Quality Evaluation of College English Teaching Based on Decision Tree based Class Association Rule Mining Algorithm

College English Teaching (CET) refers to the instruction and education of English language, literature, and communication skills to students in higher education institutions, typically colleges and universities. The quality evaluation of CET is a comprehensive process that examines the teaching and learning procedures, results, and environments to ensure that students receive high-quality English language education, preparing them for academic, professional, and personal success. Various methods have been proposed previously to evaluate the quality of CET, although these existing methods have overfitting, interpretability, data quality, and non-linear relationship problems. To overcome these problems a new method is proposed using Decision Tree based Class Association Rule Mining Algorithm (DT-CARMA) which improves the performance of the model and leads to precise classification. The Programme for International Student Assessment (PISA) dataset is used to estimate the effectiveness of DT-CARMA approach. Mean imputation, Min-max scaling, One-hot encoding, and t-distributed Stochastic Neighbor Embedding (t-SNE) approaches are considered for preprocessing. Correlation Analysis approach is taken for feature selection process. For classification DT-CARMA is considered which attained better accuracy, precision, recall, and specificity of $98.74 \%, 97.53 \%, 96.38 \%$, and 97.43% as compared to the existing approaches like Apriori algorithm.