Toward Predicting Student�s Academic Performance Using Artificial Neural Networks (ANNs)
Student 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. Gi...
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my.uniten.dspace-269822023-05-29T17:38:22Z Toward Predicting Student�s Academic Performance Using Artificial Neural Networks (ANNs) Baashar Y. Alkawsi G. Mustafa A. Alkahtani A.A. Alsariera Y.A. Ali A.Q. Hashim W. Tiong S.K. 56768090200 57191982354 57218103026 55646765500 57216243342 57208663036 11440260100 15128307800 Student 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. Final 2023-05-29T09:38:22Z 2023-05-29T09:38:22Z 2022 Article 10.3390/app12031289 2-s2.0-85123402369 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123402369&doi=10.3390%2fapp12031289&partnerID=40&md5=7fcf7eff9000c3a4fe4b54308f7217e1 https://irepository.uniten.edu.my/handle/123456789/26982 12 3 1289 All Open Access, Gold MDPI Scopus |
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Student 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. |
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56768090200 |
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56768090200 Baashar Y. Alkawsi G. Mustafa A. Alkahtani A.A. Alsariera Y.A. Ali A.Q. Hashim W. Tiong S.K. |
| format |
Article |
| author |
Baashar Y. Alkawsi G. Mustafa A. Alkahtani A.A. Alsariera Y.A. Ali A.Q. Hashim W. Tiong S.K. |
| spellingShingle |
Baashar Y. Alkawsi G. Mustafa A. Alkahtani A.A. Alsariera Y.A. Ali A.Q. Hashim W. Tiong S.K. Toward Predicting Student�s Academic Performance Using Artificial Neural Networks (ANNs) |
| author_sort |
Baashar Y. |
| title |
Toward Predicting Student�s Academic Performance Using Artificial Neural Networks (ANNs) |
| title_short |
Toward Predicting Student�s Academic Performance Using Artificial Neural Networks (ANNs) |
| title_full |
Toward Predicting Student�s Academic Performance Using Artificial Neural Networks (ANNs) |
| title_fullStr |
Toward Predicting Student�s Academic Performance Using Artificial Neural Networks (ANNs) |
| title_full_unstemmed |
Toward Predicting Student�s Academic Performance Using Artificial Neural Networks (ANNs) |
| title_sort |
toward predicting student�s academic performance using artificial neural networks (anns) |
| publisher |
MDPI |
| publishDate |
2023 |
| _version_ |
1806426501227216896 |
| score |
13.222552 |