The application of deep learning in the innovation of intelligent English teaching mode

IF 0.5 Q4 ENGINEERING, MULTIDISCIPLINARY
Yafang Chen
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引用次数: 0

Abstract

With the rapid development of deep learning technology, its application in various fields is increasingly extensive. Especially in the field of education, the application of deep learning technology has brought great challenges and changes to the traditional teaching mode. This research is aimed at the application of deep learning in intelligent English teaching mode. Firstly, the theory of deep learning is studied in depth, and the application cases of deep learning in other fields are discussed. Secondly, the research designs and implements an intelligent English teaching model based on deep learning, and carries out a lot of experiments and tests. The experimental results show that this new teaching mode can effectively improve the efficiency and effect of students’ English learning. However, it is also found that the model has some problems, such as model training needs a lot of computing resources, has certain requirements for hardware equipment, and some students have poor adaptability to the new learning mode. To solve these problems, a series of solutions are proposed. In general, although there are still some challenges in the application of deep learning in intelligent English teaching mode, its potential is huge and it has a profound impact on improving the quality of teaching.
深度学习在智能英语教学模式创新中的应用
随着深度学习技术的飞速发展,其在各个领域的应用也越来越广泛。尤其是在教育领域,深度学习技术的应用给传统的教学模式带来了巨大的挑战和变革。本研究旨在探讨深度学习在智能英语教学模式中的应用。首先,深入研究了深度学习的理论,探讨了深度学习在其他领域的应用案例。其次,研究设计并实施了基于深度学习的智能英语教学模式,并进行了大量的实验和测试。实验结果表明,这种新型教学模式能有效提高学生的英语学习效率和效果。但同时也发现该模式存在一些问题,如模型训练需要大量的计算资源,对硬件设备有一定的要求,部分学生对新的学习模式适应性较差等。为了解决这些问题,我们提出了一系列解决方案。总的来说,虽然深度学习在智能英语教学模式中的应用还存在一些挑战,但其潜力巨大,对提高教学质量有着深远的影响。
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来源期刊
CiteScore
0.80
自引率
0.00%
发文量
152
期刊介绍: The major goal of the Journal of Computational Methods in Sciences and Engineering (JCMSE) is the publication of new research results on computational methods in sciences and engineering. Common experience had taught us that computational methods originally developed in a given basic science, e.g. physics, can be of paramount importance to other neighboring sciences, e.g. chemistry, as well as to engineering or technology and, in turn, to society as a whole. This undoubtedly beneficial practice of interdisciplinary interactions will be continuously and systematically encouraged by the JCMSE.
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