## 4 Results and Discussion
### Performance Evaluation and Result Analysis  
This study used a behavioral dataset of 100 students, which covers students' behavior, physiological signals, emotional state, task completion, etc. In order to comprehensively evaluate the effect of path recommendation of the model in this paper, the model in this paper is compared with other mainstream models for the following evaluation indicators, and the results are shown in Table 3.  
With an accuracy of 95.2%, the calculated 95% confidence interval ranges from approximately 93.85% to 96.55%. This indicates that at a 95% confidence level, the true accuracy is within this interval. The 95% confidence interval for a coverage rate of 96.4% ranges from approximately 95.22% to 97.58%, while the 95% confidence interval for a recall rate of 96% ranges from approximately 94.76% to 97.24%. This means that at a 95% confidence level, the true coverage and recall rates are likely to fall within these intervals. For continuous variables, such as response time, with an average of 1.5 s and a standard deviation of 0.2 s, the 95% confidence interval ranges from approximately 1.4876 s to 1.5124 s.  
Table 3 shows the comparison results of the model in this paper and other mainstream models in the personalized learning path recommendation task. The model cited in this paper is superior to other comparison models in multiple evaluation indicators. It not only accurately recommends paths and proposes appropriate learning tasks, but also achieves an efficient performance at multiple levels. In particular, the performance of this paper's model for recall and coverage is 0.96 and 96.4%, respectively, which is much higher than other models. This shows that this paper's model presents a significant improvement in accuracy in TOP-N recommendation tasks. The system response time and system stability of the model in this paper reached 1.5s and 0.97, respectively, which are values far superior to other models. This shows that the system can complete the recommendation of learning paths and feedback of behavioral data in a very short time, and the system can maintain a trouble-free operation under long-term or high-concurrency operations. In contrast, the BERT model is at the lowest level for all indicators, especially in precision (85.3%) and recall (0.91), indicating that its recommended path accuracy is low and it cannot provide \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Precision (\%)** & **Recall** & **Coverage (\%)** & **Response Time (s)** &
\begin{tabular}{c} **Processing** \\ **Sequence Length** \\ \end{tabular} \\ \hline BERT & 85.3 & 0.91 & 87 & 2.9 & 512 \\ RoBERTa & 86.4 & 0.92 & 89.6 & 2.4 & 512 \\ XLNet & 89.8 & 0.93 & 91.7 & 2.7 & 1024 \\ GPT-3 & 92.1 & 0.95 & 92.5 & 2.2 & 1024 \\ This Paper & 95.2 & 0.96 & 96.4 & 1.5 & 2048 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of different models. students with effective learning paths and tasks. Although RoBERTa has improved over BERT in terms of precision and recall, it still has a large gap in coverage and response time indicators compared with the model in this paper, indicating that its coverage of different types of students and its response speed in the system are not as good as the model in this paper. The application of the GPT-4 model enables the system to effectively explore the dependencies between features in data sequences and effectively improves the ability of multimodal feature extraction. After combining the self-attention mechanism of the model, it is easier for the system to find the features that have the most influence on the recommendation effect, making the model's path recommendations more accurate in a variety of complex feature data. Combining the real-time feedback mechanism with incremental learning technology, the system can dynamically adjust the learning strategy based on the data fed back by the background, thus improving the real time and accuracy of the recommendation effect.