Publication:
Assessment and Evaluation of Different Machine Learning Algorithms for Predicting Student Performance

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dc.citedby3
dc.contributor.authorAlsariera Y.A.en_US
dc.contributor.authorBaashar Y.en_US
dc.contributor.authorAlkawsi G.en_US
dc.contributor.authorMustafa A.en_US
dc.contributor.authorAlkahtani A.A.en_US
dc.contributor.authorAli N.en_US
dc.contributor.authorid57216243342en_US
dc.contributor.authorid56768090200en_US
dc.contributor.authorid57191982354en_US
dc.contributor.authorid57218103026en_US
dc.contributor.authorid55646765500en_US
dc.contributor.authorid54985243500en_US
dc.date.accessioned2023-05-29T09:40:47Z
dc.date.available2023-05-29T09:40:47Z
dc.date.issued2022
dc.descriptionDecision trees; Learning algorithms; Nearest neighbor search; Neural networks; Students; Support vector machines; Academic achievements; Effective tool; Key feature; Large volumes; Machine learning algorithms; Machine learning approaches; Student performance; Systematic searches; Tertiary institutions; Top qualities; Forecasting; algorithm; Bayes theorem; human; machine learning; student; support vector machine; Algorithms; Bayes Theorem; Humans; Machine Learning; Neural Networks, Computer; Students; Support Vector Machineen_US
dc.description.abstractStudent performance is crucial to the success of tertiary institutions. Especially, academic achievement is one of the metrics used in rating top-quality universities. Despite the large volume of educational data, accurately predicting student performance becomes more challenging. The main reason for this is the limited research in various machine learning (ML) approaches. Accordingly, educators need to explore effective tools for modelling and assessing student performance while recognizing weaknesses to improve educational outcomes. The existing ML approaches and key features for predicting student performance were investigated in this work. Related studies published between 2015 and 2021 were identified through a systematic search of various online databases. Thirty-nine studies were selected and evaluated. The results showed that six ML models were mainly used: decision tree (DT), artificial neural networks (ANNs), support vector machine (SVM), K-nearest neighbor (KNN), linear regression (LinR), and Naive Bayes (NB). Our results also indicated that ANN outperformed other models and had higher accuracy levels. Furthermore, academic, demographic, internal assessment, and family/personal attributes were the most predominant input variables (e.g., predictive features) used for predicting student performance. Our analysis revealed an increasing number of research in this domain and a broad range of ML algorithms applied. At the same time, the extant body of evidence suggested that ML can be beneficial in identifying and improving various academic performance areas. � 2022 Yazan A. Alsariera et al.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo4151487
dc.identifier.doi10.1155/2022/4151487
dc.identifier.scopus2-s2.0-85130259445
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85130259445&doi=10.1155%2f2022%2f4151487&partnerID=40&md5=d931bbd6967f19b4cb68e6662c8f3dd1
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27194
dc.identifier.volume2022
dc.publisherHindawi Limiteden_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleComputational Intelligence and Neuroscience
dc.titleAssessment and Evaluation of Different Machine Learning Algorithms for Predicting Student Performanceen_US
dc.typeReviewen_US
dspace.entity.typePublication
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