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<p>Exploring the path of college education and teaching reform is the way to improve the quality of college teaching and promote the development of college education. Based on big data technology, this paper proposes a wisdom education cloud platform, which is used to realize the comprehensive utilization of teaching resources for users. Based on the personalized teaching resource recommendation demand of users, a recommendation algorithm of high-order preference propagation based on a heterogeneous knowledge graph is jointly constructed by using a meta-path and knowledge graph based on a heterogeneous information network so as to realize the personalized recommendation of the platform to users and simulate and analyze the platform. In terms of platform performance, the random write and sequential write rates of the platform reach 4765 rows/s and 3218 rows/s, respectively, and the data transmission time is reduced by 68.35% and 69.35% compared with that of NetEase Cloud Classroom and Tencent Classroom, respectively. In terms of satisfaction, the platform in this paper has improved by 5.41% and 6.7% compared with NetEase Cloud Classroom and Tencent Classroom, respectively. This shows that the intelligent education cloud platform can be the exploration direction of education teaching reform in colleges and universities.</p>
</abstract>

Exploring the path of college education and teaching reform is the way to improve the quality of college teaching and promote the development of college education. Based on big data technology, this paper proposes a wisdom education cloud platform, which is used to realize the comprehensive utilization of teaching resources for users. Based on the personalized teaching resource recommendation demand of users, a recommendation algorithm of high-order preference propagation based on a heterogeneous knowledge graph is jointly constructed by using a meta-path and knowledge graph based on a heterogeneous information network so as to realize the personalized recommendation of the platform to users and simulate and analyze the platform. In terms of platform performance, the random write and sequential write rates of the platform reach 4765 rows/s and 3218 rows/s, respectively, and the data transmission time is reduced by 68.35% and 69.35% compared with that of NetEase Cloud Classroom and Tencent Classroom, respectively. In terms of satisfaction, the platform in this paper has improved by 5.41% and 6.7% compared with NetEase Cloud Classroom and Tencent Classroom, respectively. This shows that the intelligent education cloud platform can be the exploration direction of education teaching reform in colleges and universities.

Research and strategy of university education and teaching reform based on intelligent education platform

Zhengzhou Technology and Business University

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Exploring the path of college education and teaching reform is the way to improve the quality of college teaching and promote the development of college...

Research and strategy of university education and teaching reform based on intelligent education platform

Online veröffentlicht: 30. Sept. 2023

Seitenbereich: -

Eingereicht: 22. Okt. 2022

Akzeptiert: 10. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00419

Schlüsselwörter
Heterogeneous information networks, Knowledge graphs, Meta-paths, Smart education and platforms

© 2023 Juan Zhang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research and strategy of university education and teaching reform based on intelligent education platform

Online veröffentlicht: 30. Sept. 2023

Seitenbereich: -

Eingereicht: 22. Okt. 2022

Akzeptiert: 10. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00419

SchlüsselwörterHeterogeneous information networks, Knowledge graphs, Meta-paths, Smart education and platforms

© 2023 Juan Zhang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Heterogeneous information networks, Knowledge graphs, Meta-paths, Smart education and platforms