Analysis of Higher Education Teaching Data Based on Data Mining Technology

To efficiently extract and leverage the vast amounts of data stored within university academic management systems, a novel approach utilizing data mining techniques for analyzing higher education teaching data has been proposed. This study delves into the performance data recorded in the academic affairs system, aiming to uncover meaningful insights. Initially, data collection and preprocessing are conducted to ensure clean and structured input. Subsequently, a comprehensive evaluation of student performance is performed using factor analysis. For predictive purposes, an enhanced decision tree model is developed by integrating the K-means clustering algorithm with the C5.0 algorithm. This hybrid approach is benchmarked against alternative methodologies to assess its effectiveness. Experimental results demonstrate that the enhanced decision tree achieves the highest prediction accuracy of 64.8%, characterized by minimal tree depth and a reduced number of leaf nodes. This not only enhances model precision but also ensures greater robustness while mitigating the risk of overfitting. These findings highlight the superiority of the proposed method in analyzing and predicting academic performance within the context of higher education.