Model Interpretability in the Educational Data Mining Context: A Systematic Literature Mapping
DOI:
https://doi.org/10.5753/rbie.2026.6984Keywords:
Educational Data Mining, Interpretability, Explainability, Explainable AI, Fairness, Systematic Literature Mapping, Systematic Literature ReviewAbstract
Machine learning (ML) techniques in the Educational Data Mining (EDM) context enable the development of increasingly efficient prediction models. This evolution benefits from the improvement of ML techniques, the current massive collection of heterogeneous data, and the high availability of computational power. As a result, efficient models are obtained; however, they are complex and challenging to comprehend. Therefore, the field of model interpretability, also known as Explainable Artificial Intelligence (XAI) research, becomes a pivotal piece in consolidating and giving credibility to the solutions developed in the EDM context. In this sense, we presented a systematic mapping to understand how studies in the area of EDM address interpretability, aiming to answer questions related to the (i) interpretability context, (ii) interpretability methods, metrics, and objectives, (iii) education levels, (iv) educational data, and (v) ML techniques. To achieve this goal, we conducted a Systematic Literature Mapping (SLM), which involved defining a research protocol with planning, conducting, and reporting phases. These phases included defining research questions, establishing digital reference libraries, and establishing inclusion and exclusion criteria. The findings indicate that, despite the peculiarities of each study, interpretability is frequently addressed as a post hoc component rather than as a core objective of model design, limiting its systematic evaluation and comparative analysis across models. There is a need for studies in which interpretability is a central objective, including the comparison of interpretability across models from different ML techniques, the exploration or proposal of interpretability metrics (particularly agnostic ones), and the investigation of the relationship between interpretability and algorithmic fairness. Overall, this study offers a comprehensive perspective on the applicability of interpretability methods in various educational contexts, synthesizes best practices and limitations in measuring and comparing model interpretability, and highlights the importance of involving stakeholders in the development of transparent and effective EDM applications.
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