Student performance predictive analysis has played a vital role in education in recent years. It allows for the understanding students’ learning behaviours, the identification of at-risk students, and the development of insights into teaching and learning improvement. Recently, many researchers have used data collected from Learning Management Systems to predict student performance. This study investigates the potential of clickstream data for this purpose. A total of
5341 sample students and their click behaviour data from the OULAD (Open University Learning Analytics Dataset) are used. It is found that the LSTM algorithm outperformed
other approaches on a range of evaluation metrics, with up to 90.25% accuracy. Four out of twelve
learning sites (content, subpage, homepage, quiz) are identified as critical in influencing student
performance in the course. The insights from these critical learning sites can inform the design of
future courses and teaching interventions to support at-risk students.

https://doi.org/10.3390/educsci13010017

Andrej Košir

University of Ljubljana