Prediction of Students’ self-confidence using multimodal features in an experiential nurse training environment

Simulation-based experiential learning environments used in nurse training programs offer numerous advantages, including the opportunity for students to increase their self-confidence through deliberate repeated practice in a safe and controlled environment. However, measuring and monitoring students’ self-confidence is challenging due to its subjective nature. In this work, we show that students’ self-confidence can be predicted using multimodal data collected from the training environment. By extracting features from student eye gaze and speech patterns and combining them as inputs into a single regression model, we show that students’ self-rated confidence can be predicted with high accuracy. Such predictive models may be utilized as part of a larger assessment framework designed to give instructors additional tools to support and improve student learning and patient outcomes.

Vatral, C., et al. "Prediction of Students’ Self-confidence Using Multimodal Features in an Experiential Nurse Training Environment" Proceedings of the 24th International Conference on Artificial Intelligence in Education. (2023): pp. 266-271.
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