Tinto’s (1993) Theory of Individual Departure from Institutions of Higher Education conceptualized the decision making process students navigate when committing to institutions of higher education and persisting to graduation. Transfer students are gaining the attention of administrators and policy-makers because of the high level of transfer activity in higher education. Many of these students face a uniquely difficult transition when moving from one institutional environment to another. The phenomenon, known as transfer shock, is the overall integration and adaptation difficultly that transfer students face (Hills, 1965). Since previous institution cumulative grade point average (GPA) is a criterion used in transfer admissions decisions, it was studied. Six-year graduation totals were also observed in the study because they indicates student commitment to their institution and goals toward graduation. The purpose of this study was to examine Ball State University entering transfer students and how to predict post-transfer GPA and six-year graduation based on previous institution cumulative grade point average, age, sex, previous institution type, and BSU college. Regression analysis was used to make predictive models for post-transfer GPA and six-year graduation using the observed variables (i.e., previous institution cumulative GPA, age, sex, previous institution type, and BSU college). The sample consisted of 1,857 entering transfer students at Ball State University, a state-assisted, residential university with high research activity in Muncie, Indiana. Previous institution cumulative GPA averaged 2.994 while the average post-transfer GPA was 1.681. Nearly 60% of the sample achieved six-year graduation. The results of the revised model for predicting post-transfer GPA found only previous institution cumulative GPA, age, previous institution type, and whether or not the student was in CAST to be statistically significant predictors. The model used to predict six-year graduation found previous institution type, age, sex, previous institution type, and three of the BSU colleges (CAST, CCIM, and TC) to be statistically significant predictors. When applied to the data, the six-year graduation prediction model correctly predicted six-year graduation at a rate of 79.6% and had an overall correct prediction percentage of 63.6% of the time. Suggestions for practice and recommendations for future research were included. / Department of Educational Studies

Thesis (M.A.)--Wheaton College, 2005. Action Research Paper (M.A.)--Wheaton College, 2005. / Includes bibliographical references (l. 125-129).

Thesis (Ph.D.)--Mississippi State University. Department of Leadership and Foundations. / Title from title screen. Includes bibliographical references.

This study compares the demographic characteristics, academic experiences, and socioeconomic factors of California community college students who transfer to private for-profit four-year institutions with those who transfer to public or non-profit higher education institutions. Using logistic regression analysis, this study showed that academic experiences have the greatest influence on the decision to enroll at a for-profit institution. Controlling for academic experiences and socioeconomic factors, transfer students who are African American, female, and over the age of 25 are the most likely to enroll at for-profit institutions. Students receiving financial aid at community colleges also have a higher probability of choosing for-profits as their transfer destination. Using data from the largest and most diverse community college system in the world, the results of this study determined that community college students who transfer to for-profit institutions are indeed different from students who follow traditional routes defined as public and non-profit institutions. Transfer to· four-year institutions remains a critical mission of the community colleges, especially in providing opportunities towards a bachelor degree for economically and academically disadvantaged students. Therefore, an understanding of the impact of higher education privatization on transfer choice is critical. The report discusses implications of these study results for community college and higher education administrators and policymakers.

This study concerned student transfer and articulation between Kent State University and Cuyahoga Community College District.

The purpose of the study was to determine whether age, ethnicity, gender, full-time/ part-time status, and the community college academic variables of cumulative GPA, total transferable hours, and number of completed core courses predicted students' persistence or GPA at a four-year university.

While the connection between major choice and career goals seems logically obvious, research exploring this process is limited, particularly concerning how socio-economic class, based on parents' educational levels, influences the choice process. An important initial step in understanding this larger process is to explore how SES-based differences affect the process of choosing a major, a career goal and the way in which students link their major to a possible career. This study utilizes a comparative interview design to explore the lived experiences regarding major and career aspirations of first generation and traditional college seniors who have transferred from a community college to Portland State University. This study considers a first generation student to be any student that does not have a parent that has graduated from a four-year university in the United States. A traditional student is any student that has one or more parents who have earned at least a four-year degree in the U.S. Using a conceptual framework based on Pierre Bourdieu's work on social reproduction, this qualitative interview study examines how social and cultural capital as well as habitus influences first generation and traditional community college transfer students' choice of career, major and the link these students make between the two. This research found that the majority of students, both first generation and traditional community college transfer students, gained domain specific information that helped them with their major and or career goals from mentors such as, professors and academic advisers. However, Traditional students received "life advice" and encouragement from family members and employers that helped them to stay on track and gain inside information regarding their career choices. Traditional students used their past and current work history to assist them in strengthening their chances at realizing their career goals. Many traditional students planned to use the degrees they earned at college to advance within fields they already were working in. In comparison, it was only after they started college and settled on specific majors that first generation students looked for work experiences to help explore possible occupational outcomes. Another major difference between the two groups of students was that traditional students linked their majors to multiple jobs in an occupational area while first generation students linked their major to specific occupational positions. While there has been a large amount of research in the United States using Bourdieu's theory to examine how micro processes of language and teacher's expectations are utilized to maintain social stratification in K-12 education, there has been little research done on the micro processes that occur in college that lead to the reproduction of social class. This thesis illustrates how family background-based advantages that lead to differences in students' K-12 success actually continue after they enter higher education. By drawing attention to the importance of how family-background impacts major and career choices for community college transfer students after they arrive at the university, this thesis contributes to Bourdieu's explanation of how education at all levels contributes to the reproduction of a socially stratified society.

In an effort to improve retention and promote library services to new incoming students, the Sherrod Library staff at East Tennessee State University discussed different ideas and decided to expand the Lending Technology Program & Personal Librarian (PL) program to include First Generation and Transfer students. To help decrease library anxiety, students received by email a weekly newsletter of events and workshops happening in the library. These emails also personally reached out and regularly informed the students of information literacy skills such as navigating the library website, subject guides, tutoring center, and citation help. Additionally, the students received a short two-minute YouTube video bi-monthly that showed a different department in the library. The PL acts as a guide to help direct the students to the proper resources while presenting a friendly and approachable person for the students to interact with when asking for help.

The purpose of this study was to discover factors about first-time freshmen that began at one of the six 4-year universities in the former Tennessee Board of Regents (TBR) system, transferred to any other institution after their first year, and graduated with a degree or certificate. These factors would be used with predictive models to identify these students prior to their initial departure. Thirty-four variables about students and the institutions that they attended and graduated from were used to perform principal component analysis to examine the factors involved in their decisions. A subset of 18 variables about these students in their first semester were used to perform principal component analysis and produce a set of 4 factors that were used in 5 predictive models. The 4 factors of students who transferred and graduated elsewhere were “Institutional Characteristics,” “Institution’s Focus on Academics,” “Student Aptitude,” and “Student Community.” These 4 factors were combined with the additional demographic variables of gender, race, residency, and initial institution to form a final dataset used in predictive modeling. The predictive models used were a logistic regression, decision tree, random forest, artificial neural network, and support vector machine. All models had predictive power beyond that of random chance. The logistic regression and support vector machine models had the most predictive power, followed by the artificial neural network, random forest, and decision tree models respectively.

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