion and academic success in the community college remedial mathematics pipeline? What are the relationships between gender, ethnicity, and age with respect to course placement, progression, completion and academic success in the remedial mathematics pipeline? 2. What factors predict the academic success of minority students in the remedial mathematics pipeline? Does gender, ethnicity, or age predict course completion in the remedial mathematics pipeline? Does high school GPA, placement exam score, or performance levels (overall GPA) predict the course completion in remedial math pipeline? Research Design This was a quantitative secondary data analysis. The TRUCCS research project database was used to answer the research questions stated in the research questions section of this proposal. Initial data analyses were performed to understand the population. This was accomplished by performing the frequency procedure for the ethnicity, age, and gender variables. After careful data analysis a data reduction Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 43 procedure was performed to extract the selection of the ethnicities (Asian, African American, Latino, and White) that were; defined in the definition of terms, and had a sufficient sample size for statistical analysis procedures. Additionally, a data reduction procedure was performed to extract the selection of the age population between 19 and 54 years since these were the ages that had ample sample sizes to perform statistical analysis to be presented and discussed in the examination of enrollment and completion in the remedial mathematics pipeline for minority students in an urban community college. Population and Sample The population of this research was the student sample from the project on Transfer and Retention of Urban Community College Students (TRUCCS), which was a five-year longitudinal and comprehensive study of goals, success and academic patterns of Community College students in the urban Los Angeles Community College District. The U.S. Department of Education Office of Educational Research and Improvement funded the study in 1999 with a $1.1 million Field Initiated Studies grant conducted through 2003, awarded to the Rossier School of Education at the University of Southern California. The study, headed by the Center for Urban Education at the University of Southern California (USC), is a joint project with the Higher Education Research Institute at the University of California Los Angeles (UCLA) and the Los Angeles Community College District (LACCD). The TRUCCS sampling plan included a stratified random sampling procedure of Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 44 5,000 students attending college at nine campuses, designed to represent transfer students, older students, occupational students, ESL students, day students, and evening students. The survey was administered during the spring 2001 semester. In the summer of 2001 transcript data were collected. In the fall of 2002, focus group interviews were conducted with students, faculty, and administration across the district, and a follow-up questionnaire was mailed and posted on the Internet for the original students. The sample reflected ethnic distributions, disabilities, full and part- timers, day and evening as well as various age groups (http://www.usc.edu/dept/education/truccs/). Instrumentation The data from the TRUCCS research project was used for this research. The survey questions (Appendix A) that were used in the research design are: • Q1. How important was each reason in your decision to come here? My parent wanted me to come here, my spouse, partner or other family member wanted me to come here, a high school counselor advised me, this college's graduates get good jobs, and my employer encouraged me to enroll. • Q10. As things stand today, do you think you will: permanently stop attending college, transfer to a 4-year college or university, and change your major. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 45 • Q12. If there were no obstacles, what is the highest academic degree you would like to attain in your lifetime? • Q13. Approximately how many times in the past 7 days did you; skip a class. • Q15. In the past 7 days, approximately how many hours did you work at a job and study (alone at home, alone in the college library, with students from this course, with students from other courses)? • Q16. How large a problem do you expect each of the following to be while getting your education at this college: paying for college and scheduling class for next semester? • Q22. How long does it take you to travel to this college? • Q24. The average grade in high school. • Q34. Excluding yourself, how many people (children, grandchildren, brothers, sisters, parents, etc.) are you supporting? • Q37. My teachers here give me a lot of encouragement in my studies, I always complete homework assignments, and I wait until the day before an assignment is due before starting it. • Q47. How much education do you think is needed for type of work you are planning? The questions from the follow-up questionnaire used in the research design are: Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 46 • Q3. Since January 2001, how large of a problem has each of the following been in obtaining your college education? • Q6. Rate the accuracy of the information provided by the counseling services. • Q7. Rate the helpfulness of the information provided by the counseling services. • Q8. Rate the availability of the counselors to meet with you. • Q15. Have you experienced any of the following since January 2001: divorce or separation from your spouse, birth of your own child, serious financial difficulties, death of a close friend or family member, legal problems, etc.? Additional transcript data from the LACCD database used for this research are: • the number of math courses attempted and passed (total, remedial, and college level), • math success ratio (total, remedial, and college level), • math progression (level 0 - remedial, level 1 - basic, level 2 - intermediate, and level 3 - advanced), • highest math placement exam score, • highest English placement exam score, • units (average number of credits per semester), Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 47 • age, • gender, • ethnicity, • and overall GPA. Data Analysis Where Minority Students Rank. The crosstabs procedure was used to explore the relationship between two categorical variables. Each of these variables can have two or more categories. This procedure presented a multiple comparisons by ethnicity, gender and age of the highest math placement and the progression from remedial to transfer level math of the minority students in the remedial math pipeline. The compare means procedure was used to compare the means for course completion, that is, the average number of classes attempted with respect to the average number of classes passed. This procedure was also used to compare the means for the academic success, that is, the average of the success ratios from remedial to transfer level math courses. The means were compared across ethnicities, gender and age of the minority students in the remedial math pipeline. A grouped percentage distribution was constructed across ethnicity, gender, and age for the course completion by dividing the mean (or average) of the number of classes attempted by the mean (or average) of the number of classes passed and multiplying Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 48 the results by one hundred. The percentage distributions provided an indication of where minority students rank in course completion for remedial to transfer level courses in the remedial mathematics pipeline. Relationships between Gender, Ethnicity, and Age. The tests for normality were performed to determine the appropriateness of using a parametric or nonparametric statistical procedure to test for significant relationships. A non-significant result (Sig. value of more than .05) indicates normality (Pallant, 2001). In the case of the course placement, progression, completion and academic success with respect to ethnicity, gender and age the Sig. value was .000 for each group suggesting a violation of the assumption of normality. Accordingly, the nonparametric tests were performed since they do not require that the populations that produced the data be normally distributed (Maxwell, 2004). The Chi-square test for independence is used to explore the relationship between two categorical variables (Pallant, 2001). Consequently, the chi-square test of independence was executed to determine if significant relationships exist between age, gender, or ethnicity, of the highest math placement that will be equal to the proportions of age, gender, or ethnicity of the total student enrollment in the remedial mathematics pipeline. Additionally, the chi-square test of independence was performed to identify any significant relationships between age, gender, or ethnicity, and the progression for remedial to transfer level math classes. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 49 The non-parametric Mann-Whitney test is used to test the difference between two independent variables (male and female) on a continuous group (Pallant, 2001). In this study the Mann-Whitney test was conducted in place of the independent samples t-test to evaluate the relationships between gender and the academic success, which is, the average success ratio, and the course completion, that is, the average classes attempted and average classes passed for remedial to transfer math courses in the remedial math pipeline. The non-parametric Kruskal- Wallis test allows the comparison of the scores on some continuous variable and three or more groups (Pallent, 2001). In this study the Kruskal-Wallis test was used in place of the ANOVA to evaluate the relationships between ethnicity and age with the academic success, that is, the average success ratio, and the course completion, that is, the average classes attempted and average classes passed for remedial to transfer math courses in the remedial math pipeline. Factors that Predict Academic Success. An initial analysis was conducted, of the independent variables to describe the range, mean and standard deviation for each variable from the TRUCCs data survey questions used in the Bean and Metzner (1985,1987) conceptual model of nontraditional student attrition (Table 13). The multiple regression statistics procedure was performed in order to predict academic success of minority students Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 50 in the remedial math pipeline from the TRUCCs data survey questions using the Bean and Metzner (1985, 1987) conceptual model of nontraditional student attrition. Predictors of Course Completion. A path analysis model was constructed utilizing ethnicity, gender, age, and number of math classes passed from remedial level 0 to transfer level 3 math courses in order to determine whether ethnicity, gender, and/or age will predict course completion in the remedial math pipeline. Additionally, a path analysis model was constructed utilizing the high school GPA, counselor advice, placement exam score, overall GPA, and number of math classes passed from remedial level 0 to transfer level 3 math courses in order to determine whether the high school GPA, counselor advice, placement exam score, and/or overall GPA will predict course completion in the remedial math pipeline. The multiple regression procedures was performed to obtain the path coefficients and their significance, and in doing so, evaluate the viability of the model. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 51 CHAPTER 4 FINDINGS Initial Findings. The finding from the descriptive statistics showed that there were sufficient Asian and (489), African American (563), Latino (2059) and White (412) students for this study. The Latino (58.4%) student enrollment (Chart 1) was the largest on campus, followed by African American (16.0%), Asian (13.9%), and White (11.7%), respectively, of this selected group. The female (2173) population (Chart 2) comprised 2/3 as many students enrolled than the male (1350) population. The 21-24 year old student (28.5%) was the largest population, followed by, 30-39 year old student (17.4%), 20 year olds (14.8%), 25-29 year olds (14.8%), 19 year olds (14.5%), and lastly 40-54 year olds (10.1%). Chart 1 Minority Enrollment Chart 2 Gender Enrollment M ale 38% 62% Hispanic Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 52 Where Minority Students Rank with respect to the Course Placement, Progression, Completion and Academic Success in the Community College Remedial Math Pipeline. African American and Latino Females, Age 30 and over more Prevalent Placement in Remedial Math Courses. The findings from the crosstabs procedure illustrating the highest math placement by ethnicity are shown in Table 1. For example, a significant number of African American students 54.8% (n = 227), of the sampled population were placed in remedial math courses. Additionally, five African American students that was a significant 1.2% of the sampled population were placed in transfer math courses. The most frequent mathematics placement (Table 1) for most Asian (31.1%) students was in intermediate level 2 math courses. While the predominant math placement for most African American (54.8%) and Latino (51.9%) students was in remedial level 0 math courses. Lastly, the most common placement for White (29.2%) students was in basic level 1 math courses. For the total student population 45.3% of the student’s were more commonly placed in remedial level 0 math courses, 27.8% placed in basic level 1 math courses, 13.7% placed in intermediate level 2 math courses, and 3.8% placed in transfer level 3 math courses. