Approaches to Applying Deep Learning for Early Prediction of Learners' Academic Outcomes
Main Article Content
Abstract
In the context of global digital transformation, the increase in the amount of educational data is the driving force behind modern technological methods applied in educational analysis, notably the application of deep learning in predicting student outcome. Given its potential to mitigate academic challenges and foster positive educational outcomes, the prediction of student learning outcomes emerges as a pivotal research area within educational data mining, capturing the interest of researchers worldwide. This article presents an overview of the application of deep learning in educational data analysis, with a special focus on the problem of predicting student learning outcomes. Some modern research trends in educational science based on data analysis techniques are also mentioned.
Keywords:
Machine learning, deep learning, predicting learners' learning outcomes, 4.0 industrial revolution
References
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[4]. Arsad, P.M., Buniyamin, N. and Manan, J-lA. (2013). Prediction of engineering students' academic performance using Artificial Neural Network and Linear Regression: A comparison, International Conference on Engineering Education, https://doi.org/10.1109/ICEED.2013.6908300
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[8]. Bendangnuksung, E. & Prabu, D. (2018), Student’s Performance Prediction Using Deep Neural Network, International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 2 (2018) pp. 1171-1176.
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[10]. Brooker, Corrin, L., De Barba, P., Lodge, J. & Kennedy, G. (2018), A tale of two MOOCs: How student motivation and participation predict learning outcomes in different MOOCs. Australasian Journal of Educational Technology, 34(1).
[11]. Capuano, N. & Toti, D. (2019). Experimentation of a smart learning system for law based on knowledge discovery and cognitive computing. Computers in Human Behavior, 92, 459- 467. 10.1016/j.chb.2018.03.034.
[12]. Corrigan, O. & Smeaton, A. F. (2017). A Course Agnostic Approach to Predicting Student Success from VLE Log Data Using Recurrent Neural Networks. European Conference on Technology Enhanced Learning, 545–548. Springer.
[13]. Costa, B., Fonseca, B., Santana, M.A., Araújo F.F. and Rego, J., Evaluating the effectiveness of educational data mining techniques for early prediction of students' academic failure in introductory programming courses, Computers in Human Behavior, Volume 73, 2017, Pages 247-256,
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[20]. Emmert-Streib, F., and Dehmer, M. (2019a). Defining data science by a data-driven quantification of the community. Mach. Learn. Knowl. Extract. 1, 235–251. doi: 10.3390/make1010015
[21]. Emmert-Streib, F. and Dehmer, M. (2019b). Evaluation of regression models: model assessment, model selection and generalization error. Mach. Learn. Knowl. Extract. 1, 521–551. doi: 10.3390/make1010032
[22]. Emmert-Streib, F., Yang, Z., Feng, H., Tripathi, S. and Dehmer, M. (2020) An Introductory Review of Deep Learning for Prediction Models With Big Data. Front. Artif. Intell. 3:4. doi: 10.3389/frai.2020.00004
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[40]. Lin T-Y, Goyal P, Girshick R, He K, Dollar P (2017) Focal loss for dense object detection. arXiv:1708.02002
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[47]. Mubarak, A.A., Cao, H. and Hezam, I.M. (2022), Modeling student’s performance using graph convolutional networks. Complex Intell. Syst. 8, 2183–2201 (2022). https://doi.org/10.1007/s40747-022-00647-3
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[49]. Murtaugh, P. A., Burns, L. D. & Schuster, J. (1999). Predicting the retention of university students. Research in Higher Education, 40(3), 355-371.
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[51]. Pernkopf, F., Peharz, R. and Tschiatschek, S. (2013). Introduction to Probabilistic Graphical Models Introduction. Graz, Austria.
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[54]. Rosenblatt, F. (1957). The Perceptron, A Perceiving and Recognizing Automaton Project Para. Cornell Aeronautical Laboratory.
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[57]. Sarwat, S.; Ullah, N.; Sadiq, S.; Saleem, R.; Umer, M.; Eshmawi, A.A.; Mohamed, A.; Ashraf, I. Predicting Students’ Academic Performance with Conditional Generative Adversarial Network and Deep SVM. Sensors 2022, 22, 4834. https://doi.org/10.3390/s22134834
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