Emotional Engagement Assessment: Self-Reports Versus Facial Expressions

Abstract

Current research utilizes self-reports and facial expression recognition analysis to provide a more continuous and objective insight into how students’ emotional engagement unfolds and impacts learning. Analysis of nursing students learning with virtual reality simulation revealed that only the facial expression data channel, compared to self-reports, was sensitive to fluctuations in engagement which varied throughout the different learning session phases. In addition, findings show that learning achievements were negatively associated with facial expressions of anger and positively associated with positive self-reported emotions. Hence, this study demonstrates that the methodology of using multimodal data channels which encompass different types of measures, can provide insights into a more holistic understanding of engagement in learning and learning achievement.