Abstract:

Educational Data Mining (EDM) is increasingly used to inform instruction, personalize support, and improve outcomes for learners with disabilities. In this article we attempt to synthesize current evidence on how EDM, in tandem with Learning Analytics, impacts Special Education across three domains: early identification and screening, adaptive intervention, and equity-centered governance. Systematic reviews and empirical studies show that predictive modeling (e.g., classification with tree-based ensembles or deep learning) can flag risk and guide tiered instruction, supporting timely data-driven adjustments to Individualized Education Programs (IEPs) and Response to Intervention (RTI) frameworks. Explainable AI techniques (e.g., SHAP) are gaining traction to increase transparency and educator trust, while multimodal and extended-reality learning environments are expanding the kinds of data available for personalization. Despite promising gains in engagement, retention, and achievement, the literature highlights persistent challenges, such as limited representation of disability subgroups in datasets; portability of models across contexts; and governance concerns around privacy, consent, and bias that are amplified for vulnerable populations.

The goal of this narrative review is to critically examine the existing literature on the application of data mining techniques in Special Education, with a specific focus on its utility in improving:
(i) participatory problem framing with special educators and families,
(ii) robust, accessible feature sets (behavioral, performance, assistive-tech interaction, and contextual supports),
(iii) interpretable modeling with continuous validation, and
(iv) governance aligned with FERPA/GDPR and disability rights principles.

Future work should prioritize inclusive data standards, impact evaluations that report disability-disaggregated effects, and cross-institutional benchmarks to move from promising pilots to equitable, scalable practice.

Keywords:

Special Education, Educational Data Mining, Learning Analytics, Individualized Education Programs, tiered instruction.