Tri-Branch Convolutional Neural Networks for Top-$k$ Focused Academic Performance Prediction

TL;DR

This paper introduces a novel Tri-Branch CNN architecture that leverages campus smartcard data to predict students' academic performance as a top-$k$ ranking problem, significantly improving prediction accuracy.

Contribution

The paper proposes a new Tri-Branch CNN with specialized convolution and attention mechanisms for comprehensive student behavior analysis in academic prediction.

Findings

Outperforms recent methods on real-world datasets

Effectively captures persistence, regularity, and temporal behavior patterns

Demonstrates the effectiveness of top-$k$ focused loss in identifying at-risk students

Abstract

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.