Construction of network mathematics model for college English mixed teaching-Reference-Cited by-同舟云学术

Construction of network mathematics model for college English mixed teaching

Published:2023-06-22 Issue:2 Volume:8 Page:2665-2676
ISSN:2444-8656
Container-title:Applied Mathematics and Nonlinear Sciences
language:en
Short-container-title:

Author:

Chen Siwen¹,Tian Yanyan¹

Affiliation:

1. 1 International Education College, Wuchang Institute of Technology , Wuhan , , China .

Abstract

Abstract To better carry out college English blended teaching and make students adapt better, author Z proposes a method of college English blended teaching model based on the Internet of Things network platform. According to the development of today’s Internet of Things technology, this method combines English learning and the use of cloud computing to develop the Internet network platform, so it is suitable for professionals teaching in college to complete English education., thus improving the quality of education. According to the test results: the average score of 4th grade online students is 523, 78.3% scored above 425; The average score of 4th graders in the non-network course was 394, and 62.4% scored above 425. The 4th-grade score for the online course was 129 points higher than the average score nia average for the offline course and 15.9% higher than the average score of 425. Conclusion: The author’s approach can contribute to the creation of mixed English courses in colleges and universities sad.

Publisher

Walter de Gruyter GmbH

Subject

Applied Mathematics,Engineering (miscellaneous),Modeling and Simulation,General Computer Science

Link

https://www.sciendo.com/pdf/10.2478/amns.2023.1.00447

Reference25 articles.

1. Sugiharti, G., & Ananda, W. R. (2021). The effect of instruction model using media and motivation on chemical learning results (Study of the effect of instruction models using media and motivation in learning). Journal of Physics: Conference Series, 1940(1), 012116 (6pp).

2. Theobald, M., Bellhuser, H., & Imhof, M. (2021). Deadlines don’t prevent cramming: Course instruction and individual differences predict learning strategy use and exam performance. Learning and Individual Differences, 87(4), 101994.

3. Meana, V., Rico, J. C., Valio, G., et al. (2021). Flipped Classroom and gamification in Automated Manufacturing lab classes. IOP Conference Series: Materials Science and Engineering, 1193(1), 012134-.

4. Yoo, B., Sylvain, T., Bengio, Y., et al. (2020). Joint learning of generative translator and classifier for visually similar classes. IEEE Access, 8, 219160-219173.

5. Zhu, T. (2021). The reform of digital media art professional curriculum system based on internet technology. Journal of Physics: Conference Series, 1992(3), 032121 (4pp).