A Fusion Framework for Student Performance Prediction Using Deep Learning and Blockchain Technologies

This paper presents an innovative fusion framework that integrates deep learning and blockchain technologies for student performance prediction. To validate the effectiveness of this framework, we designed and conducted a series of experiments. The results show that our fusion framework significantly outperforms the methods using deep learning or blockchain alone in terms of prediction performance. Specifically, our method achieved the best results in four evaluation metrics: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-Squared. These results confirm the effectiveness of deep learning in extracting useful features from learning records. Meanwhile, the use of blockchain ensures the security and integrity of data, further enhancing the accuracy of prediction. This study provides a new perspective on student performance prediction using deep learning and blockchain, demonstrating the potential of deep learning and blockchain in the field of educational technology.