Transforming education with deep learning: A systematic review on predicting student performance and critical challenges
Deep learning (DL) is recognized as a breakthrough in the educational technology arena, more so in the sense that it can be applied for forecasting student performance and critical issues in academic systems. This systematic review is used to investigate advances in the DL-based system-to-predicting...
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| Format: | Article |
| Language: | en |
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American Scientific Publishing Group (ASPG)
2025
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| Online Access: | http://umpir.ump.edu.my/id/eprint/43706/1/Transforming%20education%20with%20deep%20learning.pdf http://umpir.ump.edu.my/id/eprint/43706/ https://doi.org/10.54216/FPA.180207 |
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| author | Nazir, M. Noraziah, Ahmad Rahmah, Mokhtar Fakherldin, Mohammed Khawaji, Ahmad |
| author_facet | Nazir, M. Noraziah, Ahmad Rahmah, Mokhtar Fakherldin, Mohammed Khawaji, Ahmad |
| author_sort | Nazir, M. |
| building | UMPSA Library |
| collection | Institutional Repository |
| content_provider | Universiti Malaysia Pahang Al-Sultan Abdullah |
| content_source | UMPSA Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Deep learning (DL) is recognized as a breakthrough in the educational technology arena, more so in the sense that it can be applied for forecasting student performance and critical issues in academic systems. This systematic review is used to investigate advances in the DL-based system-to-predicting student performance and emphasizes its applicability, methodologies, and limitations. The paper analyses key technologies such as neural networks (NNs) and ensemble models used in educational data mining. The paper also points out limitations in previous studies, for example, data imbalance model interpretability, and issues of scalability. This review highlights the potential of DL to improve educational quality, provide personalized learning experiences, and mitigate learning hazards by synthesizing ideas from different studies. Future directions will comprise hybrid models, improvements in data preprocessing, and merging with real-time educational systems to optimize the performance of the prediction model in several academic environments. For this review, 58 papers were collected from the year 2017-2024 respectively based on DL in education, Risk in education, and student education performance analysis. Subsequently, the aim, technique used, dataset used, performance score attained, significance, and limitations of the existing studies were discussed in this review. |
| format | Article |
| id | my.ump.umpir.43706 |
| institution | Universiti Malaysia Pahang |
| language | en |
| publishDate | 2025 |
| publisher | American Scientific Publishing Group (ASPG) |
| record_format | eprints |
| spelling | my.ump.umpir.437062025-01-31T01:28:45Z http://umpir.ump.edu.my/id/eprint/43706/ Transforming education with deep learning: A systematic review on predicting student performance and critical challenges Nazir, M. Noraziah, Ahmad Rahmah, Mokhtar Fakherldin, Mohammed Khawaji, Ahmad LB Theory and practice of education QA75 Electronic computers. Computer science Deep learning (DL) is recognized as a breakthrough in the educational technology arena, more so in the sense that it can be applied for forecasting student performance and critical issues in academic systems. This systematic review is used to investigate advances in the DL-based system-to-predicting student performance and emphasizes its applicability, methodologies, and limitations. The paper analyses key technologies such as neural networks (NNs) and ensemble models used in educational data mining. The paper also points out limitations in previous studies, for example, data imbalance model interpretability, and issues of scalability. This review highlights the potential of DL to improve educational quality, provide personalized learning experiences, and mitigate learning hazards by synthesizing ideas from different studies. Future directions will comprise hybrid models, improvements in data preprocessing, and merging with real-time educational systems to optimize the performance of the prediction model in several academic environments. For this review, 58 papers were collected from the year 2017-2024 respectively based on DL in education, Risk in education, and student education performance analysis. Subsequently, the aim, technique used, dataset used, performance score attained, significance, and limitations of the existing studies were discussed in this review. American Scientific Publishing Group (ASPG) 2025 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/43706/1/Transforming%20education%20with%20deep%20learning.pdf Nazir, M. and Noraziah, Ahmad and Rahmah, Mokhtar and Fakherldin, Mohammed and Khawaji, Ahmad (2025) Transforming education with deep learning: A systematic review on predicting student performance and critical challenges. Fusion: Practice and Applications (FPA), 18 (2). pp. 79-99. ISSN 2692-4048. (Published) https://doi.org/10.54216/FPA.180207 https://doi.org/10.54216/FPA.180207 |
| spellingShingle | LB Theory and practice of education QA75 Electronic computers. Computer science Nazir, M. Noraziah, Ahmad Rahmah, Mokhtar Fakherldin, Mohammed Khawaji, Ahmad Transforming education with deep learning: A systematic review on predicting student performance and critical challenges |
| title | Transforming education with deep learning: A systematic review on predicting student performance and critical challenges |
| title_full | Transforming education with deep learning: A systematic review on predicting student performance and critical challenges |
| title_fullStr | Transforming education with deep learning: A systematic review on predicting student performance and critical challenges |
| title_full_unstemmed | Transforming education with deep learning: A systematic review on predicting student performance and critical challenges |
| title_short | Transforming education with deep learning: A systematic review on predicting student performance and critical challenges |
| title_sort | transforming education with deep learning: a systematic review on predicting student performance and critical challenges |
| topic | LB Theory and practice of education QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/43706/1/Transforming%20education%20with%20deep%20learning.pdf http://umpir.ump.edu.my/id/eprint/43706/ https://doi.org/10.54216/FPA.180207 https://doi.org/10.54216/FPA.180207 |
| url_provider | http://umpir.ump.edu.my/ |