Design and Development of a Clustering-Based Quality Evaluation System for College English Classroom Teaching

This research presents a new clustering algorithm-based method to evaluate the quality of English classroom instruction at the college level using multidimensional data such as student feedback, performance data, and engagement metrics. Conventional evaluation systems are heavily dependent on subjective judgment, manual surveys, or fixed rubrics, which generate inconsistency, inefficiency, and bias in quality evaluations. Current models are not capable of revealing underlying patterns in student-teacher interaction dynamics and are not scalable when applied across heterogeneous educational settings. Traditional methods like KMeans and hierarchical clustering have proven inadequate in handling noisy, high-dimensional educational data. The suggested model utilizes the concept of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, which is excellent for identifying outliers and identifying clusters of any shape without requiring explicit specification of the number of clusters. Experimental assessment validates that the technique offers better performance and better interpretability. Comparative analysis with conventional models verifies the stability and flexibility of the method, providing a more objective and data-driven process for ongoing teaching improvement.