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<p>This paper explores the digital learning model from the 4 paths of macro education reform. The four paths of macro education reform include improving the education system, changing the education model, updating education content, and establishing a comprehensive evaluation system. The construction of a learning port is based on the aspects of the learning environment and learning service, as well as research on the characteristics of digital learning modes. Based on BP neural networks, an evaluation model for community education digital resources has been established. The defects of the BP neural network are optimized by using an artificial fish school-frog jump hybrid algorithm, and 19 index factors are selected to construct the evaluation system of digital learning resources. Students’ digital learning abilities and the degree of innovation of digital learning resources in community education were separately analyzed using pre-and post-tests. The average performance of students in the experimental class improved by 9.89 points, and the score interval improved to [85,98], which was significantly better than the control class. The most obvious effect is the improvement of resource use performance, from 7.596 to 9.161. Improved learning ability and innovation of learning resources can be achieved through the use of digital learning modes.</p>
</abstract>

This paper explores the digital learning model from the 4 paths of macro education reform. The four paths of macro education reform include improving the education system, changing the education model, updating education content, and establishing a comprehensive evaluation system. The construction of a learning port is based on the aspects of the learning environment and learning service, as well as research on the characteristics of digital learning modes. Based on BP neural networks, an evaluation model for community education digital resources has been established. The defects of the BP neural network are optimized by using an artificial fish school-frog jump hybrid algorithm, and 19 index factors are selected to construct the evaluation system of digital learning resources. Students’ digital learning abilities and the degree of innovation of digital learning resources in community education were separately analyzed using pre-and post-tests. The average performance of students in the experimental class improved by 9.89 points, and the score interval improved to [85,98], which was significantly better than the control class. The most obvious effect is the improvement of resource use performance, from 7.596 to 9.161. Improved learning ability and innovation of learning resources can be achieved through the use of digital learning modes.

Digital Learning Models in Macro-Educational Reform

Shanghai University of Medicine & Health Sciences

Applied Mathematics and Nonlinear Sciences

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

{"article-title":"Digital Learning Models in Macro-Educational Reform"}

This paper explores the digital learning model from the 4 paths of macro education reform. The four paths of macro education reform include improving the...

Digital Learning Models in Macro-Educational Reform

Online veröffentlicht: 29. Nov. 2023

Seitenbereich: -

Eingereicht: 02. Jan. 2023

Akzeptiert: 09. Juni 2023

DOI: https://doi.org/10.2478/amns.2023.2.01297

Schlüsselwörter
BP Neural Networks, Artificial Fish Swarm-Frog Hopping, Algorithm Optimization, Macro-Educational Reform, Digital Learning

© 2023 Ying Guo, published by Sciendo

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

Digital Learning Models in Macro-Educational Reform

Online veröffentlicht: 29. Nov. 2023

Seitenbereich: -

Eingereicht: 02. Jan. 2023

Akzeptiert: 09. Juni 2023

DOI: https://doi.org/10.2478/amns.2023.2.01297

SchlüsselwörterBP Neural Networks, Artificial Fish Swarm-Frog Hopping, Algorithm Optimization, Macro-Educational Reform, Digital Learning

© 2023 Ying Guo, published by Sciendo

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

Schlüsselwörter
BP Neural Networks, Artificial Fish Swarm-Frog Hopping, Algorithm Optimization, Macro-Educational Reform, Digital Learning