## 3 Methods  
### Research Design  
The purpose of this study is to solve the problems in the existing learning path recommendation system, such as poor adaptability, insufficient personalization, and difficulty in handling long sequences of students' behavior and interest data. By introducing LLM technology, especially GPT-4, the accuracy and efficiency of personalized learning path recommendation in the second classroom can be improved. According to the experimental design principles proposed by [PERSON] et al. [34] and the qualitative research design framework proposed by [PERSON] and [PERSON] [35], system planning was carried out. This study defines the demand for personalized learning path recommendation in the second classroom, and determines the function and technical framework of the system. These theoretical frameworks were chosen because they provide scientific research method guidance and help to ensure the rigor of the research process and the effectiveness of the results. Through user interviews and demand surveys, we can deeply understand user needs and provide a solid foundation for the subsequent design. We made a systematic overall research plan, which is divided into the following stages. Data collection and pretreatment:  
Considering the complexity and diversity of students' behavior data, a data source covering a variety of information was selected to fully reflect students' learning status. The purpose of the preprocessing step is to improve the data quality, ensure that the data of different modes have the same weight in the model, and thus optimize the model's performance. The data source used a simulated student learning dataset (including 1200 records of 100 students), including multimodal data, such as physiological signals, facial expressions, activity levels, and emotional states. In the preprocessing step, the missing and abnormal values were eliminated, the frequency characteristics of time-series data were extracted by Fourier transform, and the Z-score method was used for standardization. Feature extraction and fusion:  
Effective features were extracted from multimodal data and fused to form a comprehensive student portrait. Feature extraction uses the GPT-4 model to extract the depth features of text data, combined with the features of other modal data (such as frequency domain features). Feature fusion uses the weighted summation method or other fusion techniques to integrate features of different modes to form a comprehensive portrait of students. GPT-4 was selected for feature extraction because of its powerful long-sequence modeling ability and self-supervised learning ability, which can effectively capture the long-term dependence in students' behavior. The feature fusion strategy was used to integrate information from different data sources, provide a comprehensive view of students, and generate personalized learning paths. Generating the pre-training model and its training process:  
GPT-4 was selected as the basic model, and its long sequence modeling ability and self-attention mechanism were utilized. Pre-training was performed on simulation datasets, optimizing parameters to improve the model's performance. The cross-entropy loss function was used to evaluate the error between the predicted path and the actual path, and the model parameters were updated by the Adam algorithm. GPT-4 was chosen because of its excellent performance in natural language processing tasks and its ability to handle multimodal input. Specific parameter configuration was used to balance calculation efficiency and model performance and achieve the best results while ensuring efficient training. Personalized learning path generation:  
According to students' behavior data and feature vectors, personalized learning paths are generated. Based on the autoregressive modeling formula, the optimal learning path was generated by maximizing the conditional probability.