Abstract
College English classroom teaching evaluation is an important basis for understanding teaching level and improving teaching quality. The traditional college English classroom teaching evaluation is mainly carried out through questionnaires and scales, but this method is time-consuming and laborious, inevitably introduces subjective errors, and reduces the accuracy and credibility of the evaluation results. In recent years, the rise and development of wisdom education not only provides a more convenient and efficient modern education form but also brings new ideas for classroom teaching evaluation. A subjective and objective fusion statistical evaluation model based on multidirectional genetic variation method and optimized neural network is proposed. The algorithm avoids subjective errors and improves the accuracy and reliability of the evaluation results, and a comprehensive evaluation model is constructed. Finally, according to different evaluation indexes, a systematic visualization scheme is designed to generate students’ classroom learning evaluation report and teachers' classroom teaching evaluation report, respectively, and visualize them on the web.
Highlights
Discrete Dynamics in Nature and Society multidimensional audio, and video data can be deployed and collected in the classroom
Is paper is divided into five parts. e first part is the research background, and the second part is the literature review to analyze the research results of the problem. e third part is the introduction of multidirectional mutation genetic algorithm and optimized neural network model. e fourth part is the concrete empirical analysis and completes the visualization of the college English classroom teaching evaluation system. e fifth part is the conclusion of the article
Aiming at accelerating the construction of educational informatization and intelligent education, this paper constructs a set of college English classroom teaching evaluation system based on multidirectional mutation genetic algorithm and its optimized neural network model
Summary
Received 22 October 2021; Revised 12 November 2021; Accepted 18 November 2021; Published 3 December 2021. E EduBrain data-driven smart classroom launched by qingfan technology is deployed by cameras, pickups, and other equipment in the classroom to noninvasively collect multidimensional data such as teachers’ and students’ facial expressions, voice intonation, and behavior actions in the classroom Based on this data, it uses AI technologies such as knowledge map, emotion calculation, and posture recognition to calculate classroom concentration interactive degree and other index data and generate visual college English classroom teaching evaluation results, which are visually presented on PC and mobile terminals together with college English classroom teaching audio and video. “teacher’s teaching type evaluation” is divided into “indoctrination type/natural type/interactive type.” e indicators of the presentation map, such as “classroom cloud map,” “S-T teaching analysis map,” and “knowledge point extraction map,” are presented directly through visualization and are not calculated here
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