Predicting Student Engagement in Online Learning Environments using Machine Learning

Strategic Insights -

Researchers at Sidi Mohamed Ben Abdellah University in Fez, Morocco have published a study on the use of machine learning algorithms to predict student engagement in online learning environments. The study, published in the journal Informatics, used a dataset from a Kaggle database to develop predictive models that can identify students who are at risk of disengagement. The researchers found that engagement in quizzes and exams was a strong predictor of overall engagement, and that active learners tend to perform better in assessments.

Key Takeaways:

  • The study used a machine learning algorithm to predict student engagement in online learning environments, with a focus on behavioral, emotional, and cognitive dimensions of engagement.
  • The researchers found that engagement in quizzes and exams was a strong predictor of overall engagement, with a correlation of 97% and 98.49% accuracy in predicting quiz and exam engagement, respectively.
  • The Decision Tree model was the best performer among those evaluated, achieving 97% and 98.49% accuracy in predicting quiz and exam engagement, respectively.
  • The K-Nearest Neighbors (KNN) and Gradient Boosting models also showed commendable performance, but were less effective than the Decision Tree model.
  • The research concluded that early identification of at-risk learners allows for targeted interventions, optimizing their engagement.
  • The study highlights the potential of machine learning algorithms in improving student engagement and learning outcomes in online learning environments.
  • The authors include Youssra Bellarhmouch, Hajar Majjate, Adil Jeghal, Hamid Tairi, and Nadia Benjelloun.

Statistics:

  • 97% accuracy in predicting quiz engagement using the Decision Tree model
  • 98.49% accuracy in predicting exam engagement using the Decision Tree model
  • 93% correlation between engagement predictions across quizzes, exams, and lessons
  • 44 contingents in the study on detecting student engagement in online learning environments using machine learning algorithms

Sources:

  • "Detecting Student Engagement in an Online Learning Environment Using a Machine Learning Algorithm." Informatics, vol. 12, no. 2, 2025, p. 44. (Informatics, http://www.mdpi.com/journal/informatics)
  • NewsRx. "Reports Outline Machine Learning Research from Sidi Mohamed Ben Abdellah University (Detecting Student Engagement in an Online Learning Environment Using a Machine Learning Algorithm)." Journal of Engineering, July 7, 2025, p. 3057.