Explainable Optimized Machine Learning for Predicting Student Self-Regulated Learning

doi:10.1080/00220973.2026.2719482

Explainable Optimized Machine Learning for Predicting Student Self-Regulated Learning

2026-08-29

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Konstantinos Zervoudakis

Konstantinos Mastrothanasis

DOI：10.1080/00220973.2026.2719482

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This study examined the utility of an optimized, explainable machine learning (ML) model for predicting students’ self-regulated learning (SRL). Participants were 823 middle school students (52.6% girls) from randomly selected public schools who completed in-person measures of relevant social, motivational, and contextual factors. Results showed that a tree-based ML model combined with explainability techniques showed strong internal predictive performance and identified influential predictors of SRL. Task interest was the strongest predictor of SRL, followed by subjective well-being and perceived teacher support, with moderate contributions from responsibility for learning and smaller effects of social well-being. These findings demonstrate the value of explainable ML for identifying key contributors to SRL and translating predictive models into actionable educational insights.

Keywords:

Self-regulated learning

optimized machine learning

predictors

students

middle schools

Explainable Optimized Machine Learning for Predicting Student Self-Regulated Learning

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