Review
BibTex RIS Cite

Year 2025, Volume: 8 Issue: 1, 152 - 170, 28.03.2025

Ahmet Kala , Orhan Torkul , Tuğba Yıldız , İhsan Hakan Selvi

https://doi.org/10.35377/saucis...1635558

Abstract

References

  • S. Keskin, F. Aydın, and H. Yurdugül, ‘Eğitsel Veri Madenciliği ve Öğrenme Analitikleri Bağlamında E-Öğrenme Verilerinde Aykırı Gözlemlerin Belirlenmesi’, Eğitim Teknolojisi Kuram ve Uygulama, vol. 9, no. 1, pp. 292–309, Jan. 2019, doi: 10.17943/etku.475149.
  • K. Akgün and M. Bulut Özek, ‘Eğitsel Veri Madenciliği Yöntemi İle İlgili Yapılmış Çalışmaların İncelenmesi: İçerik Analizi’, Uluslararası Eğitim Bilim ve Teknoloji Dergisi, vol. 6, no. 3, pp. 197–213, Dec. 2020, doi: 10.47714/uebt.753526.
  • J. Berens, K. Schneider, S. Görtz, S. Oster, and J. Burghoff, ‘Early Detection of Students at Risk – Predicting Student Dropouts Using Administrative Student Data from German Universities and Machine Learning Methods,’ Journal of Educational Data Mining, vol. 11, no. 3, p. 41, 2019.
  • L. C. Yu et al., ‘Improving early prediction of academic failure using sentiment analysis on self‐evaluated comments’, Journal of Computer Assisted Learning, vol. 34, no. 4, pp. 358–365, Aug. 2018, doi: 10.1111/jcal.12247.
  • C. Romero and S. Ventura, ‘Guest Editorial: Special Issue on Early Prediction and Supporting of Learning Performance’, IEEE Transactions on Learning Technologies, vol. 12, no. 2, pp. 145–147, Apr. 2019, doi: 10.1109/TLT.2019.2908106.
  • B. Albreiki, N. Zaki, and H. Alashwal, ‘A Systematic Literature Review of Student’ Performance Prediction Using Machine Learning Techniques’, Education Sciences, vol. 11, no. 9, p. 552, Sep. 2021, doi: 10.3390/educsci11090552.
  • P. Chakrapani and C. D, ‘Academic Performance Prediction Using Machine Learning: A Comprehensive & Systematic Review’, in 2022 International Conference on Electronic Systems and Intelligent Computing (ICESIC), Chennai, India: IEEE, Apr. 2022, pp. 335–340. doi: 10.1109/ICESIC53714.2022.9783512.
  • K. Alalawi, R. Athauda, and R. Chiong, ‘Contextualizing the current state of research on the use of machine learning for student performance prediction: A systematic literature review’, Engineering Reports, vol. 5, no. 12, Dec. 2023, doi: 10.1002/eng2.12699.
  • A. Nabil, M. Seyam, and A. A. Elfetouh, ‘Predicting students’ academic performance using machine learning techniques: a literature review’, International Journal of Business Intelligence and Data Mining, vol. 20, no. 4, p. 456, 2022, doi: 10.1504/IJBIDM.2022.123214.
  • N. Iam-On and T. Boongoen, ‘Generating descriptive model for student dropout: a review of clustering approach’, Human-centric Computing and Information Sciences, vol. 7, no. 1, p. 1, Dec. 2017, doi: 10.1186/s13673-016-0083-0.
  • Y. K. Hui and L. F. Kwok, ‘A review on learning analytics’, International Journal of Innovation and Learning, vol. 25, no. 2, p. 197, 2019, doi: 10.1504/IJIL.2019.097673.
  • A. Namoun and A. Alshanqiti, ‘Predicting Student Performance Using Data Mining and Learning Analytics Techniques: A Systematic Literature Review’, Applied Sciences, vol. 11, no. 1, p. 237, Dec. 2020, doi: 10.3390/app11010237.
  • M. Ingle, ‘A Review On Research Areas In Educational Data Mining And Learning Analytics’, INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH, vol. 8, 2019, [Online]. Available: www.ijstr.org
  • A. M. Shahiri, ‘A Review on Predicting Student’s Performance Using Data Mining Techniques’, Procedia Computer Science, p. 9, 2015, doi: 10.1016/j.procs.2015.12.157.
  • R. Ordoñez-Avila, N. S. Reyes, J. Meza, and S. Ventura, ‘Data mining techniques for predicting teacher evaluation in higher education: A systematic literature review’, Heliyon, vol. 9, no. 3, p. e13939, Mar. 2023, doi: 10.1016/j.heliyon.2023.e13939.
  • P. S. Pawar and R. Jain, ‘A review on Student Performance Prediction using Educational Data mining and Artificial Intelligence’, in 2021 IEEE 2nd International Conference on Technology, Engineering, Management for Societal impact using Marketing, Entrepreneurship and Talent (TEMSMET), Pune, India: IEEE, Dec. 2021, pp. 1–7. doi: 10.1109/TEMSMET53515.2021.9768773.
  • W. Xiao, P. Ji, and J. Hu, ‘A survey on educational data mining methods for predicting students’ performance’, Engineering Reports, vol. 4, no. 5, May 2022, doi: 10.1002/eng2.12482.
  • H. Nawang, M. Makhtar, and W. M. A. F. W. Hamza, ‘A systematic literature review on student performance predictions’, International Journal of Advanced Technology and Engineering Exploration, vol. 8, no. 84, Nov. 2021, doi: 10.19101/IJATEE.2021.874521.
  • K. Aulakh, R. K. Roul, and M. Kaushal, ‘E-learning enhancement through educational data mining with Covid-19 outbreak period in backdrop: A review’, International Journal of Educational Development, vol. 101, p. 102814, Sep. 2023, doi: 10.1016/j.ijedudev.2023.102814.
  • M. Saqr, R. Elmoazen, M. Tedre, S. López-Pernas, and L. Hirsto, ‘How well centrality measures capture student achievement in computer-supported collaborative learning? – A systematic review and meta-analysis’, Educational Research Review, vol. 35, p. 100437, Feb. 2022, doi: 10.1016/j.edurev.2022.100437.
  • D. A. Shafiq, M. Marjani, R. A. A. Habeeb, and D. Asirvatham, ‘Student Retention Using Educational Data Mining and Predictive Analytics: A Systematic Literature Review’, IEEE Access, vol. 10, pp. 72480–72503, 2022, doi: 10.1109/ACCESS.2022.3188767.
  • M. Zaffar, M. A. Hashmani, K. S. Savita, and S. A. Khan, ‘A review on feature selection methods for improving classification performance in educational data mining’, International Journal of Information Technology and Management, vol. 20, no. 1/2, p. 110, 2021, doi: 10.1504/IJITM.2021.114161.
