|UFDC Home||myUFDC Home | Help|
This item has the following downloads:
FACTORS THAT CONTRIBUTE TO S UCCESS IN A FIRST YEAR ENGINEERI NG SUMMER BRIDG E PROGRAM By JEFFREY M. CITTY A PROPOSAL PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF EDUCATION UNIVERSITY OF FLORIDA 2011
2 2011 Jeffrey M. Citty
3 To my family, and all engineering educators
4 ACKNOWLEDGMENTS As I reflect on the past four years, the journey to complete my doctoral studies has be en long, winding and exciting. Many times I did not know the direction I was going but I trusted in the support and guidance that was provided by the many individuals th at God provided along the way. I take this opportunity to express my sincerest thanks and appreciation to my family, colleagues, and my extended family, for their faith, support, patience, and their unconditional love. I extend my thanks and love to my f amily, Sandra, Meghan and Matthew. I am extremely thankful for the prayers, time and understanding that were provided along this journey. To my wife Sandra, whom without, this accomplishment would not have been possible I am most thankful. From the decisio n to apply to this program, to encouraging me to hang in there during the difficult times and challenging me to grow my perspectives, angles, views, to guide my thoughts before coming to any conclusion, you have been my inspiration and my rock to lean on, for which I am most indebted. My educational journey at UF has been filled with many individuals, colleagues, and mentors that have provided support, guidance in throughout my time as student. I extend my sincerest gratitude to the College of Engineering and Engineering Student Affairs staff. The College of Engineering has continued to support me in my endeavor with resources needed to complete this project and the Engineering Student Affairs has provided unwavering suppor t, patience and understanding. Bo th of these units provided support that was invaluable to me and for that I want to extend special thanks. Throughout my program of study, I have studied with and had the privilege to be supported by great mentors and colleagues. I would like to give spe cial thanks to my doctoral committee members, Dr. Dale Campbell, Dr. David Honeyman, Dr. Bernard
5 Oliver, and Dr. Paul Chadik for their input, support, encouragement, patience and willingness to work with me. I would also like to thank Dr. Cynthia Garvan fo r her statistical guidance and support throughout my data analysis. Additionally, I would also like to thank Dr. Jonathan Earle and Dr. Angela Lindner for their mentoring, encouragement and thoughtfulness while providing the opportunity work full time in a supportive work enviro nment throughout this process. anding throughout this process. I am extremely thankful for the daily pray ers, encouragement, support, and phone calls which encouraged me every day. I would like to especially thank my Mom and Dad for their love, encouragement and support for which I am gratefully thankful. Last, but not least, I would like to express my grat eful appreciation to the College necessary resources to allow me to focus on providing a product that is informative, accurate and one that others can utilize to benefit th e study of engineering summer bridge program s and higher education.
6 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 9 LIST OF FIGURES ................................ ................................ ................................ ........ 10 LIST OF ABBREVIATIONS ................................ ................................ ........................... 11 CHAPTER ABSTRACT ................................ ................................ ................................ ................... 12 1 INTRODUCTION ................................ ................................ ................................ .... 14 Purpose of the Study ................................ ................................ .............................. 17 Research Questions ................................ ................................ ............................... 18 Research Hypotheses ................................ ................................ ............................. 19 Definition of Terms ................................ ................................ ................................ .. 20 Limitations ................................ ................................ ................................ ............... 20 Significance of the Study ................................ ................................ ........................ 21 2 REVIEW OF LITERATURE ................................ ................................ .................... 24 Summer Bridge Programs ................................ ................................ ...................... 24 Components That Foster Student Success in Summer Bridge Programs .............. 27 Peer Mentoring ................................ ................................ ................................ 27 Living Learning Communities ................................ ................................ ........... 28 Commitment to Career and Educational Goals ................................ ................ 29 Tutoring ................................ ................................ ................................ ............ 29 Faculty Interaction ................................ ................................ ............................ 30 Academic Advising ................................ ................................ ........................... 31 Components That Challenge Student Success in Summer Bridge Programs ........ 33 Gateway Courses ................................ ................................ ............................. 33 Curriculum Design ................................ ................................ ............................ 35 Financial Needs ................................ ................................ ................................ 37 Family Issues ................................ ................................ ................................ ... 37 College Preparation ................................ ................................ ................................ 38 Engineering Education Retention S tudies and Summer Bridge Programs ............. 40 Summary of Literature Review ................................ ................................ ................ 43 3 METHODOLOGY ................................ ................................ ................................ ... 45 Population Sample ................................ ................................ ................................ .. 45 Subjectivity Statement ................................ ................................ ............................ 45 Dep endent Variables ................................ ................................ .............................. 46
7 Independent Variables ................................ ................................ ............................ 46 Use of Secondary Data Source ................................ ................................ .............. 49 Data Analysis ................................ ................................ ................................ .......... 49 Ethical Considerations ................................ ................................ ............................ 50 Threats to Internal Validity ................................ ................................ ...................... 51 4 DATA ANALYSIS AND RESULTS ................................ ................................ .......... 53 Preliminary Analysis ................................ ................................ ................................ 53 Profile and Descriptive Statistic s for the Institutional Sample ................................ 54 Descriptive Statistics for Institutional Sample Continuous Dependent Variables .... 55 Descriptive Statistics for Student Classification and Categorical Variables ............ 56 Investigation Differences between Participants and Non Participants .................... 57 Binary Logistic Regression ................................ ................................ ..................... 61 Model 1: Characteristics of Retention ................................ ................................ ..... 62 Model 2: Predictors of Summer Bridge Program Inclusion ................................ ..... 63 Chapter Summary and Conclusion ................................ ................................ ......... 64 5 DISCUSSION AND CONCLUSIONS ................................ ................................ ...... 76 Purpose of Study Revisited ................................ ................................ ..................... 77 Retention of Participants and Non Participants ................................ ...................... 78 Academic Performance and Gender of Participants and Non Participants ............. 80 Academic Performance ................................ ................................ .................... 81 Gender ................................ ................................ ................................ ............. 82 Impact on Quantitative Gateway Courses and Withdrawal rates ............................ 83 Quantitative Gateway Courses ................................ ................................ ......... 83 Withdrawal Rates from Quantitative Gateway Courses ................................ .... 84 Contributions to Engineering Summer Bridge Programs ................................ ........ 85 Implications for Practice ................................ ................................ .......................... 86 National, State and Institution Policy Recom mendations ................................ ........ 87 Suggestions for Future Research ................................ ................................ ........... 90 Closing Summary ................................ ................................ ................................ ... 92 APPENDIX A INSTITUTIONAL REVIEW BOARD (IRB) APPR OVAL LETTER ............................ 94 B SUMMER BRIDGE PROGRAM INVITATION L ETTER ................................ .......... 95 C SUMMER BRIDGE PROGRAM W EB A PPLICATION ................................ ............ 96 D SUMMER BRIDGE PROGAM CONTRACT ................................ ........................... 97 E SUMMER BRIDGE PROGRAM R ELEASE AND HOLD HARMLESS AGREEMENT ................................ ................................ ................................ ......... 99 F SUMMER BRIDGE PROGRAM L ETTER OF ACCEPTANCE .............................. 100
8 G SUMMER BRIDGE PROGRAM FINAL L ETTER ................................ .................. 101 LIST OF REFERENCES ................................ ................................ ............................. 102 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 112
9 L IST OF TABLES Table page 3 1 Bridge program summer daily schedule ................................ ............................. 52 4 1 Sample demog raphic description ................................ ................................ ....... 66 4 2 Frequency of mathematics and chemistry courses ................................ ............. 67 4 3 Frequency of retention of engineering students after first year ........................... 67 4 4 Descriptive statistics for continuous independent variables ................................ 67 4 5 Examination of high school GPA and retention ................................ .................. 68 4 6 Analysis of means on high school GPA a nd retention ................................ ........ 68 4 7 Comparison of participants & non participants on categorical variables ............. 68 4 8 Comparison of participants & non participants on continuous variables ............. 69 4 9 An alysis of means on participants & non participants on continuous variables .. 69 4 10 Frequency of mathematics and chemistry courses ................................ ............. 70 4 11 Analysis of means for participants & non participants on math & chemistry courses ................................ ................................ ................................ ............... 71 4 12 Relationship of participation in the summer bridge program and withdrawal rate ................................ ................................ ................................ ..................... 72 4 13 Analysis of means withdrawal rates for participants & non participant groups .. 73 4 14 Logistic regression model measures M odel 1: characteristics of retention ......... 74 4 15 Results for binary logistic regression M odel 1: characteristics of retention ......... 74 4 16 Logistic regression model measures M odel 2: summer bridge program inclusion ................................ ................................ ................................ ............. 75 4 17 Results for binary logistic regression M odel 2: summer bridge program inclusion ................................ ................................ ................................ ............. 75
10 LIST OF FIGURES Figure page 2 1 Literature review diagram ................................ ................................ ................... 44
11 LIST OF ABBREVIATION S AAU The Association of American Universities is a nonprofit association of 59 U.S. and two Canadian preeminent public and private research universities. AP Represents advance placement college level courses and exams, taught to high school students where they can earn college credit. ASEE The American Society for Engineering Education is committed to furthering education in engineering and engineering technology. DE Dual Enrollment i nvo lves students being enrolled in two separate, academically related institutions such as a high school and a college or university. EDW Engineering Data Warehouse is a storage center for multiple data that are stored for authorized access. ETS Educational Testing Service serves as the administrator of the yearly advance placement examinations IB The International Baccalaureate is a two year educational program for students aged 16 18 t hat provides an internationally accepted qualification for entry into higher education, and is recognized by universities worldwide. NSF The National Science Foundation is an independent U.S. government agency responsible for promoting science and engineer ing through research programs and education SES Socio economic status is an economic and sociological combined total measure of a person's work experience and of an individual's income education and occupation. STEM Commonly used to represent the Science, Technology, Engineering, & Mathematics field s of study. STEPUP The Successful Transition through Enhanced Preparation for Undergraduates Program is an engineering summer bridge program focused on promoting academic excellence for underrepresented groups. SUCCEED The Southeastern University and Coll ege Coalition for Engineering Education is a multi million dollar National Science Foundation program geared toward revitalizing undergraduate education.
1 2 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for th e Degree of Doctor of Education FACTORS THAT CONTRIBUTE TO SUCCESS IN A FIRST YEAR ENGINEERING SUMMER BRIDGE PROGRAM By Jeffrey M. Citty December 2011 Chair: Dale F. Campbell Major: Higher Education Administration This study explored the factors that contributed to the success of a first year engineering summer bridge program designed for all incoming engineering freshman at a southeastern large land grant research unive rsity over the 2007 2010 years. A examine the effects of the engineering summer bridge program. Additionally, characteristics of participants of the engineering summer bridge program were compared w ith non participants to investigate if any similarities existed. Moreover, predictors of retention between the participants and n on participants were explored This study attempted to determine if there were relationship s between this intervention and an i ncrease on retention by utilizing predictive and descriptive statistics to explain the phenomena between the two groups. Participation in the summer bridge program was voluntary with 415 students completing the program over the four year period (2007 2010) This data was compared with 3751 non participants. Utilizing various quantitative methods including logistic regression, results indicated that the engineering summer bridge program was successful in retaining students at a higher rate than the non parti cipants. Additionally,
13 the students who decided to participate and completed the engineering summer bridge program were significantly different that the non p articipants high school grade point average and SAT quantitative scores
14 CHAPTER 1 INTRODUCTION In the coming years, the engineering profession and engineering education are unlikel y to produce enough engineers in the United States. The U.S. Bureau of Labor Statistics has projected a need for 12,200 more engineering positions over the 10 year per iod between 2008 2018 This 6.7 % increase does not include the replacement of many retiring engineers. The number of engineering bachelor degrees awarded in the U.S is also contributing to this problem. According to the 2010 American Society of Engineering Edu cation (ASEE) Profiles of Engineering and Engineering Technology Colleges, undergraduate engineering enrollment grew by 5.3% from 427,503 to 450,685 ( during 2010 which is a notable improvement from the 1% growth from 2005 2009 (Gibbons, 2010). However, w ea kening interest in studying engineering among graduating high school students also continues Furthermore, of the students entering U.S. universities as engineering majors and completi ng degree requirements approximately only half matriculate (Wulf & Fisher, 2002; Frotenberry, Sullivan, Jordan, & Knight, 2007) A recent report from the I nstitute for Higher Education Leader s hip & Policy and The Education Trust describes the United States becoming less globally competitive as our college completion rat es stager (Offerstein, Moore, & Shulock, 2010) Currently, underrepresented populations continue to complete college at low rates however to regain our global competitiveness educational attainment must be a national goal The C ol lege Board Advocacy and Policy C enter recommends that the U S raise the number of 25 35 year olds with a n associate in arts degrees or higher degree to 55% by the year 2025 so the U S can regain is dominance in educational attainment (Lee & Rawls,
15 2010) Previous reports support these initiatives stating the workforce in the United States and the rest of the world will have demands for a more educated workforce with postsecondary skills and credentials (U.S. Department of E ducation 2006 ; Kirsch, Braun, Yamamoto, & Sum, 2007) Demographic changes in the world economy and the United States suggests that an older, more diverse and a significant population of immigrants will make up our workforce. Further, a majority of the fas test growing jobs in the new information and service economy will requir e some postsecondary education (U.S. Department of Education, 2006) The Departmen t of Labor projects that by 2018 there will be close to four million new job openings combined in health care, education, and computer and m athematical sciences Compounding this issue is the completion rate for underrepresented and nontraditional students who continue to make up an incr easing portion of those who att end colleges and universities. The nation will begin to rely on these students as major source of new employees as the demographic shifts in the population continues (Offerstein, Moore, & Shulock, 2010 ) Raising degree production in the engineering disciplines is necessary to sustain and increase our competitiveness in the global market. Universities have played an essential role in producing talented and h ighly trained engineers. In 2010 the Unit ed States univers ities graduated approximately 79 ( Gibbons, M. T. 2010 ) If U.S. universities continue to remain constant in degree production, as it has in the past 20 years, the U.S. will under produce bachelor level e ngineers ( National Science Board, 2006) Retaining and graduating all students who are interested in the engineering field is essential to meeting the workforce needs.