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 53 Table 1 Ethnicity by Highest Math Placement Levels Ethnicity Not Assessed Remedial Level 0 Basic Level 1 Intermediate Level 2 Transfer Level 3 Asian 42 (10.7%) 75 (19.1%) 107 (27.3%) 122 (31.1%)* 46 (11.7%) African American 40 (9.7%) 227 (54.8%)* 118 (28.5%) 24 (5.8%) 5 (1.2%)* Latino 132 (7.6%) 901 (51.9%)* 489 (28.2%) 185 (10.7%) 28 (1.6%)* White 54 (16.9%) 93 (29.2%) 81 (25.4%) 62 (19.4%) 29 (9.1%) Total 268 (9.4%) 1296 (45.3%) 795 (27.8%) 393 (13.7%) 108 (3.8%) * indicates results significant beyond the .05 level The findings by gender showed that the modal (or most popular) mathematics placement (Table 2) of both the female (49.2%) and male (38.9%) students was in remedial level 0 math courses. There was a more common occurrence of the percentage of females placed in remedial level 0 math courses than males. Additionally, there were more males with predominant placement in basic level 1 (29.3%), intermediate level 2 (17.5%), and transfer level 3 (4.5%) math courses than females (20.9%, 11.4%, 3.4%), respectively. The highest math placement was significant at all levels. Table 2 Gender by Highest Math Placement Levels Gender Not Assessed Remedial Level 0* Basic Level 1* Intermediate Level 2* Transfer Level 3* Male 106 (9.8%) 419 (38.9%) 316 (29.3%) 189 (17.5%) 48 (4.5%) Female 162 (9.1%) 877 (49.2%) 479 (20.9%) 204 (11.4%) 60 (3.4%) Total 268 (9.4%) 1296 (45.3%) 795 (27.8%) 393 (13.7%) 108 (3.8%) * indicates results significant beyond the .05, highest math placement was significant for all levels Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 54 The findings by age showed that the most prevalent mathematics placement (Table 3) was in remedial level 0 math courses for all ages. The most frequent percentage of students that placed in remedial level 0 math courses was from the age 40-54 (54.7%) and the least frequent percentage of students was age 20 (39.8%). The most common occurrence of the percentages of students that were placed in basic level 1 math courses was from the age 21-24 (31.5%). The least common occurrence of the percentage of students that were placed in basic level 1 math courses was from age 40-54 (19.3%). The more prevalent percentages of students that were placed in intermediate level 2 math courses was age 20 (20.4%). The least prevalent percentage of students that were placed in intermediate level 2 math courses was from age 30-39 (6.1%). The more prevalent percentage of students that were placed in transfer level 3 math courses was age 19 (5.7%). The least prevalent percentage of students that were placed in transfer level 3 math courses was from age 30-39 (2.0%). Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 55 Table 3 Age by Highest Math Placement Levels Age Not Assessed Remedial Level 0 Basic Level 1 Intermediate Level 2 Transfer Level 3 19 24 (5.7%) 175 (41.5%) 113 (26.8%) 86 (20.4%)* 24 (5.7%)* 20 30 (7.1%) 167 (39.8%) 119 (28.3%) 95 (22.6%)* 9 (2.1%) 21-24 68 (8.5%) 330 (41.5%) 251 (31.5%)* 109 (13.7%) 38 (4.8%)* 25-29 36 (8.1%) 203 (45.8%) 130 (29.3%) 53 (12.0%) 21 (4.7%)* 30-39 64 (12.7%) 271 (53.7%)* 129 (25.5%) 31 (6.1%) 10 (2.0%) 40-54 46 (16.8%) 150 (54.7%)* 53 (19.3%) 19 (6.9%) 6 (2.2%) Total 268 (9.4%) 1296 (45.3%) 795 (27.8%) 393 (13.7%) 108 (3.8%) * indicates results significant beyond the .05 level African American and Latino’s, Age 30 and over had Math Course Progression Drops Off from Remedial to Transfer Level. The progression of minority students in the remedial math pipeline by ethnicity (Table 4) showed gains in the percentage of Asians progressing from remedial level 0 (5.6%), basic level 1 (9.5%) and from intermediate level 2 (14.9%) math courses. The percentage for progression of African Americans and Latinos dropped off from remedial level 0 (17.1%, 68.3%), basic level 1 (14.6%, 67.4%) and from intermediate level 2 (12.5%, 63.9%) math courses, respectively. The progression percentages reduced slightly from remedial level 0 (9.0%) to basic level 1 (8.6%) and remained steady from intermediate level 2 (8.6%) math courses for White students. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 4 Math Pipeline Progression by Ethnicity 56 Ethnicity Progressed from Level 0* Progressed from Level 1* Progressed from Level 2* Asian 37 64 75 (5.6%) (9.5%) (14.9%) African American 112 99 63 (17.1%) (14.6%) (12.5%) Latino 448 456 321 (68.3%) (67.4%) (63.9%) White 59 58 43 (9.0%) (8.6%) (8.6%) * indicates results significant beyond the .05 level, progression significant at all levels The progression for minority students in the remedial math pipeline in terms of gender (Table 5) showed improvement in the percentages of males progressing from remedial level 0 (29.1%), basic level 1 (37.5%) and from intermediate level 2 (38.2%) math courses. The progression percentage of female students tapered off from remedial level 0 (70.9%), basic level 1 (62.6%), and from intermediate level 2 (61.8%) math courses. Table 5 Math Pipeline Progression by Gender Gender Progressed from Level 0* Progressed from Level 1 Progressed from Level 2 Male 191 253 192 (29.1%) (37.4%) (38.2%) Female 465 424 310 (70.9%) (62.6%) (61.8%) * indicates results significant beyond the .05 level, progression significant at level 0 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 57 The progression for minority students in the remedial math pipeline in terms of age (Table 6) showed that for the ages 19 and 20 there was improvement in the progression from remedial level 0 (9.3%, 10.8%), basic level 1 (11.5%, 11.7%) and from intermediate level 2 (13.5%, 17.5%) math courses, respectively. The progression percentages for students, ages 21-24 and 25-29 elevated from remedial level 0 (27.4%, 16.6%) to basic level 1 (33.4%, 17.1%), but declined from basic level 1 to intermediate level 2 (32.5%, 16.5%) math courses, respectively. Lastly, the progression percentages for students, ages 30-39 and 40-54 diminished from remedial level 0 (23.5%, 12.3%), basic level 1 (18.5%, 7.8%) and from intermediate level 2 (13.9%, 6.0%) math courses, respectively. Table 6 Math Pipeline Progression by Age Age Progressed from Level 0* Progressed from Level 1* Progressed from Level 2* 19 61 78 68 (9.3%) (11.5%) (13.5%) 20 71 79 88 (10.8%) (11.7%) (17.5%) 21-24 180 226 163 (27.4%) (33.4%) (32.5%) 25-29 109 116 83 (16.6%) (17.1%) (16.5%) 30-39 154 125 70 (23.5%) (18.5%) (13.9%) 40-54 81 53 30 (12.3%) (7.8%) (6.0%) * indicates results significant beyond the .05 level, progression significant at all levels Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 58 Asian had more Prevalent Course Completion Percentages in Transfer Level Math Courses. The course completion percentage was calculated by dividing the average number of classes attempted by the average number of classes passed and multiplying the results by one hundred. The course completion percentage of minority students in the remedial math pipeline by ethnicity is shown in Table 7, for example, Asian students attempted an average 1.69 remedial math level 0 courses and passed an average 1.18 remedial math level 0 courses that resulted a significant 69.8% course completion percentage. The course completion percentage was 77.6% for the White student, 69.8% for the Asian student, 60.9% for the Latino student, and 50.0% for the African American student in the remedial level 0 courses. The course completion percentage for basic level 1 math courses showed 58.7% for the Asian student, 56.8% for the White student, 50.6% for the Latino student, and 42.6% for the African American student. The course completion percentage for intermediate level 2 math courses showed 62.4% for the Asian student, 60.3% for the White student, 53.8% for the African American student, and 53.2% for the Latino student. The course completion percentage for transfer level 3 math courses showed 70.2% for the Asian student, 62.0% for the White student, 58.1% for the African American student, and 57.0% for the Latino students. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 59 Table 7 Math Pipeline Averages Success Ratio by Ethnicity Ethnicity Attempted Level 0* Passed Level 0* Attempted Level 1 Passed Level 1* Attempted Level 2 Passed Level 2 Attempted Level 3* Passed Level 3* Asian 1.69 1.18 (69.8%)* 1.48 0.87 (58.7%) 1.25 0.78 (62.4%) 2.32 1.63 (70.2%)* African American 1.84 0.92 (50.0%)* 1.78 0.76 (42.6%) 1.32 0.71 (53.8%) 1.72 1.00 (58.1%)* Latino 1.74 1.06 (60.9%)* 1.68 0.85 (50.6%) 1.39 0.74 (53.2%) 1.93 1.10 (57.0%)* White 1.56 1.21 (77.6%)* 1.46 0.83 (56.8%) 1.21 0.73 (60.3%) 1.84 1.14 (62.0%)* * indicates results significant beyond the .05 level, average number of classes attempted at level 0, 3 and passed at level 0,1,3 are significant, the course completion percentage is significant at level 0,3. The course completion percentage of minority students in the remedial math pipeline by gender (Table 8) showed that at remedial level 0 math courses the prominent percentage was 59.2% for female students, and 56.1% for males. The course completion for basic level 1 math courses showed the prominent percentage 51.6% for the male student, while it was 49.4% for the female student. The course completion for intermediate level 2 math courses showed that most prevalent percentage was 57.4% for the female student and 52.5% for the male student. The course completion for transfer level 3 math courses showed the most prevalent percentage 65.7% for the female student, with 56.1% for the male student. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 60 Table 8 Math Pipeline Averages Success Ratio by Gender Gender Attempted Level 0 Passed Level 0* Attempted Level 1 Passed Level 1 Attempted Level 2* Passed Level 2 Attempted Level 3* Passed Level 3 Male 1.71 0.96 (56.1%) 1.61 0.83 (51.6%) 1.41 0.74 (52.5%) 2.20 1.23 (55.9%) Female 1.84 1.09 (59.2%) 1.68 0.83 (49.4%) 1.29 0.74 (57.4%) 1.81 1.19 (65.7%) * indicates results significant beyond the .05 level, average number o f classes passed at level 0, 3 and attempted at level 2, 3 are significant The course completion percentage of minority students in the remedial math pipeline by age (Table 9) showed that for remedial level 0 math courses the predominant percentage 64.2% was for the 30-39 year old student, with 63.8% for the 40-54 year old student, 63.3% for the 25-29 year old student, 57.5% for the 21-24 year old student, 56.5% for the 20-year-old student, and the least prevalent 54.9% for the 19-year-old student. The course completion percentage for basic level 1 math courses showed that the predominant percentage 56.5% was for the 30-39 year old student, with 55.5% for the 40-54 year old student, 51.1% for the 25-29 year old student, 50.0% of the 21-24 year old student, 44.9% for the 19-year-old student, and the least predominant 43.9% for the 20-year-old student. The course completion percentage for intermediate level 2 math courses showed that the most prevalent percentage 63.3% was for the 30-39 year old student, with 61.0% for the 40-54 year old student, 59.2% for the 25-29 year old student, 53.0% for the 19-year-old student, 52.5% for the 21-24 year old student, and the least prevalent 51.9% for the 20-year-old student. The course completion percentage Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. for transfer level 3 math courses showed that the predominant 65.2% was for the 20 year old student, with 65.0% for the 30-39 year old student, 61.3% for the 21-24 year old student, 58.6% for the 19-year-old student, 57.0% for the 25-29 year old student, and the least prevalent 56.7% for the 40-54 year old student. Table 9 Math Pipeline Averages Success Ratio by Age Age Attempted Level 0* Passed Level 0* Attempted Level 1 Passed Level 1* Attempted Level 2 Passed Level 2 Attempted Level 3* Passed Level 3 19 1.53 0.84 (54.9%)* 1.56 0.70 (44.9%) 1.34 0.71 (53.0%) 1.81 1.06 (58.6%) 20 1.70 0.96 (56.5%)* 1.64 0.72 (43.9%) 1.35 0.70 (51.9%) 1.78 1.16 (65.2%) 21-24 1.74 1.00 (57.5%)* 1.61 0.80 (50.0%) 1.39 0.73 (52.5%) 2.04 1.25 (61.3%) 25-29 1.80 1.14 (63.3%)* 1.76 0.90 (51.1%) 1.30 0.77 (59.2%) 2.23 1.27 (57.0%) 30-39 1.73 1.11 (64.2%)* 1.68 0.95 (56.5%) 1.31 0.83 (63.3%) 1.97 1.28 (65.0%) 40-54 1.96 1.25 (63.8%)* 1.73 0.96 (55.5%) 1.23 0.75 (61.0%) 2.03 1.15 (56.7%) * indicates results significant beyond the .05 level, average number o f classes attempted at level 0, 3 and passed at level 0, 1 are significant, the course completion percentage is significant at level 0. African American and Asian Females had more Prevalent Academic Success in Transfer Math Courses. Academic success was interpreted by using the calculated mean (or average) of the success ratio for level 0, 1,2, and 3 math courses of minority students in the remedial math pipeline. The academic success by ethnicity (Table 10) showed that Asians (0.73) and African Americans (0.66) had the most prevalent academic Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 62 success for transfer level 3 math courses and the least prevalent academic success (0.68, 0.52), respectively, for basic level 1 math courses. The predominant academic success for Whites (0.82) and Latinos (0.68) was for remedial level 0 math courses. The least prevalent academic success for Whites (0.64) was for transfer level 3 math courses and at basic level 1 math courses for Latino (0.59) students. The academic success was the same for Asians (0.72) for remedial level 0 and intermediate level 2 math courses. The academic success for African Americans (0.64) and Latinos (0.64) was the same for intermediate level 2 math courses. Table 10 Math Pipeline Averages Success Ratio by Ethnicity Ethnicity Success Ratio Level 0* Success Ratio Level 1* Success Ratio Level 2 Success Ratio Level 3* Asian 0.72 0.68 0.72 0.73 African American 0.55 0.52 0.64 0.66 Latino 0.68 0.59 0.64 0.62 White 0.82 0.66 0.67 0.64 * indicates results significant beyond the .05 level, average success ratio is significant for levels The academic success of minority students in the remedial math pipeline by gender (Table 11) showed that the predominant academic success for male (0.64) and female (0.68) students was for transfer level 3 math courses and their least prevalent academic success (0.60, 0.59), respectively, was basic level 1 math Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 63 courses. The academic success was the same for female (0.68) students for remedial level 0 and transfer level 3 math courses. Table 11 Math Pipeline Averages Success Ratio by Gender Gender Success Ratio Level 0 Success Ratio Level 1 Success Ratio Level 2 Success Ratio Level 3* Male 0.63 0.60 0.64 0.61 Female 0.68 0.59 0.66 0.68 * indicates results significant beyond the .05 level, average success ratio is significant at level 3 The academic success of minority students in the remedial math pipeline by age (Table 12) showed the most prevalent academic success for ages 19 (0.63), 21-24 (0.63), 25-29 (0.68), 30-39 (0.74), and 40-54 (0.71) was for intermediate level 2 math courses and their least prevalent academic success (0.52, 0.59, 0.64, 0.60, & 0.65), respectively, was at basic level 1 math courses. The academic success was the same for 25-29 (0.64) and 40-54 (0.65) at basic level 1 and transfer level 3 math courses. The academic success was the same for 21-24 (0.63) and 30-39 (0.74) for intermediate level 2 and transfer level 3 math courses. The academic success was the same for 21-24 (0.63) at remedial level 0, intermediate level 2, and transfer level 3 math courses. The descending orders for the academic success for remedial level 0 math courses was; 19 (0.62), 21-24 (0.63), 20 (0.64), 40-54 (0.67), 25-29 (0.70), and 30-39 (0.72). The academic success for age 20 (0.52) at basic level 1 math courses was lower than the age 20 (0.61) at intermediate level 2 math courses. