Journal article
A Comparison of Supervised Machine Learning Methods for Classifying Students’ RAI‐Based Admission Eligibility: An Exploratory Study
Applied computational intelligence and soft computing, Vol.2026(1), 1652369
01/2026
DOI: 10.1155/acis/1652369
Abstract
This study compares five supervised machine learning models in their ability to classify students as Regent Admission Index (RAI)–eligible or not RAI‐eligible—that is, whether they meet the RAI automatic admission threshold. Because this binary outcome is computed deterministically from the same three predictors used by the models, the task is framed as a methodological comparison of how accurately and stably different algorithm families recover an existing threshold rule, rather than as a prediction of an independent admission, college‐readiness, or college‐success outcome. The study compares linear discriminant analysis (LDA) and k‐nearest neighbor (KNN) as distance‐based models, conditional inference tree (CIT) and random forest (RF) as tree‐based models, and Naïve Bayes as a Bayes’ theorem‐based model, using ACT composite scores, high school GPA, and number of core courses completed as predictors. Results show that all five models achieve high classification accuracy, with RF achieving the highest accuracy (99%). Bootstrapping with 10 resamples indicates stable performance, as reflected by low standard deviations across resamples. Small adjustments to the cutoff score cause slight changes in accuracy rankings, but the top and bottom performers remain consistent. Across models, ACT scores are strongly associated with the eligibility classification; however, the relative importance of predictors differs across models and from the policy weights assigned by the operational RAI formula, reflecting how each algorithm separates students near the threshold rather than evidence about college readiness.
Details
- Title: Subtitle
- A Comparison of Supervised Machine Learning Methods for Classifying Students’ RAI‐Based Admission Eligibility: An Exploratory Study
- Creators
- Jeongmin Ji - Korea Institute for Curriculum and EvaluationCatherine Welch - University of IowaStephen Dunbar - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Applied computational intelligence and soft computing, Vol.2026(1), 1652369
- DOI
- 10.1155/acis/1652369
- ISSN
- 1687-9724
- eISSN
- 1687-9732
- Publisher
- Wiley
- Language
- English
- Date published
- 01/2026
- Academic Unit
- Psychological and Quantitative Foundations
- Record Identifier
- 9985220841902771
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