Publication:
Imbalanced Classification Methods for Student Grade Prediction: A Systematic Literature Review

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dc.citedby11
dc.contributor.authorAbdul Bujang S.D.en_US
dc.contributor.authorSelamat A.en_US
dc.contributor.authorKrejcar O.en_US
dc.contributor.authorMohamed F.en_US
dc.contributor.authorCheng L.K.en_US
dc.contributor.authorChiu P.C.en_US
dc.contributor.authorFujita H.en_US
dc.contributor.authorid24467381700en_US
dc.contributor.authorid24468984100en_US
dc.contributor.authorid14719632500en_US
dc.contributor.authorid55416008900en_US
dc.contributor.authorid57188850203en_US
dc.contributor.authorid36968467900en_US
dc.contributor.authorid35611951900en_US
dc.date.accessioned2024-10-14T03:22:06Z
dc.date.available2024-10-14T03:22:06Z
dc.date.issued2023
dc.description.abstractStudent success is essential for improving the higher education system student outcome. One way to measure student success is by predicting students' performance based on their prior academic grades. Concerning the significance of this area, various predictive models are widely developed and applied to help the institution identify students at risk of failure. However, building a high-accuracy predictive model is challenging due to the dataset's imbalanced nature, which caused biased results. Therefore, this study aims to review the existing research article by providing a state-of-the-art approach for handling imbalanced classification in higher education, including the best practices of dataset characteristics, methods, and comparative analysis of the proposed algorithms, focusing on student grade prediction context problems. The study also presents the most common balancing methods published from 2015 to 2021 and highlights their impact on resolving imbalanced classification in three approaches: data-level, algorithm-level, and hybrid-level. The survey results reveal that the data-level approach using SMOTE oversampling is broadly applied in determining imbalanced problems for student grade prediction. However, the application of hybrid and feature selection methods supporting the generalization of the predictive model to boost student grade prediction performance is generally lacking. Other than that, some of the strengths and weaknesses of the proposed methods are discussed and summarized for the direction of future research. The outcomes of this review will guide the professionals, practitioners, and academic researchers in dealing with imbalanced classification, mainly in the higher education field. � 2013 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/ACCESS.2022.3225404
dc.identifier.epage1989
dc.identifier.scopus2-s2.0-85144071000
dc.identifier.spage1970
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85144071000&doi=10.1109%2fACCESS.2022.3225404&partnerID=40&md5=67bf6048abdbb544d3d3f4a653579c5f
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34726
dc.identifier.volume11
dc.pagecount19
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.sourceScopus
dc.sourcetitleIEEE Access
dc.subjecteducation
dc.subjectImbalanced classification
dc.subjectmachine learning
dc.subjectprediction model
dc.subjectstudent grade prediction
dc.subjectsystematic literature review
dc.subjectClassification (of information)
dc.subjectEducation computing
dc.subjectForecasting
dc.subjectLearning systems
dc.subjectGrade predictions
dc.subjectHigh educations
dc.subjectImbalanced classification
dc.subjectMachine-learning
dc.subjectPrediction modelling
dc.subjectPredictive models
dc.subjectStudent grade prediction
dc.subjectStudent success
dc.subjectStudents' grades
dc.subjectSystematic literature review
dc.subjectStudents
dc.titleImbalanced Classification Methods for Student Grade Prediction: A Systematic Literature Reviewen_US
dc.typeReviewen_US
dspace.entity.typePublication
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