Hierarchical TSK Fuzzy Classification Through Positive Intervention for Teaching Evaluation

Currently, most existing traditional teaching evaluation models are difficult to truly reflect the contribution and guiding role of administrative policies in teaching decision-making, and lack the interpretability of teaching semantics in assessing actual teaching methods and effectiveness. In addition, the substantive strategies of intervention are also difficult to quantify and evaluate. This study proposes a hierarchical Takagi–Sugeno–Kang (TSK) fuzzy classification model (Pgt-TC) with positive intervention guidance ability. The study stacks several interpretable zero-order TSK fuzzy classifiers as the basic training units (BTUs), ensuring that the final model has high interpretability of teaching semantics. Firstly, the fuzzy rule base corresponding to BTUs is determined according to the contribution level of each rule, and residual is used to reduce the error interference between adjacent BTUs, achieving the goal of improving the generalization ability of the training model. In addition, this study designed a strategy to generate posterior parameters by solving the approximation error of adjacent BTUs, which improved the classification and generalization performance of the fuzzy system. The proposed model is evaluated on five datasets, including one core educational research dataset (177 samples, 19 features) and four UCI benchmark datasets (163–5000 samples, 4–27 features). Results are reported using 5-fold cross-validation with mean values. Experimental results show that Pgt-TC achieves competitive classification performance across all datasets. On the educational dataset, it attains an average test accuracy of 93.47 and an average test SE value of 98.60, while also offering interpretability to explain educational intervention decisions.