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

Naif Radi Aljohani, Ayman Fayoumi and Saeed-Ul Hassan
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Naif Radi Aljohani: Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Ayman Fayoumi: Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Saeed-Ul Hassan: Department of Computer Science, Information Technology University, Lahore 54600, Pakistan

Sustainability, 2019, vol. 11, issue 24, 1-12

Abstract: 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 Encouragingly, our model superseded the baseline logistic regression and artificial neural networks by 18.48% and 12.31%, respectively, with 95.23% learning accuracy.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.

Keywords: students-at-risk; virtual learning environment (VLE); classification; deep learning; long short-term memory (LSTM) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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