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

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

Xiaofeng Wu
DOI: 10.4018/IJITSA.322096
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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.
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Introduction

The proliferation of new media and the internet, as well as the ongoing development of cutting-edge information technology, have led to a shift away from the more conventional model of higher education found in colleges and universities (Abdullah et al., 2021; Huo X., 2019). Multiple channels and dimensions of English knowledge have a significant impact on how the language is taught to students. Baker was the first person formally to propose using flipped classrooms in the year 2000. Khan and the Khan Academy have actively promoted and expanded this model since then. Now, the term more commonly refers to the use of short videos and other resources to assist students in preparing for class by reviewing previously taught fundamental concepts.

Teachers presented audio or video learning resources, had students read along and do exercises while listening, then revealed the answers, discussed the concepts, and played the materials again. In non-English-speaking countries, this method taught English. Practice and review end learning. Course exercises reinforce subjects. English curriculum updates address this. Teachers should encourage student participation and knowledge development with mobile devices. Mobile gadgets can boost student involvement and knowledge. Flipped classroom students need free time and a clean space. Outside of class, students should study as they please. Traditional education does not allow subject choices. Students use mobile devices for group tasks and research. Flipped mobile classroom for English hearing and speaking.

Flipped classrooms can facilitate student learning, provide teachers with a higher sense of accomplishment, and foster student creativity and analytic abilities. According to certain research, flipped learning can improve the English skills of college students. Evaluation of English instruction in the context of new media communication is a thorough procedure comprising numerous levels, aspects, and indicators. AHP is the most popular evaluation approach, but, owing to cognitive differences among experts in a particular professional field, experts will increase the weight of the indications differently, resulting in an unfair weighting calculation of the entire indicators. Some researchers have coupled the AHP method with the BP neural network method, trained and tested the AHP weighting results using the BP neural network algorithm, and created an enhanced AHP-BP teaching evaluation model with favorable assessment findings.

As a result of the proliferation of new forms of media communication, English is now ubiquitous in many aspects of daily life. In order to improve the convenience of teaching evaluation of similar practical courses, BP neural network was constructed by taking single index data of practical course evaluation as input samples and teaching evaluation results as output samples, and finally, a teaching evaluation model with practical courses as the main body was established. As a result of these implications, language education is significantly affected. This paper examines the impact of flipped teaching on college students’ foreign language acquisition through a statistical analysis of the distribution of learners’ linguistic competence and individual differences. This paper begins with a discussion of the flipped practice of college English in a provincial university. The interaction between them will be examined in greater detail in the near future. In addition, this paper develops an AHP and BP neural-network-based teaching evaluation model to improve the objectivity, speed, and accuracy of evaluations.

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