Leveraging the Nbeats Model for Predictions of Student Academic Outcomes

The paper relies on the team's “WeChat Mini Program Development — From Basics to Practice” course at Chinese universities' MOOCs. By analyzing approximately 60,000 learning data points in course development, the study aims to predict students' grades. This paper addresses traditional models' complexity and subpar performance in prediction processes by utilizing and optimizing the time-series model Nbeats. It compares the predictive performance of this model with mainstream forecasting models to explore its superiority. In addition to the data collected by the team, three publicly available datasets are incorporated to eliminate randomness in model predictions. Through ablation experiments, it demonstrates the Nbeats model's greater robustness and effectiveness.