16 Retention rates of undergraduates in engineering remain at 56% nationally (Frontenberry, Sullivan, Jordan, & Knight, 2007). Since studies have concluded that there is no difference in academic status between students staying in engineering and students that choose to leave for other majors (Besterfield Sacre, Atman, & Shuman, 1997) the high student attrition rates cannot be attributed to lack of academic ability. Methods to combat this current situation are needed to improve the engineering retention rates and provide the nation with more engineers. Loss of students majoring in engineering a ffects institutions in two primary areas: revenue and loss of human potential represented by student dropouts (Lenning, Beal, & Sauer, 1980; Pascrella & Terenzini, 1991 ; Tinto, 1993). This issue has been studied extensively and several initiatives have bee n launched to address the problem (Astin, 1993; Noel, Levit z, & Saluri, 1991; Seidman, 2005 ; Tinto, 1993). For example, e ngineering colleges and employers have partnered with universities to develop programs aimed at improving the diversity and retention o f historically underrepresented students in engineering but significantly more work in retaining undergraduate engineering underrepresented students must be done to solve this issue (Lindner, et al., 2009) Research regarding the loss of undergraduate engi neering student s has found that retention has not improved o ver time (Bernold, Spurlin, & Anson, 2007). However, studies do suggest factors surrounding this phenomenon including, among others, inadequate preparation, high school GPA, SAT Math score, miscon ceptions about the engineering profession, and classroom dynamics. Students who exit higher education voluntarily before receiving their degree usually leave for one of three reasons: (1) a
17 lack of psychological and social support (Austin, 1999) ; (2) a lac k of institutional fit and campus integration (Swail, Redd, & Perna, 2003) ; and (3) financial hardships associated with increases in college tuition and fees (Orfield & Paul, 1988) In spite of current research on retaining engineering students and programmatic solutions, attrition remains stable. Evidence supports that pedagogies of engagement such as cooperative learning, problem based learning, student feedback, and supplemental instruction may enhance student persistence and learning (Seidman, 2005) Research on engineering undergraduate retention shows that a few programmatic solutions such as the summer bridge programs that focus primarily on underrepresented populations can i mprove college retention rates (Garcia, 1991) retention and diversity issues, there is little empirical evidence assessing their effectiveness (Ackerman, 1991, Gandara & Maxwel l Jolly, 1999, Garcia, 1991 Kluepfel, 1994; Pascarella & Terenzini, 2005, Rita & Bacote, 1997) Most studies offer details on the critical and non critical factors surrounding retention or models and indicators for retention and lack solutions that can be institutionalized (Heckel, 1996; Besterfield Sacre, Atman, Shuman 1997; French, Immekus, Oakes 2005; Felder, Forrest, Baker Ward, Dietz & Mohr 1993). If successful retention solut ions can be identified then these solution s could be institutionalized. T his study will seek to investigate the factors that contribute to the outcomes of a first year engineering summer bridge program offered to all students entering engineering, to determine if it improves their retention rate. Purpose of the Study The purpose o f this study was to explore the factors that contributed to the outcomes of a first year engineering summer bridge program designed for all incoming
18 engineering freshman at a southeastern large land grant research university over the 2007 2010 years. Addit ionally, charact eristics of participants of this summer bridge program were compared with non participants to investigate if any similarities existed. Moreover, predicto rs of retention between the participants and non pa rticipants were determined. This stu dy will demonstrate if there is relationship between this intervention and an increase on retention by utilizing predictive and descriptive statistics to explain the phenomena between the two groups. This study will focus on a summer bridge program that i s offered to all incoming engineering students, not only underrepresented groups. This model includes review style sessions similar to supplemental instruction in a collaborative learning environment of quantitative gateway courses coupled with a n engineer ing design course a nd a peer mentoring component. This summer bridge model incorporates processes to enhance student learning through the use of technology based delivery systems (e.g. multimedia, electronic delivery, electronic advising and mentoring). On e goal of the program is to help students become better acclimated to the rigor of the engineering curriculum before they begin their first semester. Findings from this study could be used to redesign or guide the development of a summer bridge program tha t benefits undergraduate engineering students. Research Questions By addressing issues that have been neglected in previous literature, this study aims to expand the knowledge base of information regarding summer bridge programs Further, this study also aims to evaluate several factors that may prove to assist the summer bridge programs become successful in retaining students. Accordingly, the following research questions guide the focus of this empirical study:
19 1. What is the relat ionship between the students who completed the summer bridge program performance and their engineering pee rs who do not enroll in the summer bridge program in Calculus 1 and Chemistry 1 when compared by final course grades and withdrawal rates? 2. How did en gineering summer bridge program participants and non participants compare on high school GPA, SAT Quantitative scores, first year college GPA, and gender? 3. What is the relationship be tween engineering summer bridge program participants and n on participants, SAT Quantitative scores, first semester grades in calculus and high school GPA on retention? These research questions will be studied using quantitative research methods. This study utilize d a quasi experimental retrospective research design using a seco ndary data source. The quasi experimental d esign utilizing a static group comparison will be employed becau se the two treatment groups were not r andomly assigned. Additionally, this study utilized predictive and descriptive statistics to explain the phenomena between the two groups Research Hypotheses The following research hypotheses were developed with the previous research questions in mind. These null hypotheses are all tested at the .05 level and correspond with each research question resp ectively. Each hypothesis will be tested, analyzed and disc ussed in subsequent chapters. 1. H 0 : No relationship exists between summer bridge participants and non participants in c alculus course grades and withdrawal rates at the .05 level. 2. H 0 : Summer brid ge program participants have equivalent high school GPA, SAT Quantitative scores, first year college GPA and gender ratios when compared to non participants at the .05 level. 3. H 0 : No rela tionship will exist between summer bridge participants and non part icipants on SAT Quantitative scores, first semester grades in calculus and high school GPA on retention at the .05 level.
20 Definition of Terms The following alphabetized list contains definitions pertinent to this study. ATTRITION Students who fail to reenroll at an institution in consecutive semesters including summer semesters DISMISSAL A student who is not permitted by the institution to continue enrollment DRO POUT A student whose initial educational goal was to complete as least a bachel gree but did not obtain that goal. ENGINEERING DESIGN The process of devising a system, component, or process to meet desired needs (Oakes, et al., 2000). ENGINEERING DESIGN COURSE Course content designed to teach fundamentals of engineering design PERS ISTANCE The desire an d action of a student to stay within the system of higher education from beginning year through degree completion RETENTION The ability of an institution to retain a student from admission to the university through graduation SUMME R BRIDGE PROGRAM Program designed to expose and help newly admitted college students to make the transition to college level coursework and campus resources in the summer before they start their college careers (Kezar, 2000). WITHDRAWAL The departure of a student from a college or university campus Limitations There were several noteworthy limitations to this study concerning the structure. Using data fr om multiple academic years (2007 2010 ) made available through the Engineering Data Warehouse (EDW) this study speci fically examined the retention and academic success of first year engineering students that completed an engineering summer bridge program. Data were used to me asure student performance for students who complete the summer bridge program re tention versus students who did not participate in an engineering summer bridge program
21 through first year college GPA, high school GPA, SAT Quantitative scores, retention, calculus and chemistry course grades and withdrawal rates. As with any study that c onducts a secondary data analysis this particular dat aset has its own limitations. T his study and its analyses relied exclusively on data collected by one institution and errors in data collection and extraction by EDW are very likely. T hese potential err ors may have re sulted in erroneous results and or findings. Additionally, the sample of this study was limited to a southeastern large land grant research university Data were analyzed for this designated institution over a four year period. This reduces the generalizability of the findings to similar type and size institutions that have a college of engineering. Further, the timing of data collected, with respect to retention could be premature. The retention points for this study consisted of the beginning matriculation date at the beginning of the second part of the summer session and ended at the end of their first spring semester, after spring grades were poste d. It is realistic to consider students who performed poorly may have waited until after summer to change their major or that a student who left after the first semester may work over summer to correct some poor grades and reenroll in engineering. Signifi cance of the Study Previous research has been conducted on summer bridge program models that focus on underrepresented populat ions including female students. One study found of color actually benefitted all students and aligned the engineering undergraduate (Davis & Finelli, 2007) Other studies have found educators should increase their
22 emph asis on early success to beginning engineering students (Gilmer, 2007) Scholars have called for research on summer bridge programs but researchers have not adequately investigated the programs and their effects (Walpole, Simmerman, Mack, Mills, Scales, & Albano, 2008) This study wi ll attempt to bridge this gap. This p resent study makes a contribution for further practice, research and policy in higher education by providing a resource for first year engineering student success The results will be useful for administrators and directors of engineering summer bridge programs, policymakers at the institutional, state and federal levels. Additionally, this study will have significance due to the broad nature of this summer bridge program model in that it is offered to all fi rst year engineering students. If it can be determined that the outcomes of the calculus and chemistr y primer courses within an engineering summer bridge program positively affects first attempt final grades versus non participants and tho se grades were predictors of retention in engineering, then this would provide evidence that an engineering summer bridge programs possesses the ability to prepar e first year engineering student s in calculus and chemistry and can be used to predict student retention. This model could then be replicated at similar type size and type engineering schools to see if the findings are comparable. This stud y will assist in guiding future research in examining summer bridge programs The current study examines the factors that contribute to success in a first year engineering summer bridge program designed for all incoming engineering students over four years, 200 7 2010. Other methods could be explored to provide a richer evidence based data on summer bridge progra ms to allow practitioners more studies to draw from to develop in improve their pa rticular summer bridge program.
23 Lastly, with these findings legislatures and administrators will become more informed on the outcomes of summer bridge programs and will be m ore likely to make data driven decisions regarding support ing colleges and universities. In particular, administrators such as deans who have financial resources and oversight responsibilities over student success, i.e. retention rates and graduation rates will be better equipped to make knowledgeable assessments of summer bridge programs in terms of funding support, personal support, and recruitment and retention strategies. At the state and federal level, governing bodie s could use the results of this study to help pre college students make informed decisions about entering and persisting in a large research engineering program. For example, the results could be disseminated to all public state high sc hools. Further, the governing bodies could use this study to assist in identifying the gaps in secondary education (K 12) chemistry and mathematics to improve curricul um development and instruction.
24 CHAPTER 2 REVIEW OF LITERATURE This chapter begins with a review of the relevant literatu re surrounding summer bridge program s. Beginning with an overview of the development of summer bridge programs discussed are the strategies that support successful summer bridge programs including peer mentoring, living learning communities, commitment t o career and educational goals, tutoring, faculty interaction and academic advising. Following these are components that challenge the success of summer bridge programs including gateway courses, engineering curriculum, financial needs and family issues ar e discussed. Lastly, the engineering studies and programs that have previously been designed and engineering summer bridge programs a re discussed to explore previous methods of educational initiatives to support engineering education. The review of the li terature provided in this chapter was organized with two primary purposes in mind. First, to discover th e issues surrounding summer bridge programs including when and why they were developed. Secondly, to explore the literature for significant factors that foster and challenge student success in summer bridge programs and how they are utilized in engineering education Summer Bridge Programs The increase in African American students attending institutions of higher education coincides with the Civil Rights Movement of the 1960s, a turbulent decade for U.S. public colleges and universities (Noel, Levitz, Saluri et al., 1985). This was a turning point decade for the entrance of African Americans in institutions of higher education, facilitated in part by the passage of important pieces of legislation. The Civil Rights Act of 1964 for example, was a landmark act that outlawed discrimination in
25 public and private facilities based on race, color, religion, sex, national origin. This act had huge implications for colleges and universities that received federal funds because it mandated equal admission practices. This act facilitated the acceptance, at least on paper, of more African Americans in higher education (Kaplin & Lee, 1997; Levin & Levin, 1991; Robert& Th omson, 1994; Swail, Redd, & Perna, 2003). For the first time, institutions were forced to openly address equal opportunity for underrepresented students or disadv antaged groups. During the 1970 s, the retention of African American students, generally less a cademically prepared than White students, became a major issue. Therefore, early retention programs were created to avoid dropout and increase persistence by meeting the changing demands and needs of students to provide a staying envir onment for all studen ts Early programs sought to provide review style classes in basic mathematics science and English courses along with peer mentoring from successful students. One of the main strategies of many engineering institutions was to implement summer bridge programs for underrepresented minorities and women in Science, Technology, Engineering and Ma thematics, (STEM) disciplines. These programs sought to provide a more inclusive workforce that draws on all available talent, and provide equal opportunity for mi norities and women who traditionally have been excluded from STEM areas. These programs sought to provide entering freshman, within these minorities, with peer study groups, strong study habits, time management skills, analytic problem solving capacity and the willingness to use available department and university resources (Maton, Harbowski III, & Schmitt, 2000) Simply, this strategy was aimed at combating the high attrition and dropout rates of the large quantitative gateway courses
26 that are part of a sy stem within the STEM disciplines that limits access to degrees by (Massey, 1992, Seymour & Hewitt, 1997). Through the development of these summer bridge programs, studen ts who have participated in summer bridge programs have fou nd numerous positive benefits. These students have higher academic achievement and higher self efficacy than similar students who were not involved in such programs (Strayho rn, 2011) S tudies also report students who participated in bridge pro grams also persist in school longer and have higher college completion rates than similarly prepared non partic i pants. Furthermore, participants in bridge programs have an increased sense of control, increased confidence, and increased self esteem, important factors related to meeting the social and academic challenges of the first year (Ackermann, 1991) Additionally, students who participate in bridge programs also have closer cont act with other students and faculty during their first year and complete more core courses than non program students (Ackermann, 1991) Moreover, students develop leadership ability, have more extensive involvement in the campus community, and are more lik ely to use tutoring and counseling during the academic year than their non bridge peers (Fitts, 1989) Some concern about the lack of empirical evidence to support the efficacy of summer bridge programs improving GPA and achievement test scores, however th ere is substantial research in support for the effect of summer bridge programs on retention. (Ackermann, 1991, Balz & Esten, 1998, Buck, 1985, Garcia, 1991, Logan & Spence, 2000, McElryo & Armesto, 1998, Suhr, 198 0, Valeri Gold & Stone, 1992).
27 Given the s uccess of the engineering summer bridge programs on retention, the bridge programs, this study seeks to expand the current summer bridge program to all entering freshman re gardless of gender, race, or socio economic status. This program will run simultaneously along side the underrepresented minority based summer bridge program and utilize a similar model of delivery. Components That Foster Student Success in Summer Bridge Programs The following section describes the strategies that support successful summer bridge programs including peer mentoring, living learning communities, commitment to career and educational goals, tutoring, faculty interaction and academic advising. These components are rooted in previous literature and provide support for inclusion in any summer bridge program. However, this list is not comprehensive as more research is has been called for on the components of summer bridge programs. Peer Mentoring which the wise and experienced mentor inducts the aspiring protg into a particular, s definition fits well with this summer bridge program where relationships were encouraged between freshmen and upper class students. Peer mentoring has gained popularity as an intervention over thirty years ago and continues to be a strategy to increase retention among women, minorities, first gen eration college students and at risk students (Terrion, J.L.; Leonard, D. 2007). Because it appears to be a vital approach to providing role models and leadership within higher education, it has been ad o pted in university settings as a means to assist e ntering freshman students as they transition in to the university
28 environment. For example, to improve retention the use of peer mentoring assists to develop social support networks among new students (Brawer, 1996). Additionally, the impact of mentoring on numerous outcome variables ranging from retention and graduation rates to comfort with the educational environment has been widely studied in recent years. Overall, findings have been positive and have indicated a positive relationship or an impact of men toring on student persistence and/or grade point average of undergraduate students (Campbell and Campbell 1997; Freeman 1999; Kahveci et al. 2006;Mangold et al. 2003; Pagan and Edwards Wilson 2003; Ross Thomas and Bryant 1994;Salinitri 2005; Sorrentino 200 7; Wallace et al. 2000). Due to the past success of peer mentoring and its commonly wide use to imp r ove retention it is important that it be included within this summer bridge program. This solution will provide academic and social support that has been t heorized to retain students. Additionally, peer mentoring will provide each student with a model that exemplifies a pathway to success. This is particu larly important to engineering students given the rigor and the rigidity of the curriculum. Living Learn ing Communities Living learning community programs have been introduced at postsecondary institutions across the United States to bridge the academic experiences with co curricular experiences, to integrate learning across the curriculum. Prior research on living learning community programs indicate many positive outcomes such as increased persistence, stronger academic achievement, more faculty interaction, gains in student autonomy and personal independence, intellectual dispositions and orientations (I nkelas, Vogt, Longerbeam, Owen, & Johnson, 2006) Other studies reported significant positive student outcomes that also support student persistence. Some of the
29 outcomes included students in living learning programs are significantly more likely than students in traditional housing to be more involved on campus activities, show greater gains in or higher levels of intellectual development, use campus resources and seek assistance from peers, faculty and staff (Inkelas, 1999; Pike, 1999); experience a s moother transition to college and report their residence hall communities academically and socially supportive (Inkelas, 1999; Scholnick,1996). Commitment to Career and Educational Goals Commitment to the engineering curriculum is one of several essentia l factors for success in persistence in engineering (Burtner, 2004). Students beginning their engineering education who already are committed to the field should possess a higher motivation for persisting through to graduation. For example, in one study re searchers found that students who had a high impression of engineering and liked engineering as a career path had higher freshman retention rates (Besterfield Sacre, 1997). In another study, it was proven that there is a higher probability of a student gra duating in engineering if their peers are also engineering majors (Astin & Astin, 1992). Through participating in an engineering summer bridge progra m students such as this one students are provided the opportunity to work through their level of commitment and discern whether engineering is appropriate for them or not. Tutoring Another method that is utilized to increase or support retention is tutoring, either by professional tutors or peers tutors. In nearly 94% of federal funded student support services grants awarded, tuto r ing was a strong part of the programs and close to 8 4% used peer tutoring (Cahalan, Muraskin & Goodwin 1994). Some studies indicate that tutoring and mentoring programs which encourage pos itive socially interactive
30 experie nces, help address the problem of retention by increasing intentions and commitments both to degree completion and to the institution (Tinto, 1987). Previous studies also indicate that some peer tutoring experiences, which improved attit udes about subject matter and about college education involve peer tutoring programs (Cohen, Kulik, & Kulik 1982).One such study, went on to state that tutoring programs were observed to drastically increase mathematics achievements scores among tutees (Robinson, Schofield, & Steers We ntzell, 2005). Many studies have been conducted to assess the success of tutoring at the K 12 level however not many studies exist for assessing tut oring at the pos t secondary level (Naidu, 2006 ). Some research indicates that tutoring helps students earn higher grades. One study indicated that if students were divided based on academic record into three groups and provided tutoring to half of each group, the students who received tutoring, regardless of academic record, earned significantly higher grades t han did their non tutored counterparts (Maxwell, 1990). The literature clearly show s that postsecondary retention is problematic. However, while only limited research is available to validate the effectiveness of tutoring, some evidence exists that a supp ort system of tutoring, especially peer tutoring may contribute to persistence and graduation rates of college students. Faculty Interaction Engineering faculty members may or may not realize the critical role they play in in engineering studies (Vogt, 2008). In one study, high attrition rates of engineering students were attributed to the intimidating nature of the classroom or chilly environment, the dullness of the lecture model, and inadequate faculty guidance (Seymour & Hewitt, 1997) However, when faculty understand the
31 significance of their student relationships and seriously undertake measures to be come personally available to students, increases in student self efficacy, effort and critical thinking have been measu red (Vogt, 2008) Further, It has been proven in several higher education studies that one of the key college experie nces associated with student development is interacting with faculty (Kim & Sax, 2009). This is further supported by two higher education s tudies that review student faculty interaction and its relationship with college outcomes. With in this review on effects of informal student faculty interactions on various college student outcomes five categories were developed (Pascarella, 1980). The ca tegories include: career plans and educational aspirations, satisfaction with college, intellectual and personal development, academic achievement and college persistence (Pascarella, 1980). In a ed by adding a number of studies from the 1980 through 2000s by also finding including formal and informal student faculty interaction to demonstrate that the amount and quality of student faculty interaction pos i tively affect various student outcomes, inc luding subject competence, cognitive skills, and intellectual growth, attitudes and values, educational attainment, and career choice (Kim & Sax, 2009). As previously stated, participating i n a summer bridge program offer s the opportuntiy for students to have closer contact with faculty during their first year and complete more core courses that non program studen t s thus providing fu r ther evidence that faculty interactions are key compone n t to student retention. Academic Advising Academic advising suppo rts retention of college students in a variet y of ways. Most notably, through academic advising students learn to become members of their
32 higher education community, to think critically about their roles and responsibilities as students, and to prepare to be educated citizens of a democratic society and a global community (NACADA, 2003 ). Additionally, through advising students develop an educational plan, use information to set goals, assume responsibility for meeting their academic program requirements, cu ltivate intellec tu al habits that lead to a lifetime of learning, and act as citizens who engage in the wider world around them (NACADA, 2003 ). Empirical studies on advising supporting or effecting retention are limited to only a handful of studies. Noel, Levitz, Saluri and Associates identified themes of attrition and the role of academic advisi ng in retention efforts The themes included: acad e mic boredom, uncertainty about major and career goals, transition and adjustment difficulties, limited or unreali stic expectations of college, academic under preparedness, institution incompatibility, and course relevance. In a study of minority students on probation, identified eight factors that had implications for advising and retention. The factors included, ina ppropriate course selection, poor course scheduling, low use of support services, faculty member s with limited familiarity with resources available on campus, external factors, inability to anticipate and adjust to the impact of personal life changes, lack a mandatory, comprehensive advising process (Ramirez & Evans 1988). In another study, on a national survey conducted by the American College Testing Program and the National Center for Higher Education Management Systems, noted 45).
