Date of Graduation

5-2021

Document Type

Thesis

Degree Name

Bachelor of Science in Agricultural, Food and Life Sciences

Degree Level

Undergraduate

Department

Agricultural Economics and Agribusiness

Advisor/Mentor

Popp, Jennie

Committee Member/Reader

Wood, Lisa

Committee Member/Second Reader

Popp, Michael

Abstract

University honors programs provide students with challenging yet rewarding opportunities. Pursuing honors often offers students opportunities (such as access to uique coursework or specialized mentorship) that are not available to the general student popultion. However, honors programs also hold students to more or higher educational milestones in order to graduate with honors. Data from the University of Araksas Fayetteville (UAF) suggest students who start in honors as new freshmen typically graduate at rates much higher than students who were not honors freshmen. However, the percentage of those honors freshmen who complete their honors requirements is much lower than those who graduate at all. The objective of this study is to better identify the factors that largely impact an honors student’s liklihood of graduating and of graduating with honors.

A neural network analysis was conducted on 13 factors that were expected (either based on literature or preliminary T-test and chi square test analyses) to influence an honors freshman’s likelihood of graduating, with and without honors. Final results suggest that 1st Term GPA and major have the largest explanatory impact on graduation with honors, while 1st Term GPA has a significantly larger impact compared to other explanatory varaiblebs on graduating at all. With little to no explanatory impact from ethnic or gender variables, results imply there is no ethnic or gender bias in UAF honors program success. While this analysis lacks information related to potentially important variables such as prior research experience, service-learning experience, and study abroad that likely contribute positively to retention and success, this study establishes a good baseline model for predicting student success that can be built upon in further research.

Keywords

Honors; Neural Networks; Student Success; Education

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