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Research on the Construction of a Precision Teaching Model Based on Big Data

Author(s)

Xiaoxiao Gu, Wenjuan Shao, Kun Liu*

Affiliation(s)

College of Applied Science and Technology, Beijing Union University, Beijing, China *Corresponding Author

Abstract

With the explosive growth of educational big data, traditional experience-driven teaching models have gradually transitioned toward data-driven instruction. However, existing precision teaching research commonly faces bottlenecks such as limited data dimensions, inadequate algorithm adaptation to teaching scenarios, and static teaching strategies. In light of this, this study aims to construct a dynamic adaptive precision teaching model based on the integration of multimodal educational big data. It further designs a closed-loop operational mechanism comprising “multi-source data analysis – precision teaching implementation – data feedback iteration”. First, learner data is integrated from multiple sources—including LMS systems, behavioral tracking technology, and sensor devices—to construct a comprehensive digital learner profile. Second, a precision teaching model is built using Graph Neural Networks (GNN), embedding educational psychology theories and subject-specific pedagogical principles into the algorithm's core architecture to achieve a layered adaptive algorithm design. Finally, we propose a dynamic adaptive model based on rule-based reasoning and reinforcement learning, which can optimize teaching strategies in real time according to the dynamic changes in learning behavior. Experimental results on our self-built dataset of authentic educational practice scenarios demonstrate that this model significantly enhances teaching precision and learner learning efficiency, fully validating its technical feasibility and application effectiveness.

Keywords

Big Data; Precision Teaching; Learner Profiling; Reinforcement learning; Knowledge Tracking

References

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