Systematic integration of information big data and physics teaching based on deep learning algorithms

被引：0

作者：

Tao G. ^{[1
]}

Wang Y. ^{[2
]}

Fan Y. ^{[1
]}

机构：

[1] Experimental and Practical Education Innovation Center, Beijing Normal University at Zhuhai, Guangdong, Zhuhai

[2] Faculty of Arts and Sciences, Beijing Normal University at Zhuhai, Guangdong, Zhuhai

来源：

Applied Mathematics and Nonlinear Sciences | 2024年 / 9卷 / 01期

关键词：

Attention mechanism; Deep learning; Information big data; Physics teaching; Teaching system;

D O I：

10.2478/amns.2023.2.01044

中图分类号：

学科分类号：

摘要：

This paper first investigates the main performance of the shackles of traditional physics teaching on students and its negative consequences and then designs a physics teaching process model based on the teaching environment of information big data and constructs a framework of the physics teaching system by utilizing data mining technology. Then, the physics teaching system is modeled based on the artificial neuron model and recurrent neural network, and the attention mechanism is used to improve the overall generalization performance of the model. Finally, the application effect of the physics teaching system is compared through empirical evidence. The results show that there is a deviation in the mean value of the two classes; the average score of the experimental class is 75.552 points, and the average score of the control class is 65.910 points, and there is a difference of 9.642 points between the two classes’ mean values. The data show that applying a physics teaching system can improve the learning status quo of students’ mechanical imitation, help students better master knowledge, enhance students’ ability to use information technology and achieve better teaching results than traditional teaching. © 2023 Guanqi Tao, Yinshu Wang and Yina Fan, published by Sciendo.

引用

共 18 条

[1]

Sun M., Hao R., Wang B., Wang G., Yue C., Research on the computer assisted multimedia teaching platform application in university education reform, Boletin Tecnico/Technical Bulletin, 55, 4, pp. 544-550, (2017)

[2]

Chen H., Yang J., Application of it-integrated project-based learning in the teaching reform of undergraduate education, International Journal of Emerging Technologies in Learning (iJET), 5, (2021)

[3]

Gottlieb, Steven, Changes at the nsf and computing in the physics curriculum, Computing in Science & Engineering, 19, 3, pp. 4-5, (2017)

[4]

Klanja-Milievi A., Ivanovi M., Budimac Z., Data science in education: big data and learning analytics, Computer Applications in Engineering Education, 25, 6, (2017)

[5]

Andino M., Miftachul H., Shukri M.T.K., Ghafar D.A., Bushrah B., Azmi J.K., Et al., Understanding modern learning environment (mle) in big data era, International Journal of Emerging Technologies in Learning, 13, 5, (2018)

[6]

Yun E., Language gap between college introductory physics textbooks and high school physics textbooks, European Journal of Physics, 41, 6, (2020)

[7]

Gurbuz S., Adguzel A., Zcan V.E., Krpc S.M., Ylmaz A., Experimental high energy physics summer school for high schools, Canadian Journal of Physics, 97, 3, pp. 1-6, (2019)

[8]

Fu G., Clarke A., Individual and collective agencies in china’s curriculum reform: a case of physics teachers, Journal of research in science teaching, 56, 1, pp. 45-63, (2019)

[9]

Viennot L., Incomplete explanations in physics teaching: discussing the rainbow with student teachers, European Journal of Physics, 42, 5, (2021)

[10]

Ramankulov S.Z., Dosymov E., Turmambekov T., Azizkhanov D., Bekbayev S., Integration of case study and digital technologies in physics teaching through the medium of a foreign language, International Journal of Emerging Technologies in Learning (iJET), 15, 4, (2020)

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