Examining the college English classroom teaching quality using the fuzzy evaluation model

Abstract

English teaching at college levels is more sophisticated and advanced compared to high schools and professionals. The teaching must have high-quality meetings, real-world interactions, and professional applications. Therefore teaching quality evaluation periodically is performed internally and externally through skill validation and joint training. This article introduces a Regressive Fuzzy Evaluation Model (RFEM) for analyzing the quality of college classroom English teaching quality. This evaluation model operates over the teaching quality metrics such as performance, student understandability, and application. The understandability and English application to the real world is modeled by referring to the performance as the regressive factor. The regressive factor is analyzed for two fuzzification outputs: high and low, by analyzing the individual factors over cumulative teaching grades. The regression for low fuzzy outputs is analyzed using mean understandability and application score from the previous assessment instance. This is required for training the fuzzification from the mean score rather than the low level. Therefore the quality improvements from the lagging features are addressed by providing a new teaching method. Further fuzzy regression is initiated from the mean to the high level reducing the computation time and errors.