33 Further, developing a friendsh ip with at least one faculty membe r along with effective academic and career advising were identified as campus factors associated with persistence (Webb, 1987). Additionally, many other researchers have described improved student retention as a result of better quality advising. Academic advising has been de s cribed as a corner stone of student retention, when provided effectively, it helps students develop better education and career goals, reinforces the relationship between academic preparation and indu stry, and cont ributes to the development of a increased pos i tive attitude and academic performance has shown that a well developed advising program is an important re tention strategy. Advisor s who are knowledgeable, enthusiastic, and like working with students can often C omponents That Challenge Stu d ent Success in Summer Bridge P rogr ams The following section describes the strategies that challenge successful summer bridge programs including gateway courses, engineering curriculum, financial needs, and family issues. These components are rooted in previou s literature and provide an out line of challenges that need attention in summer bridge programs. However, this list is not comprehensive as more research is has been called for on the components of summer bridge programs. Gateway Courses Students aspiring to become scientists begin thei r post secondary education in challenging, sequentially organized, quantitative courses including calculus, chemis t ry, physics and the like. Students usually take these courses in the first two years of study and then proceed through their chosen curriculu m. However, these courses can open
34 doors or gates to the major specific courses or block admission to them if not completed successfully. What has become evident over many years is students switch out of majors that require these types of courses in dispr oportionately high numbers. For example, stude nts who entered into a humanities degree pro grams remain at a total of 74% within that major, compared with just 56% in science, mathematics and engineering majors (Seymour & Hewitt, 1994). Si milar patterns can be found amo ngst men and women. Approximately 60% of men remain in their selected science, mathematics and engineering majors where only 48% of women remain in science, mathematics and engineering majors (Gainen, J, & Willemsen E. 1995). It has been repor ted that the cru c ial experiences mathematics or engineering majors often occurs during the first year of study at the college level, when they must complete required gateway courses (Gain en, J, & Willemsen E. 1995). The issue that arises out of this problem is students who have chosen science, math or engineering degr ee program are generally well p r e pared in math e matics and science (Rawls, 1991). Although success in these type course doe s not always come easy, evidence exist on factors that influences success in calculus and chemistry which are vital classes in the engineering curriculum. For these courses, previous research suggests that influentia l factors fall within three cate gories: demographic background, general education background and p revious science learning experie nces (Tai, R., Sadler, P, & Loehr, J. 2005). Researchers found that repeating chemistry labs for understanding, high school mathematics grades, course taking patterns high
35 standardized test scores, enrollment in AP chemistry or calculus were associated with higher introductory chemistry grades in college (Tai, R., Sadler, P, & Loehr, J. 2005). n mathematics, self motivation was reported by stude nts and lecturers as being most likely to influence success. Other success factors included quality and availability of support and use of help services (Anthony, G. 2000). Curriculum Design Engineering colleges across the U.S. have been criti ci zed for not including enough hands on, practical design courses esp ecially at the freshman level. Thus, students were not sufficiently prepared in teamwork and team approached to problem solving. However, as early as the 1960, engineering educators began to notice the lack of understanding of design by their students. This spaw ned several studies which r ecommended the inclusion of an enginee r i ng design course back into the curriculum. Since the late 1980 s, there was a push to increase the amount of exposure undergraduate engineer ing students receive in design. This was particularly true of the freshman year as this y ear is critical for engineering students. In 1990 the National Science Foundation (NSF) establishe d a small number of major coalitions of U.S. institutions in a multi year effort to incre a se the quality of engineering education, develop new appr o ached to deliver engineering education, create significant intellectual exchange and resources linkages amon g major U. S. engineering institutions ( Engineering Education Coalitions 1990). Other factors that contributed to enhancing the curriculum with a freshman design course included, the engineering i ndustry and the Accrediting Board of Engineering and Technol ogy (ABET). Industries that employed engineers were taking aggressive stands on the needs of industry, the state of current
36 education and what cooperative roles industry and academia play in making change (McMasters, J.H., Ford, S.D. 1990). ABET developed new guidelines in which the ins titutions were responsible for articulating the goals of the program, the logic used in the selection of engineering topics to meet goals and identify the major, meaningful design experie nces and how they are integrated throu ghout the curriculum (ABET, 1995). These new pressures to infuse engineering design developed a myriad of enginee ring freshman design programs. Today it is widely accepted that engineering design is expected in the freshman year not only to begin the unde rstanding of the engineering design process but to a lso reduce freshman attrition. For example, at Purdue University Fort Wayne, Introduction to Engineering, Technology and Computer Science, a freshman success course, the main reported objective of the cou rse was to increase retention (Pomalaza Rez, C., Groff, B. 2003). Further, at Purdue University in Indianapolis a design oriented introduction to engineering course was developed to assist in major choice but also to increase retention (Yokomoto, C.F., Ri zkalla, M.E., The success that freshman design programs have experi e nced over the past 15 years have shown that students have a better understand ing for the engineering design process and that these courses contributed to increased retention (Pomalaza Rez, C., Groff, B. 2003). This provides support in providing a design project for our freshman stu dents who partic i pate in the summer bridge program. Additionally, this course will offer engineering faculty student contact t hat has also been shown to improve retention in undergr aduate engineering students.
37 Financial Needs Today, fewer than four % of Americans can afford to pay the sticker price for four years of college (Levine, 2008). This is especially true for lower and lower middle income students and their families (Perna & Li, 2006). The issues surrounding the ability to afford college are many and range from a growing income inequality, decline employer provided benefits, increased health care costs, increasing debt, declining personal savings to increasing college tuition, declining value of Pell grants, unmet financial need, increased borrowing among middle income students etc. (Perna & Li, 2006). What we know about the general effects of financial aid on college st udent achievement is that without need based aid and merit based aid student experience negative effects on college GPA in the first year through the fourth year (Stater, 2009) Additionally, higher GPA s are correlated with higher retention, graduation and performance on standardized test (Stater, 2009) Further, it has been found that potential students from low socioeconomic status (SES) families are significantly more responsive to the net price of co llege that those form more affluent families, holding all else equal. Given this, one can conclude that a bigger return per public dollar can be expected for support targeted toward student from low SES backgrounds. Family Issues Students leave college f or a mix of individual and institutional reasons and included among the many reasons is family demands (Astin, Korn, & Green, 1987; Bean, 1990; Braxton, Hirschy, & McClendon, 2004; Cabrera, Nora, & Casteneda, 1992; Kuh, Kinzie, Buckley, Bridges, & Hayek, 2 007; Pascarella, 1980; Peltier, Laden, & Matranga, 1999; Tinto, 1993). One study concluded that one of primary reasons
38 students leave college is lack of financial aid and holding a part time jo b (Koirala, Davis & Cid, 2010). and are viewed as the family member with the most flexible schedule; they are family problem solvers and resources for the family. Because of this situation, they often are drawn off into family needs which in turn bec ome a priority over academics. They a lso have problems creating boundaries and telling their families they ar These students deal first (Koirala, Davis & Cid, 2010). Further, evidence suggest that families fr om lower socio economic status tend to plan for one semester of finances and then leave it up to the student to figure out finances past the first semester (Koirala, Davis & Cid, 2010). To increase the retention and graduation rate of students that have t hese issues many initiatives have been de veloped to combat this problem. These practices and policies include designated high level of student engagement in campus activities and programs; well de veloped first year programs; efforts to improve particularly in mathematics; early warning and advising systems; and ample academic and social support services such as advisement and special programs for a t risk populations (Astin & Oseguera, 2005; Carey, 2004; Gansemer Topf,& Schuh, 2004; The Pell Institute for the Study of Opportunity in Higher Education, n.d., 2004). Still, more work is needed to ensure the success of students with these family issues. College Preparation Success in college is related to the degree to which pervious educational and personal experie nces have equipped students for the expectation and demands college will place upon them (Conley, 2008). Data has shown that first year stude nt study less,
39 write less and read less than they thought they would. However, when institutions emphasize certain activities students are more likely to engage in them (Kuh 2007). An emphasis of this summer bridge program is the development of student ma stery of study skills because college courses require significant amounts of time out of class to study. Study skills must encompass active learning strategies that go far beyon d reading and homework. Some of the most widely reported study skills include, time management, stress management, task prior i tizing, using information resources, taking class notes, and communicating with faculty and advisors (Robbins et.al, 2004). Additional l y, some have reported understanding the value of participating successfull y in a study group and understanding its value (Conley, 2008). Another issue regarding student success involves the growth of the Advanced Placement (AP) program o ver the past 50 years. The AP program has been accused of hindering student success in that public schools have adopted these courses as a barometer to enhance admission to colleges and universities but the courses have been reported to offer superficial cove n i or year in preparing them for colleg e (Hammond, 2008; AACU, 2002). Many colleges and universities are now encouraging more in depth, investigative, or research based learning even in the first year of college (AACU, 2002). Prior research on the predictive power of AP course experience on college success is not comp elling. Studies from the College Board, owner of the AP trademark, and the Educational Testing Service (ETS), administrator of the yearly AP examinations, are frequently cited by AP Program proponents (Morgan and Maneckshana 2000; Willingham and Morris 198 6). The descriptive nature of these studies, however, is insufficient for isolating the independent impact of the AP Program
40 given that the typical AP student is bright, motivated, and likely to experience positive college outcomes regardless of AP experie nce. Given these circumstance in which incoming students are less prepared with study and academic skills the college must address these needs to imp r ove student success. In this summer bridge program students are exposed to time management, study skills, note taking, stress management, task prior i tizing, using information resources and communicating with faculty, administrators, and advisors. This exposure attempts to provide a solution for th e deficiencies and supply participants with the needed skills to be successf ul students in engineering. Engineering Education Retention Studies and Summer Bridge Programs Engineering educators are interested in retention r esearch because predictors for the engineering profession were beginning to report negative results for college freshman planning to major in a technical field. (Frotenberry, Sullivan, Jordan, & Knight, 2007) National engineering enrollment saw its peak in the early 1980 s and has remained relatively flat for the past twenty five years (Moller Wong & Eide, 1997) and 1988 the proportion of college freshmen planning to major in a technical field fell by clo (Green, 1989) Additionally, the trend of attrition was continuing to increase. In 1975, attrition among engineering freshman was about 12 % ; by 1990, freshman attrition had doubled and was over 24 % (Beaufait, 1991) With this knowled ge the engineering education community began to look at retention of engineering students from various angles. Early studies sought to learn and understand more about the predictors of success or failur e in during the freshman year. Richard Fedler et al. s tudied 124 freshmen in an
41 introductory chemical engineering course. They correlated the assessment data (family and educational background, profile on the Myers Briggs Type Indicator, the Learning and Study Strategies Inventory along with responses to a qu estionnaire regarding attitudes and expectations) to student performance in the course (Felder, Forrest, Baker Ward, Dietz, & Mohr, 1993) Additionally, Cheryl Moller Wong and Arvid Eide sought to establish descriptive data base that would allow continuous longitudinal tracking of students through their college careers, develop a diagnostic tool that could identify students who are at risk of leaving the institution and the introduction of retention in terventions to those students. They found that estimati ng the prediction of a student either staying in engineering or leaving is not completely accurate and should be designed by individual institutions based on their student characteristics. Other studies indicate the freshman year is critical. Lebold and Wa rd indicated the best predictors of engineering persistence were the first and second semester of college grades and cumulative GPA (Lebold & Ward, 1988) self perceptions of math, science and problem solving abilities wer e strong predictors of engineering persistence. Further, Brian French et al. also studied the non cogitative and cognitive variables that effected student persistence and concluded that SAT math and h igh school rank were significant predictors of college G PA. Most recently, a statistical retention model focused on the pre college characteristics of freshman engineering students was proposed based on the prevalent engineering education empirical studies to better predict first year GPA and thus predict rete ntion in engineering from the first year to the second year (Veenstra, Dey, & Herrin, 2009) In this study, the strongest predictors of success included high school
42 achievement, quantitative skills, commitment to career and educational goals and confidence in quantitative skills and they predicted student success (First Year college GPA). These results differed from general college education empirical studies in significant predictors of the GPA and retention. In the engineering education empirical studies, it was common, that the SAT Quantitative or ACT Quantitative scores were significant for predicting the GPA for the engineering students however, in the general college education empirical studies there were no cases where the SAT Quantitative score or th e ACT Quantitative score significantly predicted the first year GPA. This suggests that the engineering curriculum is different from the general college education and should be studied with this difference in mind. With this consideration one must remember that the general engineering curriculum was originally developed from the practical base until the 1955 Grinter Report (Grinter, 1955). This report developed a focus to the scientific base with more emphasis on theoretical approaches an engineering (Sheppard & Jennison, 1997) In the mid 1960 s the curriculum shifted back to more design classes throughout the curriculum due to the lack of des ign fundamentals from students. As attrition gained more att e ntion in the 1970 s and early 1980s in the engineering educational community, a national movement began to increase the exposure of freshman to engineering design through various means (Sheppard & Jennison, 1997) Many engineering colleges and universities that were accredited by ABET formally the Accreditation Board for Engineering and Technology were introducing freshman design courses as a way to incorporate design throug hout
43 the four year curriculum. Simultaneously, other strategies including engineering summer bridge programs were developing to combat the attrition issue. Summary of Literature Review This chapter began with a discussion on the development of summer bridge programs and the issues surrounding their initial goals and aims. Next, a discussi on on the components that foster success in summer b ridge programs was undertaken. These components included peer mentoring, living learning communities, facilitating commitment to career and educational goals, tutoring, faculty interacti on, quality academ ic advising. Also discussed were some of the mos t common challenges for summer bridge programs to overcome These challenges included gateway courses in mathematics, and science, the design of the engineering curriculum, financial needs of student and fami ly issue that inhibit student success. Additionally, college preparation of high school students in the U.S. was discussed and continues to also inhib it college success and is also a challenge for summer bridge programs In sum, the literature suggests t hat a multi faceted approach to increase engineering student success will provide an environment for possible increases to engineering degree completion It also suggests engineering summer bridge program s should possess this multi faceted approach and sh ould be utilized on a broad scale. This approach guides this study in the proper placement of compo nents to ensure best results.
