Tri-Branch Convolutional Neural Networks for Top-$k$ Focused Academic Performance Prediction
This work addresses academic performance prediction for educational applications like personalized teaching and early warning, but it is incremental as it builds on existing CNN and ranking approaches with domain-specific adaptations.
The paper tackles academic performance prediction by analyzing student behavior trajectories from campus smartcard records, proposing a Tri-Branch CNN with row-wise, column-wise, and depth-wise convolutions and attention to capture persistence, regularity, and temporal distribution, and introducing a top-k focused loss for ranking; it shows substantial outperformance over recent methods on a large-scale real-world dataset.
Academic performance prediction aims to leverage student-related information to predict their future academic outcomes, which is beneficial to numerous educational applications, such as personalized teaching and academic early warning. In this paper, we address the problem by analyzing students' daily behavior trajectories, which can be comprehensively tracked with campus smartcard records. Different from previous studies, we propose a novel Tri-Branch CNN architecture, which is equipped with row-wise, column-wise, and depth-wise convolution and attention operations, to capture the characteristics of persistence, regularity, and temporal distribution of student behavior in an end-to-end manner, respectively. Also, we cast academic performance prediction as a top-$k$ ranking problem, and introduce a top-$k$ focused loss to ensure the accuracy of identifying academically at-risk students. Extensive experiments were carried out on a large-scale real-world dataset, and we show that our approach substantially outperforms recently proposed methods for academic performance prediction. For the sake of reproducibility, our codes have been released at https://github.com/ZongJ1111/Academic-Performance-Prediction.