A study on improving the teaching quality of college english classrooms based on fuzzy evaluation

In today's globalized world, having a strong command of written and spoken English is becoming increasingly essential for achieving success in both academic and professional settings. The increasing demand for trained English teachers in schools has been a consequence of the growth of international education. Conventional methods of assessment may not always take into consideration critical components of instruction, such as student participation, unambiguous instructions, and constructive feedback. This results in evaluations that are not reliable and restricts the variety of methods that can be used in instruction. The Bayesian Optimization-Fuzzy Neural Network Hybrid This study provides evidence that quality evaluation is a more accurate and equitable technique to assessing the quality of college English instruction. The FNN-BOQE was created in order to resolve these issues. Linguistic variables are generated through the use of fuzzy logic by FNN-BOQE, which transforms questionnaires from students, expert reviews, and classroom observations. The FNN is capable of modeling the intricate interactions that exist between the quality of teaching and those variables. At the same time, Bayesian Optimization is employed to optimize the parameters of the system, hence increasing its accuracy. In comparison to the conventional techniques employed in college English instruction, FNN-BOQE displayed a 22 percent improvement in the correctness of evaluation and a 19 percent improvement in the consistency of feedback. FNN-BOQE offers practical insights that make evaluations more dependable and enhances the efficacy of instruction. Therefore, it contributes to the improvement of English courses in both colleges and universities.