Machine Learning in Admissions?: Use of Chi-Square Automatic Interaction Detection (CHAID) to Predict Matriculants to Physical Therapy School.

PURPOSE

Machine learning algorithms provide methods by which patterns in admissions data may be discovered that predict admissions yields in education programs. We used a chi-square automatic interaction detection (CHAID) analysis to examine characteristics that predict applicants most likely to matriculate into a physical therapy program after being admitted.

METHODS

Data from applicants admitted to our physical therapy program from the 2015-2016 through 2021-2022 admissions cycles were evaluated (n=413). Variables included applicants' ages, grade point averages, graduate record examination (GRE) scores, admissions and behavioral interview scores, sex/gender, race/ethnicity, home state classification, undergraduate major classification, institutional classification, socioeconomic status, and first generation to college status. A CHAID algorithm identified which variables predicted matriculation after being admitted.

RESULTS

Overall, 47.2% of admitted applicants matriculated. The CHAID algorithm generated a 3-level model with 5 terminal nodes that classified matriculants with 64.9% accuracy. Applicants more likely to matriculate than to decline an admission offer included in-state applicants and White/Caucasian border-state/out-of-state applicants with GPAs below 3.65.

DISCUSSION

While findings are program-specific, the CHAID analysis provides a tool to analyze admissions data that admissions committees may use to analyze their admissions processes and outcomes.