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. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.