AHP-BP-Based Algorithms for Teaching Quality Evaluation of Flipped English Classrooms in the Context of New Media Communication

Abstract

Big data analytics constitutes a key component in the pursuit of enhancing educational efficiency. This study defines the concept of the flipped classroom in the context of new media communication, evaluates the state of audiovisual instruction in higher education, and advocates for the use of this methodology to enhance college students' English listening and speaking skills. Utilizing multiple linear regression and a conditional quantile model, this research quantifies the range of impact of flipped instruction on college students' acquisition of a foreign language. To address the deficiencies in the current evaluation process for flipped classroom teaching, it proposes a teaching quality evaluation model based on the AHP and BP neural network. The AHP constructs the teaching quality evaluation index system for the flipped classroom and ascertains the combined weights of the indices. The simulated experiment's results show that utilizing the proposed evaluation model to assess flipped classroom instruction enhances objectivity, efficiency, and precision in the evaluation process.