Predicting and analyzing secondary education placement-test scores: A data mining approach

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dc.authoridSEN, BAHA/0000-0003-3577-2548
dc.authoridUCAR, EMINE/0000-0002-6838-3015
dc.authoridDelen, Dursun/0000-0001-8857-5148
dc.contributor.authorSen, Baha
dc.contributor.authorUcar, Emine
dc.contributor.authorDelen, Dursun
dc.date.accessioned2024-09-29T15:57:10Z
dc.date.available2024-09-29T15:57:10Z
dc.date.issued2012
dc.departmentKarabük Üniversitesien_US
dc.description.abstractUnderstanding the factors that lead to success (or failure) of students at placement tests is an interesting and challenging problem. Since the centralized placement tests and future academic achievements are considered to be related concepts, analysis of the success factors behind placement tests may help understand and potentially improve academic achievement. In this study using a large and feature rich dataset from Secondary Education Transition System in Turkey we developed models to predict secondary education placement test results, and using sensitivity analysis on those prediction models we identified the most important predictors. The results showed that CS decision tree algorithm is the best predictor with 95% accuracy on hold-out sample, followed by support vector machines (with an accuracy of 91%) and artificial neural networks (with an accuracy of 89%). Logistic regression models came out to be the least accurate of the four with and overall accuracy of 82%. The sensitivity analysis revealed that previous test experience, whether a student has a scholarship, student's number of siblings, previous years' grade point average are among the most important predictors of the placement test scores. (C) 2012 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.eswa.2012.02.112
dc.identifier.endpage9476en_US
dc.identifier.issn0957-4174
dc.identifier.issue10en_US
dc.identifier.scopus2-s2.0-84859217470en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage9468en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2012.02.112
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4626
dc.identifier.volume39en_US
dc.identifier.wosWOS:000303281800100en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems With Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectData miningen_US
dc.subjectClassificationen_US
dc.subjectPredictionen_US
dc.subjectSensitivity analysisen_US
dc.subjectSETSen_US
dc.titlePredicting and analyzing secondary education placement-test scores: A data mining approachen_US
dc.typeArticleen_US

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