  • A. Abu Saa, M. Al-Emran, and K. Shaalan, ‘Factors Affecting Students’ Performance in Higher Education: A Systematic Review of Predictive Data Mining Techniques’, Tech Know Learn, vol. 24, no. 4, pp. 567–598, Dec. 2019, doi: 10.1007/s10758-019-09408-7.
  • E. Alyahyan and D. Düştegör, ‘Predicting academic success in higher education: literature review and best practices’, Int J Educ Technol High Educ, vol. 17, no. 1, p. 3, Dec. 2020, doi: 10.1186/s41239-020-0177-7.
  • J. López-Zambrano, J. A. Lara Torralbo, and C. Romero, ‘Early Prediction of Student Learning Performance Through Data Mining: A Systematic Review’, Psicothema, no. 33.3, pp. 456–465, Ağustos 2021, doi: 10.7334/psicothema2021.62.
  • H. Q. Alatawi and S. Hechmi, ‘A Survey of Data Mining Methods for Early Prediction of Students’ Performance’, in 2022 2nd International Conference on Computing and Information Technology (ICCIT), Tabuk, Saudi Arabia: IEEE, Jan. 2022, pp. 171–174. doi: 10.1109/ICCIT52419.2022.9711642.
  • S. M. Muthukrishnan, M. K. Govindasamy, and M. N. Mustapha, ‘Systematic mapping review on student’s performance analysis using big data predictive model’, Journal of Fundamental and Applied Sciences, vol. 9, no. 4S, p. 730, Jan. 2018, doi: 10.4314/jfas.v9i4S.41.
  • X. Bai et al., ‘Educational Big Data: Predictions, Applications and Challenges’, Big Data Research, vol. 26, p. 100270, Nov. 2021, doi: 10.1016/j.bdr.2021.100270.
  • A. Hernández-Blanco, B. Herrera-Flores, D. Tomás, and B. Navarro-Colorado, ‘A Systematic Review of Deep Learning Approaches to Educational Data Mining’, Complexity, vol. 2019, pp. 1–22, May 2019, doi: 10.1155/2019/1306039.
  • Kitchenham Barbara and Charters Stuart M., ‘Guidelines for performing systematic literature reviews in software engineering’, School of Computer Science and Mathematics, Keele University., Durham, UK, EBSE Technical Report EBSE-2007-01, 2007.
  • X. Li, Y. Zhang, H. Cheng, M. Li, and B. Yin, ‘Student achievement prediction using deep neural network from multi-source campus data’, Complex Intell. Syst., vol. 8, no. 6, pp. 5143–5156, Dec. 2022, doi: 10.1007/s40747-022-00731-8.
  • B. Venkatachalam and K. Sivanraju, ‘Predicting Student Performance Using Mental Health and Linguistic Attributes with Deep Learning’, Revue d’Intelligence Artificielle, vol. 37, no. 4, pp. 889–899, Aug. 2023, doi: 10.18280/ria.370408.
  • A. A. Almahdi and B. T. Sharef, ‘Deep Learning Based An Optimized Predictive Academic Performance Approach’, in 2023 International Conference on IT Innovation and Knowledge Discovery (ITIKD), Manama, Bahrain: IEEE, Mar. 2023, pp. 1–6. doi: 10.1109/ITIKD56332.2023.10099652.
  • X. Wen and H. Juan, ‘Early Prediction of Students’ Performance Using a Deep Neural Network Based on Online Learning Activity Sequence’, Applied Sciences, vol. 13, no. 15, p. 8933, Aug. 2023, doi: 10.3390/app13158933.
  • C. A. Anjali and V. R. Bai, ‘An Early Prediction of Dropouts for At-risk Scholars in MOOCs using Deep Learning’, in 2022 International Conference on Computing, Communication, Security and Intelligent Systems (IC3SIS), IEEE, Jun. 2022, pp. 1–6. doi: 10.1109/IC3SIS54991.2022.9885328.
  • H. Waheed, S.-U. Hassan, R. Nawaz, N. R. Aljohani, G. Chen, and D. Gasevic, ‘Early prediction of learners at risk in self-paced education: A neural network approach’, Expert Systems with Applications, vol. 213, p. 118868, Mar. 2023, doi: 10.1016/j.eswa.2022.118868.
  • B. T. Sayed, M. Madanan, and N. Biju, ‘An Efficient Artificial Intelligence-Based Educational Data Mining Approach for Higher Education and Early Recognition System’, SN Computer Science, vol. 4, no. 2, p. 130, Dec. 2022, doi: 10.1007/s42979-022-01562-7.
  • S. Leelaluk, T. Minematsu, Y. Taniguchi, F. Okubo, T. Yamashita, and A. Shimada, ‘Scaled-Dot Product Attention for Early Detection of At-risk Students’, in 2022 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE), Hung Hom, Hong Kong: IEEE, Dec. 2022, pp. 316–322. doi: 10.1109/TALE54877.2022.00059.
  • F. A. Al-azazi and M. Ghurab, ‘ANN-LSTM: A deep learning model for early student performance prediction in MOOC’, Heliyon, vol. 9, no. 4, p. e15382, Apr. 2023, doi: 10.1016/j.heliyon.2023.e15382.
  • H. Wan, M. Li, Z. Zhong, and X. Luo, ‘Early Prediction of Student Performance with LSTM-Based Deep Neural Network’, in 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC), IEEE, Jun. 2023, pp. 132–141. doi: 10.1109/COMPSAC57700.2023.00026.
  • K. Qin, X. Xie, Q. He, and G. Deng, ‘Early Warning of Student Performance With Integration of Subjective and Objective Elements’, IEEE Access, vol. 11, pp. 72601–72617, 2023, doi: 10.1109/ACCESS.2023.3295580.
  • S. S. Kusumawardani and S. A. I. Alfarozi, ‘Transformer Encoder Model for Sequential Prediction of Student Performance Based on Their Log Activities’, IEEE Access, vol. 11, pp. 18960–18971, 2023, doi: 10.1109/ACCESS.2023.3246122.
  • H.-C. Chen et al., ‘Week-Wise Student Performance Early Prediction in Virtual Learning Environment Using a Deep Explainable Artificial Intelligence’, Applied Sciences, vol. 12, no. 4, p. 1885, Feb. 2022, doi: 10.3390/app12041885.
  • F. D. Pereira et al., ‘Explaining Individual and Collective Programming Students’ Behavior by Interpreting a Black-Box Predictive Model’, IEEE Access, vol. 9, pp. 117097–117119, 2021, doi: 10.1109/ACCESS.2021.3105956.
  • E. Tanuar, Y. Heryadi, Lukas, B. S. Abbas, and F. L. Gaol, ‘Using Machine Learning Techniques to Earlier Predict Student’s Performance’, in 2018 Indonesian Association for Pattern Recognition International Conference (INAPR), Jakarta, Indonesia: IEEE, Sep. 2018, pp. 85–89. doi: 10.1109/INAPR.2018.8626856.
  • H. Waheed, S.-U. Hassan, N. R. Aljohani, J. Hardman, S. Alelyani, and R. Nawaz, ‘Predicting academic performance of students from VLE big data using deep learning models’, Computers in Human Behavior, vol. 104, p. 106189, Mar. 2020, doi: 10.1016/j.chb.2019.106189.
