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Predicting student's performance using machine learning methods: A systematic literature review

Decision trees; Forecasting; Nearest neighbor search; Neural networks; Search engines; Students; Support vector machines; Academic achievements; Academic performance; Higher education institutions; K nearest neighbor (KNN); Machine learning methods; Predicting and analyzing; Student's performan...

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Main Authors: Baashar Y., Alkawsi G., Ali N., Alhussian H., Bahbouh H.T.
Other Authors: 56768090200
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers Inc. 2023
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spelling my.uniten.dspace-260902023-05-29T17:06:43Z Predicting student's performance using machine learning methods: A systematic literature review Baashar Y. Alkawsi G. Ali N. Alhussian H. Bahbouh H.T. 56768090200 57191982354 54985243500 55430817100 6508133865 Decision trees; Forecasting; Nearest neighbor search; Neural networks; Search engines; Students; Support vector machines; Academic achievements; Academic performance; Higher education institutions; K nearest neighbor (KNN); Machine learning methods; Predicting and analyzing; Student's performance; Systematic literature review; Learning systems Student's performance is a success factor in higher education institutions. The excellent record of academic achievements raises the institution's ranking as one of the criteria for a high-quality university. Predicting and analyzing the performance of the student is essential to assist educators in identifying weaknesses and enhancing the academic scores. However, achieving accurate predictions is challenging due to huge amount of educational data. The main reason behind this, is the lack of research on exploring different prediction methods and key attributes that influence the student's academic performance. Hence, this systematic review intends to explore the current machine learning methods and attributes used in predicting the student's performance. Several online databases were used to perform a systematic search of data-driven studies. The analysis and assessment of 30 selected articles revealed five main prediction methods: artificial neural networks (ANNs), decision trees, support vector machine (SVM), k-nearest neighbor (KNN) and na�ve Bayes. Our findings revealed that the ANN method has the best performance with a high level of accuracy. Demographic, academic, family/personal and internal assessment were found to be the most frequently used attributes in prediction the student performance. � 2021 IEEE. Final 2023-05-29T09:06:43Z 2023-05-29T09:06:43Z 2021 Conference Paper 10.1109/ICCOINS49721.2021.9497185 2-s2.0-85112437325 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112437325&doi=10.1109%2fICCOINS49721.2021.9497185&partnerID=40&md5=3740aeb1ff595a0b4e5642847baeb44c https://irepository.uniten.edu.my/handle/123456789/26090 9497185 357 362 Institute of Electrical and Electronics Engineers Inc. 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; Forecasting; Nearest neighbor search; Neural networks; Search engines; Students; Support vector machines; Academic achievements; Academic performance; Higher education institutions; K nearest neighbor (KNN); Machine learning methods; Predicting and analyzing; Student's performance; Systematic literature review; Learning systems
author2 56768090200
author_facet 56768090200
Baashar Y.
Alkawsi G.
Ali N.
Alhussian H.
Bahbouh H.T.
format Conference Paper
author Baashar Y.
Alkawsi G.
Ali N.
Alhussian H.
Bahbouh H.T.
spellingShingle Baashar Y.
Alkawsi G.
Ali N.
Alhussian H.
Bahbouh H.T.
Predicting student's performance using machine learning methods: A systematic literature review
author_sort Baashar Y.
title Predicting student's performance using machine learning methods: A systematic literature review
title_short Predicting student's performance using machine learning methods: A systematic literature review
title_full Predicting student's performance using machine learning methods: A systematic literature review
title_fullStr Predicting student's performance using machine learning methods: A systematic literature review
title_full_unstemmed Predicting student's performance using machine learning methods: A systematic literature review
title_sort predicting student's performance using machine learning methods: a systematic literature review
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806424499720028160
score 13.211869

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