A Student Emotions Recognition Based Online Texts with Large Language Models for Learning Prediction

with the rapid development of artificial intelligence (AI) and Large Language Models (LLM) technologies, it offers innovative approaches to model and analyze to predict student performance with educational behavior and learning data. But the challenges of data diversity, technical complex, and lack of semantic comprehension ability that limited the use of AI-based tools for learning performance prediction. In the paper, student emotions recognition based online learning data especially forum texts using LLMs is proposed, and an improved online learning performance prediction with online learning data, including student emotions using Signed Graph Neural Networks (SGNN) and LLMs, namely ER-SGNN-LLM, is constructed. In the model, the keywords are extracted from both forum texts and answers generated by students using LLM, and the keywords extracted from student’s forum texts are used for student emotions recognition, the keywords extracted from student’s answers, as well as the student emotions are used for student learning prediction. The relationships between each keyword extracted from texts and knowledge points of the course are encoded with SGNN. A graphic contrastive learning model is used to handle the noise in the dataset caused by students' subjective reasons. The combination of GNN and LLM is used to student emotions recognition and learning prediction. To verify the performances of the model, many experiments are conducted using the public dataset. The results demonstrate that, the model achieved better effectiveness in learning prediction, compared with other models. The values of F1 score of the proposed model is improved 0.032 compared that of the exist SGNN-LLM model.