Abstract: The continuous advancement of artificial intelligence technologies has accelerated the intelligent transformation of education,with student behavior analysis emerging as a key research area supporting precision teaching and personalized learning.However,existing approaches often rely on specialized models for behavior feature extraction and classification,with outputs typically presented as abstract labels,lacking interpretability and intuitiveness.To enable natural language descriptions of students' attentiveness in online learning scenarios,this paper constructs a vision-language alignment dataset for online education contexts,consisting of student learning images paired with corresponding attention-related behavior descriptions.The dataset includes both single-frame images and multi-frame image sequences.Building upon this dataset,it proposes a multimodal fine-tuning method tailored for the task of describing students' attentive behaviors.Experiments are conducted on the Qwen2.5-VL-3B and Qwen2.5-VL-7B vision-language models.The proposed method incorporates prompt design based on head pose,gaze direction,and facial expressions to guide the model in learning attention-related features.Furthermore,this paper introduces an attention-perception loss to enhance the model's understanding of student behavior.Experimental results demonstrate that the fine-tuned models achieve superior accuracy in describing student attentiveness compared to existing vision-language models.

Key words: Visual language model, Student behavior analysis, Online learning, Engagement access