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In educational data mining, student classification is an important and popular task by predicting final study status of each student. In the existing works, this task has been considered in many various contexts at both course and program levels with different learning approaches. However, its real-world characteristics such as temporal aspects, data imbalance, data overlapping, and data shortage with sparseness have not yet been fully investigated. Making the most of deep learning, our work is the first one addressing those challenges for the program-level student classification task. In a simple but effective manner, convolutional neural networks (CNNs) are proposed to exploit their well-known advantages on images for temporal educational data. As a result, the task is resolved by our enhanced CNN models with more effectiveness and practicability on real datasets. Our CNN models outperform other traditional models and their various variants on a consistent basis for program-level student classification.

Enhanced CNN Models for Binary and Multiclass Student Classification on Temporal Educational Data at the Program Level