44 Figure 2 1 L iterature review diagram Summer Bridge Program Postive Components Challenges Engineering Summer Bridge Program
45 CHAPTER 3 METHODOLOGY C hapter 3 describes the population and sample subjectivity statement, dependent and independent variables design, data analysis and ethical considerations. Possible threats to validity are al so discussed. Population Sample The setting for this study was a four year public research and doctoral degree granting institution in the southeast between the 2007 2010 academic school years. It serves approximately 50,000 graduate and undergraduate stud ents with approximately 6,500 incoming freshman each year. The university has a comprehensive curriculum that includes the liberal arts curriculum as well as science and engineering. The engineering college enrolls approximately 5 0 00 undergraduate student s and 2 710 graduate students with approximately 1, 1 00 incoming freshman each year. The data were catalogued in the arehouse. Acce ss to the data warehouse was granted to the researcher from the Associate Dean of Student Affairs whose responsibilities includes oversight of the engineering freshman programs facilitated by the Engineer ing Student Affairs office. Subjectivity Statement The researcher has conducted quantitative analyses utilizing a secondary data source in several studies investigating retention of undergraduate engineering students at a large four year public institution. In prior research, the researcher has used t test and chi square analyses how ever has not run logistic regression analysis. The researcher ha s presented quantitative findings at relevant professional conferences. The researcher currently administ ers and has administered this summer bridge program
46 over the past eight ye ars. Given th e nature of this role, the findings of this study may not be as objective and forthcoming than an outside researcher studying this program. Dependent Variables The continuous dependent variables for this study included: high school GPA, SAT quantitative s cor es, SAT verbal scores and age. The dichotomous dependent variables included: 1 st semester grades in calculus, 1 st semester grade in chemistry, withdrawals from calculus, and withdrawals from chemistry, retention in engineering after the spring semester in the first year, gender, and race. Both numerical and categorical dependent data warehouse. Students who began enrollment in either summer b or fall semesters and had a final major listed with the university registrar as engineering after the spring semester of the first year of enrollment were considered retained in the engineering program. Independent Variables The independent vari able for this study was the engineering summer bridge program. This program was developed as a spin off of the Successful Transition through Enhanced Preparation for Undergraduates Program (STEPUP) which was created as part of the Southeastern University and College Coalition for Eng ineering Education (SUCCEED) initiative from Coalitions. This program was introduced in 2003 as a one week program offered to all incoming engineering freshmen designed to offer support to incoming students in ca lculus, chemistry, design, student success, and career decisions. The program was offered again in this model the following year. The next two years the program was changed to a six week program with components in calculus, chemistry, and design that
47 offered three academic credits. In 2007 the program added three more components to encompass a full six academic credit program th at has continued through 2010. The data analyzed in this study considers data from the 2007 2010 summer bridge programs. E ngineering Summer Bridge Program : O ver the past four years the summer bridge program has been a six week summer bridge program that introduces freshman to the college of engineering through six principal courses which include various levels of both c alcul us and c hemistry, AutoCAD, computer programming, engineering design, student success seminar an d introduction to engineering. E ach student is assigned an upper division engineering student peer mentor who meets with them during the design class during the summer and on a weekly basis during the fall a nd spring semesters to provide tailored academic and professional support Peer mentors are selected during the spring semester prior to the summer program beginning. Peer mentor applicants apply through a web application that includes short essay on skill, attributes, and d esire to work as a peer mentor. The current year peer mentors select approximately 20 25 applicants to interview. Applicants attend a panel interview of the director and the current year ment ors that ask questions rel evant to the position. After all applicants are interviewed the director and the current peer mentors select the top applicants and offer them a position in the program. After selection and position acceptance all peer mentors are required to attend five training sessions were they are trained on all aspects of the program including: history, philosophy, definition of a mentor, parts of the program, design project, computer programming, team building, university academic policies, student success,
48 and goal setting. After the trainings, mentors are expected to participate in the orientation and throughout the summer. Peer mentors are required to meet with their ject and build a relationship with the students assigned to them. All incoming freshmen who have indicated engineering as their major on their application for admission are invited via letter from the Associate Dean of Student Affairs to participate in the program (Appendix B) Interested students complete an online web application (Appendix C ) and are sent contract and release forms to compete with their parents. The contract (Appendix D ) asks for them to participate in all of the summer bridge program activities during the summer, fall and spring semesters. The release form absolves the university of any law suit to sue the university arising out of any loss, damage or injury while the student is a part of the program (App endix E ) Once the contract and release forms are c omplete and returned to the program director the student is accepted into the program. The open enrollment nature of the program is currently i n place because demand has yet to exceed capacity limits. The program is currently designed to enroll up to approximately 120 130 students a year ; however it has only exceeded 120 students only once in the 2007 2010 study time period. Once accepted, the student is sent a letter of acceptance and detai ls on how to sign up for the summer bridge pr ogram courses during orientation (Appendix F) Once th e student registers for the courses they are sent a final letter with instructions to attend the summer bridge program orientation held the weekend befo re the summer B session begins (Appendix G) At th e orientation, students are introduced to the staff, administration, instructors, their peer mentor, and are giv en an overview of the program.
49 Once the program begins the students attend courses throughout the summer b semester see Table 3 1 for the course sc hedule. Once the summer se mester is completed the participants are required to meet with their peer mentors on a weekly basis throughout the fall and spring semesters. Mentors submit a weekly report on all assigned st udents to the program director. The we integration. If any problems are reported the student receives additional attention from mentor and program director as needed. Additionally, fall and spring social events are held to reunite all students in the program and discuss registration for the following semester. The program concludes at the end of the spring semester after exams with the mentors working with their students to assist in the development of goal for the sophomore year. Use of Secondary Data Source Secondary data for this study was provided by the Data Warehouse. office which houses the official records for un dergraduate educat ion in a hierarchical database. This database allows interaction through the C onversational I nteractive C ontent S ystems known as CICS. Participants were id entified through the completion of an online application. The data were also stored in the Engineering Data Warehouse. used in aggregate form. Data Analysis Data analysis for this study was conducted in a preliminary analysis and advanced analysis. The preliminary analysi s included descriptive analysis, t test and chi square test for student data. The data was analyzed using IBM SPSS 19 statistical software
50 package For H ypothesis 1, mean calculus grades and withdrawal rates for the experimental and control groups were an alyzed using t tests and chi square test to assess for statistical differences between the means for the different groups. For H ypothesis 2, the mean high school GPA s SAT quantitative scores first semester grades in calculus and gender were analyzed usin g t test and chi square test to assess for statistical differences between the means For H ypothesis 3, the high school GPA s SAT quantitative scores first sem ester grades in calculus were analyzed using logistic regression analysis. Demographic and frequ ency data will also be analyzed for age, gender and ethnicity. The data analyses conducted in the advanced stage of this study included the use of logistic regression. Logistic regression, (y) = a + bx, was used to explore the multiple pre college and coll ege student factors in predicting student retention. Early in retention research logistic regression was called for to study college student retention because of (Tinto, 1975) Demographic and fre quency data for age, gender, ethnicity, also analyzed between groups using tests for 2 independent samples (Mann Whitney U test). Ethical Considerations committee, the College of Engineering and the Internal Review Board from the University The benefit risk ratio was assessed for this study, indicating minimal risk to participants. The benefits to the participants potentially include enhanced understanding of calculus and chemistr y concepts, immersion in engineering topics, and mentoring by engineering professionals and peers There are no other direct benefits fo r participation in this study.
51 Threats to Internal Validity The main threat to internal validity in this design is whether differences between groups can be attributed to characteristics of the groups other than the experimental conditions to which they were assigned (Gall, Gall, & Borg, 2007) Additionally, uncontrolled threats to validity include mortality of experi mental participants, instrumentation with respect to the intervention, and restricted generalizability outside of sample characteristics.
52 Table 3 1 Bridge program summer daily schedule Period Monday Tuesday Wednesday Thursday Friday 1(8:00 9:15) Chemistry Chemistry Chemistry Chemistry Chemistry 2(9:30 10:45) Calculus Calculus Calculus Calculus Calculus 3(11:00 12:15) Succ. Skills Comp.Prog. AutoCAD Comp. Prog. AutoCAD 4(12:30 1:45) Lunch Lunch Lunch Lunch Lunch 5(2:00 3:15) Design Design Design Design Design 6(3:30 4:45) Intro to EG Intro to EG 7(5:00 6:15) Intro to EG Intro to EG
53 CHAPTER 4 DATA ANALYSIS AND RE SULTS This chapter begins with a presentation of results for the descriptive statistics (univariate and bivariate), t t ests, and chi square analysis. The binary logistic regression models were constructed by using dependent variables which included pre college and college characteristics that will be further described and discussed later in the chapter. The selected indepe ndent variables for the regression models were regressed against the d ependent variables representing first year retention in engineering and pre college and college characteristics. Chapter 4 concludes with a brief summary of significant results from the statistical analyses that were performed. Preliminary Analysis The preliminary analysis section of this study provides a foundation for the advanced statistical methods (binary logistic regre ssions) that were employed. The preliminary analysis section begins with a presentation of results from the descriptive analyses of the institutional sample, including the distribution of the students by cohort year. Additionally, this section describes a discussion of the average age, high school grade point average (HS GPA), SAT Quantitative scores, and first year in college GPA. The preliminary analysis is followed by a presentation of comparison resu lts from the summer bridge program participants and no n participant groups. Results from the conducted t tests and chi square test s are presented and discussed. The final portion of the preliminary analysis includes an examination of predictor factors and retention (i.e., gender, summer bridge program pa rtici pation, HS GPA).
54 Profile and Descriptive Statistics for the Institutional Sample This study was performed at an Association of American Universities ( AAU ) designated university in the southeast at large public, land grant institution. The engineering sch ool serves approximately 50 00 undergraduates and 2 710 graduate students. Research funding for this engineering college reaches over 100 million dollars a year. In comparison to a similar AAU type and size institution, this university encompasses similar st udent body make up and size and procures similar research funding. A total of 4,166 student s were included in this study. Of the students included in the sample, the summer bridge program participants represented 9.9% (n=415) while the non participating students included 90.1% (n=3751) (Table 4 1). Male students consisted of 74.5% (n=3104) while female students r epresented 25.5% of the sample. White students made up the largest percentage of the race/ethnicity with 65.8% (n=2728) while American Indian stu dents represented the smallest percentage of race/ethnicity with .4% (n=15). The freshmen cohort years 2007 2010 range in size from 892 in 2007 to 1163 in 2010 with each year containing 21.4 % to 27.9% of the sample size. Using the data provided by the Eng ineering Data Warehouse (EDW) the, frequency of the mathematics and chemistry courses taken by beginning engineering students was explored (Table 4 2). Additionally, the frequency of r etaining students that were summer bridge program completer s and student s who were non participating students is explored. In calculus, 43 % (n=1808) tak e cal culus 1 and 15% (n=625 ) take calculus 2 Additionally, 13% (n=524) undertake pre calculus and 12% (n=488) begin their
55 mathematics curriculum by taking calculus 2 for adva nce placements students. The combination of these four courses outlines 83% of the mathematics courses taken in the first full semester by incoming engineering students. The remaining 17% of students take the honors version of either calcul us 1, 2 or 3. In chemistry courses, 51% take chemistry 1 (n=2130). Further, 17% (n=696) undertake pre chemistry and 15% (n=614) begin their chemistry curriculum by t aking chemistry for engineers. Only 4% (n=153) undertake chemistry 2 during their fir st full semester. Th e combination of these four courses outlines 86% of the chemistry courses tak en in the first full semester. An additional 12% (n=482) did not take a chemistry course in the first year either because the y obtained advance placement (AP), dual enrollment (DE ) or International Baccalaureate (IB) credit in high school for chemistry or have chosen an engineering major that does not require chemistry 2. Examining frequency data on students that were retained as engineering majors after completing their first ye ar in engineering show ed a difference between the program participants and non participants groups. Students who completed the summer bridge prog ram retained at 88% (n = 365) while non participants retained at 84.1% (n = 3154). A chi square test of indepen dence was performed to examine the relationship betw een the two student groups (participants and non participants ) and retention. The relationship between these variables was significant, (1, N= 4164) = 4.17, p =.04. Participants were more likely to be ret ained than non participants students (Table 4 3). Descriptive Statistics fo r Institutional Sample Continuous Dependent Variables The following section examines the sample characteristics of mean age, high school grade point average, SAT quantitative scores, and the college grade point average at the end of the first year in the in cluded samples (see Table 4 4). The results
56 from this descriptive analysis for the continuous variable revealed the mean ag e of incoming engineering students is 18.34 (SD = 0.51), the mean incoming HS GPA was a 4.13 (SD = .50) out of a 5.0 scale, the mean SAT Quantitative scores was a 679.5 (SD = 64.07) out of a possible 800 and the mean of the first year college GPA was 3.33 (SD = 0.57) out of a 4.0 scale. An additional investigation into high school grade point average and its relationship to retention was undertaken. A t test failed to reveal a statistically significant difference between the mean of the High School GPA of the entering engineering students and retention after the first year. Students not retained had the following results: (M = 4 1, SD = 4677) (see Table 4 5). There was not a significant effect for HSGPA, t (4 135) = 1.76, p > .05 (Table 4 6) for those students who were retained versus those who were not retained. Descriptive Statistics for Student Classification and Categorical Variables Using the data provided by the Engineering Data warehouse, the sample was divided in to tw o classifications, the participant completers and the students who did not participate in the summer bridge program (non participants ). Chi Square analyses were performed to understand if any differences existed between the groups on gender and race. Descr iptive statistics for categorical variables were explored on both groups (see Table 4 7). Both groups had similar gender representation with 77.1% (n=320) male and 22.9% (n=95) female in the participant group compared to the 74.2% (n=2784) male and 25.8 % ( n=967) female in the non participant group. Some notable differences existed between the two group s in the Race category. The participant group had 6.1% 5.1% Asian studen ts (n=21) compared with the non participant group which had 11.2% Asian students (n=42 1). Additionally, the participant group had 9.0% more white
57 students 71.8% (n=298) than the non participant group 64.8% (n=2430). Most notable was a significant difference between the two group frequencies existed on race (6, N = 4166) = 26.75, p = .000. Regarding gender, no significant difference was found between frequencies the groups with (1, N = 4166) = 1.64, p = .200. Invest igation Differences between Participants and Non Participants Independent sample t test were conducted to investigate group statistics and if d ifferences between participants and non participants Quantitative scores and first year in college GPA were significantly different. Additionally, academic performance and course withdrawal rates are investigat ed to determine if any significant differences existed between participants and non participants The group statistics of the average age, high school grade point average (HS GPA), SAT Quantitative scores and first year in college grade point average also conducted using the Engineering Data warehouse dat a comparing the participants and non participant groups (Table 4 8). The results from the descriptive analysis for the continuous variables indicated that the mean age in year s for students who completed t he summer bridge program had a mean age of 18.34 (SD = 0.52) compared to students in t he non participant group who had a mean age of 18.34 (SD = 0.51). Further, the m ean high school GPA for the participant students was 4.07 (SD = 0.35) and the mean high sc hool GPA for the non participant students was 4.13 (SD = 0.52). The mean SA T Quantitative score for the participant students was 656.46 (SD = 62.95) and the mean SAT Qua ntitative score for the non participant students was 682.0. (SD = 63.69) The mean first year in college GPA for the participating students was 3.28 (SD =
