## 5 Experiment Design-Accuracy Verification of Process Evaluation Method
### Experimental Design
Further analysis from Figures 3-5 show that, among the five algorithm prediction models, the experimental group and the control group have a high degree of fit, and the average absolute percentage error is also small, which indicates that the evaluation model designed in this study can effectively assist teachers to evaluate students more accurately. Among them, KNN has the largest R Squared Score, and RMSE and MAPE have the smallest value. According to specific indicators, KNN algorithm has the smallest error in the prediction model, which is closer to the expert rating. Therefore, KNN algorithm has the strongest stability in this system model. Finally, KNN algorithm is deployed in the system. In the later stage, more labeled real data will be added to the evaluator model, and the iterative procedural evaluator model will be constantly updated to make its performance more powerful and its evaluation more accurate. ## 6 Conclusions and Future Work

In the context of the networked era, students are engaged in richer learning, so the evaluation of students should be more multimodal, especially in the field of vocational education. In this paper, we focus on the field of practical training education and build a model of a secondary skill-based practical training process data evaluation system in a virtual reality environment, which consists of four subsystems. These are namely VR practical training process activity recording, process data processing and storage, practical training process data analysis, and practical training process evaluation. The evaluation index system of the process evaluator is constructed in the subsystem of the process evaluation model of practical training, and the learners are evaluated from three dimensions on their knowledge acquisition, skill mastery, and ability development. When a new user uses this evaluation system, the system can score the user's practical training. In this paper, we train an evaluator model by machine learning and demonstrate through experiments that our approach can accurately evaluate the process of the trainers by taking the practical training of secondary vocational auto mechanics as an example. In the process of algorithm validation of the evaluator model, this study mainly adopts five algorithms suitable for this model: Linear Regression, Decision Tree Regressor, SVR, KNN, and Random Forest Regressor. In the actual validation process we found that the KNN algorithm is more effective in predicting the overall model. We found that the KNN algorithm was more effective in predicting the overall model, and its accuracy rate reached over 90%. Moreover, the evaluator model can be continuously updated and iterated with more real data in the future, and its process evaluation of students' practical

Figure 5: Mean Absolute Percentage Error of five prediction models. training operations will be more accurate. This study allows teachers and students to more accurately identify problems in practical training operations by providing accurate feedback on the skill-based practical training process in auto mechanics. Teachers can better plan their teaching priorities and students can better master their professional skills and improve them in a targeted manner. In this study, our proposed model of a process evaluation system in a virtual reality environment can indeed solve the problem of inaccurate and non-objective evaluation in skill-based practical training, and the method has been verified to accurately evaluate learners' practical training by evaluating it in actual teaching. However, the proposed model is only a small-scale study with the example of practical training in auto mechanics, and the limitation of small sample size limits the generalization of its findings. In the future, it is necessary to study the expansion to different grade levels as well as other practical training specialties. It can promote the wide application and model innovation of process-based assessment services for skill-based practical training learning. Conceptualization, Q.X. and F.Q.