Vol. 19 No. 1 (2026): Special Issue on Predicting Students' Learning Outcomes Using Machine Learning

Published: 2026-03-31

Full Issue

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• A Systematic Approach to Predicting Students' Academic Performance A Review of Recent Literature

  Anselmus Yata Mones

  1–14

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• Actionable Learning Analytics Predicting University Performance Levels with Interpretable Machine Learning

  Enrique De La Hoz, Carlos Garcia-Yerena, Ingrid Torres-Rojas

  15–27

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• Unpacking the Black Box A Hybrid XAI Framework for AutoGluon-Based Multiclass Student Outcome Prediction

  Marwan Nawae, Siripa Chankua, Massaya Longsaman

  28–39

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• Machine Learning Predictions of Student Outcomes The Role of Educational Structure and Social Stressors in Czech Municipalities

  Martin Flegl, Marketa Matulova, Kristyna Vltavska

  40–56

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• Measuring Academic Efficiency in High-Impact Scholarships A Two-Stage Windows DEA and Gaussian Mixture Model Approach

  Andres Acero, Miguel Alejandro Garzón-Parra, Jesús Isaac Vázquez-Serrano

  57–71

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• Imbalanced Multi-class Prediction of Student Drop-out and Graduation A Systematic Literature Review

  Ridwan Setiawan, Edi Noersasongko, Abdul Syukur, Fikri Budiman, Dede Kurniadi

  72–90

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• Academic Productivity Dynamics in Colombian Social Science Programs A PCA–Malmquist Index Approach (2020–2023)

  Enrique De La Hoz, Carlos Garcia-Yerena, Rohemi Zuluaga-Ortiz

  91–100

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