Graph Neural Networks for Predicting Student Performance: A Deep Learning Approach for Academic Success Forecasting
Keywords:
Graph Neural Network, Student Performance, Knowledge GraphAbstract
Student Academic Success Forecasting in higher education sector especially for technical courses has become increasingly popular, but success rates are typically low, and evaluating student performance can be challenging. Therefore, a framework for effective evaluation and prediction of student outcomes is highly needed for educational institutions. The proposed Performance Evaluation approach models the association in between students and their respective courses as a knowledge graph and uses a graph neural network to extract insightful patterns for better prediction. In addition, it utilizes a recurrent neural network to capture the sequential patterns in students' behavioural data over time and forecast their academic outcomes in a specific course. This research work demonstrates the effectiveness of this approach in predicting student performance, and ablation feature analysis is conducted to gain insights into the underlying factors that contribute to performance prediction.
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