Personalized teaching management and quality evaluation system for intelligent education based on machine learning

With the increasing demand for efficient intelligent education management and personalized learning, this study designs a personalized teaching management and quality evaluation system based on machine learning. The primary goal is to improve intelligent education management efficiency, offer precise teaching quality assessment, and provide tailored learning recommendations. The decision tree algorithm was employed to analyze student data, allowing the system to plan learning paths based on students' learning behaviors and grades. Additionally, it evaluates teaching effectiveness in real time. The UCI dataset, which contains intelligent education student performance data, was utilized for this study. Pre-processing steps such as data purification, standardization, and feature extraction were performed to enhance data quality and consistency. The system demonstrated excellent performance in both functional and performance testing, operating efficiently with datasets of varying sizes and maintaining stability under high-load conditions. The test results indicate that the system achieves high prediction accuracy and rapid response times, aligning with the need for personalized teaching in large-scale intelligent educational environments. The personalized teaching management system based on decision trees effectively optimizes the teaching quality evaluation process, introducing intelligent management tools into the intelligent education sector.