  • N. R. Aljohani, A. Fayoumi, and S.-U. Hassan, ‘Predicting At-Risk Students Using Clickstream Data in the Virtual Learning Environment’, Sustainability, vol. 11, no. 24, p. 7238, Dec. 2019, doi: 10.3390/su11247238.
  • C.-C. Yu and Y. (Leon) Wu, ‘Early Warning System for Online STEM Learning—A Slimmer Approach Using Recurrent Neural Networks’, Sustainability, vol. 13, no. 22, p. 12461, Nov. 2021, doi: 10.3390/su132212461.
  • M. Y. S. L. Z. Aljuid, ‘Deep Learning Based Method For Prediction of Software Engineering Project Teamwork Assessment in Higher Education’, Journal of Theoretical and Aplied Information Technology, vol. 99, no. 9, 2021, [Online]. Available: www.jatit.org
  • Y. Mao, S. Marwan, and T. W. Price, ‘What Time is It? Student Modeling Needs to Know’, p. 12, 2020.
  • H. Mi, Z. Gao, Q. Zhang, and Y. Zheng, ‘Research on Constructing Online Learning Performance Prediction Model Combining Feature Selection and Neural Network’, Int. J. Emerg. Technol. Learn., vol. 17, no. 07, pp. 94–111, Apr. 2022, doi: 10.3991/ijet.v17i07.25587.
  • N. M. Aslam, I. U. Khan, L. H. Alamri, and R. S. Almuslim, ‘An Improved Early Student’s Academic Performance Prediction Using Deep Learning’, International Journal of Emerging Technologies in Learning (iJET), vol. 16, no. 12, p. 108, Jun. 2021, doi: 10.3991/ijet.v16i12.20699.
  • B.-H. Kim, E. Vizitei, and V. Ganapathi, ‘GritNet: Student Performance Prediction with Deep Learning’. arXiv, Apr. 19, 2018. Accessed: Sep. 08, 2022. [Online]. Available: http://arxiv.org/abs/1804.07405
  • X. Wang, P. Wu, G. Liu, Q. Huang, X. Hu, and H. Xu, ‘Learning performance prediction via convolutional GRU and explainable neural networks in e-learning environments’, Computing, vol. 101, no. 6, pp. 587–604, Jun. 2019, doi: 10.1007/s00607-018-00699-9.
  • X. Li, X. Zhu, X. Zhu, Y. Ji, and X. Tang, ‘Student Academic Performance Prediction Using Deep Multi-source Behavior Sequential Network’, in Advances in Knowledge Discovery and Data Mining, vol. 12084, H. W. Lauw, R. C.-W. Wong, A. Ntoulas, E.-P. Lim, S.-K. Ng, and S. J. Pan, Eds., in Lecture Notes in Computer Science, vol. 12084. , Cham: Springer International Publishing, 2020, pp. 567–579. doi: 10.1007/978-3-030-47426-3_44.
  • O. Ojajuni et al., ‘Predicting Student Academic Performance Using Machine Learning’, in Computational Science and Its Applications – ICCSA 2021, vol. 12957, O. Gervasi, B. Murgante, S. Misra, C. Garau, I. Blečić, D. Taniar, B. O. Apduhan, A. M. A. C. Rocha, E. Tarantino, and C. M. Torre, Eds., in Lecture Notes in Computer Science, vol. 12957. , Cham: Springer International Publishing, 2021, pp. 481–491. doi: 10.1007/978-3-030-87013-3_36.
  • R. C. Raga and J. D. Raga, ‘Early Prediction of Student Performance in Blended Learning Courses Using Deep Neural Networks’, in 2019 International Symposium on Educational Technology (ISET), Hradec Kralove, Czech Republic: IEEE, Jul. 2019, pp. 39–43. doi: 10.1109/ISET.2019.00018.
  • S. Surenthiran, R. Rajalakshmi, and S. S. Sujatha, ‘Student Performance Prediction Using Atom Search Optimization Based Deep Belief Neural Network’, Optical Memory and Neural Networks, vol. 30, no. 2, pp. 157–171, Apr. 2021, doi: 10.3103/S1060992X21020119.
  • H. Karimi, T. Derr, J. Huang, and J. Tang, ‘Online Academic Course Performance Prediction using Relational Graph Convolutional Neural Network’, in The International Conference on Educational Data Mining (EDM), 2020, pp. 7–7. [Online]. Available: https://github.com/hamidkarimi/dope.
  • F. Chen and Y. Cui, ‘Utilizing Student Time Series Behaviour in Learning Management Systems for Early Prediction of Course Performance’, JLA, vol. 7, no. 2, pp. 1–17, Sep. 2020, doi: 10.18608/jla.2020.72.1.
  • Q. Hu and H. Rangwala, ‘Reliable Deep Grade Prediction with Uncertainty Estimation’, in Proceedings of the 9th International Conference on Learning Analytics & Knowledge, ACM, Mar. 2019, pp. 76–85. doi: 10.1145/3303772.3303802.
  • S. Hassan, H. Waheed, N. R. Aljohani, M. Ali, S. Ventura, and F. Herrera, ‘Virtual learning environment to predict withdrawal by leveraging deep learning’, Int J Intell Syst, vol. 34, no. 8, pp. 1935–1952, Aug. 2019, doi: 10.1002/int.22129.
  • L. Wang, A. Sy, L. Liu, and C. Piech, ‘Learning to Represent Student Knowledge on Programming Exercises Using Deep Learning’, in The International Conference on Educational Data Mining (EDM), 2017, pp. 6–6.
  • X. Du, J. Yang, J.-L. Hung, and B. Shelton, ‘Educational data mining: a systematic review of research and emerging trends’, Information Discovery and Delivery, vol. 48, no. 4, pp. 225–236, May 2020, doi: 10.1108/IDD-09-2019-0070.
  • D. Uliyan, A. S. Aljaloud, A. Alkhalil, H. S. A. Amer, M. A. E. A. Mohamed, and A. F. M. Alogali, ‘Deep Learning Model to Predict Students Retention Using BLSTM and CRF’, IEEE Access, vol. 9, pp. 135550–135558, 2021, doi: 10.1109/ACCESS.2021.3117117.
  • H. S. Park and S. J. Yoo, ‘Early Dropout Prediction in Online Learning of University using Machine Learning’, JOIV : International Journal on Informatics Visualization, vol. 5, no. 4, p. 347, Dec. 2021, doi: 10.30630/joiv.5.4.732.
  • A. Nabil, M. Seyam, and A. Abou-Elfetouh, ‘Prediction of Students’ Academic Performance Based on Courses’ Grades Using Deep Neural Networks’, IEEE Access, vol. 9, pp. 140731–140746, 2021, doi: 10.1109/ACCESS.2021.3119596.
  • M. Adnan et al., ‘Predicting at-Risk Students at Different Percentages of Course Length for Early Intervention Using Machine Learning Models’, IEEE Access, vol. 9, pp. 7519–7539, 2021, doi: 10.1109/ACCESS.2021.3049446.
  • B. K. Yousafzai et al., ‘Student-Performulator: Student Academic Performance Using Hybrid Deep Neural Network’, Sustainability, vol. 13, no. 17, p. 9775, Aug. 2021, doi: 10.3390/su13179775

