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Analysis and Prediction of At-Risk Students Using Machine Learning Algorithms

◐ medium · 2026-06-23learning-analytics, higher-ed, dropout-reduction, student-modeling, rct, llm

Gheisari and Salarian (2026) apply supervised machine learning classification to identify at-risk students before they withdraw from higher education programs. The study evaluates Logistic Regression, Random Forest, Support Vector Machines (SVM), and K-Nearest Neighbors (KNN) using academic performance, demographic data, and enrollment records. Logistic Regression and linear SVM achieved the highest predictive accuracy, demonstrating ML's capability to detect at-risk students for proactive intervention. This Learning Analytics research contributes to Student Modeling for dropout-reduction in Higher Ed contexts, providing a data-driven foundation for strategic retention decisions and connecting to the broader AI Adoption Training Public Sector discussion on AI-supported institutional decision-making.

Connected Concepts

  • Learning Analytics
  • Student Modeling
  • Higher Ed

    • Connected Articles

  • AI Adoption Training Public Sector

    • Citation

    Soheila Gheisari, Hamid Salarian (2026). Analysis and Prediction of At-Risk Students Using Machine Learning Algorithms. arXiv:2606.20617. arXiv:2606.20617 (cs.CY; cs.LG)