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dc.contributor.authorYakubu,Mohammed Nasiru
dc.contributor.authorAbubakar, Abubakar Mohammed
dc.date.accessioned2022-03-31T12:26:48Z
dc.date.available2022-03-31T12:26:48Z
dc.date.issued2021
dc.identifier.citationYakubu, M. N. & Abubakar, A. M. (2021). Applying machine learning approach to predict students’ performance in higher educational institutions. Kybernetes, 51(2), 916-934.en_US
dc.identifier.urihttp://hdl.handle.net/20.500.12566/1083
dc.description.abstractPurpose – Academic success and failure are relevant lifelines for economic success in the knowledge-based economy. The purpose of this paper is to predict the propensity of students’ academic performance using early detection indicators (i.e. age, gender, high school exam scores, region, CGPA) to allow for timely and efficient remediation. Design/methodology/approach – A machine learning approach was used to develop a model based on secondary data obtained from students’ information system in a Nigerian university. Findings – Results revealed that age is not a predictor for academic success (high CGPA); female students are 1.2 times more likely to have high CGPA compared to their male counterparts; students with high JAMB scores are more likely to achieve academic success, high CGPA and vice versa; students from affluent and developed regions are more likely to achieve academic success, high CGPA and vice versa; and students in Years 3 and 4 are more likely to achieve academic success, high CGPA. Originality/value – This predictive model serves as a classifier and useful strategy to mitigate failure, promote success and better manage resources in tertiary institutions.en_US
dc.description.sponsorshipNo sponsoren_US
dc.language.isoengen_US
dc.publisherKybernetesen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectInformation systemsen_US
dc.subjectBilgi sistemitr_TR
dc.subjectEducationen_US
dc.subjectEğitimtr_TR
dc.subjectICTen_US
dc.subjectArtificial intelligenceen_US
dc.subjectYapay zekatr_TR
dc.subjectAcademic successen_US
dc.subjectAkademik başarıtr_TR
dc.subjectMachine learningen_US
dc.subjectMakine öğrenmetr_TR
dc.subjectLogistic regressionen_US
dc.subjectLojistik regresyontr_TR
dc.subjectEnrollment dataen_US
dc.subjectKayıt verileritr_TR
dc.subjectHigher educationen_US
dc.subjectYüksek öğrenimtr_TR
dc.subjectNigeriaen_US
dc.subjectNijeryatr_TR
dc.subjectInternational publicationen_US
dc.titleApplying machine learning approach to predict students’ performance in higher educational institutionsen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.identifier.wosWOS:000663043800001
dc.identifier.scopus2-s2.0-85108161735
dc.identifier.volume51
dc.identifier.issue2
dc.identifier.startpage916
dc.identifier.endpage934
dc.contributor.orcid0000-0002-1163-0185 [Abubakar, Abubakar Mohammed]
dc.contributor.abuauthorAbubakar, Abubakar Mohammed
dc.contributor.yokid255914 [Abubakar, Abubakar Mohammed]
dc.contributor.ScopusAuthorID57193113146 [Abubakar, Abubakar Mohammed]
dc.identifier.doihttps://doi.org/10.1108/K-12-2020-0865


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