Early Prediction of Students’ Performance Through Deep Learning: A Systematic and Bibliometric Literature Review

Year 2025, Volume: 8 Issue: 1, 152 - 170, 28.03.2025

Ahmet Kala , Orhan Torkul , Tuğba Yıldız , İhsan Hakan Selvi

https://doi.org/10.35377/saucis...1635558

Abstract

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.

Keywords

Education Educational data mining Early prediction Student performance Deep learning Bibliometric literature review Systematic literature review

References

  • S. Keskin, F. Aydın, and H. Yurdugül, ‘Eğitsel Veri Madenciliği ve Öğrenme Analitikleri Bağlamında E-Öğrenme Verilerinde Aykırı Gözlemlerin Belirlenmesi’, Eğitim Teknolojisi Kuram ve Uygulama, vol. 9, no. 1, pp. 292–309, Jan. 2019, doi: 10.17943/etku.475149.
  • K. Akgün and M. Bulut Özek, ‘Eğitsel Veri Madenciliği Yöntemi İle İlgili Yapılmış Çalışmaların İncelenmesi: İçerik Analizi’, Uluslararası Eğitim Bilim ve Teknoloji Dergisi, vol. 6, no. 3, pp. 197–213, Dec. 2020, doi: 10.47714/uebt.753526.
  • J. Berens, K. Schneider, S. Görtz, S. Oster, and J. Burghoff, ‘Early Detection of Students at Risk – Predicting Student Dropouts Using Administrative Student Data from German Universities and Machine Learning Methods,’ Journal of Educational Data Mining, vol. 11, no. 3, p. 41, 2019.
  • L. C. Yu et al., ‘Improving early prediction of academic failure using sentiment analysis on self‐evaluated comments’, Journal of Computer Assisted Learning, vol. 34, no. 4, pp. 358–365, Aug. 2018, doi: 10.1111/jcal.12247.
  • C. Romero and S. Ventura, ‘Guest Editorial: Special Issue on Early Prediction and Supporting of Learning Performance’, IEEE Transactions on Learning Technologies, vol. 12, no. 2, pp. 145–147, Apr. 2019, doi: 10.1109/TLT.2019.2908106.
  • B. Albreiki, N. Zaki, and H. Alashwal, ‘A Systematic Literature Review of Student’ Performance Prediction Using Machine Learning Techniques’, Education Sciences, vol. 11, no. 9, p. 552, Sep. 2021, doi: 10.3390/educsci11090552.
  • P. Chakrapani and C. D, ‘Academic Performance Prediction Using Machine Learning: A Comprehensive & Systematic Review’, in 2022 International Conference on Electronic Systems and Intelligent Computing (ICESIC), Chennai, India: IEEE, Apr. 2022, pp. 335–340. doi: 10.1109/ICESIC53714.2022.9783512.
  • K. Alalawi, R. Athauda, and R. Chiong, ‘Contextualizing the current state of research on the use of machine learning for student performance prediction: A systematic literature review’, Engineering Reports, vol. 5, no. 12, Dec. 2023, doi: 10.1002/eng2.12699.
  • A. Nabil, M. Seyam, and A. A. Elfetouh, ‘Predicting students’ academic performance using machine learning techniques: a literature review’, International Journal of Business Intelligence and Data Mining, vol. 20, no. 4, p. 456, 2022, doi: 10.1504/IJBIDM.2022.123214.
  • N. Iam-On and T. Boongoen, ‘Generating descriptive model for student dropout: a review of clustering approach’, Human-centric Computing and Information Sciences, vol. 7, no. 1, p. 1, Dec. 2017, doi: 10.1186/s13673-016-0083-0.
  • Y. K. Hui and L. F. Kwok, ‘A review on learning analytics’, International Journal of Innovation and Learning, vol. 25, no. 2, p. 197, 2019, doi: 10.1504/IJIL.2019.097673.
  • A. Namoun and A. Alshanqiti, ‘Predicting Student Performance Using Data Mining and Learning Analytics Techniques: A Systematic Literature Review’, Applied Sciences, vol. 11, no. 1, p. 237, Dec. 2020, doi: 10.3390/app11010237.
  • M. Ingle, ‘A Review On Research Areas In Educational Data Mining And Learning Analytics’, INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH, vol. 8, 2019, [Online]. Available: www.ijstr.org
  • A. M. Shahiri, ‘A Review on Predicting Student’s Performance Using Data Mining Techniques’, Procedia Computer Science, p. 9, 2015, doi: 10.1016/j.procs.2015.12.157.
  • R. Ordoñez-Avila, N. S. Reyes, J. Meza, and S. Ventura, ‘Data mining techniques for predicting teacher evaluation in higher education: A systematic literature review’, Heliyon, vol. 9, no. 3, p. e13939, Mar. 2023, doi: 10.1016/j.heliyon.2023.e13939.
  • P. S. Pawar and R. Jain, ‘A review on Student Performance Prediction using Educational Data mining and Artificial Intelligence’, in 2021 IEEE 2nd International Conference on Technology, Engineering, Management for Societal impact using Marketing, Entrepreneurship and Talent (TEMSMET), Pune, India: IEEE, Dec. 2021, pp. 1–7. doi: 10.1109/TEMSMET53515.2021.9768773.
