TY  - EJOU
AU  - Wang, Xinzheng 
AU  - Guo, Bing 
AU  - Shen, Yan 

TI  - Two-Way Neural Network Performance Prediction Model Based on Knowledge Evolution and Individual Similarity
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2024
VL  - 138
IS  - 2
SN  - 1526-1506

AB  - 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.
KW  - Computer; education; performance prediction; deep learning

DO  - 10.32604/cmes.2023.029552