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

Decision 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 institutio...

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Main Authors: Alsariera Y.A., Baashar Y., Alkawsi G., Mustafa A., Alkahtani A.A., Ali N.
Other Authors: 57216243342
Format: Review
Published: Hindawi Limited 2023
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id my.uniten.dspace-27194
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spelling my.uniten.dspace-271942023-05-29T17:40:47Z Assessment and Evaluation of Different Machine Learning Algorithms for Predicting Student Performance Alsariera Y.A. Baashar Y. Alkawsi G. Mustafa A. Alkahtani A.A. Ali N. 57216243342 56768090200 57191982354 57218103026 55646765500 54985243500 Decision 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 Machine Student 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. Final 2023-05-29T09:40:47Z 2023-05-29T09:40:47Z 2022 Review 10.1155/2022/4151487 2-s2.0-85130259445 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130259445&doi=10.1155%2f2022%2f4151487&partnerID=40&md5=d931bbd6967f19b4cb68e6662c8f3dd1 https://irepository.uniten.edu.my/handle/123456789/27194 2022 4151487 All Open Access, Gold, Green Hindawi Limited Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Decision 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 Machine
author2 57216243342
author_facet 57216243342
Alsariera Y.A.
Baashar Y.
Alkawsi G.
Mustafa A.
Alkahtani A.A.
Ali N.
format Review
author Alsariera Y.A.
Baashar Y.
Alkawsi G.
Mustafa A.
Alkahtani A.A.
Ali N.
spellingShingle Alsariera Y.A.
Baashar Y.
Alkawsi G.
Mustafa A.
Alkahtani A.A.
Ali N.
Assessment and Evaluation of Different Machine Learning Algorithms for Predicting Student Performance
author_sort Alsariera Y.A.
title Assessment and Evaluation of Different Machine Learning Algorithms for Predicting Student Performance
title_short Assessment and Evaluation of Different Machine Learning Algorithms for Predicting Student Performance
title_full Assessment and Evaluation of Different Machine Learning Algorithms for Predicting Student Performance
title_fullStr Assessment and Evaluation of Different Machine Learning Algorithms for Predicting Student Performance
title_full_unstemmed Assessment and Evaluation of Different Machine Learning Algorithms for Predicting Student Performance
title_sort assessment and evaluation of different machine learning algorithms for predicting student performance
publisher Hindawi Limited
publishDate 2023
_version_ 1806428133484658688
score 13.222552

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