Predicting and Mitigating Freshmen Student Attrition: A Local-Explainable Machine Learning Framework

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dc.authoridDelen, Dursun/0000-0001-8857-5148
dc.authorwosidDelen, Dursun/O-6938-2015
dc.contributor.authorDelen, Dursun
dc.contributor.authorDavazdahemami, Behrooz
dc.contributor.authorRasouli Dezfouli, Elham
dc.date.accessioned2024-05-19T14:39:00Z
dc.date.available2024-05-19T14:39:00Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractWith the emergence of novel methods for improving machine learning (ML) transparency, traditional decision-support-focused information systems seem to need an upgrade in their approach toward providing more actionable insights for practitioners. Particularly, given the complex decision-making process of humans, using insights obtained from group-level interpretation of ML models for designing individual interventions may lead to mixed results. The present study proposes a hybrid ML framework by integrating established predictive and explainable ML approaches for decision support systems involving the prediction of human decisions and designing individualized interventions accordingly. The proposed framework is aimed at providing actionable insights for designing individualized interventions. It was showcased in the context of college students' attrition problem using a large and feature-rich integrated data set of freshman college students containing information about their demographics, educational, financial, and socioeconomic factors. A comparison of feature importance scores at the group- vs. individual-level revealed that while group-level insights might be useful for adjusting long-term strategies, using them as a one-size-fits-all strategy to design and implement individual interventions is subject to suboptimal outcomes.en_US
dc.identifier.doi10.1007/s10796-023-10397-3
dc.identifier.issn1387-3326
dc.identifier.issn1572-9419
dc.identifier.pmid37361887en_US
dc.identifier.scopus2-s2.0-85152466776en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1007/s10796-023-10397-3
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4671
dc.identifier.wosWOS:001196137400001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofInformation Systems Frontiersen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.subjectAnalyticsen_US
dc.subjectPredictionen_US
dc.subjectDeep Learningen_US
dc.subjectShapen_US
dc.subjectExplainable Aien_US
dc.subjectStudent Attritionen_US
dc.titlePredicting and Mitigating Freshmen Student Attrition: A Local-Explainable Machine Learning Frameworken_US
dc.typeArticleen_US

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