  • W. Xiao, P. Ji, and J. Hu, ‘A survey on educational data mining methods for predicting students’ performance’, Engineering Reports, vol. 4, no. 5, May 2022, doi: 10.1002/eng2.12482.
  • H. Nawang, M. Makhtar, and W. M. A. F. W. Hamza, ‘A systematic literature review on student performance predictions’, International Journal of Advanced Technology and Engineering Exploration, vol. 8, no. 84, Nov. 2021, doi: 10.19101/IJATEE.2021.874521.
  • K. Aulakh, R. K. Roul, and M. Kaushal, ‘E-learning enhancement through educational data mining with Covid-19 outbreak period in backdrop: A review’, International Journal of Educational Development, vol. 101, p. 102814, Sep. 2023, doi: 10.1016/j.ijedudev.2023.102814.
  • M. Saqr, R. Elmoazen, M. Tedre, S. López-Pernas, and L. Hirsto, ‘How well centrality measures capture student achievement in computer-supported collaborative learning? – A systematic review and meta-analysis’, Educational Research Review, vol. 35, p. 100437, Feb. 2022, doi: 10.1016/j.edurev.2022.100437.
  • D. A. Shafiq, M. Marjani, R. A. A. Habeeb, and D. Asirvatham, ‘Student Retention Using Educational Data Mining and Predictive Analytics: A Systematic Literature Review’, IEEE Access, vol. 10, pp. 72480–72503, 2022, doi: 10.1109/ACCESS.2022.3188767.
  • M. Zaffar, M. A. Hashmani, K. S. Savita, and S. A. Khan, ‘A review on feature selection methods for improving classification performance in educational data mining’, International Journal of Information Technology and Management, vol. 20, no. 1/2, p. 110, 2021, doi: 10.1504/IJITM.2021.114161.
  • A. Abu Saa, M. Al-Emran, and K. Shaalan, ‘Factors Affecting Students’ Performance in Higher Education: A Systematic Review of Predictive Data Mining Techniques’, Tech Know Learn, vol. 24, no. 4, pp. 567–598, Dec. 2019, doi: 10.1007/s10758-019-09408-7.
  • E. Alyahyan and D. Düştegör, ‘Predicting academic success in higher education: literature review and best practices’, Int J Educ Technol High Educ, vol. 17, no. 1, p. 3, Dec. 2020, doi: 10.1186/s41239-020-0177-7.
  • J. López-Zambrano, J. A. Lara Torralbo, and C. Romero, ‘Early Prediction of Student Learning Performance Through Data Mining: A Systematic Review’, Psicothema, no. 33.3, pp. 456–465, Ağustos 2021, doi: 10.7334/psicothema2021.62.
  • H. Q. Alatawi and S. Hechmi, ‘A Survey of Data Mining Methods for Early Prediction of Students’ Performance’, in 2022 2nd International Conference on Computing and Information Technology (ICCIT), Tabuk, Saudi Arabia: IEEE, Jan. 2022, pp. 171–174. doi: 10.1109/ICCIT52419.2022.9711642.
  • S. M. Muthukrishnan, M. K. Govindasamy, and M. N. Mustapha, ‘Systematic mapping review on student’s performance analysis using big data predictive model’, Journal of Fundamental and Applied Sciences, vol. 9, no. 4S, p. 730, Jan. 2018, doi: 10.4314/jfas.v9i4S.41.
  • X. Bai et al., ‘Educational Big Data: Predictions, Applications and Challenges’, Big Data Research, vol. 26, p. 100270, Nov. 2021, doi: 10.1016/j.bdr.2021.100270.
  • A. Hernández-Blanco, B. Herrera-Flores, D. Tomás, and B. Navarro-Colorado, ‘A Systematic Review of Deep Learning Approaches to Educational Data Mining’, Complexity, vol. 2019, pp. 1–22, May 2019, doi: 10.1155/2019/1306039.
  • Kitchenham Barbara and Charters Stuart M., ‘Guidelines for performing systematic literature reviews in software engineering’, School of Computer Science and Mathematics, Keele University., Durham, UK, EBSE Technical Report EBSE-2007-01, 2007.
  • X. Li, Y. Zhang, H. Cheng, M. Li, and B. Yin, ‘Student achievement prediction using deep neural network from multi-source campus data’, Complex Intell. Syst., vol. 8, no. 6, pp. 5143–5156, Dec. 2022, doi: 10.1007/s40747-022-00731-8.
  • B. Venkatachalam and K. Sivanraju, ‘Predicting Student Performance Using Mental Health and Linguistic Attributes with Deep Learning’, Revue d’Intelligence Artificielle, vol. 37, no. 4, pp. 889–899, Aug. 2023, doi: 10.18280/ria.370408.
  • A. A. Almahdi and B. T. Sharef, ‘Deep Learning Based An Optimized Predictive Academic Performance Approach’, in 2023 International Conference on IT Innovation and Knowledge Discovery (ITIKD), Manama, Bahrain: IEEE, Mar. 2023, pp. 1–6. doi: 10.1109/ITIKD56332.2023.10099652.
  • X. Wen and H. Juan, ‘Early Prediction of Students’ Performance Using a Deep Neural Network Based on Online Learning Activity Sequence’, Applied Sciences, vol. 13, no. 15, p. 8933, Aug. 2023, doi: 10.3390/app13158933.
