An Optimal Fuzzy Neural Network Prediction Model for Student Performance Prediction in Online Education

In the time of boost of the Internet, online education is also thriving, but how to grasp the performance of students in the context of online education has become a major problem in the current online education. In response to the above problems, the study proposes the Principal Component Analysis Algorithm (PCA), Adaptive Network-based Fuzzy Inference System (ANFIS) and Genetic Algorithm (GA) for secondary school students&quot performance prediction model, i.e., PCA-GA-ANFIS model, and the performance of PCA-GA-ANFIS, ANFIS and XGBoost were analyzed and compared through experiments. The experiment illustrated that the loss value of PCA-GA-ANFIS model was about 0.15, while the loss values of ANFIS model and XGBoost model were about 0.3 and 0.25, respectively, which were higher than PCA-GA-ANFIS model. The absolute and relative errors of ANFIS model and XGBoost model were about 3.6, 2% and 3.2, 1.8%, respectively; the absolute and relative errors of the PCA-GA-ANFIS model are about 2.2 and 1.6%, respectively, and the minimum absolute error tends to be close to 0, which demonstrated that the forecasting outcomes of the PCA-GA-ANFIS model were closer to the true values. The PCAGA-ANFIS model performed with an average accuracy of 89.4% and precision of 90.3%. The results demonstrated that the PCA-GA-ANFIS model outperformed the other two models in terms of accuracy and precision. Comparatively, the ANFIS and XGBoost models exhibited an average accuracy and precision of approximately 86.8% and 87.8%, and 87.0% and 89.1%, respectively. Therefore, the results showed that the PCA-GA-ANFIS model produced more precise prediction results and better prediction performance than the ANFIS and XGBoost models.