Lai Lin
School of Foreign Languages, Xihua University, Chengdu, China.
Front Psychol. 2022 Jul 13;13:945273. doi: 10.3389/fpsyg.2022.945273. eCollection 2022.
With the development of modern information technology, the flipped classroom teaching mode came into being. It has gradually become one of the hotspots of contemporary educational circles and has been applied to various disciplines at the same time. The domestic research on the flipped classroom teaching mode is still in the exploratory stage. The application of flipped classroom teaching mode is still in the exploratory stage. It also has many problems, such as low class efficiency, poor teacher-student interaction, outdated teaching modes, not student-centered, etc., which lead to poor students' enthusiasm for learning. Therefore, the current English flipped classroom teaching mode still needs to be tested and revised in practice. Combined with emotion recognition technology, this paper analyzes speech emotion recognition, image emotion recognition, and audition emotion recognition technology and conducts a revision test for the current English flipped classroom teaching mode. It uses the SVM algorithm for one-to-one method and dimension discretization for emotion recognition, and finds that the recognition results after different dimension classification recognition are improved for each emotion. Among them, the recognition rate of different dimension classification recognition methods is 2.6% higher than that of one-to-one method. This shows that under the same conditions, the emotion recognition technology of different dimension classification recognition methods is higher.
随着现代信息技术的发展，翻转课堂教学模式应运而生。它逐渐成为当代教育界的热点之一，同时也被应用于各个学科。国内对翻转课堂教学模式的研究仍处于探索阶段。翻转课堂教学模式的应用也仍处于探索阶段。它还存在许多问题，如课堂效率低下、师生互动差、教学模式陈旧、不以学生为中心等，这些导致学生学习积极性不高。因此，当前的英语翻转课堂教学模式仍需在实践中进行检验和修正。本文结合情感识别技术，分析了语音情感识别、图像情感识别和听觉情感识别技术，并对当前的英语翻转课堂教学模式进行了修正测试。它使用支持向量机算法的一对一方法和维度离散化进行情感识别，发现不同维度分类识别后的情感识别结果均有提高。其中，不同维度分类识别方法的识别率比一对一方法高2.6%。这表明在相同条件下，不同维度分类识别方法的情感识别技术更高。
