  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.
