{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T15:36:22Z","timestamp":1780500982120,"version":"3.54.1"},"reference-count":17,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T00:00:00Z","timestamp":1756857600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Student success is a multifaceted outcome influenced by academic, behavioral, contextual, and socio-environmental factors. With the growing availability of educational data, machine learning (ML) offers promising tools to model complex, nonlinear relationships that go beyond traditional statistical methods. However, the lack of interpretability in many ML models remains a major obstacle for practical adoption in educational contexts. In this study, we apply explainable artificial intelligence (XAI) techniques\u2014specifically SHAP (SHapley Additive exPlanations)\u2014to analyze a synthetic dataset simulating diverse student profiles. Using LightGBM, we identify variables such as hours studied, attendance, and parental involvement as influential in predicting exam performance. While the results are not generalizable due to the artificial nature of the data, this study reframes its purpose as a methodological exploration rather than a claim of real-world actionable insights. Our findings demonstrate how interpretable ML can be used to build transparent analytic pipelines in education, setting the stage for future research using empirical datasets and real student data.<\/jats:p>","DOI":"10.3390\/info16090763","type":"journal-article","created":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T15:15:57Z","timestamp":1756912557000},"page":"763","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Exploring Predictive Insights on Student Success Using Explainable Machine Learning: A Synthetic Data Study"],"prefix":"10.3390","volume":"16","author":[{"given":"Beatriz","family":"Santana-Perera","sequence":"first","affiliation":[{"name":"Department of Specific Didactics, University of Las Palmas de Gran Canaria, 35001 Las Palmas de Gran Canaria, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-8369-6027","authenticated-orcid":false,"given":"Carmen","family":"Garc\u00eda-Barcel\u00f3","sequence":"additional","affiliation":[{"name":"Department of Computer Science Technology and Computation, University of Alicante, 03690 Alicante, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mauricio","family":"Gonz\u00e1lez Arcas","sequence":"additional","affiliation":[{"name":"Department of Computer Science Technology and Computation, University of Alicante, 03690 Alicante, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0791-8298","authenticated-orcid":false,"given":"David","family":"Gil","sequence":"additional","affiliation":[{"name":"Department of Computer Science Technology and Computation, University of Alicante, 03690 Alicante, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,3]]},"reference":[{"key":"ref_1","unstructured":"Xu, Y., Ma, L., Wang, Z., and Sun, S. 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