58 0.55) and the mean first year in college GPA for the non participating students was 3.33 (SD = 0.58). Independent t test were conducted on the principle that the assumptions of independen t t test (e.g., normal distribution of data, homogeneity of variance, data are i ndependent) were not violated. The results supported that the equal assumptions had not be violated by returning p not significant (Table 4 9). From the conducted t tests, significant differences were found for mean HSGPA between n on participant ing students and participating students (Table 4 8). A difference in HSGPA .066 5 was found between the n on participants and parti cipating students. The differences between these two groups HSGPA were significant at the .05 alpha level t(4137) = 2.51, p< .05. Additionally, a difference in the SAT Q was found between n o n participants and participating students. The differences between these two groups were significant at the .05 alpha leve l t(4009) = 7.64, p<.001. From the results for the t test, conclusions can be drawn based on the samp le for this study, that n on participants students have a significantly higher HSGPA and SAT Quantit ative scores but not a significantly higher 1 st year college GPA or age. Using the data provided by the EDW the, frequency of the mathematics and chemistry courses taken by participants and non participating students were explored (Table 4 10). In calcul us, the majority of the participating students and in the non participating students began taking calculus 1, 53.7% (n=223) and 42.3% (n=1585) respectively. The second largest class percentage of students diffe red between participants and non
59 participants Participating students took pre calculus as their second largest class at 1 6.4% (n=68) whereas the non participating student s took calculus 2 at their second largest mathematic course at 15.4% (n=578). However, if the calculus 2 class is combined with the calculus 2 for AP students for both groups calculus 2 becomes the second largest math ematics course at 17.8% for participating students and 27.7% for non participating students. Additionally, pre calculus becomes the third most frequently taken class by b oth groups at 16.4% (n=68) for participants and 12.2% (n=456) for non participating students. The combination of these four courses outlines 87.9% for participating stu dents and 82.2% for the non participating students of the mathematics courses taken in t he first full semester by incoming engineering studen ts. The remaining 12.1% in participating and 17.8% in non participating students take the honors version of either calculus 1, 2 or 3. In chemistry courses, the majority of students in the participating and non participating groups take chemistry 1, 62.2% (n=258) a nd 49.9% (n=1872) respectively. Further, the se cond largest course for the participants and non participants was pre chemistry at 17.1% (n=71) and 16.7% (n=625) respectively. Addit ionally, 12.5% (n=52) of the participating students undertook c hemistry 1 for engineers course whereas 15.0% (n=562) of the non participating students took the c hemistry 1 for engineers cours e. Only 2.7% (n=11) of the participating students undertook chemi stry 2 during their first full semester and only 3.8 (n=142) of the non participating students also took chemistry 2. The combination of these five courses outlines 94.5% of the chemistry courses taken in the first full semester for the participating stu de nts and 85.4% for the non participating students. An ad ditional 5.1% (n=21) of the participating group a nd 12.3% (n=461) of the
60 non participating group did not take a chemistry course in the first year either because they obtained advance placement (AP), d ual enrollment (DE) or International Baccalaureate (IB) credit in high school for chemistry and have chosen an engineering major that does not require chemistry 2. From the conducted t tests, significant differences were not found for any of the compariso ns between the participants and non participants groups on course grade values (Table 4 11). For mathematics, the means from the calculus 1 course were closest to being significantly different at the .05 alpha leve l t(1806) = 1 63 p>.05. For chemistry, the means from the pre chemistry course were closest to being significantly different at the .05 alp ha level t(694) = 1.70, p>.05. This provides evidence that the students in both groups participants and non participants are receiving relatively the same average grades in their first attempts at the math ematics and chemistry courses. A descriptive analysis of the relat ionship in the participants and n on participants regarding withdrawal rates in calculus and chemistry was also conducted using the ED W dat a comparing the participants and no n participants groups (see Table 4 12) The participants withdrew at a high er rate than non participants in Calculus 1 and p re calculus with participants withdrawing 13.5% (n=30) in calculus and 13.2% (n=9) in pre calcu lus. Whereas the non participants withdrew 10.7% (n=169) in calculus 1 and 10.1% (n=46) in pre calculus. In chemistry 1, the participants withdrew at a lower r ate than the non participants at 8.5% (n=22) versus 9.3 (n= 174) for the non participants Additionally, in pre chemistry the participants withdrew a t a lower rate than the non participating students at 5.6% (n=4) versus 7.0% (n=44). Further investigation is needed to determine the
61 significance of the relationships between both the groups and t heir mathematics and chemistry withdrawal rates t o understand group differences. A t test was conducted to determine if any differences existed amongst the participants and the non participating groups on withdrawal rates from the mathematics and chemist ry cou rses (Table 4 13). From the conducted t test, significant differences were found for Honors calculus 1 with mean differences of .100 was found between the participants and non participant groups. This difference between the participants and non pa rticipants groups was significant at the .05 alpha level t (76) = 2.71, p<.05. From these results, there was a significant effect for Hon ors Calculus 1 with the non participant group withdrawing less than t he participants group from this course. Binary Lo gistic Regression Binary logistic regression statistical methods were employed to better understand the factors that best predict the likelihood students will be retained or not retained in an engineering major after their first year of attendance. For t his study, logistic regression was used to explore the multiple pre college and college student factors in predi cting student retention and participant inclusion. Early in retention research logistic regression was called for to study college student reten (Tinto, 1975) Logistic regressions statistical procedures are also helpful when researchers are interested in estimating the probability that an event will occur (i.e. college retention, graduation, grade attainment etc.) for students with specific characteristics (Pen g, Kee, & Ingersoll, 2002) The following section will present and discuss significant findings from the conducted logistic regression analyses. Results are discussed in terms of odds ratios ( Exp ( )), which represents the odds change for a one unit cha nge in the predictor or
62 independent variable, when all other predictor variables in the equation are held at a constant value. The presentation of results for the logistic regressions begin with the results for regressions Model 1 (characteristics of ret ention) followe d by Model 2 (predictors of Summer Bridge Program inclusion). Model 1: Characteristics of Retention A significant p value of less than p< .001for Model 1 was found, which suggest that the regression equation including predictor variables we re significantly improved, or a better fit, than the model with only the constant being considered. Model 1 held in an engineering major (Table 4 14). Four of th e 12 predictor variables in the equation were found to be significant (see Table 4 15). Gender, SAT quantitative scores, calculus grade values & program participation: For M odel 1, gender, SAT Quantitative scores and Calculus Grade Values were all found to be significant factors in predicting retention of engineering students after their first year. Gender was a significant factor in predicting retention after the first year of engineering school. The results outlined that females were found to be 53.5% les s likely ( = .625, p<.001) than males to retain in an engineering major holding all other variables constant. Further, SAT quantitative scores were also a significant factor in predicting retention after the first year of engineering school. The results found that students with higher SAT Quantitative scores to be 1.003 times more likely to ( =.003, p<.000) retain in an engineering major holding all other variables constant. Furthermore, the first year calculus GPA was found to be a significant factor in prediction retention. Here the results showed tha t students with higher math GPA s are
63 1.613 more likely ( = .478, p<.000) to retain in an engineering major. Lastly, the result s found the students in the summer bridge program to be 0.585 less likely ( = .535, p<.05) to be retained in an engineeri ng major after the first year. Model 2: Predictors of Summer Bridge Program Inclusion As with the first regression model, a significant p value of less than p< .001for Model 2 was found, which suggested that the regression equation including predictor variables were significantly improved, or a better fit, than the model with only the constant being considered. Model 2 held several factors that were significant in predicting studen ts that would enroll in the summ er bridge program (Table 4 16). Four of the 12 predictor variables in the equation were found t o be significant (Table 4 17). In M odel 2, significant predictors for summer bridge program inclusion included gender, ethnicity, HS GPA, and SAT Quantitative scores (Table 4 17). Gender was a significant factor in predicting stude nts who would enroll in the summer bridge program. The results outlined that females were found to be 77.2% less lik ely ( = .259, p<.05) than males to participate in the summer bridge program holding all other variables constant. Under ethnicity, Asian students were also found to be a significant factor in predicting summer bridge program inclusion. The results showed that Asian students were 41.1% less likely ( = .889, p <.05) to participate in the summer bridge program than other incoming engineering minority students. Students who possessed a higher high school GPA were found to have decreased o dds of participating in the summer bridge program, compared to their inverse reference group. Students who had elevated high school GPA s were nearly 70% less likely ( = .36 summer bridge program. Lastly, students who obtained high SAT quantitative scores
64 were less likely ( = participate in the summer bridge program. Chapter Summary and Conclusion The preliminary and advanced analytic methods used in this study were designed to provide an overall view of the similarities and differences between Non participating students and participating students. In review, the descriptive analyses provided furthe school GPA, SAT Quantitative scores The non participants have higher high school GPAs and SAT Quantitative scores. Additionally, the results from the descriptive analysis indicate th at stud ents who participate in the summer bridge program retain at a signifi cantly higher rate than non participating students in the first year This begins to underscore the importance and the effectiveness of a summer bridge program designed to incorpor ate more incoming engineering students to raise the overall retention rate and it also begins to develop a profile for students to be targeted for this type of program. With regard to how students were similar in both groups, the analysis showed that bot h student groups were of similar age, had si milar first year in college GPA s, took beginning math and chemistry courses at similar distributions and withdrew from these beginning math and chemistry courses at similar rates. This begins to show the students in the incoming engineering class ultimately are similar with regard to age, grade performance, course selection and withdrawal from math and chemistry courses. This begins the outli ne that the students in the participant group, with lower SAT Quantita tiv e scores and high school GPA s are retained at a higher rate than the Non participating students.
65 The primary purpose of this study was to explore the factors that contributed to the success of a first year engineering summer bridge program designed for al l incoming engineering. The evidence supports the notion that significant differen ces are present between the participants and the non participating students. Participating students have similar first year college GPA s, take the same math and chemistry cou rses and withd rawal at the same rates as the non participating students. However participating itative scores, high school GPA s are lower but they retain at a higher rate th an non participating students. Results from the logistic regress ion suggest that higher SAT Quantitative s cores, g ender, and first s emester math course grades are significant predictors of re tention. Additionally, the second regression model found that female students along with students who possessed a lower high school GPA and SAT Quantitative scores were less l ikely to participate in the summer bridge program. The last chapter of this study will further discuss the results of the conducted preliminary and advanced analyses, considering the previous literature on this topic.
66 Table 4 1. Sample demographic d escription Demograph ic Categories Percentage Frequency Total 100 (4166) Participants 9.9 (415) Non participants 90.1 (3751) Gender Male 74.5 (3104) Female 25.5 (1062) Race/Ethnicity Asian 10.5 (442) African American 4.6 (192) Hispanic 15.8 (664) American Indian 0 .4 (15) Non Resident Alien 0 .8 (35) White 65.7 (2728) Unknown 2.2 (90) Cohort 2007 21.4 (892) 2008 24.7 (1028) 2009 26.0 (1083) 2010 27.9 (1163)
67 Table 4 2. Frequency of mathematics and c hemistry courses Mathematics & Chemistry Courses Frequency Percentage Pre Calculus 524 12.6 Calculus 1 1808 43.4 Calculus 2 624 15.0 Calculus 2 for AP students 488 11.7 Calculus 3 303 7.3 Pre Chemistry 696 16.7 Chemistry 1 2130 51.1 Chemistry for Engineers 614 14.7 Chemistry 2 153 3.7 Table 4 3 Frequency of retention of engineering students after first year Student Classification __________ Frequency Percentage_ _____ Participants 365 88.0 Non participants 3154 84.1 Table 4 4 Descriptive statistics for continuous independent variables Sample Characteristics Mean (SD) Age in Years 18.34 (0.518) HS GP A (5.0 scale) 4.13 (0.5097) SAT Quantitative 679.5 (64.077) First Year College GPA 3.33 (0.578) ____________________ ____________________ ____________________ __________
68 Table 4 5 Examination of high school GPA and retention Not Retained M (SD) Retained M (SD) p value H.S. G.P.A. 4.1 (0.47) 4.1 (0.52) .078 Table 4 6 Analysis of means on high school GPA and retention Comparisons D t Mean Std. p equal variance difference Error F Sig Non retained vs. Retained .039 .844 4135 1.76 .038 .021 .078 Table 4 7. Comparison of participants & non participants on categorical variables Participants Non participants Percentage Count Percentage Count p value Gender Male 77.1 (320) 74.2 (2784) 0 .200 Female 22.9 (95) 25.8 ( 967) Race Asian 5.1 (21) 11.2 (421) 0 .000* African American 6.5 (27) 4.4 (165) Hispanic 13.7 (57) 16.2 (607) American Indian 0 .7 (3) 0 .3 (12) White 71.8 (298) 64.8 (2430) Non Resident Alien Unknown 2.2 (81) ____________________________________________ __________________________ *Mean differe nce is significant at the p< .000 significance level
69 Table 4 8. Comparison of participants & non participants on continuous variables S ample Characteristics Participants Non participants Mean (SD) Mean (SD) Age in Years 18.34 (.528) 18.34 (.517) HS GP A 4.07 (.354) 4.13 (.523) SAT Quantitative 656.46 (62.95) 682 (63.69) First Year College GPA 3.28 (.552) 3.33 (.581) ______________________________________________________________________ Table 4 9 Analysis of means on participants & non participants on continuous variables Comparisons D t Mean Std. p equal variance difference Error F Sig Non Participants vs. Participants Age 0.115 0.73 4164 0.95 0.003 0.027 0 .924 Non Participants vs. Participants HSGPA 1.938 0.164 4137 2.514 0.0665 0.026 0 .012* Non Participants vs. Participants SAT Q 0.328 0.567 4009 7.644 25.59 3.349 0 .000* Non Participants vs. Participants 1 st Year College GPA 0.671 0.413 4142 1.556 0.04655 0.029 0 .120 Significant at the p< .05 level
70 Ta ble 4 10 Frequency of mathematics and chemistry courses Participants (n=415) Non Participants (n=3751) Courses Frequency (%) Frequency (%) Pre Calculus 68 (16.4) 456 (12.2) Calculus 1 223 (53.7) 1585 (42.3) Calculus 2 46 (11.1) 578 (15.4) Honors Calculus 1 10 (2.4) 68 (1.8) Honors Calculus 2 6 (1.4) 85 (2.3) Calculus 2 / AP students 28 (6.7) 460 (12.3) Pre Chemistry 71 (17.1) 625 (16.7) Chemistry 1 258 (62.2) 1872 (49.9) Chemistry for Engineers 52 (12.5) 562 (15.0) Chemistry 2 11 (2.7) 142 (3.8)
71 Table 4 11 Analysis of means for participants & non participants on math & chemistry courses Comparisons D t Mean Std. p equal variance difference Error F Sig Non Participants vs. Participants Pre Calculus 1.597 0.207 522 .585 .099 .176 .570 Non Participants vs. Participants Calculus 1 4.375 0.037 1806 1.63 .156 .096 .103 Non Participants vs. Participants Calculus 2 4.187 0.041 622 .704 .13210 .187 .481 Non Participants vs. Participants Honors Calculus 1 3.629 0.061 76 .781 .20147 .257 .437 Non Participants vs. Participants Honors Calculus 2 0.019 0.891 89 .406 .16927 .416 .686 Non Participants vs. Participants Calculus 2 for AP Stu. .548 .459 486 .934 .1989 .213 .351 Non Participants vs. Participants Pre chemistry 1.249 .264 694 1.70 .272 .159 .088 Non Participants vs. Participants Chemistry 1 1.687 .194 2128 1.08 .093 .086 .278 Non Participants vs. Participants Chemistry for Engineers 1.237 .266 612 1.125 .218 .194 .261 Non Participants vs. Participants Chemistry 2 0.072 0.788 151 .157 .0583 .371 .875 *Mean difference is significant at the p< .05 significance level
72 Table 4 12 Relationship of participation in the summer bridge program and withd rawal rate Course Participants Non Participants Percentage Frequency Percentage Frequency Pre Calculus 13.2 (9) 10.1 (46) Calculus 1 13.5 (30) 10.7 (169) Calculus 2 6.5 (3) 7.8 (45) Honors Calculus 1 10.0 (1) 0.0 (0) Honors Calculus 2 0.0 (0) 4.7 (4) Calculus 2 for AP students 7.1 (2) 5.4 (25) Pre Chemistry 5.6 (4) 7.0 (44) Chemistry 1 8.5 (22) 9.3 (174) Chemistry 2 9.1 (1) 5.6 (8)
73 Table 4 13 Analysis of means withdrawal rates for participants & non participant groups Comparisons D t Mean Std. p equal variance difference Error F Sig Non Participants vs. Participants Pre Calculus 2.354 0.126 522 .789 .031 .040 .431 Non Participants vs. Participants Calculus 1 5.90 0.015 1806 1.246 .028 .022 .213 Non Participants vs. Participants Calculus 2 .392 0.531 622 .309 .013 .041 .757 Non Participants vs. Participants Honors Calculus 1 37.2 0.00* 76 2.71 .100 .037 .008* Non Participants vs. Participants Honors Calculus 2 1.28 0.26 89 .538 .047 .087 .592 Non Participants vs. Participants Calculus 2 for AP Stu. .568 .451 486 .383 .017 .045 .702 Non Participants vs. Participants Pre chemistry .805 .370 694 .433 .014 .032 .658 Non Participants vs. Participants Chemistry 1 .649 .421 2128 .400 .008 .019 .689 Non Participants vs. Participants Chemistry for Engineers .192 .661 612 .216 .012 .054 .829 Non Participants vs. Participants Chemistry 2 .818 .367 151 .467 .035 .074 .641 S ignificant at the p< .05 level
74 Table 4 14 Logistic regression model measures model 1: characteristics of retention Chi Sig. Nagelkerke % predicted square R Square correctly Probability of retention 327.479 12 .000*** .142 85.2 ***Significant at the p .001 level Table 4 15 Results for binary logistic regression model 1: characteristics of retention Variable Beta Exp ( ) Std. Error Wald Gender .625 *** 535 104 36.039 Ethnicity Asian .483 1.621 .349 1.913 African American .314 1.369 .376 .699 Hispanic .296 1.345 .332 .795 American Indian .803 .448 .695 1.335 Other 2.055 7.086 1.140 3.248 White .045 1.047 .313 .021 HS GPA .078 1.081 .123 .401 SAT Quantitative .003*** 1.003 .001 15.324 First Year College GPA .032 1.032 .093 .115 Calculus GPA .478*** 1.613 .041 13 5.131 Summer Bridge Program .535** .585 .173 9.568 **p<.01, ***p<.001.
