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.

研究了多种指标（包括表现、行为参与度和情感参与度）的组合，通过分析来自学习管理系统的数字痕迹和学生网络摄像头拍摄的图像，识别学习困难的学生。利用法国一所工程学校2022至2023学年收集的真实数据，观察到积极情绪状态与学术成果的改善之间存在相关性。这些初步发现支持情绪在区分高、低成就学生方面起到关键作用的观点。

从学习管理系统到情感辅导系统：初步研究