Irmgard U Willcockson1, Craig W Johnson, William Hersh, Elmer V Bernstam. 1. School of Health Information Sciences, Department of Internal Medicine, The University of Texas Health Science Center at Houston, 7000 Fannin Street, Suite 600, Houston, TX 77030, USA.
Abstract
OBJECTIVE: To predict student performance in an introductory graduate-level biomedical informatics course from application data. DESIGN: A predictive model built through retrospective review of student records using hierarchical binary logistic regression with half of the sample held back for cross-validation. The model was also validated against student data from a similar course at a second institution. MEASUREMENTS: Earning an A grade (Mastery) or a C grade (Failure) in an introductory informatics course. RESULTS: The authors analyzed 129 student records at the University of Texas School of Health Information Sciences at Houston (SHIS) and 106 at Oregon Health and Science University Department of Medical Informatics and Clinical Epidemiology (DMICE). In the SHIS cross-validation sample, the Graduate Record Exam verbal score (GRE-V) correctly predicted Mastery in 69.4%. Undergraduate grade point average (UGPA) and underrepresented minority status (URMS) predicted 81.6% of Failures. At DMICE, GRE-V, UGPA, and prior graduate degree significantly correlated with Mastery. Only GRE-V was a significant independent predictor of Mastery at both institutions. There were too few URMS students and Failures at DMICE to analyze. Course Mastery strongly predicted program performance defined as final cumulative GPA at SHIS (n=19, r=0.634, r2=0.40, p=0.0036) and DMICE (n=106, r=0.603, r2=0.36, p<0.001). CONCLUSIONS: The authors identified predictors of performance in an introductory informatics course including GRE-V, UGPA and URMS. Course performance was a very strong predictor of overall program performance. Findings may be useful for selecting students for admission and identifying students at risk for Failure as early as possible.
RCT Entities:
OBJECTIVE: To predict student performance in an introductory graduate-level biomedical informatics course from application data. DESIGN: A predictive model built through retrospective review of student records using hierarchical binary logistic regression with half of the sample held back for cross-validation. The model was also validated against student data from a similar course at a second institution. MEASUREMENTS: Earning an A grade (Mastery) or a C grade (Failure) in an introductory informatics course. RESULTS: The authors analyzed 129 student records at the University of Texas School of Health Information Sciences at Houston (SHIS) and 106 at Oregon Health and Science University Department of Medical Informatics and Clinical Epidemiology (DMICE). In the SHIS cross-validation sample, the Graduate Record Exam verbal score (GRE-V) correctly predicted Mastery in 69.4%. Undergraduate grade point average (UGPA) and underrepresented minority status (URMS) predicted 81.6% of Failures. At DMICE, GRE-V, UGPA, and prior graduate degree significantly correlated with Mastery. Only GRE-V was a significant independent predictor of Mastery at both institutions. There were too few URMS students and Failures at DMICE to analyze. Course Mastery strongly predicted program performance defined as final cumulative GPA at SHIS (n=19, r=0.634, r2=0.40, p=0.0036) and DMICE (n=106, r=0.603, r2=0.36, p<0.001). CONCLUSIONS: The authors identified predictors of performance in an introductory informatics course including GRE-V, UGPA and URMS. Course performance was a very strong predictor of overall program performance. Findings may be useful for selecting students for admission and identifying students at risk for Failure as early as possible.
Authors: Elmer V Bernstam; William R Hersh; Stephen B Johnson; Christopher G Chute; Hien Nguyen; Ida Sim; Meredith Nahm; Mark G Weiner; Perry Miller; Robert P DiLaura; Marc Overcash; Harold P Lehmann; David Eichmann; Brian D Athey; Richard H Scheuermann; Nick Anderson; Justin Starren; Paul A Harris; Jack W Smith; Ed Barbour; Jonathan C Silverstein; David A Krusch; Rakesh Nagarajan; Michael J Becich Journal: Acad Med Date: 2009-07 Impact factor: 6.893