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 education system, changing the education model, updating education content, and establishing a comprehensive evaluation system. The construction o...
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| Format: | Article |
| Language: | English |
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Sciendo
2024-01-01
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| Series: | Applied Mathematics and Nonlinear Sciences |
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| Online Access: | https://doi.org/10.2478/amns.2023.2.01297 |
| _version_ | 1797340571036024832 |
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| author | Guo Ying |
| author_facet | Guo Ying |
| author_sort | Guo Ying |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T10:06:03Z |
| format | Article |
| id | doaj.art-b9325b55108845e79630261e46a5fa57 |
| institution | Directory Open Access Journal |
| issn | 2444-8656 |
| language | English |
| last_indexed | 2024-03-08T10:06:03Z |
| publishDate | 2024-01-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Mathematics and Nonlinear Sciences |
| spelling | doaj.art-b9325b55108845e79630261e46a5fa572024-01-29T08:52:41ZengSciendoApplied Mathematics and Nonlinear Sciences2444-86562024-01-019110.2478/amns.2023.2.01297Digital Learning Models in Macro-Educational ReformGuo Ying01Shanghai University of Medicine & Health Sciences, Shanghai, 201318, China.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.https://doi.org/10.2478/amns.2023.2.01297bp neural networksartificial fish swarm-frog hoppingalgorithm optimizationmacro-educational reformdigital learning97b20 |
| spellingShingle | Guo Ying Digital Learning Models in Macro-Educational Reform Applied Mathematics and Nonlinear Sciences bp neural networks artificial fish swarm-frog hopping algorithm optimization macro-educational reform digital learning 97b20 |
| title | Digital Learning Models in Macro-Educational Reform |
| title_full | Digital Learning Models in Macro-Educational Reform |
| title_fullStr | Digital Learning Models in Macro-Educational Reform |
| title_full_unstemmed | Digital Learning Models in Macro-Educational Reform |
| title_short | Digital Learning Models in Macro-Educational Reform |
| title_sort | digital learning models in macro educational reform |
| topic | bp neural networks artificial fish swarm-frog hopping algorithm optimization macro-educational reform digital learning 97b20 |
| url | https://doi.org/10.2478/amns.2023.2.01297 |
| work_keys_str_mv | AT guoying digitallearningmodelsinmacroeducationalreform |