Classroom Management Effectiveness Assessment Model of English Teaching Based on Gustafson-Kessel Clustering Based Takagi-Sugeno Fuzzy Model with Wild Geese Algorithm

Abstract

The classroom's evaluation of the teaching effect English has a demanding workload, complex statistics, among other factors. The assessment of classroom instruction quality plays a crucial role in creating a supportive environment that encourages and guides the improvement of university administration and services, as well as in igniting teacher enthusiasm, boosting their efficacy as teachers, and raising the standard of talent development. Strong ambiguity, fuzziness, and inexactness are the main questions that come up while thinking about the case of teaching quality evaluation. The Gustafson - Kessel Clustering Based Takagi-Sugeno Fuzzy (GK-TSF) model along with wild geese algorithm (GKTSF-WGA) method is proposed for classify the skills, attitudes, and verbal, classifications to improve the above issues. Initially, the data’s are gathered via the dataset of teaching assistant evaluation dataset. Afterward, the data’s are fed to pre-processing. In pre-processing segment; it removes the noise and enhances the input data’s utilizing adaptive robust cubature kalman filtering. The feature extraction section receives the output of the preprocessing. Fluency is extracted as a feature using the One - dimensional quantum integer wavelet S-transform in this case. After that, the extracted features are given to the GK-TSF model with wild geese algorithm for effectively classify skills, attitudes, and verbal. The proposed ET-GKTSF-WGA approach is implemented in MATLAB. The proposed ET-GKTSF-WGA method attains 23.05%, 28.95%, and 26.56% higher accuracy for skills; 23.12%, 25.43%, and 27.45%, higher accuracy for attitudes; 25.86%, 27.73%, and 21.04%, higher accuracy for verbal. The computational time performance of the proposed method attains 70.85%, 45.7%, 60.12%, and 35.34%, lower computation. The proposed ET-GKTSF-WGA method is in contrast to the current techniques like ET-FCE, ET-q-ROFs, and ET-K-MC, respectively.