  • C. A. Anjali and V. R. Bai, ‘An Early Prediction of Dropouts for At-risk Scholars in MOOCs using Deep Learning’, in 2022 International Conference on Computing, Communication, Security and Intelligent Systems (IC3SIS), IEEE, Jun. 2022, pp. 1–6. doi: 10.1109/IC3SIS54991.2022.9885328.
  • H. Waheed, S.-U. Hassan, R. Nawaz, N. R. Aljohani, G. Chen, and D. Gasevic, ‘Early prediction of learners at risk in self-paced education: A neural network approach’, Expert Systems with Applications, vol. 213, p. 118868, Mar. 2023, doi: 10.1016/j.eswa.2022.118868.
  • B. T. Sayed, M. Madanan, and N. Biju, ‘An Efficient Artificial Intelligence-Based Educational Data Mining Approach for Higher Education and Early Recognition System’, SN Computer Science, vol. 4, no. 2, p. 130, Dec. 2022, doi: 10.1007/s42979-022-01562-7.
  • S. Leelaluk, T. Minematsu, Y. Taniguchi, F. Okubo, T. Yamashita, and A. Shimada, ‘Scaled-Dot Product Attention for Early Detection of At-risk Students’, in 2022 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE), Hung Hom, Hong Kong: IEEE, Dec. 2022, pp. 316–322. doi: 10.1109/TALE54877.2022.00059.
  • F. A. Al-azazi and M. Ghurab, ‘ANN-LSTM: A deep learning model for early student performance prediction in MOOC’, Heliyon, vol. 9, no. 4, p. e15382, Apr. 2023, doi: 10.1016/j.heliyon.2023.e15382.
  • H. Wan, M. Li, Z. Zhong, and X. Luo, ‘Early Prediction of Student Performance with LSTM-Based Deep Neural Network’, in 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC), IEEE, Jun. 2023, pp. 132–141. doi: 10.1109/COMPSAC57700.2023.00026.
  • K. Qin, X. Xie, Q. He, and G. Deng, ‘Early Warning of Student Performance With Integration of Subjective and Objective Elements’, IEEE Access, vol. 11, pp. 72601–72617, 2023, doi: 10.1109/ACCESS.2023.3295580.
  • S. S. Kusumawardani and S. A. I. Alfarozi, ‘Transformer Encoder Model for Sequential Prediction of Student Performance Based on Their Log Activities’, IEEE Access, vol. 11, pp. 18960–18971, 2023, doi: 10.1109/ACCESS.2023.3246122.
  • H.-C. Chen et al., ‘Week-Wise Student Performance Early Prediction in Virtual Learning Environment Using a Deep Explainable Artificial Intelligence’, Applied Sciences, vol. 12, no. 4, p. 1885, Feb. 2022, doi: 10.3390/app12041885.
  • F. D. Pereira et al., ‘Explaining Individual and Collective Programming Students’ Behavior by Interpreting a Black-Box Predictive Model’, IEEE Access, vol. 9, pp. 117097–117119, 2021, doi: 10.1109/ACCESS.2021.3105956.
  • E. Tanuar, Y. Heryadi, Lukas, B. S. Abbas, and F. L. Gaol, ‘Using Machine Learning Techniques to Earlier Predict Student’s Performance’, in 2018 Indonesian Association for Pattern Recognition International Conference (INAPR), Jakarta, Indonesia: IEEE, Sep. 2018, pp. 85–89. doi: 10.1109/INAPR.2018.8626856.
  • H. Waheed, S.-U. Hassan, N. R. Aljohani, J. Hardman, S. Alelyani, and R. Nawaz, ‘Predicting academic performance of students from VLE big data using deep learning models’, Computers in Human Behavior, vol. 104, p. 106189, Mar. 2020, doi: 10.1016/j.chb.2019.106189.
  • N. R. Aljohani, A. Fayoumi, and S.-U. Hassan, ‘Predicting At-Risk Students Using Clickstream Data in the Virtual Learning Environment’, Sustainability, vol. 11, no. 24, p. 7238, Dec. 2019, doi: 10.3390/su11247238.
  • C.-C. Yu and Y. (Leon) Wu, ‘Early Warning System for Online STEM Learning—A Slimmer Approach Using Recurrent Neural Networks’, Sustainability, vol. 13, no. 22, p. 12461, Nov. 2021, doi: 10.3390/su132212461.
  • M. Y. S. L. Z. Aljuid, ‘Deep Learning Based Method For Prediction of Software Engineering Project Teamwork Assessment in Higher Education’, Journal of Theoretical and Aplied Information Technology, vol. 99, no. 9, 2021, [Online]. Available: www.jatit.org
  • Y. Mao, S. Marwan, and T. W. Price, ‘What Time is It? Student Modeling Needs to Know’, p. 12, 2020.
  • H. Mi, Z. Gao, Q. Zhang, and Y. Zheng, ‘Research on Constructing Online Learning Performance Prediction Model Combining Feature Selection and Neural Network’, Int. J. Emerg. Technol. Learn., vol. 17, no. 07, pp. 94–111, Apr. 2022, doi: 10.3991/ijet.v17i07.25587.
  • N. M. Aslam, I. U. Khan, L. H. Alamri, and R. S. Almuslim, ‘An Improved Early Student’s Academic Performance Prediction Using Deep Learning’, International Journal of Emerging Technologies in Learning (iJET), vol. 16, no. 12, p. 108, Jun. 2021, doi: 10.3991/ijet.v16i12.20699.
