A comparative study of Student Engagement algorithms in E-learning Environments
DOI:
https://doi.org/10.29304/jqcsm.2026.18.32820Keywords:
Student Engagement, Machine Learning, Recommendation Systems, Education DataAbstract
In e-learning, student engagement is a key factor that influences learning outcomes. Despite this, disengagement and dropout continue to be a problem. In this paper, we compare popular student engagement models to predict and improve the engagement process in e-learning platforms by utilizing machine learning approaches. We rely on behavior interaction data extracted from the EdNet dataset to predict the engagement behavior of students and provide personalized, data-driven recommendations to improve their engagement .We apply the following machine learning models: Logistic Regression , Random Forest , XGBoost , LightGBM , Artificial Neural Networks .
We compare the performance of each model with several metrics. We also use an experimental control design to evaluate the impact of our recommendation on student engagement. Our results show that the tree-based ensemble learning models, especially the XGBoost model, outperform the other models in terms of engagement prediction. In addition, the proposed recommendation system has a positive impact on improving the engagement of students. Our results indicate the effectiveness of our AI-based intervention in early identification and retention of students in online courses.
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Copyright (c) 2026 Susan Nadhum Ahmed, Manar Joundy Hazar, Mustafa Jawad Radif
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