Date of Award

Spring 5-2021

Document Type

Honors Thesis

Degree Name

Bachelor of Arts

Department

Business Administration

Advisor/Committee Chair

Eliot Rich

Abstract

Once you attract the best students to your campus, how do you keep them? University honors programs, a common means of attracting students in the competition for excellent students, have had difficulty retaining students in completing their honors education. Given the investment into honors students, strategies to improve outcomes would focus scholarship and teaching resources on some combination of students most likely to complete the program and assist those most likely to drop out. Additionally, by identifying predictors of retention and completion, the Honors College will be able to improve its admissions process to better select students most likely to complete the program.

Strategic decisions should rest upon accurate operational data. Due to the diverse campus units handling different datasets, limited staffing, and the accumulation of historical data, the Honors College has a slow and error-prone data management process, relying on manual transcription of data across isolated and distributed data sets. The goal of this project is to provide UAlbany’s Honors College with the tools necessary to efficiently manage their student records and achieve higher retention rates by 1) identifying and improving data collection and curation processes for Honors College students, 2) developing and implementing a student information system (SIS), and 3) using regression analysis to identify predictors of honors program retention and completion for honors students.

When these tasks are completed, the University will be able to deploy its resources better, intervene earlier, and increase retention of its most promising student cohort. By reducing time spent managing student records with the SIS, the administration can dedicate more time engaging with students, potentially those at a higher risk of dropping out of the program. The results of the logistic regression analysis provide insight on this, identifying which pre-entry and post-entry variables are significant predictors of retention and completion. HSGPA is the most significant pre-entry predictor for 1-Year Retention and 4-Year Completion, and Term 1 GPA is the most significant post-entry predictor of 1-Year Retention, 1-Year to 2-Year Retention, and 4- Year Completion.

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