Abstract—This article undertakes a rigorous examination of the learning analytics model, with a particular focus on its application in the context of clustering algorithms. The integration of students' academic performance data enables the implementation of hierarchical tagging and trend tracking of learning situations. The model's initial phase involves the construction of a data layer, integrated with machine learning mechanisms, which are capable of efficiently identifying potential student groups that are at risk in terms of academic performance. Subsequently, an analytics layer is developed using Power BI, and the designed dynamic signage enables educational administrators to comprehensively understand the learning situation of students in the transition stage in real time. Ultimately, the DeepSeek API is integrated into the application layer, thereby providing precise teaching intervention suggestions. The model for teaching reform was successfully implemented during a one-year study encompassing over 1,500 students from three institutions. It provides a management framework for the transition from secondary to undergraduate vocational education. 
 

Keywords—secondary vocational education, undergraduate vocational education, learning analytics model, clustering algorithms

Cite: Yikun Xu, Sha Xian, and Linjie Xiang, "Data-Driven Intelligent Model for Learning Analytics: Bridging Secondary and Undergraduate Vocational Education," International Journal of Learning and Teaching, Vol. 11, No. 3, pp. 139-142, 2025.

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