Dual Path Convolutional Neural Network for Student Performance Prediction

Abstract

Student performance prediction is of great importance to many educational domains, such as academic early warning and personalized teaching, and has drawn numerous research attention in recent decades. Most of the previous studies are based on students’ historical course grades, demographical data, in-class study performance, and online activities from e-learning platforms, e.g., Massive Open Online Courses (MOOCs). Thanks to the widely used of campus smartcard, it supplies an opportunity to predict students’ academic performance with their off-line behavioral data. In this study, we seek to capture three student behavioral characters, including duration, variation and periodicity, and predict students’ performance based on the three types of information. However, it is highly challenging to extract efficient features manually from the huge amount of raw smartcard records. Besides, it is not trivial to construct a good predictive model for some majors with limited student samples. To address the above issues, we develop a novel end-to-end deep learning method and propose Dual Path Convolutional Neural Network (DPCNN) for student performance prediction. Moreover, we introduce multi-task learning to our method and predict the performance of students from different majors in a unified framework. Experimental results demonstrate the superiority of our approach over the state-of-the-art methods.