<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The purpose of exploring the development mode of online teaching resources in colleges and universities is to enhance the utilization rate of online teaching resources to promote the diversification of teaching methods. In this paper, based on big data technology, a cloud education platform integrating online teaching resources in colleges and universities is constructed under the perspective of integrating industry and education, and the architecture and functions of the cloud education platform are explained. Then the collaborative filtering algorithm is used to filter and collect the information on online teaching resources, and the Pearson correlation formula is used to pre-process the data and help users to recommend teaching resources. Finally, the pre-processed data are filtered and analyzed using the multilayer perceptron technology under the deep learning model. To verify the practicality of the cloud education platform proposed in this paper, the platform’s performance is evaluated by combining MAE metrics with RMSE metrics. In terms of load capacity, the platform has the best load capacity when the number of users is 200. From the performance test, the average transmission rate of the cloud education platform is 8.1% and 0.93% higher than that of NetEase Cloud Classroom and Tencent Classroom, and the average transmission time is 7.09 s. From the practicality analysis, the average value of the MAE index is 0.317%, and the average value of the RMSE index is 0.232%. This shows that the cloud education platform can effectively integrate university online teaching resources and help universities achieve diversified development of teaching methods.</p>
</abstract>

The purpose of exploring the development mode of online teaching resources in colleges and universities is to enhance the utilization rate of online teaching resources to promote the diversification of teaching methods. In this paper, based on big data technology, a cloud education platform integrating online teaching resources in colleges and universities is constructed under the perspective of integrating industry and education, and the architecture and functions of the cloud education platform are explained. Then the collaborative filtering algorithm is used to filter and collect the information on online teaching resources, and the Pearson correlation formula is used to pre-process the data and help users to recommend teaching resources. Finally, the pre-processed data are filtered and analyzed using the multilayer perceptron technology under the deep learning model. To verify the practicality of the cloud education platform proposed in this paper, the platform’s performance is evaluated by combining MAE metrics with RMSE metrics. In terms of load capacity, the platform has the best load capacity when the number of users is 200. From the performance test, the average transmission rate of the cloud education platform is 8.1% and 0.93% higher than that of NetEase Cloud Classroom and Tencent Classroom, and the average transmission time is 7.09 s. From the practicality analysis, the average value of the MAE index is 0.317%, and the average value of the RMSE index is 0.232%. This shows that the cloud education platform can effectively integrate university online teaching resources and help universities achieve diversified development of teaching methods.

Research on the integration of online teaching resources in higher education institutions under the perspective of industry-education integration

Nanjing University of Aeronautics and Astronautics

Applied Mathematics and Nonlinear Sciences

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

The purpose of exploring the development mode of online teaching resources in colleges and universities is to enhance the utilization rate of online teaching...

Research on the integration of online teaching resources in higher education institutions under the perspective of industry-education integration

Data publikacji: 28 sie 2023

Zakres stron: -

Otrzymano: 10 lis 2022

Przyjęty: 08 mar 2022

DOI: https://doi.org/10.2478/amns.2023.2.00255

Słowa kluczowe
Collaborative filtering algorithm, Multilayer perceptron, Pearson correlation, Industry-education integration, Cloud education platform

© 2023 Yu Shen et al., published by Sciendo

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

Research on the integration of online teaching resources in higher education institutions under the perspective of industry-education integration

Data publikacji: 28 sie 2023

Zakres stron: -

Otrzymano: 10 lis 2022

Przyjęty: 08 mar 2022

DOI: https://doi.org/10.2478/amns.2023.2.00255

Słowa kluczoweCollaborative filtering algorithm, Multilayer perceptron, Pearson correlation, Industry-education integration, Cloud education platform

© 2023 Yu Shen et al., published by Sciendo

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

Słowa kluczowe
Collaborative filtering algorithm, Multilayer perceptron, Pearson correlation, Industry-education integration, Cloud education platform