Evaluating Personality Impact on Engagement and Confusion Detection in Learning Environments

This study investigates sensor-free emotion detection techniques in Intelligent Learning Environments, focusing specifically on detecting engagement and confusion. Traditional sensor-based methods of emotion detection face challenges, especially in low- and middle-income countries due to resource constraints. Consequently, our research utilizes data mining and machine learning algorithms to develop models that accurately detect student emotions using only interaction data. We also examine the potential of incorporating personality traits to boost the effectiveness of these models. Our analysis involves a dataset of interactions and annotated emotions from students using a Math step-based Intelligent Tutoring System. We implemented a structured machine learning pipeline designed to prevent data leakage and ensure robust model development. The findings indicate that while the baseline models for detecting engagement and confusion are reliable and show improvements over previous models tested on the same dataset, the inclusion of personality traits did not significantly enhance model performance. This research highlights the complexity of emotion detection in learning environments and discusses the implications of using personal characteristics to improve emotion detection accuracy.