Which noncognitive features provide more information about reading performance? A data-mining approach to big educational data

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dc.authoridARICAK, Osman Tolga/0000-0001-8598-5539
dc.authoridGuldal, Hakan/0000-0002-1566-9378
dc.authoridErdogan, Irfan/0000-0003-4535-4956
dc.authorwosidARICAK, Osman Tolga/B-9039-2008
dc.authorwosidGüldal, Hakan/IXE-0139-2023
dc.contributor.authorAricak, Osman Tolga
dc.contributor.authorGuldal, Hakan
dc.contributor.authorErdogan, Irfan
dc.date.accessioned2024-06-12T11:13:24Z
dc.date.available2024-06-12T11:13:24Z
dc.date.issued2023
dc.departmentTrakya Üniversitesien_US
dc.description.abstractThe purpose of this study is to discover which noncognitive variables provide more information about reading performance. To answer this question, data mining based on information gain, decision tree and random forest methods were utilized in the study. The participants of the study consisted of 606,627 15-year-old students (49.8% female) in a total of 78 countries or economies, 37 of which are OECD members. Reading performance and plausible values of reading, the Student, ICT Familiarity, Financial Literacy, Educational Career, Well-Being and Parent Questionnaire data in PISA 2018 were analyzed to answer the research questions. When 108 features were analyzed as independent variables, it was found that SES (home possessions, cultural possessions, and ICT resources at home), metacognitive skills (assessing credibility and summarizing), and liking/enjoying reading were major variables predicting reading performance. The path analysis revealed that these variables explain 53.3% of the variability in reading performance. It is also remarkable that the decision tree model has a 74.61% accuracy value in estimating the reading performance.en_US
dc.identifier.doi10.1177/18344909231164025
dc.identifier.issn1834-4909
dc.identifier.scopus2-s2.0-85152926500en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1177/18344909231164025
dc.identifier.urihttps://hdl.handle.net/20.500.14551/23539
dc.identifier.volume17en_US
dc.identifier.wosWOS:000973085400001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSage Publications Ltden_US
dc.relation.ispartofJournal Of Pacific Rim Psychologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectReading Performanceen_US
dc.subjectSESen_US
dc.subjectMetacognitionen_US
dc.subjectData Miningen_US
dc.subjectAchievementen_US
dc.subjectPredictorsen_US
dc.subjectAlgorithmsen_US
dc.subjectTimssen_US
dc.titleWhich noncognitive features provide more information about reading performance? A data-mining approach to big educational dataen_US
dc.typeArticleen_US

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