## 4 Results and Discussion
### Discussion  
In this paper, we utilized the collected dataset on students' learning levels to develop a prediction model for evaluating these levels. The dataset is divided into two parts in this study, with 70% used for training the model and the remaining 30% utilized to assess the accuracy of prediction results. To ensure that the model maintained high precision in predicting different datasets, we enhanced its generalization ability. Additionally, we enhanced the complexity of the model by combining SSA, ISSA, and KPCA algorithms with an initial classification model SVM. Consequently, three composite models (SSA-SVM, ISSA-SVM, and KPCA-ISSA-SVM) have been constructed. Each classifier underwent training and testing using the dataset to compare predictions and determine which composite model exhibits superior prediction accuracy. It is worth noting that this study only predicts and evaluates students' learning levels through five commonly used parameters, namely, PEG, LPR, STR, SCG, and STG. Other factors, such as students' participation in learning goals, interest level, and learning enthusiasm, also have certain impacts on students' learning levels. This study used machine learning models to fully analyze the presented information on students' learning to determine their learning level. Although the input parameters for the predictive analysis are lacking, this approach provides a method to automatically evaluate students' learning levels, and provides a theoretical basis for accurately evaluating students' learning levels in the future, as well as proposing corresponding teaching programs. From the experimental data presented in this paper, it can be seen that different algorithms have different effects on the hyperparameter tuning of the SVM model. The ISSA algorithm had the best tuning effect, and its fitness reached a minimum value of 0.0245 when the number of iterations reached four. The fitness of the more complex KPCA-ISSA-SVM integrated model was only 0.035, although this was still lower than that of the SSA-SVM model (0.067). This means that, regarding the prediction accuracy of the model, hyperparameter adjustment can effectively improve the degree of fitness of the model to the dataset, and thus improve the prediction accuracy of the model. The prediction accuracies of four models were compared to facilitate a comprehensive comparison and analysis of their predictive performances on UNS. Figure 4 illustrates that both the SVM and SSA-SVM models exhibited overfitting during the training stage. In other words, the prediction accuracy of the training stage was much higher than that of the  
Figure 6: Importance analysis. test stage, which may have been caused by the insufficient complexity of the model. By observing the prediction results of ISSA-SVM and KPCA-ISSA-SVM, it can be seen that, with increases in the complexity of the model, the gap between the prediction accuracy of the model in the training stage and the test stage gradually narrowed. This means that the construction of two of the models, ISSA-SVM and KPCA-ISSA-SVM, was effective. Improving the complexity of the model can improve the ability of the model to predict students' learning levels. A confusion matrix has been used to visualize the test results. From the matrix, it can be clearly inferred that there are some different inaccuracies in the prediction results of the four models. Taking the second type of prediction as an example, from the point of view of the data, the accuracy of the second type of prediction increased with the increase in the complexity of the model. In the second type of prediction, the SVM model showed 15.6% wrong prediction results, while the KPCA-ISSA-SVM only gave 7.7% wrong prediction results. The influences of different input parameters on the model are analyzed by analyzing a large amount of student performance data. The key variables affecting the assessment of students' learning levels are pointed out.