75 Table 4 16 Logistic regression model measures model 2: summer bridge program inclusion Chi Sig. Nagelkerke % predicted square R Square correctly Probability of retention 104.180 10 .000*** .054 90.0 ***Significant at the p .001 level Table 4 17 Results for binary logistic regression model 2: summer bridge program inclusion Variable Beta Exp ( ) Std. Error Wald Gender .259* .772 .129 4.043 Ethnicity Asian .889* .411 .424 4.402 African American .255 .798 .424 .283 Hispanic .418 .658 .384 1.189 American Indian .653 1.922 .754 .751 Other 20.477 .000 7095.08 .000 White .0 54 1.055 .361 .022 HS GPA .369** .692 .110 11.238 SAT Quantitative .007*** .994 .001 55.477 *p<.05, **p<.01, ***p<.001.
76 CHAPTER 5 DISCUSSION AND CONCL USIONS Summer bridge programs were born out of the Civil Rights Movement the 1960 s in an effort to address the retention issue of African American students in higher education (Noel, Levitz, Saluri et al., 1985). As summer bridge programs evolved over time, the benefits of these programs became apparent to college administrators as a too l for increasing retention and providing a more inclusive workforce. Engineering schools across the nation began to implement summer bridge programs for underrepresented minorities and women. While the retention of underrepresented minorities and women in engineering majors did increase, the overall retention of engineering colleges has remained steady over the past 20 years (National Science Board, 2006) This suggests that a large portion, approximately 44% nationally, of students were not being retained and could possibly benefit from participating in a summer bridge program as well. The findings, for this empirical study, reveal that students who participated in the summer bridge program did retain through their first year at a higher rate than non parti cipants and at a statistically significant rate. More specifically, findings strongly suggest that SAT quantitative scores and the first college calculus course grades are strong predictors of student retention for the first two semesters This analysis extends the previous literature on summer bridge programs and brings further awareness to the academic performance and outcomes of engineering students. This chapter begins with a focus on the preliminary analysis of the summer bridge program participants and the non participating students. Next, the focus of the discussion include s the advanced analysis where models were utilized to understand the characteristics of rete ntion and the predictors of summer bridge program inclusion.
77 In this fin al chapter of study, previous literature was drawn upon to interpret and contextualize the empirical results and to propose implications of the findings to engineering practitioners. As the study closes, C hapter 5 is concluded with a discussion of the cont ribution of this body of work to the engineering community; provide suggestions for new policies for improved retention and success of engineering students. Finally, suggestions are made for future research regarding summer bridge programs offered at engin eering colleges and schools. Purpose of Study Revisited This study extends the current literature on summer bridge programs by exploring the factors that contribute to the success of a first year engineering summer bridge program designed for all incomin g engineering freshman by challenging the assumptions that there are no differences in students who completed the summer bridge program s and non participating students in High School GPA, SAT Quantitative scores, first semester calculus grades withdrawal r ates and retention in engineering. Through this study, there are three main issues that emerge regard ing the differences between participants and non participants First, this study addresses the ide a that participation in a summer bridge program is a majo r factor for increased rates in retention after the first year of college as engineering student. In earlier chapters, it was proposed that no differences would be found when examining t he retention rates when the participant and non participant groups wer e compared on SAT Quantitative scores, first semester grades in calculus and high school GPA. Secondly, this study addresses the pre college performance and performance factors of the first year in college of the students within the participant and non pa rticipant groups. The hypothesis was proposed that the students would have
78 equivalent high school GPA, SAT Quantitative scores, first semester grades in calculus and gender ratios. Lastly, this study addresses the rela tionship between participants and no n participants in calculus cours e grades and withdrawal rates. It was proposed that no relationship will exist between participants and non participants with respect to calculus course grades and withdrawal rates. Retention of Participants and N on Partici pants What is the relationship between participating and non participating engineering students SAT Quantitative scores, first semester grades in calculus an d high school GPA on retention? Retention : The results from this study disputes the hypothesis that a relationship exists between participants and non participating students between SAT Quantitative scores, first semester grades in calculus and high school GP A and retention in engineering. A previo us study found the average first year GPA for students who returned to engineering was significantly higher than student who left for other majors (Burtner, 2004) Further, another study also fo und that within three semest ers, most students with low GPA s leave engineering (Zhang, 2004) S pecifically, the results of this study suggest that students who enroll in and complete the summer bridge program are more likely to be retained in engineering afte r their first year than non partici pants. However, the summer bridge program along with gender were both found not to be predictors of engineering retention after applying the regression M odel 1. While female engineering students have always been a minority population in engineering it is not surprising that being a female engineering student is not a predictor of retention in engineering. The poor impression that many female students have about their ability to
79 perform well in technical subjects is exacerba ted by the traditional unfriendly engineering curriculum and instruction, with continuing emphasis on individual work and competitive grading, (Felder, Felder, Mauney, Hamrin, & Dietz, 1995) Moreover, the gender populations of both participants and non pa rticipating students were not significantly differen t, which explains obtaining these results similar to the same result of previous studies where gender was found not to be a significant factor in retention (Felder, Felder, Mauney, Hamrin, & Dietz, 1995; Astin & Astin, 1992) Furthe rmore, participating in the summer bridge program not proving to be a predictor of being retained in engineering is, on the surface, a negative result for the program ; however at a deeper look we can ascertain what causes this result by looking at the characteristics of t he students who participate from regres sion Model 2: Predictors of Summer Bridge Program Inclusion. This model found that gender, ethnicity, high school GPA and SAT Quantitative scores were significant factors in predicting stude nts who would enroll in the summer bridge program. Specifically, the model found that females and Asian students were less likely to enroll. This can be interpreted that males we re more likely to enroll in summer bridge programs than females. More importantly, student s with elevated high school GPA s and SAT quantitative scores were less likely to participate in the summer bridge program. Asian students were less likely to enroll than other ethnicities. Students with lower SAT qua ntita tive scores and high school GPA s were more likely to enroll. Given these results it could be suggested that the summer bridge program has enrolled lesser academically prepared students (i.e. students with lower high school GPA and lower SAT quant itati ve scores than the non participating students). However, th e students who complete the
80 first year summer bridge program retain at a higher rate than the non participating students. As a result, students that are male, non Asian, with lower SAT Quantita tive scores and high school GPA s benefit the most from the summer bridge program because they retain at a higher r ate after going through the summer bridge program. This result is in line with previous studies on summer bridge programs that found numerous pos itive benefits such as higher academic achievement than similar students who were not involved in summer bridge programs (Ackerman, 1991) At Bowling Green State University, the Academic Investment in Math and Science program (AIMS) reported that students who participated in the AIMS program held a higher GPA than students who did not participate in the program after seven semesters (Gilmer, 2007) Further, The Program for Women in Science, Engineering, and Mathematics (PWISEM) found that after one year students who participated in the program significantly remained in the STEM fields than students who did not participate (Kahveci, Southerland, & Gilmer, 2006) Additionally, another study of a summer bridge program found the program participants academic and social engagement increased over the first two years of the program, and the retention rate after eight semester s was higher than the non participants (Walpole, Simmerman, M ack, Mills, Scales, & Albano, 2008) Given the results of these previous studies, the result of this study adds to the previous literature and corroborate s the effects summer bridge programs have on retention. Academic Perf ormance and Gender of Partic ipants and Non Participants How do participants and non participating engineering students compare on high school GPA, SAT Quantitative scores, first year college GPA and gender?
81 Academic Performance The results from this study are mixed cons idering the hypothesis that participating students will have equivalent high school GPA s, SAT Quantitative scores, first year in college GPA and gender ratios when compared to non participating students. In particular, th e results show that the non participating stude nts outperform the participating students in high school GPA and SAT quantitative scores however there was no significant difference found between the groups on first year college GPA. These results provid e positive support for this summer bridge program Since the summer bridge program is a self selected program, where students who are interested in the program select themselves in order to participate, we can only speculate that students who enroll feel they will benefit from the program. The data show th at the non participating stude nts hold higher high school GPA s and SAT Quantitative scores ; however students who completed the summer bridge program. From these data we can conclude ag ain that students who enrol l in the summer bridge program are lesser prepared students ; howe ver after going through the summer bridge program they perform similarly to the non participating students on first year college GPA providing evidence that the su mmer bridge program is effective in assisting lesser prepared students to perform simila rly to students with higher GPA s and SAT quantitative scores. This is also consistent with previous literature that found that success in college is related to the degr ee to which previous educational and personal experiences have equipped students for the expectations and demands college will place upon them (Conley, 2008; Kahveci, Southerland, & Gilmer, 2006; Walpole, Simmerman, Mack, Mills, Scales, & Albano, 2008) Gi ven that a summer bridge program is undertaken during the first semester of a
82 experience that equips students for college dema nds and expectations. Thus, the summer bri dge program can be considered to be a positive influence college success. Gender The second part of the a bove hypothesis states that participating students will have equivalent gender ratios when com pared to non participating students. The results found this part of the hypothesis to be true. When the groups were tested to see if any differences were statistically significant with regard to gender no evidence was found to support any differences existed. However, the descriptive statistics s how that slight differences exis ted between the groups. The participating group was comprised of 77.1% male an d 22.9% female where the non participating group was contained 74.2% male and 25.8% female. This is in line with current studies that show females make up approximately 18 20% of engineering majors (Noeth, Cruce, & Harmston, 2003) Although the previous results of this study the regression Model 2 showed that females were l ess likely to enroll in the summer bridge program female students were enr oll ing at similar rates as non participating female students. This suggests that both genders enrolling in engineering majors find the offering of a summer bridge program proportionally appealing. Additionally, this could suggest that female engineering st udents have similar intentions to persist in engineering as males engineering students. This has been found in previous studies where researchers were examining (Concannon & B arrow, 2009)
83 Impact on Quantitative Gateway Courses and Withdrawal rates What is t he relationship between the participating and non participating students in calculus and chemistry when compared by final grades and withdrawal rates? Quantitative Gateway Courses Students who aspire to become scientist or engineers begin their college careers by taking challenging sequentially organized, quantitative course including calculus and chemistry physics and the like. Due to the challenging nature of thes e courses they can open doors to the upper division course work or they can block any future enrollment if they are not completed successfully. Previous studies have reported that the students who decided on a science or engineering majors already have a good background in mathematics and science and performed well on the SAT and had good h igh s chool GPA s (Rawls, 1991 ; Zhang, 2004 ). The results of this study are similar by showing that no significant differences were found for any of the calculus or chemi stry fina l course grades between the participants and non participating students. Further, the results from this study also suggest those students who have chosen engineering as they major have some or a good background experience in mathematics and scienc e and were not statistically different between the participants and non participants An explanation of this re sult could suggest that the participating students who are academically lesser prepared and are just as successful as the stud ents who did not go through the summer bridge program that the two groups were both equivalent in their preparation in chemistry and calculus since the two group were not significantly different in their grade performance in either course. This provides some evidence tha t suggest that the calculus and chemistry revi ew sessions included in the summer bridge
84 program were effective at preparing lesser prepared students to perform at the same level as better prepared st udents who did not participate. Withdrawal R ates from Q uantitative Gateway Courses Quantitative Gateway course such as calculus, chemistry and physics are reported as foundat ional courses for engineering. However, according to the Mathematics Association of America, college freshmen that enroll in calculus cou rses are being filtered out of majors such as engineering, science and mathematics because of lack of success in entry level mathematic courses (Olsen, Knott, & Currie, 2009) Further, the authors stated that lack of success in these courses was due to lac k of pre college preparation. Another study found similar results at the University of Alabama where they report that 60% 70% of engineering freshmen are not calculus ready (Gleason, et al., 2010) The results of this study support the hypothesis that participants and non participants Specifically, the results show tha t the participating withdrawal rates were not statistically significa nt when compared to the non partic ipating students in all courses chemistry and calculus courses except Honors Calculus 1. However, this difference can be explained by looking closer at the frequency data. The frequency data i n Table 4 12 shows only one participating student withdrew from Hon ors Calculus 1 and zero non participating students withdrew from Honors Calculus 1 over the span of the study. Although a significant result, it only account s for one student withdraw over a span of four freshman cohorts Additionally, the nature of honors courses, being comprised of honors students, lends itself to students who are high performing student and s eldom withdrawal from courses.
