A Three-Layered Student Learning Model for Prediction of Failure Risk in Online Learning

Danial Hooshyar, Yueh Min Huang, Yeongwook Yang

Research output: Contribution to journalArticlepeer-review


Modelling students’ learning behavior has proven to be a fundamental indicator of their success or failure in online courses. However, many studies ignore properly considering such modelling while predicting students’ risk of failure. This study proposes a new educational data mining approach, called StudModel, that automatically models students based on their learning behavior, and accordingly predicts their risk of failure in courses. Briefly, a three-layered students’ learning model with respect to their content access, engagement, and assessment behavior in a course is developed, and then clustering and classification methods are employed to put students into lowand high-risk of failure categories. To evaluate the approach, three courses with different numbers of students from the Moodle system of the University of Tartu were used. Our findings showed that StudModel achieved accuracies higher than 90%, outperforming many state-of-the-art approaches in predicting students’ risk of failure in courses with different numbers of students (with deep neural network being among the best classifiers). Furthermore, using a local interpretable model-agnostic explanations approach, the StudModel provides explanations on its decisions which can nurture educators, practitioners, and learners’ trust in such predictions. These reveal that it is feasible to accurately and transparently predict students’ risk of failure in online courses by using their current activity data that is available in most online learning environments, not their past performance or demographic data that either cannot be controlled by them or might be unavailable.

Original languageEnglish
Article number28
JournalHuman-centric Computing and Information Sciences
Publication statusPublished - 2022

All Science Journal Classification (ASJC) codes

  • Computer Science(all)


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