Publication:
Toward Predicting Student�s Academic Performance Using Artificial Neural Networks (ANNs)

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dc.citedby12
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.authorAlsariera Y.A.en_US
dc.contributor.authorAli A.Q.en_US
dc.contributor.authorHashim W.en_US
dc.contributor.authorTiong S.K.en_US
dc.contributor.authorid56768090200en_US
dc.contributor.authorid57191982354en_US
dc.contributor.authorid57218103026en_US
dc.contributor.authorid55646765500en_US
dc.contributor.authorid57216243342en_US
dc.contributor.authorid57208663036en_US
dc.contributor.authorid11440260100en_US
dc.contributor.authorid15128307800en_US
dc.date.accessioned2023-05-29T09:38:22Z
dc.date.available2023-05-29T09:38:22Z
dc.date.issued2022
dc.description.abstractStudent performance is related to complex and correlated factors. The implementation of a new advancement of technologies in educational displacement has unlimited potentials. One of these advances is the use of analytics and data mining to predict student academic accomplishment and performance. Given the existing literature, machine learning (ML) approaches such as Artificial Neural Networks (ANNs) can continuously be improved. This work examines and surveys the current literature regarding the ANN methods used in predicting students� academic performance. This study also attempts to capture a pattern of the most used ANN techniques and algorithms. Of note, the articles reviewed mainly focused on higher education. The results indicated that ANN is always used in combination with data analysis and data mining methodologies, allowing studies to assess the effectiveness of their findings in evaluating academic achievement. No pattern was detected regarding selecting the input variables as they are mainly based on the context of the study and the availability of data. Moreover, the very limited tangible findings referred to the use of techniques in the actual context and target objective of improving student outcomes, performance, and achievement. An important recommendation of this work is to overcome the identified gap related to the only theoretical and limited application of the ANN in a real-life situation to help achieve the educational goals. � 2022 by the authors. Licensee MDPI, Basel, Switzerland.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo1289
dc.identifier.doi10.3390/app12031289
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85123402369
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85123402369&doi=10.3390%2fapp12031289&partnerID=40&md5=7fcf7eff9000c3a4fe4b54308f7217e1
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26982
dc.identifier.volume12
dc.publisherMDPIen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleApplied Sciences (Switzerland)
dc.titleToward Predicting Student�s Academic Performance Using Artificial Neural Networks (ANNs)en_US
dc.typeArticleen_US
dspace.entity.typePublication
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