Abstract: In intelligent education systems,knowledge point annotation is a key module for organizing teaching resources,enabling personalized recommendations,and modeling students' cognitive states.However,traditional exercise-oriented approaches to knowledge point annotation have limitations in reflecting the individual differences exhibited by students in programming learning processes.To address this issue,this paper proposes an automatic student code knowledge point annotation method based on multi-agent collaboration.This method shifts the focus from exercise-driven to code-driven annotation,constructing a three-level knowledge framework encompassing statement layer,code-block layer,and function layer.It also introduces a collaborative system composed of three agents－knowledge annotation,task analysis,and integrated feedback－with internal self-inspection and iterative optimization capabilities.Experimental evaluation is conducted on 363 student code submissions from an introductory programming course.The system demonstrates strong interpretability and group analysis capability in real-world educational scena-rios,effectively revealing students' knowledge mastery status and common cognitive deficiencies.Moreover,the study employs a LLM-based peer-review mechanism for performance assessment.Results indicate that the multi-agent collaborative approach outperforms methods based directly on LLM across five evaluation dimensions(completeness,accuracy,reasonableness,error identification ability,and educational guidance),with significantly more selections as the preferred solution.This research achieves automated and interpretable knowledge point annotation for student code,providing technical support and practical foundations for fine-grained student modeling,personalized assessment,and other downstream tasks.

Key words: Programming education, Knowledge point annotation, Student code, Large language model, Multi-agent