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November 13, 2025Scientific ReportsOpen Access

Using machine learning to predict student outcomes for early intervention and formative assessment

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Authors

BABilal Barış AlkanSKSerafettin KuzucukNANesrin Alkan

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Overview

Analysis identifies at-risk students through machine learning, suggesting timely interventions to improve academic outcomes.

Key Points

  • The predictive model significantly improves early identification of at-risk students, enhancing their academic outcomes.
  • Machine learning algorithms such as Random Forest and Support Vector Machine were evaluated for their predictive accuracy.
  • A classification model was developed to serve as an early warning system for academic failures, enabling timely interventions.
  • Interventions based on identified student needs can facilitate effective responses in educational settings, ensuring equity.

Cite This Study

Alkan et al. (2025) studied this question.

synapsesocial.com/papers/692523b2c0ce034ddc35484bhttps://doi.org/10.1038/s41598-025-23409-w
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