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Updated: Oct 27, 2025

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Early Prediction of Student Learning Performance Through Data Mining: A Systematic Review.

Javier López-Zambrano1, Juan A Lara Torralbo, Cristobal Romero

  • 1Escuela Superior Politécnica Agropecuaria de Manabí.

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|July 23, 2021
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Summary
This summary is machine-generated.

Early prediction of student learning performance using data mining is crucial. Key factors include student assessment and interaction data, with prediction timing varying by educational system type.

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Area of Science:

  • Educational Data Mining
  • Learning Analytics

Background:

  • Early prediction of student learning performance is a significant research area.
  • Data mining techniques offer valuable tools for this prediction.

Purpose of the Study:

  • To provide a comprehensive overview of current research in early prediction of student learning performance.
  • To synthesize findings on data mining techniques, variables, and prediction accuracy.

Main Methods:

  • A systematic literature review was conducted.
  • Papers were identified through major search engines and selected based on predefined criteria.

Main Results:

  • 82 selected papers from an initial 133 were analyzed.
  • Studies focused on online and face-to-face learning in secondary and tertiary education.
  • Commonly used algorithms included J48, Random Forest, SVM, Naive Bayes, logistic, and linear regression.

Conclusions:

  • Student assessment and Learning Management System (LMS) interaction data are key predictors.
  • The feasibility of early prediction is influenced by the educational system's structure.
  • Machine learning algorithms are effective for predicting student outcomes.