Abstract: The deep integration of artificial intelligence(AI) technology in the education sector has become a core strategy for national educational digital transformation.Within the domain of computer practice teaching,the precise recommendation of practical learning resources serves as a vital pathway to enhance student learning efficacy and quality.Confronting the tension between the scale of higher education and the diversification of student needs,this study proposes a lightweight educational large model-based personalized practice learning resource recommendation framework,named LightPLRec(Lightweight Personalized Learning Re-commender for Dynamic Practice Materials).The model is designed to intelligently recommend tailored practical learning materials in response to the dynamic changes in individual student characteristics.Leveraging a lightweight large model with low computational demands,it constructs the SPIR(Student Profile & Interest-based Recommender) educational large model for personalized practical learning resource recommendation through instruction fine-tuning and reinforcement learning methods.By integrating multi-source heterogeneous data and deeply incorporating the curriculum knowledge system,disciplinary frontiers,industrial development trends,and national strategic orientations,it establishes a cross-disciplinary,multimodal practical learning resource repository and designs the graph2topic method for converting knowledge graphs into thematic text.Empowered by the robust capabilities of the SPIR large model and supported by the multi-source resource repository,it proposes an intelligent workflow-based recommendation method.Specifically,it designs a thematic analysis method to extract student competency features from assessment results,applies the GCN(Graph Convolutional Network) algorithm to mine student interest features from learning behavior data,and creates dual intelligent agents:a “Competency-Recommender Agent” and an “Interest-Recommender Agent”.This constructs a dual-agent collaboratively driven intelligent workflow system,enabling a series of tasks from the intelligent generation of personalized student profiles to the dynamic recommendation of practical learning resources.Furthermore,a persona-lized resource recommendation dataset is constructed,on which the proposed model demonstrates significantly superior perfor-mance compared to baseline models.Specifically,the LightPLRec model trained on the Qwen2.5-3.0B base model demonstrates outstanding performance in both the capability recommendation and interest recommendation tasks,achieving accuracies of 0.947 and 0.939 respectively,surpassing the evaluation results of DeepSeek-V3 on the same dataset.This research provides a technical paradigm for the vertical application of educational large models in specific scenarios.Simultaneously,by creating a dynamic personalized practical learning resource recommendation model,it offers an innovative pathway to implement the principle of “tea-ching students according to their aptitude” and cultivate high-quality computer practice talents.

Key words: Lightweight educational large model, Personalized recommendation, Graph convolutional network algorithm, Intelligent workflow, Intelligent agent, Reinforcement learning