Knowledge-Based English Teaching Quality Assessment Methods: Using Data Mining Techniques to Identify Key Factors and Improve Teaching Strategies

Effective evaluation of complex learning systems requires reliable information acquisition, multidimensional data analysis, and adaptive feedback mechanisms. This study proposes a data-driven assessment framework for teaching-quality evaluation based on knowledge-oriented learning analytics and data-mining techniques. A multidimensional data acquisition architecture is developed to integrate classroom interaction records, online learning behaviors, assessment outcomes, and learner-affective information into a unified information-processing workflow. Multiple analytical modules, including cluster analysis, association-rule mining, sequence-pattern discovery, and predictive modeling, are employed to identify latent relationships between learning behaviors and educational outcomes. To improve evaluation reliability, a dynamic comprehensive indicator model is established for continuous quality monitoring and anomaly detection. Furthermore, an intelligent feedback mechanism is introduced to generate diagnostic reports, support adaptive strategy adjustment, and establish a closed-loop optimization process. The proposed framework enables systematic information extraction, pattern recognition, and decision support from heterogeneous educational datasets. By integrating data acquisition, intelligent analytics, and adaptive feedback, the framework provides an engineering-oriented methodology for information processing, behavior modeling, and dynamic performance assessment in complex data-intensive environments.