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Understanding Student Learning Behavior and Predicting Their Performance

Understanding Student Learning Behavior and Predicting Their Performance

Muhammad Wasif, Hajra Waheed, Naif R. Aljohani, Saeed-Ul Hassan
Copyright: © 2019 |Pages: 28
ISBN13: 9781522590316|ISBN10: 1522590315|ISBN13 Softcover: 9781522597230|EISBN13: 9781522590323
DOI: 10.4018/978-1-5225-9031-6.ch001
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MLA

Wasif, Muhammad, et al. "Understanding Student Learning Behavior and Predicting Their Performance." Cognitive Computing in Technology-Enhanced Learning, edited by Miltiadis D. Lytras, et al., IGI Global, 2019, pp. 1-28. https://doi.org/10.4018/978-1-5225-9031-6.ch001

APA

Wasif, M., Waheed, H., Aljohani, N. R., & Hassan, S. (2019). Understanding Student Learning Behavior and Predicting Their Performance. In M. Lytras, N. Aljohani, L. Daniela, & A. Visvizi (Eds.), Cognitive Computing in Technology-Enhanced Learning (pp. 1-28). IGI Global. https://doi.org/10.4018/978-1-5225-9031-6.ch001

Chicago

Wasif, Muhammad, et al. "Understanding Student Learning Behavior and Predicting Their Performance." In Cognitive Computing in Technology-Enhanced Learning, edited by Miltiadis D. Lytras, et al., 1-28. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-9031-6.ch001

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Abstract

Despite the increase in the adoption of online educational platforms, student retention is still a challenging task with a number of students having low performance margins during these courses. This chapter intends to predict student performance based on their learning behavior on the basis of their logging data history, using the publicly available Open University Learning Analytics Dataset. To model this problem, logistic regression (LR) is used as a baseline technique. Additionally, random forest (RF), multiple layered perceptron with multiple activation functions, and Gaussian Naïve Bayes are also deployed. The results demonstrate that RF outperforms the baseline LR and other models with 89% accuracy, 89% precision, 88% recall, and 88% F1-score. Finally, the authors conclude that using the above-mentioned models, students “at-risk” can be identified which can be managed by an alert mechanism to improve student success rate by making timely interventions.

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