Detection of Disengagement From Voluntary Quizzes: An Explainable Machine Learning Approach in Higher Distance Education

Students disengaging from their tasks can have serious long-term consequences, including academic drop-out. This is particularly relevant for students in distance education. One way to measure the level of disengagement in distance education is to observe participation in non-mandatory exercises in different online courses. In this paper, we detect student disengagement in the non-mandatory quizzes of 3 reference courses with 42 instances distributed in four semesters from a distance-based university. We carefully identified the most informative student log data that could be extracted and processed from Moodle. Then, nine machine learning algorithms were trained and compared to obtain the highest possible prediction accuracy. Using the SHAP method, we developed an explainable machine learning framework that allows practitioners to better understand the decisions of the trained algorithm. The experimental results show a balanced accuracy of 91%, where about 85% of disengaged students were correctly detected. On top of the highly predictive performance and explainable framework, we provide a discussion on how to design a timely intervention to minimize disengagement from voluntary tasks in online learning.