Explainable Early Warning Systems for Student Dropout Prediction: Insights from a Bibliometric Analysis
DOI:
https://doi.org/10.15837/ijccc.2026.4.7599Keywords:
student dropout prediction, machine learning, bibliometric analysis, explainable artificial intelligence, early warning systemAbstract
Student dropout and weak academic performance are among the most costly problems higher education institutions face, and their early prediction has become an active research field. This paper maps that field through a bibliometric analysis of 352 Scopus-indexed documents published in English between 2014 and 2025, selected from 446 records through a documented screening. Author-keyword co-occurrence analysis in VOSviewer, after thesaurus-based cleaning, yielded 39 recurrent keywords organized into four clusters: an applied core around machine learning and dropout prediction in higher education; classification algorithms and deep-learning architectures; the older educational-data-mining vocabulary; and academic performance, retention and class imbalance. A temporal overlay shows the field moving from generic classification and data mining toward deep learning, gradient boosting and explainable artificial intelligence (XAI). Against the four gaps this map exposes, interpretability, transferability, fairness and deployment, we propose X-EWS, a five-layer explainable early-warning framework spanning data acquisition to intervention and governance, in which each gap is answered by a specific architectural choice. A proof-of-concept instantiation on a public benchmark of 4,424 students illustrates that each layer of the framework can be realized with standard, auditable components.
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