85 The results p rovide some evidence that the summer bridge program is effective in preparing students to take on quantitative gateway courses and perform similarly, with respect to withdrawal rates, to non participating students. Contributions to Engineering Summer Bridge Programs This study makes a contribution to the engineering summer bridge programs by providi ng a study on students that completed an engineering summer bridge program designed for all incoming engineering students. Utilizing a longitudinal methodology to emphasize the importance of tracking the academic performance and the explanation of importan or leave for other majors. Further, this study provides a standard for future comparative studies of students who participate in an engineering summer bridge program and st udents who do not participate in an engineering summer bridge program. Additionally, this study makes a contribution to the study of higher education by providing a model and rationale for continued study of the academic performance and factors associated with the retention of engineering students who participate in summer bridge programs. This study further seeks to advance awareness of the impact student participation in summer bridge programs. Engineering summer bridge programs have the ability to make select another major. This study highlights the barriers and opportunities that an engineering summer bridge open to all students can benefit from in hopes that engineering colleges wi ll utilize such data to develop larger summer bridge programs to benefit both the success of the student and the engineering colleges. Finally, this study provides governing bodies and institutional administrators vital information on the outcomes of eng ineering summer bridge programs. This information
86 suggests areas where more human and financial resources should be directed in the future to grow the return on investment made by the state through and institutions toward increased student retention in eng ineering. Implications for P ractice Since the 1970 s institutions of higher education have been challenged to retain students to meet th e changing demands of society. One of the most common solutions utilized to retain students in higher education has bee n the summer bridge programs. Initially, t he engineering community within higher education used summer bridge program s to retain underrepres ented students in engineering. However, over time the success of the se engineering summer bridge programs has not ra ised the engineering colleges overall retention rate. From this result, it was thought by replicating the success of the former summer bridge programs but open enrollment to all incoming engineering students would positively impact the overall retention ra te of engineering schools. From this study, it has become clear that aims for increased retention rates can be attained through rigorous development of summer bridge programs designed for all incoming engineering students. Based on the results from this st udy, I present three recommendations that institutions and practitioners at engineering colleges can better support retentions efforts of engineering students at their institutions. First, this study begins to outline a profile of a student that might be nefit more from completing a summer bridge program. The development of a target profile that includes their high school GPA and SAT Quantitative scores could provide a more persuasive argument for enticing students who would benefit most from the program t o enroll and further increase the overall retention rate. Additionally, through this profile,
87 students and parents could have more data to make an informed decision on whether to participate in engineering summer bridge program or not. Second, given that first semester grades in calculus were found to be a predictor of retention, additional attentio n should be given to calculus. This could be accomplished either during the summer bridge program or by extending the summer bridge program to include the fall semester. Since the summer bridge program at the schedule is already full, adding additional attention to calculus would require removing another topic or alternatively carrying over these sessions into the fall semester. During the fall seme ster, students could attend cooperative, active learning sessions in calculus to further improve their calculus skills and ultimately their success which could translate into higher rete ntion of engineering students. Lastly, practitioners should consider sharing the profile of students, including SAT Quantitative scores and high school GPA, who are interested in studying engineering and would benefit most from participating in an engineering summer bridge program with high school official s such as guidance counselors. This would allow K 12 school officials to share this information with interested students and parents so they could adequately recommend additional or advanced track of mathematics t hat would benefit the student. Additionally, students would h ave some benchmarks to aim for earlie r in their high school career. This would also allow students to begin to understand the expectations of considering a major in engineering and other science related majors. National State and Institution Policy Recom mendations Since the early 1960s several colleges saw massive expansion in enrollment due to many governmental initiatives that made a college education desirable. The National
88 Youth Administration in an effort to lessen the effects of the Depression fund ed postsecondary educational opportunities to thousands of students, the GI Bill was introduced to help returning soldiers gain skills to reenter the workforce, finally the National Defense Education Act of 1958 and the Higher Education Act of 1965 were al so passed in an effort to encourage college enrollment (Berger & Lyon, 2005). As enrollments rose, institutions began to think about retention and began to monitor enrollments ; however only several attempts were made to systematically assess patterns of retention. The past four decades in engineering education, many attempts have been made to increase enrollment and graduation rates of students obtaining Bachelors of Science in enginee ring degrees. Nearly, every campus in the U S has at least one program designed to foster minority retention (Ohland & Crockett, 2002) This is mainly due to the poor retention rate of engineering students which continues to hover around 50% nationally. H owever, it has been reported that many of the initiatives put in place to improve retention have not been studied to determine if they are effective (Ohland & Crockett, 2002 ; Walpole, Simmerman, Mack, Mills, Scales, & Albano, 2008 ) Accordingly, propose d h ere are three possible national, state and institutional policies to enhance the probability of success of engineering summer bridge programs and students pursuing engineering degrees. First, continued national funding not only to produce summer bridge p rograms but new funding initiatives to also research them vigorously is paramount. Some results have been produced with respect to summer bridge programs ; however they are not comprehensive and leave out the study of the program components themselves
89 (Ohl and & Crockett, 2002; Walpole, Simmerman, Mack, Mills, Scales, & Albano, 2008) Further, studies of the components could provide evidence of what works in summer bridge programs. This will allow other programs to make adjustments to their program if necess ary and provide them with better overall results, mainly higher retention rates. Additionally summer bridge program s provide volumes of necessary support for incoming students ; however after the first year support wanes students are left to their own d evices to complete the rest of their college career. Engineering college s should consider developing an inter curriculum program where support can be continued for students throughout their college career until graduation. Since strides are being made in t he freshman year, advantage should be made of this success for the remaining years. Attention must be given to t he interweaving of the academic support with respect to curriculum demands and dwindling resources ; however the summer bridge program model cou ld provide the outline for this type of needed program. Lastly, institutions should consider supplementary mathematics and science instruction reviews as part of the required curriculum to capture those students who cannot attend summer bridge programs an d have scores below the profile of successful students. Although the engineering curriculum is already full with little room for electives or extra classes many students come to college with Advance Placement, International Baccalaureate, or Dual Enrollmen t credit that eliminates some college curriculum requirements. Given this situation, engineering colleges should considering requiring additional supplementary mathematics and science review sessions for students that do not have adequate SAT Quantit ative scores or high school GPA s. This initiative would allow students who did not participate in the engineering summer bridge program to
90 experience the benefits of the ma thematics and science reviews. This effort would allow students to simultaneously take the ir first college mathematics and science course while also obtaining additional instruction that would enhance their success and ultimately lead to increased retention. Suggestions for Future Research Through this study, it is anticipated that additional empirical research will be conducted on the factors that contribute to the success of engineering summer bridge programs in order to develop best practices and best components. To address these issues, the direction of future research could take on many d ifferent shapes. Based on the findings of this study, I provide several areas where future research may be important to expand the current literature on the successful factors of engineering summer bridge programs and the outcomes of students participating in engineering summer bridge programs. More research on the factors included in a summer bridge program, that contribute to the retention and graduation of an engineering students is warranted. Further understanding of the components that contribute the retention and overall graduation rates would be beneficial in designing and redesigning summer bridge programs to become more effe ctive in reaching their goals. Further, additional study on the demographic factors from students that participate in summer bridge programs is needed. A richer picture of students that benefit most from summer bridge programs would illuminate a better profile and target for potential p articipation. Possible demographic factors that should be considered include socio economic status, first generation in college, high school ranking, house hold size, cost/benefit, etc. Additionally, researchers could consider the draw of the
91 reputation of an institution as a factor. For example, the reputation of a public state university versus the reputation of a public flagship university might provide insight on what students attend and participate in retention programs. These factors would offer a gre ater understanding of students who would not only benefit the most from a summer bridge program but also allow the development of other programs and services to assist students continue in their engineering programs. These factors could also reveal themsel ves to be inhibitors to participation or predictors of exclusion which could inform program directors. Additionally, increased quantitative and qualitative research of factors that enhance retention through the second year of engineering school is needed Current retention efforts in engineering focus primarily on the first semester and first year. However, during the second year of enginee ring school approximately 25% of the sophomore level students leave the discipline for other majors, other institutio ns o r higher education altogether. This research would allow for the development of effective programmatic solutions and services would extend retention efforts and ultimately graduation rates. A longitudinal study that compares participants and non parti cipants over a longer period of time is appropriate. Other types of summer bridge programs that assist students in non engineering majors such as mathematics, chemistry, physics, biology, should be developed and examined. Additionally, a study across sever al types of different institutions that employ summer bridge programs could provide new insights. These types of studies hold the potential to expand the small body of evidence on the
92 effectiveness of summer bridge programs and provide solutions to our nee d for a highly skilled diverse workforce. Lastly, future quantitative and qualitative research on the lack of female participation in engineering summer bridge programs is suggested. Females are already a minority population in engineering programs and in creasing their participation in engineering summer programs could prove beneficial in retaining and graduating more female engineers. A greater understanding of why or why they do not participate will increase awareness of their decision making process. Th is research could prove beneficial to addressing female concerns and provide a more ef fective summer bridge program. Closing Summary If we observe what the past 20 years in engineering education has produced and glance at the future of engineering educati on, one can easily that the potential for improvements is vast. Over the past four decades, engineering summer bridge programs have proven their worth in several development areas and have made impacts on retention (Garcia, 1991) These programs in support of the institutional and college missions provide an opportunity in which institutions can further support the personal academic goals of its students. This approach of providing academic support to further the academic goals of student in areas where evi dence based practices are proven to provide positive results is a standard worthy of action. This study was intended to explore the factors that contributed to the success of a first year engineering summer bridge program designed for all incoming enginee ring freshman. T his study compared participants of a summer bridge program and non participants to investigate if any similarities existed. Moreover, predictors of retention
93 between the participants and n on participants were explored Additionally, t his st udy attempted to demonstrate if relationship s between this intervention and an increase on retention by utilizing predictive and descriptive statistics to explain the phenomena between the two groups. Utilizing various quantitative methods including logistic regression, results indicated that the engineering summer bridge program was successful in retaining students at a higher rate than the non participants. Additionally, the students who decided to pa rticipate and completed the engineering summer bridge program were significantly different that the non participants on several variables. As shown through this study, s ummer bridge programs serve an increasing need and a foundation on which increased huma n potential and effective use of resources creates benefits that are felt by many.
94 APPEND IX A INSTITUTIONAL REVIEW BOARD (IRB) APPROVAL LETTER
95 APPENDIX B SUMMER BRIDGE PROGRA M INVITATION LETTER
96 APPENDIX C SUMMER BRIDGE PROGRA M WEB APPLICATION
97 APPENDIX D SUMMER BRIDGE PROGAM CONTRACT
99 APPENDIX E SUMMER BRIDGE PROGRA M RELEASE AND HOLD H ARMLESS AGREEMENT
100 APPENDIX F SUMMER BRIDGE PROGRA M LETTER OF ACCEPTAN CE UF Engineering Student, Engineering Freshman Transition Program (EFTP) from June 28 August 6, 2010. This program is designed to equip you with essential skills for engineering s uccess. In order to participate and complete your enrollment in EFTP, several requ irements must be accomplished. First, you must go to the following web links and print out each form, complete them with your parents and return them to our office by the deadline of JUNE 11 a) Acceptance form & contract: http://engnet.ufl.edu/students/files/contract.pdf b) Release form http://engnet.ufl.edu/students/files/release_form.pdf c) Information guide http://engnet.ufl.edu/students/files/info_guide.pdf Second, if you have not registered for orientation (Preview), you must do so ASAP. Please go to http://www.dso.ufl.edu /nsp/orientation/ to register online or call the Dean of Students Office for assistance at (352) 392 1261. Please remember to sign up for one of the orientations offered for summer B entering students. Finally, you will need to register for the EFTP cour ses that will be given to you at your orientation (Preview) session. Upon completion of all three of these tasks you will be fully registered for EFTP and ready to attend our o rientation session on June 26. Details of the orientation will be sent out afte r the June 11 deadline. If your student has a fall admission date we are currently working t o have it switched to summer B. Once this is accomplished and you are notified by the admission office, you can obtain housing and register for a summer B previe w session. Once again, congratulations on your selection for EFTP. My staff and I look forward to working with you as you make this very important transition to college. If you have any questions or concerns, please contact me Mr. Jeff Citty at (352) 392 0944 or email@example.com Sincerely, EFTP Director
101 APPENDIX G SUMMER BRIDGE PROGRA M FINAL LETTER
102 LIST OF REFERENCES ABET (1995). (IV.C.3.d(3)(c)), Criteria for Accrediting Programs in Engineering in the United States, Effective for Evaluations During the 1995 1996 Cycle, Engineering Accreditation Commission, Accreditation Board for Engineering and Technology, Inc., Baltimore, Maryland. Ackermann, S. (1991). The benefits of summer bridge programs for underreprese nted a nd low income students. College & University 66 (4) 201 208. Anthony, G. (2000). Factors influencing first year students' success in mathematics. I nternational Journal of Mathematical Education in Science and Technology, 3 1 (1), 3 14. Astin, A., & Astin, H. (1992). Final report: Undergraduate science education: The impact of different college environments on the educational pipeline in the sciences. Los Angeles: Higher Education Research Institute, Graduate School of Education, UCLA. Astin, A. W., Korn, W., & Green, K (1987). Retaining and satisfy ing students. Educational Record 68, 36 42. Astin, A.W., & Oseguera, L. (2005). Pre college and institutional influences on degree a ttainment. In A. Seidman (Ed), College student retention: Formula for student success ( 245 276).Westport, CT: American Council on Education/Praeger. Austin, A. (197 7). Four Critical Years. San Francisco: Jossey Bass. Austin, A. (1993). What M atters in C ollege. San Francisco: Jossey Bass Austin, A. (1999). Student Involvement: A developmental theory for higher education. Journal of College Student Development 40 (5) 518 529. Balz, F., & Esten, M. R. (1998). Fulfilling private dreams, serving public priorities: An analysis of TRIO students' success at independen t colleges and universities. Journal of Negro Education 67 (4) 333 345. Beal, P. E., & Noel, L. (1980). What works in student retention Iowa City, Iowa: American College Testing Program. B ean, J. P. (1990). Why students leave: Insights from research. In D Hossler, J. P. Bean, and Associates (Eds.), The strategic management of college enrollments (147 169). San Francisco: Jossey Bass. Beaufait, F. (1991). Engineering Education Needs Surgery. Proceedings Frontiers in Education Conference (519 522).
103 Berger, J. B., & Lyon, S. C. (2005). Past to Present. In A. Seidman, College Student Retention (1 27). Westport: Praeger Bernold, L., Spurlin, J., & Anson, C. (2007). Understanding Our Student: A Longitudinal Study of Success and Failure in Engineering w ith Implications for Increased Retention. Journal of Engineering Education 96 (3), 263 274. Besterfield Sacre, M., Atman, C., & Shuman, L. (1997). Character istics of Freshman Engineering Students: Models for Determining Student Attrition in Engineering. Jo urnal of Engineering Education 86 (2), 139 149. Brawer, F. B. (1996). Retention attrition in the nineties ERIC. No. ED393510. Los Angeles, CA.: ERIC Clearinghouse for Community Colleges. Braxton, J. M., Hirschy, A. S., & McClendon, S. A. (2004). Toward Un derstanding and Reducing College Student Departure. ASHE ERIC Higher Education Report, 30, ( 3 ).San Francisco: Jossey Bass. Buck, J. (1985). Summer Bridge: A residential learning experiences for high risk freshman at the University of California, San Diego. Columbia : Annual Confere nce on The Freshman Year Experie nce. Burtner, J. (2004). Critical to Quality Factors Associated w i th Engineering Student Persiste n ce: The Influence of Freshman Attitudes. Proceedings of the 34th ASEE/ISEE Frontiers in Education Con ference. Session F2E 1. Cabrera, A. F., Nora, A., & Castaneda, M. B. (1992). The role of finances in the persistence process: A structural model. Research in Higher Education 33 571 593. Cahalan, M., Muraskin, L., & Goodwin, D. (1994). National Study of Student S upport Services Interim Report: Volu me 1 Program Implementation. U.S. Department of Education, Office of the Under Secretary (ED 370 512). Campbell, T. A., & Campbell, D. E. (1997) Faculty/student mentor program: effects on academic performance and retention, Research in Higher Education 38 727 742. Carey, K. (2004, May). A matter of degrees: Improving graduation rates in four year colleges and universities Washington, DC: Education Trust. Concannon, J. P., & Barrow, L. H. (2009). Men's and Wo men's Intentions to Persist in Undergraduate Engineering Degree Programs. Journal of Science Education and Technology 19 (2), 133 145. Conley, D. T. (2007). Redefining College Readiness. Eugene: Educational Policy Improvement Center.
104 Crockett, D. S. (1978). Academic advising: A cornerstone of student retention In L. Noel (Ed.), Reducing the dropout rate. San Francisco: Jossey Bass. Davis, C. S. G., & Finelli, C. (2007). Diversity and Retention in Engineering. New Directions for Teaching and Learning 111, 63 71. Engineering Education Coalitions (1990). (NSF89 107), Program Solicitation, NSF, Closing Date: April 16, 1990. Felder, R.M., G.N. Felder, M. Mauney, C.E. Hamrin, Jr., & E.J. Dietz, (1995). A l ongitudinal s tudy of e ngineering s tudent p erformanc e and r etention. III. g ender d ifferences in s tu dent performance and attitudes Journal of Engineering Education, 84 (2) 151 174. Felder, R. M., Forrest, K. D., Baker Ward, L., Dietz, E. J., & Mohr, P. H. (1993). A l ongitudinal s tudy of e ngineering s tudent p erformance and r etention I. s uccess and f ailure in the i ntroductory c ourse. Journal of Engineering Education 82, 15 21. Fitts, J. (1989). A comparison of locus of control and achievement among remedial summer bridge and non bridge students in community co llege in New Jersey. Annual Meeting of the American Educational Research Association. San Francisco: American Educational Research Association. Freeman, K. (1999). No services needed? The case for mentoring high achieving African American students. Peabody Journal of Education 74 (2), 15 26. French, B., Immekus, J. & Oakes, W. (2005). An examination of the indicators of Journal of Engineering Education 94 (4), 419 4 25. Frotenberry, N., Sullivan, J. F., Jordan, P ., & Knight, D. (2007). Enginee r ing Education Rese a r ch Aids Instruction. Science 317, 1175 1176. Gainen, J. & Willemsen. (1995) Fostering Student Success in Quantitative Gateway Courses. New Directions for Teaching and Learning, 61 1 45. Gall, M., Gall, J., & Borg, W. (2007). Educational Research: An Introduction. Boston: Pearson Education. Gandara P, Maxwell Jolly J. (1999). Priming the Pump: Strategies for Increasing the Achievement of Underrepresented Minority Undergraduates. New York: College Board. Gansemer Topf, A.M., & Schuh, J.H. (2004). Instruction and academic support expenditures: An investment in retention and graduation. Journal of College Student Retention: Research, Theory & Practice 5 (2), 135 146.