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 64 Table 12 Math Pipeline Averages Success Ratio by Age Age Success Ratio Level 0 Success Ratio Level 1* Success Ratio Level 2* Success Ratio Level 3 19 0.62 0.52 0.63 0.60 20 0.64 0.52 0.61 0.67 21-24 0.63 0.59 0.63 0.63 25-29 0.70 0.64 0.68 0.64 30-39 0.72 0.60 0.74 0.74 40-54 0.67 0.65 0.71 0.65 * indicates results significant beyond the .05 level, average success ratio is significant at level 1,2. Relationships between Gender, Ethnicity, and Age with respect to the Course Placement, Progression, Completion and Academic Success in the Community College Remedial Math Pipeline The African American and Latino Females, Age 30 and over Predominant Placement in Remedial Math were Significant. The findings from the chi-square test of independence analysis showed that the highest mathematics placement was significantly (% 2 = 372.59, df = 12, p < .0005) associated with ethnicity (Table 1). In particular, a significantly more prevalent percentage of African Americans (54.8%) and Latinos (51.9%) were placed in remedial level 0 math courses. While there was a significantly predominant percentage of Asians (31.1%) and Whites (10.7%) that were placed in intermediate level 2 math courses. Lastly, there was significantly not as many prevalent Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 65 percentages of African American (1.2%) and Latino (1.6%) student placement in transfer level 3 math courses. The findings from the chi-square test of independence showed that the highest mathematics placement was significantly (x2 = 37.89, df = 14, p < .0005) associated with gender (Table 2). In particular, a significantly predominant percentage of females (49.2%) were placed in remedial level 0 math courses than males (38.9%). While there was a significantly more prevalent percentage of males that were placed in, basic level 1 (29.3%), intermediate level 2, and transfer level 3 (4.5%) math courses than females (20.9%, 11.4%, & 3.4%, respectively). The findings from the chi-square test of independence analysis showed that the highest mathematics placement was significantly (x2 = 149.68, df = 20, p < .0005) associated with the age (Table 3). In particular, a significantly predominant percentage of ages 40-54 (54.7%) and 30-39 (53.7%) were placed in remedial level 0 math courses. While there was a significantly more prevalent percentage of 21-24 (31.5%) that are placed in basic level 1 math courses. There was a significantly predominant percentage of age 20 (22.6%) and 19 (20.4%) that were placed at the intermediate level 2 math courses. Lastly, there were significantly more prevalent percentages for age 19 (5.7%), 21-24 (4.8%) and 25-29 (4.7%) placement in transfer level 3 math courses. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 66 African American and Latino’s, Age 30 and over Decreasing Math Course Progression from Remedial to Transfer Levels was Significant. The findings from the chi-square test of independence analysis showed that ethnicity was significantly associated with the math progression from level 0 to higher (x2 = 67.31, df = 3, p < .0005), the math progression from level 1 to higher (X2 = 32.53, df=3,p< .0005), and the math progression from level 2 to higher (% 2 = 13.35, df = 3, p < .01). In particular, the gains in the percent of Asian student progression, the drop off in the percent of African American and Latino student progression, and the slight reduction in the percent of White student progression were significant from remedial to transfer level math courses in the remedial math pipeline. The findings from the chi-square test of independence analysis showed that gender was significantly associated with the math progression from level 0 to higher (X2 = 28.41, df = 1, p < .0005). Specifically, there was a significant more prevalent percent of female student progression from remedial to basic level math courses in the remedial math pipeline. Additionally, the findings showed that no significant relationship existed between gender and math progression from level 1 and 2 to higher for the minority student in the remedial math pipeline. The findings from the chi-square test of independence analysis showed that age was significantly associated with the math progression from level 0 to higher (X2 = 47.93, df = 5, p < .0005), the math progression from level 1 to higher Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 67 (X2 = 26.43, df = 5, p < .0005), and the math progression from level 2 to higher (x2 = 23.04, df = 5, p < .0005). In particular, there was a significant improvement for the ages 19 and 20 in the progression for remedial to transfer level math courses in the remedial math pipeline. Additionally, for the ages 21-24 and 25-29 the elevated progression for remedial and basic math and the declined progression for basic and intermediate math courses were significant for the minority students in the remedial math pipeline. While, diminished progression, was significant for remedial and transfer math course for the ages 30-39 and 40-54 minority students in the remedial math pipeline. The Asian Student’s Predominant Course Completion in Transfer Math Courses was Significant. The findings from the Kruskal-Wallis analysis showed that ethnicity was significantly associated with course completion, that is, the average number of classes’ attempted and average number of classes passed for remedial and transfer math courses. Specifically, the findings showed a significant relationship existed between the ethnicity and the course completion for the basic level 0 math courses, since both the number of classes attempted at level 0 (x2 = 10.90, df = 3, p < .05) and the number of classes passed at level 0 (x2 = 10.90, d f= 3, P < .05) were significant. Additionally, the findings showed a significant relationship existed between the ethnicity and the course completion for the transfer level 3 math courses, since both Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 68 the number of classes attempted at level 3 (x2 = 17.13, df = 3, p < .01) and the number of classes passed at level 3 (x2 = 45.40, df = 3, p < .0005) were significant. While there was a significant relationship between the ethnicity and the number of classes attempted at level 1 (x2 = 17.75, df = 3, p < .0005) and the number of classes attempted at level 2 (x2 = 18.53, d f= 3,p< .0005) the number of classes passed at level 1 and level 2 were not significant. The Mann-Whitney Test, a non-parametric test, was conducted in place of the independent-samples t-test to evaluate the relationship of males and females in terms of their course completion, that is, the average number of classes attempted and the average number of classes passed for remedial to transfer math courses. The findings from the Mann-Whitney test analysis showed that gender was significantly associated with the number of classes passed at level 0 (z = -3.18, p < .01), the number of classes attempted at level 2 (z = -2.95, p < .01), and the number of classes attempted at level 3 (z = -2.82, p < .01). Although, these significant relationships existed there was no significant relationship established between the course completion and the gender for the remedial to transfer level math courses. The findings from the Kruskal-Wallis analysis showed that age was significantly associated with the course completion, that is, the average classes attempted and average classes passed, for remedial math courses. Specifically, the findings showed a significant relationship existed between the age of a student and course completion since both the number of classes attempted at level 0 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 69 (X2 = 13.40, df = 5, p < .05) and the number of classes passed at level 0 (X2 = 24.00, df = 5, p < .0005) were significant. Although, the number of classes passed at level 1 (x2 = 45.97, d f= 5,p< .0005), and the number of classes attempted at level 3 (x2 = 16.20, df = 5, p < .01) were significant, it cannot be concluded that the course completion at the basic and transfer level math courses were significant since the number of classed attempted at level 1 and passed at level 3 were not significant. Additionally, there is no significant relationship between the age and the course completion for the intermediate level 2 math courses. African American Females Academic Success Ranking Improved when they Reached Transfer Math Courses. The Kruskal-Wallis Test a non-parametric test was conducted in place of the one-way between-groups ANOVA to evaluate the relationship of the ethnicity across the academic success, that is, the average success ratio for the remedial, basic, intermediate and transfer level of math courses. The findings showed that there was a significant difference in the ethnicity across the academic success for remedial level 0 (x2 = 43.77, df = 3, p < .0005) math courses, basic level 1 (x2 = 20.25, df = 36, p < .0005) math courses, and transfer level 3 (x2 = 11.59, df = 3, p < .01) math courses. The mean ranks for the groups indicated that Whites had the predominant academic success in remedial level 0 math courses and African Americans had the least prevalent academic success in remedial level 0 math courses. The Asians had Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 70 the more prevalent academic success in basic level 1 math courses and African Americans had the least prevalent academic success in basic level 1 math courses. Additionally, the Asians had the more prevalent academic success in transfer level 3 math courses while Latinos had the least prevalent academic success in transfer level 3 math courses. The Mann-Whitney Test, a non-parametric test, was conducted in place of the independent-samples t-test to evaluate the relationship of males and females in terms of their academic success in the four levels of math. The findings showed that there was significantly more prevalent academic success in transfer level 3 math courses (z = -3.142, p = .002) for the female students. There were no significant relationships of the males and females in terms of their academic success in the remedial, basic and intermediate level math courses. The findings from the Kruskal-Wallis analysis showed that age was significantly associated with the success ratio in level 1 math (x2 = 27.98, df = 5, p < .0005), and the success ratio in level 2 math (x2 = 13.53, df = 5, p < .05). The mean ranks for the groups indicated that the students age 30-39 had the more prevalent and the students age 19 had the least prevalent academic success for basic math courses. Additionally, the mean ranks for the groups indicated that the students age 40-54 had the more prevalent and the students age 20 had the least prevalent academic success for intermediate math courses. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Factors that Predict Academic Success in the Remedial Math Pipeline. Initial Analysis of the Independent Variables. The 24 independent variables were classified into 5 blocks (Table 13). The first block, background and defining variables, consisted of age, average number of credits per semester, travel time to college, educational goal, high school performance (GPA, math and English placement scores), ethnicity and gender. The second block, academic variables consisted of study habits, rating of academic advising, absenteeism, major certainty, and courses availability. The third block, environmental variables, consisted of finances paying for college, hours of employment, outside encourage, family responsibility, and opportunity to transfer. The fourth block, academic outcome was the overall GPA. The last block, psychological outcomes, consisted of the utility of the college to get graduates good jobs, satisfaction with the quality of teaching, goal commitment, and stress. Description of the Population. The range, mean, and standard deviation for the independent variables are listed in the Table 13. The background and defining variables of the population was a majority Latino female, at the age 21-24 years old, enrolled in about 8 credits per semester. These students traveled 15-30 minutes to get to college, and had high aspirations to obtain a master’s degree. Their high school performance indicated an Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 72 average grade of B-, highest math placement score of 2, and 3 for the highest English placement score. The academic variables showed that these students had good study habits. They always completed their homework on time, don’t generally start their assignment the day before it was due, studied around 3-5 hours alone at home, and rarely skipped a class (maybe one time). They probably had no intention of changing their college major and had relatively small problems scheduling classes for the next semester. The academic counseling services were rated as not at all accurate, helpful, and available. The environmental variables indicated that there was a medium problem paying for college, with most students working part time 11 - 20 hours a week. The outside encouragement was slightly unimportant. Although, they were not supporting a family and they were probably going to transfer to a 4-year college or university. The academic outcome was the cumulative GPA at the time the survey was taken. The GPA ranged from A or A+ (Extraordinary) to D or lower (Poor), there were no GPAs below the C+ (Above Average) reported. The average overall GPA for this population was C+ or above average. The psychological outcomes showed that the student’s perception that the graduates of this college get good jobs was slightly important. They found the quality of teaching a large problem. They were definitely committed to attending Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 73 college. Lastly, these students had relatively low stress, with the average student experiencing less than one of five stressful situations. Table 13 Conceptual Model Constructs Block Identifiers Variables Names Description Range Mean S.D. Dependent Variables Intent to leave Academic Success Ratio Total Success Ratio Success Ratio Level 0 Success Ratio Level 1 Success Ratio Level 2 Success Ratio Level 3 0-1 0-1 0-1 0-1 0-1 0.70 0.78 0.78 0.76 0.69 0.32* 0.35 0.34 0.38 0.39* Independent Variables 1. Background and Defining Variables Age 4-9 6.39 1.57* Enrollment Status Average number o f credits per semester 0.88- 24 7.91 2.45* Residence Travel time to college. 1-6 1.82 0.91 Educational Goal Highest academic degree desired. 1-8 5.50 1.54* High School Performance Average grade in high school. Highest Math Placement Score. Highest English Placement Score. 