Predicting Student Success in Communication Skills Learning Scenarios with Virtual Humans

Abstract

Virtual humans are frequently used to help medical students practice communication skills. Here, we show that communication skills features drawn from the literature on best practices for doctor-patient communication can be used to predict student interviewers' success in a given domain skill. We also demonstrate the viability of Bayesian Rule Lists, an interpretable machine learning model, for this use case. Bayesian Rule Lists' predictive performance is comparable to that of other other commonly used algorithms, including decision trees. This suggests that Bayesian Rule Lists, which produce simple, human-readable trained binary classifiers, may be suitable for providing feedback for educational purposes.