Analyzing Student Learning Outcome: Stratified Cox Proportional Hazards Model

By Chau-Kuang Chen.

Published by The Technology Collection

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Article: Print $US10.00
Article: Electronic $US5.00

Survival analysis allows investigators to study the timing and duration of a critical event, which is a binary and dichotomous measure. This study used the stratified Cox proportional hazards model, a branch of survival analysis, to establish the relationship between a specific student learning outcome and its relevant explanatory variables. The outcome variable of interest was the timing of experiencing academic difficulty--dismissal, withdrawal, and leave of absence--due to academic reasons. The explanatory variables included demographics, undergraduate GPAs, MCAT scores, medical school academic performances, medical school curriculum tracks, and financial aid loan amounts. The main focus of this study was to measure the effect (relative hazard) of specific explanatory variables on the academic difficulty after adjusting for other explanatory variables. The data analysis indicated that academic difficulty was significantly associated with the MCAT verbal reasoning score, number of sophomore courses failed, and other relevant variables. By identifying the occurrence of critical events along with the explanatory variables, college decision makers could implement intervention strategies to ensure student success.

Keywords: Survival Analysis, Cox Proportional Hazards Model, Student Learning Outcome, Academic Difficulty

The International Journal of Technology, Knowledge and Society, Volume 6, Issue 1, pp.167-178. Article: Print (Spiral Bound). Article: Electronic (PDF File; 927.230KB).

Dr. Chau-Kuang Chen

Associate Professor and Director of Institutional Research, Office of Institutional Research, Meharry Medical College, Nashville, Tennessee, USA

Dr. Chau-Kuang Chen has significantly contributed to the development and implementation of student and alumni tracking systems to analyze student learning outcomes, alumni specialties, and practicing locations in support of college mission. Dr. Chen has taught Biostatistics to graduate students and medical residents at Meharry Medical College for more than 20 years, specializing in generalized linear model, survival analysis, time series analysis, and artificial intelligence modeling approach. He was one of the first to incorporate a variety of sophisticated techniques--ordered logit/cloglog, proportional hazard, transfer function of ARIMA methodology, grey forecasting model, and artificial intelligence methods--into higher education processes and outcomes. Dr. Chen has won the 2008 Distinguished Graduate Educator Award, and the Dean’s Award for Excellence in Teaching in the School of Graduate Studies three times, conducted numerous statistical workshops at annual conferences of the Association for Institutional Research (AIR), and published several articles in the Web-based IR Applications, an AIR refereed journal.


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