1-9 0-5 0-5 5.31 2.40 3.14 1.86* 1.14* 1.10* Ethnicity 1-4 2.69 0.85* Gender 1-2 1.57 0.49* 2. Academic Variables Study Habits Always complete homework assignments. 1-7 5.86 1.19 Don’t start assignment until day before due. 1-7 3.19 1.71* 1-9 4.14 1.53* Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 74 Table 13 Conceptual Model Constructs (cont.) Block Identifiers Variables Names Description Range Mean S.D. Academic Advising Hours study alone at home. Rate the accuracy, helpfulness and availability of the counseling services. 1-9 0-15 4.14 6.88 1.53* 4.07* Absenteeism Skip a class. 1-6 1.51 0.84 Major Certainty Change your college major. 1-5 2.49 1.15* Courses Availability Scheduling classes for next semester. 1-5 2.09 1.13* 3. Environmental Variables Finances Paying for college. 1-5 2.51 1.36* Hours o f Employment Hours work at a job? 1-9 5.43 2.89* Outside Encouragement Teacher, parents, spouse, partner or other family member, high school or other counselor, or employer encouraged enrollment. 1-35 16.68 6.72* Family Responsibility How many people are you financially supporting? 1-12 3.07 1.03 Opportunity to Transfer Transfer to a 4-year college or university. 1-5 4.18 1.12 4. Academic Outcome GPA 1-6 3.07 0.42* 5. Psychological Outcomes Utility This college’s graduates get good jobs. 1-7 4.53 1.74* Satisfaction Quality o f teaching. 1-5 4.25 0.94 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 75 Table 13 Conceptual Model Constructs (cont.) Block Identifiers Variables Names Description Range Mean S.D. Goal Commitment Permanently stop attending college. 1-5 1.43 0.80 Stress Experienced: 0=divorce or separation from spouse, l=birth o f your own child, 2=serious financial difficulties, 3=death of a close friend or family member, 4=legal problems since January 2001. 0-4 0.59 0.76 * Normal distribution Background variables: high school GPA, enrollment status and highest math placement were factors predicting academic success in the remedial math pipeline. The regression weights for the minority students in the remedial math pipeline are presented in Table 14. The average grade in high school from the background and defining variables block made the largest significant contribution and the overall GPA from the academic outcome block, made a lesser significant contribution to explain the 21.8% of the variance in the success ratio for remedial level 0 math courses. The enrollment status from the background and defining Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 76 variables block made the largest significant contribution. The overall GPA from the academic outcome block and average grade in high school from the background and defining variables block made a lesser contribution. Lastly, the quality of teaching from the psychological outcomes block and the hours of employment from the environmental variables block made the least significant contribution to the 27.6% variance explained in predicting the success ratio for basic level 1 math courses. The environmental variables blocks’ opportunity to transfer and highest math placement from the background and defining variables block made the largest significant contribution. The overall GPA from the academic outcome block and enrollment status from the background and defining variables block made a lesser contribution to the 28.1% variance explained in predicting the success ratio for intermediate level 2 math courses. The enrollment status from the background and defining variables block made the largest significant contribution and absenteeism from the academic variables block made a lesser significant contribution to the 21.7% variance explained in predicting the success ratio for transfer level 3 math courses. The background variables played a major role in determining the academic success in the remedial math pipeline in an urban community college. This coincides with the literature findings that background variables were significant in determining the success in college mathematics for first-year college students enrolled in remedial-type mathematics courses. The findings for this study showed that the Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 77 background variables: high school GPA, enrollment status and highest math placement were significant factors that predicted academic success for the remedial to transfer level courses. Additionally the environmental variable opportunity to transfer was a significant factor that predicted academic success for intermediate math level courses. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 78 Table 14 Regression Weights Independent Variables Names Success Ratio Level 0 Success Ratio Level 1 Success Ratio Level 2 Success Ratio Level 3 Beta T-test Beta T-test Beta T-test Beta T-test Background and Defining Variables Age .085 1.013 -.005 -.073 .127 1.856 .084 1.012 Enrollment Status .143 1.663 .244 4.050*** .164 2.501* .267 3.393** Residence -.022 -.300 .045 .781 -.035 -.536 -.040 -.518 Educational Goal -.045 -.569 -.077 -.121 .078 1.163 .025 .316 High School Performance Average grade in high school. .162 2.167** .153 2.576** .114 1.671 .088 1.067 Highest Math Placement Score. -.014 -.180 .054 .927 .192 2.790** .050 .631 Highest English Placement Score .060 0.60 -.066 -1.105 -.132 -1.844 -.077 .960 Ethnicity .112 1.539 .057 1.008 .029 .439 .025 .317 Gender -.076 -1.010 .071 1.215 -.072 -1.117 .005 .063 Academic Variables Study Habits .088 1.181 .085 1.511 .027 .434 .025 .323 Academic Advising .080 1.030 -.005 -.090 -.063 -.921 .015 .205 Absenteeism .035 .491 -.055 -.949 -.058 -.893 -.254 3.408** Major Certainty .064 .862 -.036 -.638 -.067 -1.041 .038 .529 Courses Availability .038 .496 .029 .458 .108 1.563 .070 .857 Environmental Variables Finances -.095 -1.228 .033 -.510 .026 .386 -.104 -1.343 Hours of Employment .059 .751 .129 2.195* .009 .137 .082 1.070 Outside Encouragement -.104 -1.262 -.011 -.175 .032 .447 .008 .089 Family Responsibility .028 .386 .057 1.004 .062 .989 .042 .576 Opportunity to Transfer .026 .323 .092 1.481 .240 3.728*** -.010 -.127 Academic Outcome GPA .231 2.984* .243 3.971*** .172 2.529* .040 .502 Psychological Outcomes Utility -.075 -.942 -.079 -1.295 -.026 -.374 -.012 -.151 Satisfaction -.005 -.064 .151 2.631** .078 1.216 -.057 .785 Goal Commitment -.152 -1.962 .021 .352 .011 .173 -.068 .858 Stress .013 .171 -.053 -.923 -.068 -1.098 -.099-1.327 * P < .05, ** p < .01, *** p < .001 Does Gender, Ethnicity, or Age Predict Course Completion in the Remedial Math Pipeline? Results from the intercorrelations and simultaneous regression analysis are shown in Table 15. The number of math classes passed for the remedial level 0 math courses were significantly correlated to ethnicity (r = .050, p = .029), gender Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 79 (r = .073, p = .003) and age (r = .132, p = .000). The age (Beta = .137) was the best predictor for number of math classes passed for the remedial level 0 math courses. The results of the simultaneous regression analysis showed that the ethnicity, gender, and age were significant predictors (t = 2.576, p = .010, t = 2.709, p = .007, and t = 5.233, p = .000), respectively, of the number of math classes passed for the remedial level 0 math courses. Approximately 2.7% of the variation in the number of math classes passed for the remedial level 0 math courses was explained by ethnicity, gender, and age. The number of math classes passed for the basic level 1 math courses was significantly correlated to age (r = .157, p = .000). The number of math classes passed for the basic level 1 math courses was not significantly correlated with ethnicity (r = .012, p = .308), and gender (r = 004, p = .435). The student’s age (Beta = .158) was the best predictor for number of math classes passed for the basic level 1 math courses. The results of the simultaneous regression analysis showed that age was a significant predictor (t = 6.792, p = .000) of the number of math classes passed for the basic level 1 math courses. However, ethnicity and gender were not significantly related to the number of math classes passed for the basic level 1 math courses (t = .872, p = .384 and t = .022, p = .983), respectively. Approximately 2.5% of the variation in the number of math classes passed for the basic level 1 math courses was explained by age. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 80 The number of math classes passed for the intermediate level 2 math courses was significantly correlated with the student’s age (r = .073, p = .004). The number of math classes passed for the intermediate level 2 math courses was not significantly correlated with ethnicity (r = -.020, p = .240), and gender (r = 006, p = .418). The age (Beta = .072) was the best predictor for number of math classes passed for the intermediate level 2 math courses. The results of the simultaneous regression analysis showed that the age was a significant predictor (t = 2.599, p = .009) of the number of math classes passed for the intermediate level 2 math courses. However, ethnicity and gender were not significantly related to the number of math classes passed for the intermediate level 2 math courses (t = -.603, p = .547 and t = .086, p = .932), respectively. Approximately 0.6% of the variation in the number of math classes passed for the intermediate level 2 math courses were explained by age. The number of math classes passed for the transfer level 3 math courses was significantly correlated to ethnicity (r = -.157, p = .000). The number of math classes passed for the transfer level 3 math courses was not significantly correlated with gender (r = -.017, p = .054), and age (r = 045, p = .068). The ethnicity (Beta = -.155) was the best predictor for number of math classes passed for the transfer level 3 math courses. The results of the simultaneous regression analysis showed that ethnicity was a significant predictor (t = -5.247, p = .000) of the number of math classes passed for the transfer level 3 math courses. However, gender and age were not Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 81 significantly related to the number of math classes passed for the transfer level 3 math courses (t = -.680, p = .497 and t = 1.313, p = .190), respectively. Approximately 2.6% of the variation in the number of math classes passed for the transfer level 3 math courses were explained by ethnicity. Table 15 Pearson Correlation, R squared, F Statistics Ethnicity Gender Age R Squared F Number of classes passed at level 0 .050* 073** .132*** .027 13.110*** Number of classes passed at level 1 .012 .004 .157*** .025 15.476*** Number of classes passed at level 2 -.020 .006 .073** .006 2.432 Number of classes passed at level 3 -.157*** -.017 .045 .026 10.085*** * p < .05, ** p < .01, *** p < .001 Does High School GPA, Placement exam, or Performance Predict Course Completion in Remedial Math Pipeline? Results from the intercorrelations and simultaneous regression analysis are shown in Table 16. The number of math classes passed for the remedial level 0 math courses was significantly correlated with average GPA in high school (r = .166, p = .000), academic advising (r = .169, p = .000), and highest math placement (r = -.093, p = .000). The average GPA in high school (Beta = .185) was the best predictor for the number of math classes passed for the remedial level 0 math courses. The results of the simultaneous regression analysis showed that the average Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 82 GPA in high school, academic advising, and highest math placement were significant predictors (t = 6.942, p = .000, t = 6.851, p = .000, and t = -4.424, p = .000), respectively, of the number of math classes passed for the remedial level 0 math courses. However, overall GPA was not significantly related to the number of math classes passed for the remedial level 0 math courses (t = -.452, p = .651). Approximately 7.2% of the variation in number of math classes passed for the remedial level 0 math courses was explained by average GPA in high school, academic advising, and highest math placement. The number of math classes passed for the basic level 1 math courses was significantly correlated to average GPA in high school (r = .136, p = .000), academic advising (r = .155, p = .000), highest math placement (r = .043, p = .046), and overall GPA (r = .279, p = .000). The overall GPA (Beta = .284) was the best predictor for the number of math classes passed for the basic level 1 math courses. The results of the simultaneous regression analysis showed that the average GPA in high school, academic advising, and overall GPA was significant predictors (t = 5.088, p = .000, t = 7.516, p = .000, and t = 11.846, p = .000), respectively, of the number of math classes passed for the basic level 1 math courses. However, highest math placement was not significantly related to the number of math classes passed for the basic level 1 math courses (t = .246, p = .806). Approximately 12.4% of the variation in the number of math classes passed for the basic level 1 math courses was explained by Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 83 average GPA in high school, academic advising, highest math placement, and overall GPA. The number of math classes passed for the intermediate level 2 math courses was significantly correlated to average GPA in high school (r = .192, p = .000), highest math placement (r = .146, p = .000), and overall GPA (r = .242, p = .000). The overall GPA (Beta = .219) was the best predictor for the number of math classes passed for the intermediate level 2 math courses. The results of the simultaneous regression analysis showed that the average GPA in high school, highest math placement, and overall GPA were significant predictors (t = 5.638, p = .000, t = 4.712, p = .000, and t = 7.801, p = .000), respectively, of the number of math classes passed for the intermediate level 2 math courses. However, academic advising was not significantly related to the number of math classes passed for the intermediate level 2 math courses (t = -.854, p = .393). Approximately 10.3% of the variation in the number of math classes passed for the intermediate level 2 math courses was explained by average GPA in high school, highest math placement and overall GPA. The number of math classes passed for the transfer level 3 math courses was significantly correlated to average GPA in high school (r = .231, p = .000), highest math placement (r = .120, p = .000), and overall GPA (r - .362, p = .000). The overall GPA (Beta = .331) was the best predictor for the number of math classes passed for the transfer level 3 math courses. The results of the simultaneous Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 84 regression analysis showed that the average GPA in high school, and overall GPA were significant predictors (t = 4.509, p = .000, and t = 11.373, p = .000), respectively, of the number of math classes passed for the transfer level 3 math courses. However, academic advising and highest math placement were not significantly related to the number of math classes passed for the transfer level 3 math courses (t = 1.694, p = .091, and t = 1.740, p = .082), respectively. Approximately 13.0% of the variation in the number of math classes passed for the transfer level 3 math courses was explained by average GPA in high school, highest math placement and overall GPA. Table 16 Pearson Correlation, R squared, F Statistics Number of classes passed Average grade in high school Academic Advising Highest math placement Overall GPA R squared F level 0 .166*** 169*** -093*** -.030 .072 26.216*** level 1 .136*** .155*** .043* 279*** .124 55.103*** level 2 192*** -.035 .146*** 242*** .103 33.800*** level 3 .231*** .480 .120*** .362*** .159 50.878*** * p < .05, ** p < .01, *** p < .001 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 85 CHAPTER 5 SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS Nationally, remedial education has not gone unnoticed even as there has been a noticeable shift of remedial education from the four-year colleges and universities to the community colleges. There was an abundance of literature presenting the community college as the future recipient for remedial math education. Therefore, some researchers suggested reporting the data on the state of remedial education for each state or community. Consequently, many researchers have found that, in higher education, ethnic and racial minorities are disproportionately represented in remedial math courses and suggested using the percentage of students who enrolled in remedial math courses to provide a snapshot of the current availability and the demand for these courses in higher education. In fact, the percent of students at community colleges, enrolled in at least one developmental education math course are reported in the literature. The placement process of determining where students belong within a sequence of courses, as recommended by researchers, should be reviewed for validity, cultural and gender bias and appropriateness in order to institute test cut-off scores and curriculum implications for the community colleges preparing the students to meet determined levels of preparation. Several researchers Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 86 have used the Bean and Metzner (1985) model of nontraditional student attrition in an effort to understand the academic success of community college students. Bean and Metzner (1985) recommended to researchers, that if a particular factor, such as an extended orientation program, that was not included in the model due to insufficient empirical study, but that is assumed to be of importance at a particular institution, such a factor can be added to the model in its appropriate place (in this case, as an academic variable). Additionally, they strongly recommended researchers to concentrate their efforts on part of the model that can be used as a guide to the study of GPA, satisfaction, stress, goal commitment, or other intervening variables, each of which can be treated as a dependent variable. The Purpose o f the Study The purpose of this study was to examine course enrollment and completion in the remedial mathematics pipeline for minority students in an urban community college to determine which variables (examples: high school grade point average (GPA), ethnicity, gender, age, placement exam score or performance level (overall GPA)) are predictors of academic success in mathematics courses. Summary of Findings Where do minority students rank with respect to the course placement progression, completion and academic success in the community college remedial mathematics pipeline? Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 87 The data from this study show that there were a disproportionate number of African Americans with highest math placement in remedial math courses. Additionally, there were a disproportionate number of Asian students with highest math placement in intermediate math courses. There were disproportionately more male students placed in basic or higher math courses than female students who largely were placed in remedial math courses. These results suggest further evaluation of the assessment instruments used to determine the math placement of minority students. These results concur with the findings from the literature review that recommended the evaluation of existing assessments for validity, cultural bias, and appropriateness. Lastly, there were disproportionately more 30-39 and 40-54 year old students who were placed in remedial math courses and more 19 and 20 year old students that were placed in intermediate math courses. These results may be appropriate for the student 30 years and older since there is a likely break between the time that the last math course was taken and the time that the math placement score was determined. The results from this study with respect to the progression in the remedial mathematics pipeline was shown in the hierarchical progression of the students from the remedial, basic, and intermediate levels to the basic, intermediate and transfer level math courses. The percentage of Asian students progressing from lower to higher-level math courses was increasing. The percentage of African American and Latino students progressing from lower to higher-level math courses was decreasing. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 88 The male students had a steady progression from lower to higher-level math courses. The female students’ progression from lower to higher-level math courses was decreasing with the more prevalent progression being from the remedial math courses. The 20 and 21-24 year old students’ progression from lower to higher-level math courses was increasing. Lastly, the 30 - 39 and 40 - 54 year old student’s progression from lower to higher-level math courses was decreasing. Consequently, the Asian, male, and 20 - 24 year old students are progressing through the remedial math course sequence. The African American, Latino, female and 30 years and older students are at risk in their progression through the remedial math pipeline. These students need additional assistance for a positive progression through the remedial math course sequence. Mentoring, tutoring, and strong academic advising may aide in a quick transition into transfer level math courses. The literature review findings showed a call for retention efforts to quickly integrate high-risk students into the university environment. Further study is needed to determine if there is a relationship with the female students’ age and any inherent math phobias. What are the relationships between gender, ethnicity and age with respect to course placement, progression, completion and academic success in the remedial mathematics pipeline? The relationships between gender, ethnicity, and age with respect to placement showed that more African Americans, Latinos, females, 30 years and Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 89 older students were placed in remedial math courses. The male students ages 21-24 were placed at a more prevalent percentage in basic math courses. The Asian and White males ages 19 and 20 were placed in the intermediate math courses in predominant percentages. The 19 to 29 year old students were consistently placed in transfer level math courses in more prevalent percentages at the same time that African and Latino students had the least prevalent percentage of placement in the transfer level math courses. The academic success was more prevalent for Whites and least prevalent for African Americans in the remedial math courses. The academic success was predominant for Asians and least prevalent for African Americans in the basic math courses. The academic success was more prevalent for Asians and females, but least prevalent for Latinos in the transfer math courses. These findings show that the African American and Asian female students’ academic success improves when they reach the transfer level math courses. This is consistent with the literature review findings that remedial math courses help the African American students’ academic success when they follow the sequence of courses from remedial to transfer math. The Latino students were at risk in their academic success through the remedial math pipeline. Additional services may aide in a quick transition from the transfer level math courses in order to integrate Latino students into the university environment. Further study is needed to determine what factors predict academic success from the transfer level math course sequence for the Latino student. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 90 What factors predict the academic success of minority students in the remedial mathematics pipeline? The factors that predict academic success through the sequence of remedial mathematics courses were average GPA in high school and overall GPA for remedial math courses. Enrollment status, overall GPA, average GPA in high school, quality of teaching, and hours of employment were the factors that predict academic success for basic math courses. The opportunity to transfer, highest math placement, overall GPA, and enrollment status were the factors that predict academic success for intermediate math courses. The factors that predict academic success for transfer math courses were enrollment status and absenteeism. These results indicate that the academic success of minority students could be enhanced with the following curriculum implementations: remedial math course prerequisites that include a strong overall GPA, basic math course enrollments that require academic counselor approval, intermediate course enrollments that require strong overall GPA and academic counselor approval, and transfer course enrollments that require academic counselor approval and attendance criteria for continued enrollment. Further study is needed to determine if in fact these enhancements will be successful for minority students in the remedial math pipeline. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 91 Does gender, ethnicity, or age predict course completion in the remedial mathematics pipeline? In the remedial mathematics pipeline, ethnicity, gender, and age are significant predictors of course completion in the remedial math courses. The age was the best predictor of course completion in the remedial math courses. For the basic and intermediate math courses age was the significant predictor of course completion. There was no significance for ethnicity and gender as a predicator for course completion in these levels. Lastly, ethnicity was the best predictor of course completion in the transfer math courses. While, gender and age were not significant predicators for course completion in transfer math courses. Further study is needed to determine what intervening variables are associated with the student’s age and ethnicity that will predict successful course completion in the remedial, basic and transfer math courses. Does high school GPA. placement exam score, or performance levels (overall GPA) predict the course completion in remedial math pipeline? In the remedial mathematics pipeline, average grade in high school, academic advising, and highest math placement were significant predictors for course completion in remedial math courses. The average GPA in high school was the best predictor for course completion for remedial math courses. However, overall GPA was not a significant predictor for course completion in remedial math courses. The overall GPA was the best predictor for course completion in basic, intermediate, and Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 92 transfer math courses. While, average GPA in high school and highest math placement were also significant predictors for course completion in the basic, intermediate, and transfer math courses. Academic advising was a significant predictor for course completion in basic math courses, but not a significant predictor for course completion in intermediate and transfer math course. Further study is needed to determine the relationship between academic advising and successful course completion in the remedial math pipeline for the minority student. Discussion Assessing and Placing Students The results of this study indicated that African American students were disproportionately placed in remedial math courses. African American students represented 16% of the population while they represented 54.8% of the students with predominant math placement in remedial math courses. Additionally, the study showed that Asian students were disproportionately placed in intermediate math courses. Asian students represented 13.9% of the population while they represented 31.1% of the students with predominant math placement in intermediate math courses. These results are indicative of the existence of equity issues in the utilization of the math placement score in the remedial math pipeline and some cultural bias associated with the assessments used to determine the highest math placement. The Academic Senate for California (1995) recommended that existing assessments should be reviewed for validity, cultural bias, and appropriateness and Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 93 proposed the collaboration of the university and community college to determine the test cut-off scores and curriculum implications for preparing students for college level coursework. Further study is needed to determine what, if any modifications have been made starting with the assessments used a decade ago all the way through the existing assessments to promote equity and eliminate cultural bias for minority students. Enrollment Trends in Remedial Education The average age for the student was 2 1 -2 4 years old (28.5%), females makeup 62% of the community college enrollment in this study. The ethnic makeup of the student population was: Caucasian 11.7%, African American 16%, Asian 13.9% and Latinos 58.4%. According to the statistics gathered from the National Center for Education Statistics (Foote, 1997) the female (57.8%), Caucasian (11.1%), and African American (10.5%) population are similar to that of the first time freshmen in the U.S. enrolled in community colleges. But, there was a huge disparity in the Latino (4.6%) and Asian (1%) first-time freshmen in the U.S. enrolled in community college when compared to those students enrolled in the urban community college sampled in this study. Additionally, the students can be characterized as nontraditional, since they have two of the most common nontraditional