Data-Driven Insights: Predicting Foundations-Level Success and Step 1 Completion in Active Learning Curriculum

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  In Roundtable Session: Admissions in Focus: Equity, Predictive Insights, and Holistic Review

Abstract

This study explores how admissions factors predict pre-clinical academic performance and timely USMLE Step 1 completion in an active learning-based medical curriculum. Data from 125 students in the Class of 2027 at a northeastern U.S. public medical school were analyzed using linear and logistic regression. Undergraduate GPA and MCAT scores significantly predicted Foundations grades, CBSE scores, and first-year course outcomes, with the MCAT CPBS subscore predicting Step 1 completion. In contrast, CASPer and MMI scores showed limited or no predictive value. These findings highlight the importance of academic preparation for success in active learning environments and support the use of data-informed admissions and student support strategies aligned with the demands of such curricula.

Authors

• Yichen Zhao, University of Vermont

• Karen Lounsbury, University of Vermont

• Kathryn N. Huggett, University of Vermont

• Leila Amiri, University of Vermont