{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T05:25:12Z","timestamp":1761110712100,"version":"build-2065373602"},"reference-count":161,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,12]],"date-time":"2025-08-12T00:00:00Z","timestamp":1754956800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Universidad Nacional Mayor de San Marcos","award":["004305-R-24"],"award-info":[{"award-number":["004305-R-24"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>College dropout represents a significant challenge for universities, and despite advances in machine learning technologies, predicting dropout remains a complex task. This literature review focuses on investigating the factors that influence college dropout, examining the models used to predict it, and highlighting the most significant advances in explainability and simulation over the period 2012 to 2024 using the PRISMA methodology. They identified 520 factors in five categories (demographic, socioeconomic, institutional, personal, and academic), with the most studied factors in each category being, respectively, gender, scholarships, infrastructure, student identification, and grades. They also identified 83 machine learning models, with the most studied being the decision tree, logistic regression, and random forest. In addition, eight explanatory models were identified, with SHAP and LIME being the most widely used. Finally, no simulation models related to university dropout were identified. This study groups factors related to university dropout into key models for prediction and analyzes the methods used to explain the causal factors that influence university student dropout.<\/jats:p>","DOI":"10.3390\/computation13080198","type":"journal-article","created":{"date-parts":[[2025,8,12]],"date-time":"2025-08-12T15:51:02Z","timestamp":1755013862000},"page":"198","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Factors, Prediction, Explainability, and Simulating University Dropout Through Machine Learning: A Systematic Review, 2012\u20132024"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5430-0215","authenticated-orcid":false,"given":"Mauricio","family":"Quimiz-Moreira","sequence":"first","affiliation":[{"name":"Facultad de Ingenier\u00eda de Sistemas e Inform\u00e1tica, Universidad Nacional Mayor de San Marcos, Lima 15081, Peru"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4899-3008","authenticated-orcid":false,"given":"Rosa","family":"Delgadillo","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda de Sistemas e Inform\u00e1tica, Universidad Nacional Mayor de San Marcos, Lima 15081, Peru"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8558-9122","authenticated-orcid":false,"given":"Jorge","family":"Parraga-Alava","sequence":"additional","affiliation":[{"name":"Facultad de Ciencias Inform\u00e1ticas, Universidad T\u00e9cnica de Manab\u00ed, Portoviejo 130104, Ecuador"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3897-3356","authenticated-orcid":false,"given":"Nelson","family":"Maculan","sequence":"additional","affiliation":[{"name":"Systems Engineering-Computer Science and Applied Mathematics, CT & CCMN, Campus\u2014Ilha do Fund\u00e3o, Federal University of Rio de Janeiro, Rio de Janeiro 21941-617, Brazil"}]},{"given":"David","family":"Mauricio","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda de Sistemas e Inform\u00e1tica, Universidad Nacional Mayor de San Marcos, Lima 15081, Peru"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Baranyi, M., Nagy, M., and Molontay, R. 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