英语教学质量评价的深度学习算法

IF 0.8 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
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引用次数: 1

摘要

大学在培养人才方面发挥着巨大的作用。特别是在国际化的背景下,英语作为通用语言的教学变得越来越重要。本文介绍了传统的英语教学质量评价方法,以传统方法的评价指标与卷积神经网络(CNN)算法相结合,建立了用于英语教学质量评价的深度学习算法,并对CNN算法进行了仿真实验,并与支持向量机(SVM)算法进行了比较。结果表明,CNN算法得到的分数与实际分数有一定误差,但远低于SVM算法得到的分数,并且CNN算法的计算时间更短。本文采用CNN算法结合层次分析法(AHP)构建的评价指标对英语教学质量进行评价,并通过与SVM算法的对比验证了CNN算法的有效性,为英语教学质量的智能评价提供了有效参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Deep Learning Algorithm for Evaluating the Quality of English Teaching
Universities play a huge role in the cultivation of talents. Especially in the context of internationalization, the teaching of English as a common language is becoming more and more important. This paper introduced the traditional methods for evaluating the quality of English teaching, established a deep learning algorithm for evaluating the quality of English teaching with the evaluation indicators of the traditional methods combined with the convolutional neural network (CNN) algorithm, conducted simulation experiments on the CNN algorithm, and compared it with the support vector machine (SVM) algorithm. The results showed that the scores obtained by the CNN algorithm had some errors with the actual scores but were much lower than the scores obtained by the SVM algorithm, and the CNN algorithm consumed a shorter time in computing. This paper used the CNN algorithm combined with evaluation indexes constructed by the analytic hierarchy process (AHP) method to evaluate the quality of English teaching and verified the effectiveness of the CNN algorithm through a comparison with the SVM algorithm, which provides an effective reference for intelligent evaluation of English teaching quality.
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来源期刊
CiteScore
2.90
自引率
0.00%
发文量
25
期刊介绍: The International Journal of Computational Intelligence and Applications, IJCIA, is a refereed journal dedicated to the theory and applications of computational intelligence (artificial neural networks, fuzzy systems, evolutionary computation and hybrid systems). The main goal of this journal is to provide the scientific community and industry with a vehicle whereby ideas using two or more conventional and computational intelligence based techniques could be discussed. The IJCIA welcomes original works in areas such as neural networks, fuzzy logic, evolutionary computation, pattern recognition, hybrid intelligent systems, symbolic machine learning, statistical models, image/audio/video compression and retrieval.
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