Multi-granularity sentiment analysis and learning outcome prediction for Chinese educational texts based on transformer architecture

With the increasing adoption of intelligent tutoring systems, accurately interpreting students’ emotional states in educational contexts is crucial for providing personalized learning support. In computer science, natural language processing (NLP) techniques offer promising solutions for sentiment analysis and academic performance prediction. In the field of Chinese language education, students’ emotional states significantly influence their learning outcomes. However, traditional sentiment analysis methods exhibit limited adaptability to educational texts, failing to capture multi-granularity emotional expressions effectively. To bridge this gap, this study proposes a Transformer-based multi-granularity sentiment analysis framework tailored specifically for Chinese educational texts, integrating sentiment classification with learning outcome prediction. Our approach operates across three distinct levels, sentence, paragraph, and full text, to extract nuanced emotional features comprehensively. Furthermore, we develop a predictive model that integrates these sentiment features with learning behavior data to estimate students’ academic performance accurately. Experimental results demonstrate that our framework consistently outperforms traditional and deep learning baseline models in sentiment classification and learning outcome prediction tasks. These findings highlight the substantial potential of NLP techniques to enhance adaptive learning strategies and optimize personalized learning experiences.