Emotion Trajectory and Student Performance in Engineering Education: A Preliminary Study

In this study, we aim to establish the connection between the emotional trajectory of students during a pedagogical sequence and their performances. The project aims to develop an affective and intelligent tutoring system for detecting students facing difficulties and helping them. We designed this experimentation during the 2022–2023 academic year with students in a French engineering school. We collected and analyzed two primary data sources: student results from the Learning Management System (LMS) and images captured by students’ webcams during their learning activities. It is known that basic (primary) emotions (like fear or disgust) do not reflect student affective states when facing pedagogical issues (like misunderstanding or proudness). Since such “academic emotions” are not easy to define and detect, we changed the paradigm and used a 2D dimensional model that describes better the wide spectrum of emotion encountered. Moreover, it allows to build a temporal emotion trajectory reflecting the student’s emotional trajectory. Firstly, we observed a correlation between these trajectories and academic results. Secondly, we found that high-performing student trajectories are significantly different from the others. These preliminary results, support the idea that emotions are pivotal in distinguishing highly performing students from their less successful counterparts. This is the first step to assess students’ profiles and proactively identify those at risk of failure in a human learning context.