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With the digital transformation in education, students’ academic and behavioral data is becoming increasingly accessible through online learning platforms and performance tracking tools. Machine learning (ML) models offer a unique opportunity to predict academic outcomes based on this data and help improve educational processes by providing tailored recommendations. In this article we will see the role of various machine learning models in predicting students’ academic performance, comparing their efficacy, interpretability, and practical implications for education, and the challenges in Predicting Academic Performance.

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Machine Learning Models to Predict Students’ Academic Performance

  • 会议论文

  • 发表日期：

    2025年

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  • Benlalia Zakaria,
  • Khallouki Hajar,
  • Mzili ilyass,
  • Abatal Ahmed

摘要

With the digital transformation in education, students’ academic and behavioral data is becoming increasingly accessible through online learning platforms and performance tracking tools. Machine learning (ML) models offer a unique opportunity to predict academic outcomes based on this data and help improve educational processes by providing tailored recommendations. In this article we will see the role of various machine learning models in predicting students’ academic performance, comparing their efficacy, interpretability, and practical implications for education, and the challenges in Predicting Academic Performance.