Early Prediction of Students’ Performance Through Deep Learning: A Systematic and Bibliometric Literature Review
Early prediction of student performance is a critical and challenging task in the field of Educational Data Mining (EDM), encompassing all levels of education. Although there is extensive literature on student performance within EDM, studies specifically focused on early prediction are limited and m...
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| Format: | Article |
| Language: | English |
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Sakarya University
2025-03-01
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| Series: | Sakarya University Journal of Computer and Information Sciences |
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| Online Access: | https://dergipark.org.tr/en/download/article-file/4590421 |
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| _version_ | 1849728926723604480 |
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| author | İhsan Hakan Selvi Tuğba Yıldız Orhan Torkul Ahmet Kala |
| author_facet | İhsan Hakan Selvi Tuğba Yıldız Orhan Torkul Ahmet Kala |
| author_sort | İhsan Hakan Selvi |
| collection | DOAJ |
| description | Early prediction of student performance is a critical and challenging task in the field of Educational Data Mining (EDM), encompassing all levels of education. Although there is extensive literature on student performance within EDM, studies specifically focused on early prediction are limited and mostly rely on traditional machine learning methods. However, in recent years, the importance and use of deep learning (DL) methods have increased due to their ability to process large datasets. This systematic literature review focuses on the early prediction of student performance using DL techniques. A total of 39 articles selected from the Scopus and Web of Science databases were analyzed using systematic and bibliometric methods. The review addresses five key research questions, including the distribution of studies by publication year, type, and education level; the datasets and features used; DL models and techniques; the timing of early predictions; and the challenges, limitations, and opportunities encountered. The bibliometric analysis, conducted with the VOSviewer program, visualized relationships between keywords, authors, and articles. Overall, this review provides a comprehensive synthesis of existing research on the early prediction of student academic performance using DL, offering valuable insights into trends and opportunities for researchers, educators, and policymakers. |
| format | Article |
| id | doaj-art-90debce667c94300985ff98d0d60a9b0 |
| institution | DOAJ |
| issn | 2636-8129 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Sakarya University |
| record_format | Article |
| series | Sakarya University Journal of Computer and Information Sciences |
| spelling | doaj-art-90debce667c94300985ff98d0d60a9b02025-08-20T03:09:24ZengSakarya UniversitySakarya University Journal of Computer and Information Sciences2636-81292025-03-018115217010.35377/saucis...163555828Early Prediction of Students’ Performance Through Deep Learning: A Systematic and Bibliometric Literature Reviewİhsan Hakan Selvi0https://orcid.org/0000-0002-8837-2137Tuğba Yıldız1https://orcid.org/0000-0002-3207-8932Orhan Torkul2https://orcid.org/0000-0003-2690-7228Ahmet Kala3https://orcid.org/0000-0002-0598-1181SAKARYA UNIVERSITYBOLU ABANT IZZET BAYSAL UNIVERSITYSAKARYA UNIVERSITYSAKARYA UNIVERSITY OF APPLIED SCIENCESEarly prediction of student performance is a critical and challenging task in the field of Educational Data Mining (EDM), encompassing all levels of education. Although there is extensive literature on student performance within EDM, studies specifically focused on early prediction are limited and mostly rely on traditional machine learning methods. However, in recent years, the importance and use of deep learning (DL) methods have increased due to their ability to process large datasets. This systematic literature review focuses on the early prediction of student performance using DL techniques. A total of 39 articles selected from the Scopus and Web of Science databases were analyzed using systematic and bibliometric methods. The review addresses five key research questions, including the distribution of studies by publication year, type, and education level; the datasets and features used; DL models and techniques; the timing of early predictions; and the challenges, limitations, and opportunities encountered. The bibliometric analysis, conducted with the VOSviewer program, visualized relationships between keywords, authors, and articles. Overall, this review provides a comprehensive synthesis of existing research on the early prediction of student academic performance using DL, offering valuable insights into trends and opportunities for researchers, educators, and policymakers.https://dergipark.org.tr/en/download/article-file/4590421educationeducational data miningearly predictionstudent performancedeep learningbibliometric literature reviewsystematic literature review |
| spellingShingle | İhsan Hakan Selvi Tuğba Yıldız Orhan Torkul Ahmet Kala Early Prediction of Students’ Performance Through Deep Learning: A Systematic and Bibliometric Literature Review Sakarya University Journal of Computer and Information Sciences education educational data mining early prediction student performance deep learning bibliometric literature review systematic literature review |
| title | Early Prediction of Students’ Performance Through Deep Learning: A Systematic and Bibliometric Literature Review |
| title_full | Early Prediction of Students’ Performance Through Deep Learning: A Systematic and Bibliometric Literature Review |
| title_fullStr | Early Prediction of Students’ Performance Through Deep Learning: A Systematic and Bibliometric Literature Review |
| title_full_unstemmed | Early Prediction of Students’ Performance Through Deep Learning: A Systematic and Bibliometric Literature Review |
| title_short | Early Prediction of Students’ Performance Through Deep Learning: A Systematic and Bibliometric Literature Review |
| title_sort | early prediction of students performance through deep learning a systematic and bibliometric literature review |
| topic | education educational data mining early prediction student performance deep learning bibliometric literature review systematic literature review |
| url | https://dergipark.org.tr/en/download/article-file/4590421 |
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