Résumé
Due to the COVID-19 pandemic, the demand for distance learning has significantly increased in higher education institutions. This type of learning is usually supported by Web-based learning systems such as Massive Open Online Courses (Coursera, edX, etc.) and Learning Management Systems (Moodle, Blackboard-Learn, etc.). However, in this remote context, students often lack feedback and support from educational staff, especially when they face difficulties or challenges. For that reason, this work presents a Prediction-Intervention approach that (a) predicts students who present difficulties during an online learning course, based on two main learning indicators, namely engagement and performance rates, and (b) offers immediate support to students, tailored to the problem they are facing. To predict students' issues, our approach considers ten machine learning algorithms of different types (standalone, ensemble, and deep learning) which are compared to determine the best performing ones. It has been experimented with a dataset collected from the Blackboard-Learn platform utilized in an engineering school called ESIEE-IT in France during 2021-2022 academic year, showing thus quite promising results. © 2022 IEEE.