From Learning Management System to Affective Tutoring system: a preliminary study
This work addresses student difficulty detection in education, but it is incremental as it builds on existing LMS and affective computing methods.
The study investigated combining performance, behavioral, and emotional engagement indicators to identify struggling students using LMS data and webcam images, finding a correlation between positive emotions and better academic outcomes.
In this study, we investigate the combination of indicators, including performance, behavioral engagement, and emotional engagement, to identify students experiencing difficulties. We analyzed data from two primary sources: digital traces extracted from th e Learning Management System (LMS) and images captured by students' webcams. The digital traces provided insights into students' interactions with the educational content, while the images were utilized to analyze their emotional expressions during learnin g activities. By utilizing real data collected from students at a French engineering school, recorded during the 2022 2023 academic year, we observed a correlation between positive emotional states and improved academic outcomes. These preliminary findings support the notion that emotions play a crucial role in differentiating between high achieving and low achieving students.