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AN ANALYTICS APPROACH FOR MODELING COLLEGE STUDENT GRADUATION
Zheng, Jeffrey
Zheng, Jeffrey
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Thesis/Dissertation
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2025-05
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Educational Leadership
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https://doi.org/10.34944/6769-at67
Abstract
This study applied analytic models to predict student 4-year graduation rates based off pre-college and first-semester characteristics at a large, public, R1 research institution in the Northeast. The quantitative techniques applied include logistic generalized linear modeling, generalized additive models, random forests, and neural networks. This study achieved model accuracy in the 70% range across methods, found academic and financial variables as the most predictive of 4-year student graduation, and no evidence of a multiplicative interaction between gender, race, and income variables. Random forest models were most accurate overall, but the logistic regression model appeared best for practical implementation. The paper provides findings, implications, and recommendations to improve the graduation rate across higher education institutions and ideas for future research.
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