• DOI:10.1145/3303772.3303828
• Corpus ID: 67870434

Predicting Student Success in Communication Skills Learning Scenarios with Virtual Humans

@article{Carnell2019PredictingSS,
  title={Predicting Student Success in Communication Skills Learning Scenarios with Virtual Humans},
  author={Stephanie Carnell and Benjamin C. Lok and Melva T. James and Jonathan Su},
  journal={Proceedings of the 9th International Conference on Learning Analytics \& Knowledge},
  year={2019}
}

• Stephanie Carnell, B. Lok, +1 author Jonathan Su
• Published 4 March 2019
• Computer Science
• Proceedings of the 9th International Conference on Learning Analytics & Knowledge

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.