characteristics as defined by Horn (1996), delayed enrollment (60.6%) and part-time attendance (average 8 credits per semester). This study did not determine the most common nontraditional characteristic, financial independence for Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 94 this student population. Further study is needed to determine the relationships between financial independence and academic success in the remedial math pipeline. The snapshot of the percentage of course completion was particularly alarming for African American students. This study showed that the African American student’s course completion percentage was 50% for the remedial math courses. The Latino student had a 60.9% course completion for the remedial math courses. Future studies are needed to determine the relationships that are associated with the students that had successful course completion in the remedial math pipeline. Nontraditional Student Attrition Conceptual Model The results of the nontraditional student attrition conception model (Bean & Metzner, 1985) were consistent with Bean and Metzner’s (1985) assertion that students with poor academic performance are expected to drop out at higher rates than students who perform well. The background and defining variable block showed that the average GPA in high school made the greatest contribution to the academic success in remedial math courses and it made a lesser contribution to the academic success in basic math courses. While, enrollment status was the major contribution to the academic success from this block in the basic and transfer math courses it made a lesser contribution to the academic success in the intermediate math courses. The highest math placement was the chief contribution from this block Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 95 to the academic success in the intermediate math course. The academic variable block showed that the overall GPA made the lesser contribution to the academic success in the remedial, basic, and intermediate math courses and absenteeism in the transfer math course. The environmental variable block showed that the opportunity to transfer was the main significant contribution to academic success in the intermediate math courses. The hours of employment make the least significant contribution to the academic success in the basic math course. The psychological outcomes variable block showed that the quality of teaching makes the least significant contribution to the academic success in the basic math course. These results were consistent with the literature review findings that the background variables played a major role in determining success in college mathematics for first- year college students’ enrolled in remedial-type mathematics courses. Further study is needed that excludes the variables that make the least significant contribution to academic success. The literature review supports concentrating efforts on the parts of the model that can be used to guide the study of intervening variables. Academic Success Rate in Remedial Math The results from the rank of the minority students in respect to the course completion and academic success in the remedial math pipeline showed that the African American students’ course completion percentage improved substantially, from 50% for basic math courses to 58.1% for transfer math courses. While, White student course completion decreased substantially, from 77.6% for basic math Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 96 courses to 62% for transfer math courses. At the same time the African American student had the more prevalent academic success ratio (66%) in the transfer math courses, while the White student had the least prevalent academic success ratio (64%). For African Americans these results are consistent with several studies (Penny & White, 1998; Short, 1996) that the remedial math pipeline does help the student course completion. Additionally, if the African American student completed the math pipeline successfully their academic success improved in the higher level math courses. But, for White students the remedial math pipeline shows an adverse affect in course completion and academic success in the higher level math courses. The Asian and Latino course completion and academic success doesn’t change much in the remedial math pipeline. Further study is needed to establish the relationships associated with course completion and academic success for African Americans in the remedial math pipeline in order to replicate the course completion and academic success for other minority students. Factors and Variable Predicting Academic Success and Course Completion Factors Predicting Academic Success The results of this study showed that the average GPA in high school and the overall GPA were significant factors that explained 21.8% of the variance in the academic success in remedial math courses. The enrollment status, overall GPA, Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 97 average GPA in high school, quality of teaching and hours of employment were the significant factors that explained 27.6% of the variance in the academic success in basic math courses. The opportunity to transfer, highest math placement, overall GPA, and enrollment status were the significant factors that explained 28.1% of the variance in academic success in intermediate math courses. The enrollment status and absenteeism were the significant factors that explained 21.7% of the variance in academic success in transfer math courses. Further study is needed with the exclusion of the overall GPA variable in order to determine the more prevalent factors that explain the variance in course completion and academic success for minority students in the remedial math pipeline. Variables Predicting Course Completion The results of this study showed that gender, ethnicity and age were significant predictors for course completion in remedial math courses. The ethnicity and gender were not significant predictors for course completion in basic and intermediate math courses. The age was the best predictor for course completion and had a positive correlation in remedial, basic, and intermediate math courses. Essentially, the older students had more prevalent course completion in the higher level math courses. Ethnicity was the best predictor for course completion and there was a negative correlation with transfer math courses. Subsequently, Asian and African American had more prevalent course completion in transfer math courses. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 98 The average GPA in high school, academic advising, and highest math placement were significant predictors for the course completion in remedial math course sequence. The average GPA in high school was the best predictor. However, the highest math placement had a negative correlation to course completion. The overall GPA was the best predictor of course completion for basic, intermediate and transfer math courses. Additionally, the average GPA in high school and academic advising were significant predictors for the course completion in basic math courses. Although, the highest math placement was positively correlated to the course completion in basic math courses it was not a predictor of course completion. As well, the average GPA in high school and highest math placement were significant predictors for the course completion in intermediate math courses. Moreover, the average GPA in high school was a significant predictor of course completion in transfer math courses. Conclusions Nationally, 29% of all college freshmen take at least one remedial class in reading, writing or math (Weiss, 2001). While some critics are suggesting a double billing of taxpayers for the failure of the public high schools to properly prepare students, others argue of an unfair impact of remedial education on minorities and immigrants. The epidemic for remedial education is emerging at the same time when university access cannot be guaranteed for all students. State budget cuts in higher education will require community colleges to accommodate additional demands for Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 99 lower division courses to prepare students for transfer as a priority when determining course selections being offered. This puts the community college in the primary role of providing remedial education. Therefore, it is important for the community college administrator to monitor the course-taking patterns of the students. The data from this study showed that there was a disproportionate number of African American, female, and age 30 and older students with math placement in remedial math courses. There were disproportionately more Asian and 19 or 20 year old students that were placed in intermediate math courses. The African American, Latino, female, and age 30 and over students progressing from lower to higher-level math courses was decreasing. The Asian, male, and age 20, 21 -24 student progression from lower to higher-level math courses was increasing. The descriptive data gathered indicated the background and defining variables as; nontraditional, diverse population, majority Latino, female, age 21 and older, and enrolled on a part-time basis. They commuted 15 to 20 minutes to college, and had high aspirations to obtain a master’s degree. Their high school performance indicated an average grade of B- and an average score of 2 on the highest math placement. The academic variables portrayed students with good study habits and attendance. They were probably certain of their college major and had relatively small problems scheduling classes. They rated the academic counseling services as not at all accurate, helpful, and available. The environmental variables indicated Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 100 there was a medium problem paying for college, with most students working part time. The outside encouragement was slightly important; they’re not supporting a family and were probably going to transfer to a 4-year college or university. The academic outcome variable, at the time the survey was taken, revealed that the average overall GPA for this population was C+ or above average. The psychological outcomes variables showed that the student’s perception of the college to get their graduates good jobs was slightly important, the quality of teaching was a large problem, and they were definitely committed to attending college and had relatively low stress. Johnson and Pritchard (1989) suggested retention efforts should quickly integrate high risk students into the university environment and help students balance educational commitments with work responsibilities. Careful examination of the description of the population utilizing the conceptual model constructs (Table 13) will be instrumental for the community college administrator when making determinations and provisions for the services that will ultimately balance the needs of the students with the defining characteristics of student attrition. The examination of the factors that predict the academic success in the minority student math pipeline identified; highest math placement, enrollment status, attendance, hours of employment, opportunity to transfer, overall GPA and, quality of teaching were significant predictors of academic success in nontransferable and transfer math courses. The predictors of course completion in remedial math courses Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 101 were; ethnicity, gender, age, average GPA in high school, highest math placement, and academic advising. While age and average GPA in high school was the best predictors. The predictors of course completion in basic math courses were; age, average GPA in high school, highest math placement, overall GPA, and academic advising. The predictors of course completion in intermediate math courses were; age, average GPA in high school, highest math placement, and overall GPA. The predictors of course completion in transfer math courses were; ethnicity, average GPA in high school, highest math placement, overall GPA, and academic advising. Whereas, ethnicity was the best predictor for course completion in the remedial math pipeline in transfer math courses. Furthermore, overall GPA was the best predictor for course completion in the remedial math pipeline in basic, intermediate, and transfer math courses. This information is important for administration when implementing; curriculum and instruction, student program services, staff selection committees, and professional development. The literature suggest further examination of the impact of developmental courses on graduation rates and whether the levels of participation in the university’s academic support services will predict academic success as well as graduation rates. Recommendations Implications The implication of this study for the community college administration is that further research is desired to examine the current rank of minority student enrollment Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 102 in community college remedial math courses. There were equity issues from this study showing up in the highest math placement for African American, female, and age 30+ students in remedial math courses. At the same time, while predicting academic success, this study recommends shifting the focus mainly on groups of related predictors such as high school performance, enrollment status, and opportunities to transfer, and academic advising. The scheduling of remedial math courses to be varied in such a way as to meet high school performance prerequisites, enrollment status and opportunity to transfer is a recommendation for curriculum implications for the community college administrator. Additionally, in this study, the factors that have been found to be predictive of academic success in the remedial math pipeline include absenteeism, hours of employment, and the quality of teachers. This study suggests the use of separate prediction equations for ethnicity, age and quality of teaching. The implications are the utilization of these independent variables in developing and implementing retention studies and reporting their results at future meetings of community college researchers. Recommendation for Further Research Replication of this study in the Developmental Education programs of community colleges throughout this country should be conducted so that there will be an implementation of a common perspective for assessing the effectiveness of Developmental Education programs in community colleges throughout this country. More research is needed to explore the descriptive characteristics of the remedial Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 103 math teachers and the academic advisors in order to improve perceptions of the quality of teaching and the accurate, helpfulness and availability of the counseling services in the community college. Reproduced with permission of the copyright owner. 