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Japanese Teaching Quality Satisfaction Analysis with Improved Apriori Algorithms under Cloud Computing Platform
Hunan City University, Yiyang Hunan, 413000, China
* Corresponding Author: Lini Cai, e-mail:
Computer Systems Science and Engineering 2020, 35(3), 183-189. https://doi.org/10.32604/csse.2020.35.183
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In this paper, we use modern education concept and satisfaction theory to study the construction of a system used to evaluate Japanese teaching quality based on a satisfaction model. We use a cloud computing platform to mine the rules of Japanese teaching quality satisfaction by using an improved Apriori algorithm to explore the impact of measurement indicators of teaching objectives, processes and results on overall satisfaction with Japanese teaching practices, so as to improve Japanese teaching in the future. Scientific decision-making, improvement of teaching practices, transformation and innovation of students’ learning methods provide data reference and theoretical support.Keywords
Relevance Analysis; Improved Apriori Algorithm; Japanese Teaching Quality Evaluation; Satisfaction Model
Cite This Article
APA Style
Cai, L. (2020). Japanese teaching quality satisfaction analysis with improved apriori algorithms under cloud computing platform. Computer Systems Science and Engineering, 35(3), 183-189. https://doi.org/10.32604/csse.2020.35.183
Vancouver Style
Cai L. Japanese teaching quality satisfaction analysis with improved apriori algorithms under cloud computing platform. Comput Syst Sci Eng. 2020;35(3):183-189 https://doi.org/10.32604/csse.2020.35.183
IEEE Style
L. Cai, “Japanese Teaching Quality Satisfaction Analysis with Improved Apriori Algorithms under Cloud Computing Platform,” Comput. Syst. Sci. Eng., vol. 35, no. 3, pp. 183-189, 2020. https://doi.org/10.32604/csse.2020.35.183
Citations
Copyright © 2020 The Author(s). Published by Tech Science Press.This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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