  • B.-H. Kim, E. Vizitei, and V. Ganapathi, ‘GritNet: Student Performance Prediction with Deep Learning’. arXiv, Apr. 19, 2018. Accessed: Sep. 08, 2022. [Online]. Available: http://arxiv.org/abs/1804.07405
  • X. Wang, P. Wu, G. Liu, Q. Huang, X. Hu, and H. Xu, ‘Learning performance prediction via convolutional GRU and explainable neural networks in e-learning environments’, Computing, vol. 101, no. 6, pp. 587–604, Jun. 2019, doi: 10.1007/s00607-018-00699-9.
  • X. Li, X. Zhu, X. Zhu, Y. Ji, and X. Tang, ‘Student Academic Performance Prediction Using Deep Multi-source Behavior Sequential Network’, in Advances in Knowledge Discovery and Data Mining, vol. 12084, H. W. Lauw, R. C.-W. Wong, A. Ntoulas, E.-P. Lim, S.-K. Ng, and S. J. Pan, Eds., in Lecture Notes in Computer Science, vol. 12084. , Cham: Springer International Publishing, 2020, pp. 567–579. doi: 10.1007/978-3-030-47426-3_44.
  • O. Ojajuni et al., ‘Predicting Student Academic Performance Using Machine Learning’, in Computational Science and Its Applications – ICCSA 2021, vol. 12957, O. Gervasi, B. Murgante, S. Misra, C. Garau, I. Blečić, D. Taniar, B. O. Apduhan, A. M. A. C. Rocha, E. Tarantino, and C. M. Torre, Eds., in Lecture Notes in Computer Science, vol. 12957. , Cham: Springer International Publishing, 2021, pp. 481–491. doi: 10.1007/978-3-030-87013-3_36.
  • R. C. Raga and J. D. Raga, ‘Early Prediction of Student Performance in Blended Learning Courses Using Deep Neural Networks’, in 2019 International Symposium on Educational Technology (ISET), Hradec Kralove, Czech Republic: IEEE, Jul. 2019, pp. 39–43. doi: 10.1109/ISET.2019.00018.
  • S. Surenthiran, R. Rajalakshmi, and S. S. Sujatha, ‘Student Performance Prediction Using Atom Search Optimization Based Deep Belief Neural Network’, Optical Memory and Neural Networks, vol. 30, no. 2, pp. 157–171, Apr. 2021, doi: 10.3103/S1060992X21020119.
  • H. Karimi, T. Derr, J. Huang, and J. Tang, ‘Online Academic Course Performance Prediction using Relational Graph Convolutional Neural Network’, in The International Conference on Educational Data Mining (EDM), 2020, pp. 7–7. [Online]. Available: https://github.com/hamidkarimi/dope.
  • F. Chen and Y. Cui, ‘Utilizing Student Time Series Behaviour in Learning Management Systems for Early Prediction of Course Performance’, JLA, vol. 7, no. 2, pp. 1–17, Sep. 2020, doi: 10.18608/jla.2020.72.1.
  • Q. Hu and H. Rangwala, ‘Reliable Deep Grade Prediction with Uncertainty Estimation’, in Proceedings of the 9th International Conference on Learning Analytics & Knowledge, ACM, Mar. 2019, pp. 76–85. doi: 10.1145/3303772.3303802.
  • S. Hassan, H. Waheed, N. R. Aljohani, M. Ali, S. Ventura, and F. Herrera, ‘Virtual learning environment to predict withdrawal by leveraging deep learning’, Int J Intell Syst, vol. 34, no. 8, pp. 1935–1952, Aug. 2019, doi: 10.1002/int.22129.
  • L. Wang, A. Sy, L. Liu, and C. Piech, ‘Learning to Represent Student Knowledge on Programming Exercises Using Deep Learning’, in The International Conference on Educational Data Mining (EDM), 2017, pp. 6–6.
  • X. Du, J. Yang, J.-L. Hung, and B. Shelton, ‘Educational data mining: a systematic review of research and emerging trends’, Information Discovery and Delivery, vol. 48, no. 4, pp. 225–236, May 2020, doi: 10.1108/IDD-09-2019-0070.
  • D. Uliyan, A. S. Aljaloud, A. Alkhalil, H. S. A. Amer, M. A. E. A. Mohamed, and A. F. M. Alogali, ‘Deep Learning Model to Predict Students Retention Using BLSTM and CRF’, IEEE Access, vol. 9, pp. 135550–135558, 2021, doi: 10.1109/ACCESS.2021.3117117.
  • H. S. Park and S. J. Yoo, ‘Early Dropout Prediction in Online Learning of University using Machine Learning’, JOIV : International Journal on Informatics Visualization, vol. 5, no. 4, p. 347, Dec. 2021, doi: 10.30630/joiv.5.4.732.
  • A. Nabil, M. Seyam, and A. Abou-Elfetouh, ‘Prediction of Students’ Academic Performance Based on Courses’ Grades Using Deep Neural Networks’, IEEE Access, vol. 9, pp. 140731–140746, 2021, doi: 10.1109/ACCESS.2021.3119596.
  • M. Adnan et al., ‘Predicting at-Risk Students at Different Percentages of Course Length for Early Intervention Using Machine Learning Models’, IEEE Access, vol. 9, pp. 7519–7539, 2021, doi: 10.1109/ACCESS.2021.3049446.
  • B. K. Yousafzai et al., ‘Student-Performulator: Student Academic Performance Using Hybrid Deep Neural Network’, Sustainability, vol. 13, no. 17, p. 9775, Aug. 2021, doi: 10.3390/su13179775
There are 69 citations in total.