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Digest of Education Statistics 2001 (NCES 2002-130). Washington, DC: U.S. Government Printing Office.U.S. Walker, W. & Plata M. (2000). Race/Gender/Age differences in college mathematics students. Journal o f Developmental Education, (23)3, 24-32. Waycaster, P. (1998, Spr). Student should spend sufficient time on developmental mathematics. Inquiry, (2)1, 26-31. Weiss, K. R. (2001, January 24). Cal State expels 2,009 students for lack of skills College: They are kicked out for failing remedial math, English classes. Los Angeles Times, pp. A3. Weissman, J., Bulakowski, C., & Jumisko, M. K. (1997,Winter). Using Research to evaluate developmental education programs and policies. New Directions for Community Colleges. 100, 73-80. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. APPENDIX A Survey Instrument One Community College Student Survey D ear S tudent: T h is in fo rm atio n is bein g co lle c te d by researc h ers from the U niv ersity o f S o uthern C alifo rn ia and the U niversity o f C alifo rn ia at L os A ngeles in co n ju n ctio n w ith the Los A ngeles C o m m u n ity C o lleg e D istrict as part o f a larg e study o f co m m u n ity c o lleg e students in I .os A n g ele s. Y ou h a\ e been sele cted a s a p articip an t in a m u lti-y e a r p roject, Y our c o o p e ra tio n w ill assist researc h ers to h elp Los A ngeles C o m m u n ity C o lleg e stu d en ts to be su ccessfu l in th e ir e d u c atio n a l pu rsu its. Y our assistan ce is cru cial to the p roject; w e than k y ou fo r y o u r p articip atio n in this im p o rtan t research. DIRECTIONS P lease answer alt questions as com pletely and accurately a s possible. Because your resp on ses will be read by a m achine, your careful observance of th ese lew sim ple rules will be m ost appreciated. • Use only black *»ad pencil (No. 2 s deal). • Make heavy black m arks that (ill the ovals (do not circle or check the ovals). • E rase cleanly any answ er you wish to change. ~ C • O ^ X " • Make no stray markings ol any kind. EXAMPLES: Correct Mark: incorrect Mark: Social Security Number N a m e - _ _ __ Your prim ary em ail a d d re s s : Your p h o n e n u m b e r :___ i i "I . ! S JE 3D f t X ft x o . x X > 35 £ x .r ■ X - T X: •X i X £ X 4 . X : V X X 1 X £ X t X J X X £ X X X A ■X'T X X s X X X -5' X X f :c • » X .if id a :© X X X .V T •X r ‘ X ® X X ft, X * rja X c r nc < £ ■ r x ct> >, x We want to follow your p rogress for th e next tw o years: yet w e realize that many stu d en ts will m ove from tim e to time. P lea se provide th e n am es of tw o p eop le w h o are likely to know your ad dress even if you m ove. We request the nam e, a d d ress, and p hon e num ber of tw o p erson s. Contact 1. A relativ e o r friend w ho d o e s n o t live with y ou a n d w ho is likely to know your a d d re s s a t all tim es: N am e: . A d d r e s s ;. City. S ta te . Zip: ... P h o n e N u m b er: _ b m ail a d d r e s s : __ Contact 2: A nother relative or friend w ho d o c s n o t live with you a n d w h o is likely to know your a d d re s s a t nil tim es: N a rre A d d re ss City. S ta te , Zip: _ P h o n o N um ber; Em ail a d d re s s _ DO NOT W RITE IN TH IS AREA 14660 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 112 1. Below are some reasons that might have influenced your decision to attend this particular cottage. How important waa each reason in your decision to come here? [Mart one fO' eacn statem ent) I I I My parent3 wanted me to ccm e here . c J a 3 ~ 3 — My spouse, partner or other family member wanted me to come here . . . . -T 0 “ 1 _3 — This cotege has a good reputation......... <2 c 0 - O mm 1 wanted to go to a different college mm than many of my fnenas.......................... I. : .V m m Thus college has good social activities .. 2 0 '* ■ 1 cou d r t find a joo .................... 0 this college > $ atforaafcie.......................... w- C « A high school or cthor counselor mm advised m e ................................................ O .•3 0 I :: mm This college < s close to my home............. w _ 0 . 'J _ _ mm This colleges graduates get good jo o s.. o mm Tnis co^ege s students transfer to mm gocd 4-year schoo s .............................. c r; 'V j mm i couldn't find anything better to CO........ c mm I want to get a better jc-o............................ c .2 ~ 2 mm My friends are attending here ........ w ~ C mm This college in cfcse to w nere l work , ., c ■ r 3 r ~ - mm This college offers educational » programs cf special interest to me mm that other colleges dc NOT have........... n : ■ ,3 T mm I want to get a college d e g re e ................. o •r Q I _ T mm To learn English tor work........................... o 1 O 3 O “ C •m My employer e-icouragoe mo to enroll •mm h e re .................................................... o f— j O O mm This college offers the program or mm certificate 1 need for w o rk .................... o ~ 2 — C • 3 . 2. How many of your closest personal friends are also currently attending this college? (Mark one ) None of my closest Inends .................... O ne of my o o sesi I n e n d s ........................ .3 A 'ew of my closest frien d s........................3 About halt of rry closest frie n d s............. Most of my cinsosr Innnds . ~ All ol my closest friends............................ 3. In general, w hat d o the following people think about this particular college? iHiirK one fcr eacn statement.; You Your closest friends Your spouse or partner . Your parents nr guardian!; Your other relativ es........ Your high school 'CcChers Others [ i U f i ti ill ■ m 4. Which of the following statements best describes your college plans for next semester? (Mark one.i I will attend only this college ............................ I will attend cr<s co lege and 1 otner college l will attend this co lege ano 2 or more other cc'feges .. . . 1 will not sherd here, but t wi i attend t other eo’le g e ............ I will not atterd nere. but I wrl attend 2 or mote other colleges f wilt not aher/l .my college S. W here did you attend school? United (Mark a]l that apply ir each column ) S tates Elementary scnoolor equivaort (Aguti 4 to iti . . ~ ~ ........ Junior high school (Ages 12 to >4i H>gn scnoci (Ages 15 to 1 6 ) ..................................... “ C o te q e ........................................................................ ” • • • S. Not including this college, how many other colleges or universities have you evor attended? ;Mar< cne i None (I nave attended only this college)............... 1 o th e r............................................................... . _ 2 3 o th e rs ...................................................................... 4 or more o th e rs............................................................. 7. How many credits have you earned at this college in previous semesters? (Mart m e.) None ........................• 1 - 3 .................................................................................. _ 4-3 ............................................................................ ~ i c - t a .............................................................................. 1!)-2? . .. ............................................................ 2 B -36..............................................................................~ 37-60 .............................................................................. Mere than 6 0 ................... ................................... 8. Since leaving high school, have you ever taken courses at any other institution? For (Mark all that apply) Credit Yes, at another community or ]un<or c o 'ie g e :l'; . Yes. ai a 4 year college or university.................... Yes. at some otncr postscconaary school -'for o<ampie. technical, vocational, business). . . 4 in addition to this college, are you taking co u rse s at another school or college this se m e ste r1 -'Mark a-i tnat apply.) Yes. at a n o th e r co T sr-u n ity c o 'ie g e ................................ Yes. at a four-year college or university . V o-j. ai a higri school ............................... Yes. a t a v o catio n al o r tra d e sc h o o l Yes. a t a n ad u lt s c h o o l .............................................. A nother Country Not for Credit -2 • Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 113 As th in g s sta n d today, d o you l £ i . / think you w ill. . , ? * * f Mark one for oacn itale-irent. / / / / £ 111 C hange your career cho re c --IG G c G raduate w in honors ........................... r ; - r~ G c Play varsuynntercoiiegiate am-eiics O g o o Gel a bachelor's degree G ; ; ; o O ! Permanently stop attending co fege O .:;iG 3 o : Leave this college temporarily and return la t e r ................ ...................... o G G Transfer to another community ccilcgo, . . . G _• G G . Transfer to a 4-year college or unvorsity .. G ■3 - -■ D eveop close new relationships w in sludonts at this college . ......................... 3 3 Zi G - Talk regularly with me instructors at this co lle g e ...................................................... G C O C hange your college m a j o r ...................... — G . 14. For (Jx>|_cours« only, approximately how many times in the pest 7 days, did you: iM a r* o n g for e a c h s ta te m e n t \ Work m small groups during class time Teiecnono or email another student to as* a cjuesticr*. about >ou' studies Ask tno instructor questions . . . Speak uo dunng o a s s discussion G / •7' / // ij: 7/ /// 15 In the past T ^ays. approximately how many hours bid you: iM a'k on.? for -;ncn itate-rren f j i j n H ii / 3 h / # * 2 / iiH' < £ > * ■ : i - --z i _ _ . j _ 11. Indicate ait co lleg e d e g re e s earned United A nother Work ai a icb ............. r - - . i i (if any). jMar< ail mat - icpiv.'i S ta te s Country Do housework cr chiiccare t l - i - , - i Associate oegrne iA A. or equivalent) - V /nfh t\ ' P ... '■ ■ i . Oachc'er’s d eg ree \D.A., D.S . *tc | . . : S cend cn this cam pus ; including i Graduate degree iM A.. M.S . Ph D.. time in class*. . . . p i - ExD.. J.D. M.D. etc.l . .. - Spend talking with students ado j 1 1 Certificate ............................ .. G . . uvr.gs not related tu j cuurt>e 'G - . G Sfuay jione at h o m e ....................... ) ■ - - G .l.‘ Stuay aicne m tho coifoge • brary. . 12. tf th ere w are no o b sta c le s, w hat is the h ig h est academ ic Study w*tn students from this d e g re e y o u w ould tike to attain in your lifetim e? (Mark one.) course , C - O Will take classes, Dvr do ro t intend to earn a degree Study rvrft students ‘rom oinef Vocational certificate — courses <not m s courset ............ . G c o I 3 - 3 3 • 5 ) '// Associate (A A. or e quivalent)...................... B a r . n e l n r s d e g r e e IP . A . R S . e t c ) At least a Bocncfor 5. m ayoe m o re ............... M aster's degree (M.A . M.S. eic.).................. Doctoral riggreo (Ph D , Fd D . J D . etc ) Medical degree iM.D.. O.D.S.. D.V.M.. etc.) 13. Approxim ately how many times in the past 7 Pays, did you: (Mark one for each s:aiem eri,i 3 k i3 a c l a s s .............................. Talk wilt: an instructor before or alter .1 class . Taik '.vtn an instructor during ctfrce hours ................ •Jse entail or the Internet fcr homework He;p anotner L ucent unae-storcf h o m e w o r k . ............................ Sfuny >n groups o u isd e of m ats . Speak w»tn an acaueih'C ucunse.ur 16. How large a problem do you expect each of the foBowing to be white getting your education at this college? (Mark one for each statement.) P a r k in g .................... ........................... Transportation (access 10 public transportation, snanng c a r s, e tc .;.................. F j n n i y r c b u o r i i 'b i 'i t 'o s i e . g . . e r k d c a r e . parent care) ........................................ Job-related -esccncip. .t«es................................ Paymg tor cologs. . S c n e d u . i h y crosses tcr ne*i s e m e s t e r U nds's'anoing tre En^nsn anguctjje . D if ^ c u itv o f : i . ? ; s e s . 17. How often do you u se English with the following people? .M ark o n e fcr -:acn uurtem ent.} W ith m y p a ren ts Wtnf'iortfs '.Vim :«;acno?5 cr crciosst.r-; « ji tr ; M i l /i/l/l/j/// ■ w ih ti jUiiisiij • 3 * Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 114 18. How often do you u se a language other than English with the following people? (Mark one for each statem ent) With my p a re n ts .................................................... With f n e n d s ............................................................ With tea c h e rs o r p ro fesso rs at this college 19. How well are you able to do the following in English? (M ark o n e for e a c h ite m ) R e a d ................................................ W rite .......... U ocerstarw a co lleg e lecture . R e a d a co lleg e text doc* W rite a n e ssa y exam ............................ W rite a te r n p a c e r . .......................... Participate in c la ss d is c u s s io n s ............ C om m unicate w th instructors 2 a 1* English your native language? Y e s C G o to q uestion 79 No O ' C ontinue lo question 7 .1 21. How wen are you able to do the following in your native language? (M ark o r e tor e a c h item.) R ea d ............................................... W rite............... - ............................. U n d erstan d a college lecture . . . R ead a college text b o o k ............ W rite a n e s sa y e x a m ................... W nte a term p ap er . . . ................ Pan-opaie in class discussions . Communicate vwth instrjetors . I 22. How long d o es it take you to travel to this college? (Mark qne.i L ess than '5 m inutes -. 15 to 30 m m u te s ............ 31 to 4 5 m m u te s ............ 46 to 6 0 m in u te s ............ B etw een t a r d 2 nours. M ore than 2 h o u r s . . 23- Do you h a ve a disability? (Mark all that eoply.) H e a rin g ...................................................................................... S p e e c h .................................... ....................... Mobility im p a ire d .................................................................. Attention deficit d iso rd er............................................... Psychological disorder ........................ Learning d s a o u ity ............................................. Vision problem ihat cannot be c o re c te d by g lasse s o r contact t e n s e s ...................................... O th e r .......................................................................................... No d isa b ilitie s.......................................................................... 24. What w as your average grade m high school? iMar* on* A bi A* (Ext'aofdtnoryi ............................................................ A * (Supenor Oua ity )................................................................... B- (Excellent) . B (Very Good) ........................................................................... 0* (G ood]...................................................................................... C ‘ (Above Average: C (A verage).................................................... C- (Below A verage)..................................................................... 0 or lo^er fP o o r)............................ 25. Before this sem ester, what m athem atics c o u rse s have you taken? Include courses In high school or previous college work. (Mark oil that aoply.l Baste math, Business main, or Pre algebra .......................... Algebra i ................................................................... • Geometry .............................. .............................. A gebra I I .................................................................................... Trigonom etry...................................................... p*e-ca c u i u s ................................................................ C aicuius........................................................................................ 26. Before this sem ester, w hal science co u rse s have you taken? Include co u rses in high sch o o l or previous college work. (Mark al[that apply.) General Biology ......................................................................... C nem rstfy...................................................................................... PPiy3iC3.......................................................... ........................ Biology specialty (i.e.. mcrobioiogy. geneics, botany, eefi bioiogy, m a'in? fetology, e i c .) ........................................... Other Cartn science (i e . geooqy, meteorology, etc.) ......... 27. With whom do you live while attending this coflego? (Mark ail that apply) With my spouse or partner ............................................. .. W»th my p aren s or guardians ................................... W4h my children siepchfdren............................................. W4h siblings (nrothnr{s) and/or sislerlsl) With other relatives . ................. ......................................... With a roommatefsi or a fnend(s) . ..................................... 1 live alone ........ 28. Your gender: Male .......... . . Female ........... 29. How old win you be on D ecem ber 31 of this year? 1fi ynars or younger . . . I B ................................................................................ ......... 1 9 ................ ............................................................................................. 2 0 ..............................-.................................................................................................................. 2 1 -2 4 ?S -?9 .............................................. 30 -3 9 .................................................. . . . . 4 0 - 5 4 .......................... .......................... 55 or older . . . . . . . 4 . Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 30. W hat is your ethnic g roups)? (Mark all that apply.) Chines©......................................................................................' 3 Filipino.........................................................................................-J Japanese................................................................................. —' Korean...................................................................................... T h a i.............................................................................................^ Laotian ..................................................................................... ^ C am bodian...................................................................................^ Vietnam ese............................................................................. South Asian (Indian Subcontinent)....................................O A ra b ............................................................................................ O African-American/Black...................................................... M exican......................................................................................^ Mexican-American/Chicano............................................... South American ..................................................................... O Central A m erican................................................................... ~ Other Latino/Hispanic ...........................................................^ Alaskan N a tiv e .........................................................................—* American In d ian ...................................................................... —; Pacific Islander/Samoan. Hawaiian, or Guamanian . . . — Other Pacific Islander............................................................. — Caucasian/White ....................................................................— O th e r.......................................................................................... ^ 31. Are you currently married? Y e s ...............................................................................................—1 No ...............................................................................................—' 32. W ho is (are) the prim ary w age e arn e rs) in your household? (Mark ail. that apply.) Yourself.....................................................................................O Partner/Spouse....................................................................... O Parents/Guardians.................................................................O Children/Stepchildren............................................................ O O th e r......................................................................................... O 33. How many o f your children/stepchildren are living In your household? (Mark one.) N o n e ..........................................................................................O 1 -2 .....................................................................................O 3 - 4 .....................................................................................O 5 or m ore...........................................................i ......................3 34. Excluding yourself, how many people (children, grandchildren, brothers, sisters, parents, etc.) are you financially supporting? (Mark one for each item.) Under 5 years of a g e . 5 to 18 years of age . Over 18 years of a g e . 35. Which one of the following best describes your employment status at this time? (Mark one.) Employed full-time (including self-employed)----- Employed part-time (including self-employed) . . . Not employed but looking for work........................ Not employed and not presently looking for work. 36. How do you think of yourseif? (Mark one.) Primarily as a student who is employed .............. Primarily as an employee who is going to college Primarily as a parent who is going to college — Solely as a stu d en t................................................ 37. For the following items, please indicate the extent to which you agree or disagree with the following statements. (Mark one for each statement.) My teachers here give me a lot of encouragement in my studies........................... I enjoy doing challenging class assignments....................................................... What other people think of me is very O O c o o r~, O o o o G o o I start to study at least 2 or 3 days prior . _ n n n I expect to do well and earn good grades in college................................................ .......... o GG O o G a Understanding what is taught is important n c. n G n O n I always complete homework assignments — o r — ■ o o O o I keep trying even when i am frustrated n o n -s. n Cl n Learning can be judged best by the grade n n n n 3 n n It is important for me to finish the courses in my program of studies ................................ o o o o O o o Things are harder for me because of my race or ethnicity................................................ o o o Q o o o I frequently have difficulty meeting deadlines........................................................... o o o c o o o I am very determined to reach my goats.......... o o o o o o o I was initially very nervous about attending r>n n n n n o I feel most satisfied when I work hard to achieve something ........................................... o o o o o c o My family is more important than my n r - v n n n n Success in college is largely due to effort (has to do with how hard you try).................... o c o C D o o c I feel I belong at this college.............................. Q c 3 C o o o I wait until the day before an assignment is due before starting i t .................................... O o OC o c o I know I can learn all the skills taught in college........................................................... O G o o o C o I want to become involved in programs to clean up the environment............................ O c Q G c o o c C 3 3 o o Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 116 3 6 .1 hava attended an orientation te a fio n at this college. Yes............................................... O No ................................................. O 39. Are you receiving the following typ es of financial a ssista n ce ? (Mark all that apply.) L o a n ...............................................© Scholarship or g r a n t ................O 40. Do you ow n your o w n . . . ? (Mark one m each c o ljm n l Yea Home (not re n tin g ) — O Com puter (with Internet a c c c s s t........................... C l C om pjter (without interne; access) .......................... O C a r ..................................................................................... © No O o a 41. What la the h igh est level of formal education obtained by your paren ts either In the U.S. or in another country? (Mark one si each column.) M ather Father 6th grade or less........................................... O ....... C Junior high or middle school ...................................... O .......... O Som e high s c h o o l.......................................................... © ............ © Finished high school or G E D .......................O .......... O S om e com munity c o lle g e .................................. ..,. O i—> C o m p eted community co lleg e.................................... © ............ © S om e four-year colleg e...................................................O . . O C om pleted four-year co leg e d e g re e .........................© ...........O Som e graduate s c h o o l...................................................O C J G raduate deg ree ......................................................... O O I d o not k n o w ............................................................ O ...O 42. W hile y o u w ere grow ing up, m ark th e jo b th a t b e s t d e s c rib e s y o u r p a re n t's m a|or o cc u p atio n . (Mark on e in each column.) M other F ather r e t i r e d ......................... . © ...........© Day laborer (cleaning, construction, farm. _ factory, e tc .) ....................... O .. .. <_J Worker or hourly em ployee (service, hotel, hospital, agriculture, truck driver, clerical, retail sales an d service. laundry or m aintenance, e re ) O .. O Factory worker (manufacturing, w arehousing, shipping, operations, telephone operator, e tc .). . . O r _) Skilled tradesm an (machinist, plumper, tile setter, electrician, auto mechanic, nurse, secretary, chef, technician) .................................................................. — J Supervisor or m anager (professional) . Small business ow ner (retail, construction, service, e t c . ) ................................................ C ' ......................i~ > Professional, while collar (sales, finance, teaching, consulting, engineer, accounting, doctor, lawyer, e tc .) ...........................................................................•-> Housework (taking ca re of children or hom e) . .. O . .. O Unemployed or on w e lfa re ..........................................O ............© Do not know .....................................................................f —' ...........O 43. Wrll* 1 n your father s m ain Job (or. If n et working hi* m ost recent Job). 44. Write In your m other's main Jab (or. It not workln her m ost recent Job). 45. D escribe your p resen t work/career. 48. D escribe the type of w ork/career you plan to be In 7 or 8 years from now. 47. How much education do you think is n eed ed for type of work you are planning? (Mark one.) High school diploma or GED ......................................... Som e community c o lle g e ................................................ Completion of A ssociate deg ree (A A. or equivalent) Som e four-year college w o tk ......................................... Completion of a four-year college ceg ree (B.A . B.S. Completion of m ore than a lour-year college degree Completion of a professional d eg ree or credential . , Completion of a graduate degree (M aster's Degree) Completion of an advanced professional degree (Ooctorate, Ph.D.. M.D., e t c . ) . .................... Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.

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Asset Metadata

Creator Rice, Alice (author)

Core Title Examining the factors that predict the academic success of minority students in the remedial mathematics pipeline in an urban community college

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, curriculum and instruction,Education, Mathematics,OAI-PMH Harvest

Language English

Contributor Digitized by ProQuest (provenance)

Advisor Hagedorn, Linda Serra (committee chair), McComas, William (committee member), Sundt, Melora (committee member)

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c16-415059

Unique identifier UC11341075

Identifier 3145271.pdf (filename),usctheses-c16-415059 (legacy record id)

Legacy Identifier 3145271.pdf

Dmrecord 415059

Document Type Dissertation

Rights Rice, Alice

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions 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 University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus, Los Angeles, California 90089, USA