105 Garcia, P. (1991). Summer bridge: Imp r oving retention r ates for underprepared students Jour nal of the Freshman Year Experie nce 3 (2), 91 105. Gibbons, M. T. (2010 ). Engineering Statistics Retrieved October 6, 2010 from American Society for Engineering Education. http://www.asee.org/papers and publications/publications/college profiles Gilmer, T. C. (2007). An Understanding of the Imp ro ved Grades, Retention and Graduation Rates of STEM Majors at the Academic Investment in Math and Science Program at Bowling Green State University. Journal of STEM Education 8 (1&2) 11 21. Gleason, J., Boykin, K., Johnson, P., Bowen, L., Raju, D., & Slappey, C. (2010). Integrated e ngineering m ath b ased s ummer b ridge p rogram f or s tudent r etention. Advances in Engineering Education, 2 (2), 1 17. Green, K. C. (1989). A Profile of Undergraduates in the Sciences. American Scientist, 77, 475 480. Grinter, L. (1955). Report on Eval uation of Engineering Education Washington, D.C : ASEE. Hagedorn, L. S. (2005). How to Define Retention. In A. Seidman, College Student Retention (89 105). Westport: Praeger. Heckel, R., Engineering f reshman e nrollments: c ritical and n on critical f actors Journal of Engineering Education 85 (1) 15 21. Inkelas, K. K. (1999). The tide on which all boats rise: The effects of living learning participation on undergraduate outcomes at the University of Michigan. Ann Arbor, MI:University Housing. Research Office Inkelas, K., Vogt, K. E., Longerbeam, S. D., Owen, J., & Johnson, D. (2006). Measuring Outcome of Living Learning Programs: E xamining College Envir o n ments a n d Student Learning and Development. The Journal of General Ed uc ation 55 40 76. Kahveci, A., Southerland, A., & Gilmer, P. (2006). Retaining Undergraduate Women in Science, Math e matics, and Engineering. Journal of College Science Teaching 36 (3), 34 38. Kaplin, W, & Lee, B. (1997). A Legal Guide for Student Affairs Professionals. San Francisco: Jossey Bass. Kemere r, F. R. (1984 85 Winter ). The role of deans, department chairs and faculty in enrollment management. College Board Review 134, ( 4 8 ) 28 29
106 Kezar, A. (2000, August 18). Summer on Bridge Programs: Supporting all students. Washington D.C. Retrieved August 8, 2010, from George Washington University Graduate School of Education and Human Development: http://www.ericdigest.org /2001 1/summer.html Kim, Y., & Sax, L. (2009). Student faculty interaction in research universities: Differences by student gender, race, social class, and first generation status. Research in Higher Education 50, 437 459. Kirsch, I., Braun, H., Yamamoto, K., & Sum, A. (2007). America's Perfect Storm. Princeton: Educational Testing Service. Kluepfel, G. (1994 Spring ). Developing successful retention programs: An interview with Michael Hovland. Journal of Developmental Education 1 7 28 30, 32 33. Koirala, Hari P.; Davis, Marsha J.; and Cid, Carmen R. (2010). Retention of Most at Risk Entering Students at a Four Year College. NERA Conference Proceedings 2010. Paper 30.http://digitalcommons.uconn.edu/nera_2010/30 Kuh, G. (2001). Assessing what really matter s to student learning Change 33 (3) 10 17. Kuh, G. D., Kinzie, J., Buckley, J., Bridges, B., & Hayek, J. C. (2007). Piecing together the student success puzzle: Research, propositions, and recommendations ASHE Higher Education Report, 32 (5). San Francisco: Jossey Bass. Lee, J., & Rawls, A. (2010). The College Completion Agenda 2010 Progress Report. New York: The College Board Advocacy and Policy Center. Levin, M., & Levin, J. (1991). A critical examination of academic retention programs for at risk minority college students. Journal of College Student Development 32 323 334. Levine, A. (2008 ). Inside Higher Ed Retrieved 10 11, 2010, from insiderhighered.com : www.insidehighered.com/views/2008/11/10/levine Lebold, W. K., & Ward, S. (1988). Engineering Retention: National and Insit it utional Perspectives. ASEE Annual Conference 843 85, ASEE Lenning, O., Beal, P., & Sauer, K. (1980). Retention and attrition ; Evidence for action and research. Boulder: National Center for Higher Education Management Systems. Lindner, A., Roberts, S., Bray, K., Mayhew, D., Shishodia, V., Citty, J., & Ogles, J. (2009). Evaluation of Retention and Other Benefits of a Fifteen Year Residenti al Bridge Program for Underrepresented Engineering Students. Proceedings of the 2009 ASEE Annual Conference. Austin : ASEE
107 Logan, C. R., Salisbury Glennon, J., & Spence, L. D. (2000). The learning edge academic program: Toward a community of learners. Jo urnal of The First Year Experience & Students in Transition 12 (1), 77 104. Mangold, W. D., Bean, L. G., Adams, D. J., Schwab, W. A., & Lynch, S. M. (2003). Who goes who stays: An assessment of the effect of a freshman mentoring and unit registration prog ram on college persistence. Journal of College Student Retention 4 (2), 95 122. Massey, W. E., (1992). A s uccess s tory a mid d ecades of d isappointment. Science 258 (5085), 1177 1179. Maton, K. I., Hrabowski, F. A., & Schmitt, C. L. (2000). African American college stude nts excelling in the sciences: College and post college outcomes in the Meyerhoff Scholars Program. Journal of Research in Science Teaching 37 629 654 Maxwell, M. (1990). Does tutoring help? A look at the literature, Review of R esearch in D evelopmental Education 7 (4), 1 8. McElro y E., & Armesto, M. (1998). TRIO and Upward Bound: History, programs and issues past, present and future. Journal of Negro Education 67 (4), 373 381. McMasters, J. H., & S. D. Ford, (1990 July/August). An i ndustry v iew of e nhancing d esign e ducation, Engineering Education 80 (5), 526 529. Moller Wong, C., & Eide, A. (1997). An Engineering Student Retention Study. Journal of Engineering Education 86 (1), 7 15. NACADA. (2003 ). Paper presented to the Task force on defining academic advising Retrieved from NACADA Clearinghouse of Academic Advising Resources Web site: http://www.nacada.ksu. edu/Clearinghouse/Research_Related/definitions.htm Naidu, P. (2006). Literature review: Tutoring. The Journal of the Association for the Tutoring Profession, 1(1). Retrieved May 9, 2006, from http://www.jsu.edu/depart/edprof/atp/ejournal.htm National Science Bo ard. (2006). Science and Engineer ing Indicators. National Science Foundation. Noel, L., Levitz, R., & Saluri, D. (19 85 ). In creas ing student retention San Francisco: Jossey Bass. Noel, L., Levitz, R., & Saluri, D. (1991). Increas ing student retention (2nd Ed.) San Francisco: Jossey Bass.
108 Noeth, R., Cruce, T., & Harmston, M. (2003). Maintaining a strong engineering workforce. Iowa City: ACT Policy Report. Oakes, W. C., Leone, L. L., Gunn, C. J., Dilworth, J. B., Potter, M. C., Young, M. F., Diefes, H. A. & Flori, R. E. (2000). Engineering Your Future. St. Louis : Great Lakes Press Offerstein, J., Moore, C., & Shulock, N. (201 0). Advancing by Degrees: A Framework for Increasing College Completion. Sacramento: IHELP & The Education Trust. Ohland, M. W., & Crockett, E. R. (2002). C reating a Catalog and Meta Anal ysis of Freshman Programs for Engineering Students: Part 1: Summer Bridge Programs. Proceedings of the 2002 American Society for Engineering Education Annual Conference & Exposition. Montreal: American Society for Engineering Education. Olsen, J. C., Knott L., & Currie, G. (2009). Discursive Practices in College Pre Calculus Classes. In L. Knott, The Role of Mathematics Disco u rse in Producing Leaders of Discourse (41 59). Charlotte: Information Age Publishing. Orfield, G., & Paul, F. (1988). Declines in mi nority access: A tale of five cities. Educational Record 68 (4), 57 62. Pagan, R., & Edwards Wilson, R. (2003). A mentoring program for remedial students. Journal of College Student Retention 4 (3), 207 226. Parkay, F. W. (1988). Reflections of a protg. Theory into Practice 27 1 95 200. Pascarella, E. (1980). Student faculty informal contact and college outcomes. Review of Educational Research 50 (4), 545 595. Pasc a rella, E., & Terenzini, P. (1991). How college affects students: Findings and insights from twenty years of research. San Francisco: Jossey Bass. The Pell Institute for the Study of Opportunity in Higher Education. (2004). Raising the graduation rates of low Income college students. Washin gton, DC: Author. Peltier, G. L., Laden, R., & Matranga, M. (1999). Student persistence in college: A review of research. Journal of College Student Retention 1 ,357 376. Perna, L. W., & Li, C. (2006). College a ffordability: Implications for c ollege o pport unity. NASFAA Journal of Student Financial Aid 36 (1), 7 24. Pike, G. R. (1999). The effects of residential learning communities and traditional residential living arrangements on educational gains during the first year of college. Journal of College Student Development 40 269 283. Pomalaza Raez, C. & Henry Gr off, B. (2003). Retention 101: Where robots go... students follow. Journal of Engineering Education 92 (1), 85 90.
109 Ramirez, G. M., & Evans, R. J. (1988). Solving the probation puzzle: A student affirmative action program. NACADA Journal 8 (2), 34 45. Rawls, R. (1991 April 15 ). Minorities in science Chemical & Engineering News, 20 35. Rita, E. & Bacote, J. (1997 June ). The benefits of college discovery pre freshman summer program for minority an d low income students. College Student Journal 31 161 173. Robert, E. R., & Thomson, G. (1994). Learning assistance and the success of underrepresented students at Berkeley. Journal of Development Education 17 (3). 4 14. Robinson, D., Schofield, J. & Ste ers Wentzell, K. (2005). Peer and cross age tutoring in math: Outcomes and their design implications, Educational Psychology Review 17 (4), 327 362. Ross Thomas, E., & Bryant, C. E. (1994). Mentoring in higher education: A descriptive case study. Education, 115 (1), 70 76. Salinitri, G. (2005). The effects of formal mentoring on the retention rates for first year, low achieving students. Canadian Journal of Education, 28 (4), 853 873. Scholnick, E. K. (1996). A two year longitudinal study of science and math students in the College Park Scholars program. Unpublished report, University of Maryland, College Park. Seidman, A. (2005). College Student Retention. Westpoint: Praeger Publishers. Seymour, E., & Hewitt, N. M. (1997). Talking about leaving: Why undergraduates leave the sciences. Boulder: Westview Press. Sheppard, S., & Jennison, R. (1997). Freshman e ngineering d esign e xperiences an o rganization al framework International Journal of Engineering Education 13 (3), 190 197 Sorrentino, D. M. (2007). The SEEK mentoring program: An application of the goal setting theory. Journal of College Student Retention, 8 (2), 241 250. Stater, M. (2009). The Impact of Financial Aid on College GPA at Three Flagship Public Institutions. American Educational Research Journal 46 (3), 782 815. Strayhorn, T. L. (2011). Bridging the p ip e line: Increasing u nderrepresented s tudents' p reparation for c ollege t hrough a s ummer b ridge p rogram. American Behavioral Scientist 55 (2), 142 159.
110 Suhr, J. ( 1980). Study of the 1978 summer step: The summer "bridge" program at the learning skills center, University of California, Davis. Davis : Univer si ty of California, Davis: Office of Student Affairs Research & Evaluation. Swail, W., Redd, K., & Perna, L. (20 03). Retatining minority students in higher education: A framework for success. San Francisco: Jossey Bass. Tai, R, Sadler, P, Loehr, J. (2005). Factors i nfluencing s uccess in i ntroductory c ollege c hemistry. Journal of Research in Science Teaching, 42 (9), 987 1012. Terrion, J.L. & Leonard, D. (2007). A taxonomy of the characteristics of student peer mentors in higher education: Findings from a literature review. Mentoring & Tutoring 15 (2), 149 164. Tinto, V. (1975). Dropout from Higher Education: A theoret ical synthesis of recent research Review of Educational Research 45 (1), 89 125. Tinto, V. (1987). Leaving college: Rethinking the causes and cures of student attrition. Chicago: University of Chicago Press. Tinto, V. (1993). Leaving College: Rethinking the causes and cures of student attrition (2nd ed.). Chicago: University of Chicago Press. U.S. Department of Education (2006). A Test of Leadership: Charting the Future of U.S. Higher Education. Washington, D.C. : Education Publications Center. Valeri Gold, M. D., & Stone, K. (1992). The bridge: A summer enrichment program to retain African American collegians. Journal of The Freshman Year Experience 4 (2), 101 117. Veenstra, C. P., Dey, E. L., & Herrin, G. D. (2009). A Model for f reshman e nginee ring r etention Advances in Engineering Education 1 (3), 1 33. Vogt, C. M. (2008). Faculty as a c ritical j uncture in s tudent r etention and p erformance in e ngineering p rograms. Journal of Engineering Education 97 (1), 27 36. Wallace, D., Abel, R., & Ropers Huilman, B. R. (2000). Clearing a path for success: Deconstructing borders through undergraduate mentoring. The Review of Higher Education, 24 (1), 87 102. Walpole, M., Simmerman, H., Mack, C., Mills, J., Scales, M., & Albano, D. (2008). Bridge to s u ccess: Insight i nto s ummer b ridge p rograms s tudents' c ollege t ransition. J ournal of the First Year Experie nce & Students in Transition 20 (1), 11 30. Webb, E. M. (1987). Retention and excellence through student involvement: A leadership role for student af fairs. NASPA Journal 24 (4) 6 11
111 Wulf, W., & Fisher, G. (2002 Spring ). A Makeover for Engineering Education. Issues in Science and Technology 18 (3), 35. Yokomoto, C.F., Rizkalla, M.E., C.L. and L amm, N. (1998). A successful motivational freshmen design experience using attached learning, Proc. 28th ASEE/IEEE Frontiers in Education Conference, 1, 493 499 Zhang, G. A. (2004). Identifying f actors i nfluencing e ngineering s tudent g raduation: A l ongitudinal and c ross i nstitutional s tudy. Journ al of Engineering Education 93 (4), 313 320.
112 BIOGRAPHICAL SKETCH Jeffrey M. Citty was born to Robert and Libby Citty in April of 1975, in Tampa, Florida. The younger of two children, Jeff grew up in Tampa and graduated from T.R. Robinson High School in 1993. Upon completing high school, he attended Hillsborough ree during the summer of 1995. Jeff moved to Gainesville to continue his academic studies at the University of Florida (UF). At UF, he earned his Ba chelor of Science (B.S.) degree in Recreation, Parks and Tourism in 1997. After his graduation, Jeff completed a nine month extended internship with the held positions in the non profit and for profit sectors where he gained experience in managem ent, sales, and data analysis. Through these experiences Jeff felt the desire to return to UF to complete an advanced degree. education, while simultaneously working full time as a Coordinator for Student Financial Affairs. Once he ee in December of 2003, he continued his student affairs career at UF by accepting a Coordinator position in the Engineerin g Student Affairs office with dual responsibilities of advising and administering an engineering summer bridge program designed for all incoming engin eering students. After four years of full time student affairs work, Jeff decided to return to the College of Education to pursue his doctoral degree in Higher Education Administration. Again, Jeff worked simultaneously as a Coordinator in the Engineering Student Affairs office while pursuing his terminal degree. During his pursuit of his terminal degree Jeff was promoted to Assistant Director in the Engineering Student Affairs office and was
113 also recognized by the National Academic Advising Association with a certificate of merit for Outstanding Advising during the 2009 national conference. In the fall of 2011 he received his Ed.D. from the University of Florida.