Details

Primary Language English
Subjects Computer Software
Journal Section Review
Authors

Ahmet Kala 0000-0002-0598-1181

Orhan Torkul 0000-0003-2690-7228

Tuğba Yıldız 0000-0002-3207-8932

İhsan Hakan Selvi 0000-0002-8837-2137

Early Pub Date March 27, 2025
Publication Date March 28, 2025
Submission Date February 7, 2025
Acceptance Date March 6, 2025
Published in Issue Year 2025Volume: 8 Issue: 1

Cite

APA Kala, A., Torkul, O., Yıldız, T., Selvi, İ. H. (2025). Early Prediction of Students’ Performance Through Deep Learning: A Systematic and Bibliometric Literature Review. Sakarya University Journal of Computer and Information Sciences, 8(1), 152-170. https://doi.org/10.35377/saucis...1635558
AMA Kala A, Torkul O, Yıldız T, Selvi İH. Early Prediction of Students’ Performance Through Deep Learning: A Systematic and Bibliometric Literature Review. SAUCIS. March 2025;8(1):152-170. doi:10.35377/saucis.1635558
Chicago Kala, Ahmet, Orhan Torkul, Tuğba Yıldız, and İhsan Hakan Selvi. “Early Prediction of Students’ Performance Through Deep Learning: A Systematic and Bibliometric Literature Review”. Sakarya University Journal of Computer and Information Sciences 8, no. 1 (March 2025): 152-70. https://doi.org/10.35377/saucis. 1635558.
EndNote Kala A, Torkul O, Yıldız T, Selvi İH (March 1, 2025) Early Prediction of Students’ Performance Through Deep Learning: A Systematic and Bibliometric Literature Review. Sakarya University Journal of Computer and Information Sciences 8 1 152–170.
IEEE A. Kala, O. Torkul, T. Yıldız, and İ. H. Selvi, “Early Prediction of Students’ Performance Through Deep Learning: A Systematic and Bibliometric Literature Review”, SAUCIS, vol. 8, no. 1, pp. 152–170, 2025, doi: 10.35377/saucis...1635558.
ISNAD Kala, Ahmet et al. “Early Prediction of Students’ Performance Through Deep Learning: A Systematic and Bibliometric Literature Review”. Sakarya University Journal of Computer and Information Sciences 8/1 (March 2025), 152-170. https://doi.org/10.35377/saucis. 1635558.
JAMA Kala A, Torkul O, Yıldız T, Selvi İH. Early Prediction of Students’ Performance Through Deep Learning: A Systematic and Bibliometric Literature Review. SAUCIS. 2025;8:152–170.
MLA Kala, Ahmet et al. “Early Prediction of Students’ Performance Through Deep Learning: A Systematic and Bibliometric Literature Review”. Sakarya University Journal of Computer and Information Sciences, vol. 8, no. 1, 2025, pp. 152-70, doi:10.35377/saucis. 1635558.
Vancouver Kala A, Torkul O, Yıldız T, Selvi İH. Early Prediction of Students’ Performance Through Deep Learning: A Systematic and Bibliometric Literature Review. SAUCIS. 2025;8(1):152-70.
 Download Cover Image


INDEXING & ABSTRACTING & ARCHIVING


 31045 31044  31046 31047 

31043 28939 28938


29070    The papers in this journal are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License