| Title |
An Optimal Fuzzy Neural Network Prediction Model for Student Performance Prediction in Online Education |
| DOI |
https://doi.org/10.5573/IEIESPC.2024.13.5.462 |
| Keywords |
Achievement prediction; Subtractive clustering; Genetic algorithm; Adaptive fuzzy inference system |
| Abstract |
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' 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 PCA-GA-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. |