Open Access

ARTICLE

Two-Way Neural Network Performance Prediction Model Based on Knowledge Evolution and Individual Similarity

Xinzheng Wang^1,2,*, Bing Guo¹, Yan Shen³

1 College of Computer Science, Sichuan University, Chengdu, 610065, China
2 School of Information Science and Engineering, Guilin University of Technology, Guilin, 541004, China
3 School of Control Engineering, Chengdu University of Information Technology, Chengdu, 610225, China

* Corresponding Author: Xinzheng Wang. Email:

Computer Modeling in Engineering & Sciences 2024, 138(2), 1183-1206. https://doi.org/10.32604/cmes.2023.029552

Received 26 February 2023; Accepted 20 June 2023; Issue published 17 November 2023

Abstract

Predicting students’ academic achievements is an essential issue in education, which can benefit many stakeholders, for instance, students, teachers, managers, etc. Compared with online courses such as MOOCs, students’ academic-related data in the face-to-face physical teaching environment is usually sparsity, and the sample size is relatively small. It makes building models to predict students’ performance accurately in such an environment even more challenging. This paper proposes a Two-Way Neural Network (TWNN) model based on the bidirectional recurrent neural network and graph neural network to predict students’ next semester’s course performance using only their previous course achievements. Extensive experiments on a real dataset show that our model performs better than the baselines in many indicators.

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