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Article

Predicting At-Risk Students Using Clickstream Data in the Virtual Learning Environment

1
Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
2
Department of Computer Science, Information Technology University, Lahore 54600, Pakistan
*
Author to whom correspondence should be addressed.
Sustainability 2019, 11(24), 7238; https://doi.org/10.3390/su11247238
Received: 10 November 2019 / Revised: 25 November 2019 / Accepted: 26 November 2019 / Published: 17 December 2019
(This article belongs to the Special Issue Technology Enhanced Learning Research)
In higher education, predicting the academic performance of students is associated with formulating optimal educational policies that vehemently impact economic and financial development. In online educational platforms, the captured clickstream information of students can be exploited in ascertaining their performance. In the current study, the time-series sequential classification problem of students’ performance prediction is explored by deploying a deep long short-term memory (LSTM) model using the freely accessible Open University Learning Analytics dataset. In the pass/fail classification job, the deployed LSTM model outperformed the state-of-the-art approaches with 93.46% precision and 75.79% recall. Encouragingly, our model superseded the baseline logistic regression and artificial neural networks by 18.48% and 12.31%, respectively, with 95.23% learning accuracy. We demonstrated that the clickstream data generated due to the students’ interaction with the online learning platforms can be evaluated at a week-wise granularity to improve the early prediction of at-risk students. Interestingly, our model can predict pass/fail class with around 90% accuracy within the first 10 weeks of student interaction in a virtual learning environment (VLE). A contribution of our research is an informed approach to advanced higher education decision-making towards sustainable education. It is a bold effort for student-centric policies, promoting the trust and the loyalty of students in courses and programs. View Full-Text
Keywords: students-at-risk; virtual learning environment (VLE); classification; deep learning; long short-term memory (LSTM) students-at-risk; virtual learning environment (VLE); classification; deep learning; long short-term memory (LSTM)
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MDPI and ACS Style

Aljohani, N.R.; Fayoumi, A.; Hassan, S.-U. Predicting At-Risk Students Using Clickstream Data in the Virtual Learning Environment. Sustainability 2019, 11, 7238. https://0-doi-org.brum.beds.ac.uk/10.3390/su11247238

AMA Style

Aljohani NR, Fayoumi A, Hassan S-U. Predicting At-Risk Students Using Clickstream Data in the Virtual Learning Environment. Sustainability. 2019; 11(24):7238. https://0-doi-org.brum.beds.ac.uk/10.3390/su11247238

Chicago/Turabian Style

Aljohani, Naif R., Ayman Fayoumi, and Saeed-Ul Hassan. 2019. "Predicting At-Risk Students Using Clickstream Data in the Virtual Learning Environment" Sustainability 11, no. 24: 7238. https://0-doi-org.brum.beds.ac.uk/10.3390/su11247238

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