Optimization of Higher Education Teaching Methodology System Based on Edge Intelligence
This study provides an in-depth research on the dynamic allocation of resources in higher education teaching and learning, especially in the application of edge intelligence architecture. In the study, the characteristics of edge intelligence and its application in smart mobile devices (SMDs) are fi...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2024-01-01
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| Series: | Applied Mathematics and Nonlinear Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/amns-2024-0619 |
| _version_ | 1797279159963090944 |
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| author | Guo Jingjing Wei Xiaoxu |
| author_facet | Guo Jingjing Wei Xiaoxu |
| author_sort | Guo Jingjing |
| collection | DOAJ |
| description | This study provides an in-depth research on the dynamic allocation of resources in higher education teaching and learning, especially in the application of edge intelligence architecture. In the study, the characteristics of edge intelligence and its application in smart mobile devices (SMDs) are first analyzed, highlighting the role of mobile edge computing (MEC) in reducing latency and improving the quality of user experience. Then, the study adopts a data acquisition method based on deep neural network (DNN) model to optimize the edge training model. The experimental results show that the efficiency of edge computing can be significantly improved by optimizing the allocation of computing resources and reducing the data transmission delay. Specifically, the total training delay and energy consumption of the edge server are reduced under different global iteration numbers in the experiment. In addition, the study also explores the integration of 5G networks and AR/VR technology in education. It proposes a teaching optimization model based on edge intelligence, improving interaction quality and learning efficiency in AR/VR safety education classrooms. The study shows that the teaching model performs well in reducing latency and increasing transmission rate, which is especially suitable for dual-teacher classroom scenarios and provides a new perspective for future higher education teaching. |
| first_indexed | 2024-03-07T16:20:15Z |
| format | Article |
| id | doaj.art-ec36f346aea84202bae3b2fd2b3fb303 |
| institution | Directory Open Access Journal |
| issn | 2444-8656 |
| language | English |
| last_indexed | 2024-03-07T16:20:15Z |
| publishDate | 2024-01-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Mathematics and Nonlinear Sciences |
| spelling | doaj.art-ec36f346aea84202bae3b2fd2b3fb3032024-03-04T07:30:43ZengSciendoApplied Mathematics and Nonlinear Sciences2444-86562024-01-019110.2478/amns-2024-0619Optimization of Higher Education Teaching Methodology System Based on Edge IntelligenceGuo Jingjing0Wei Xiaoxu11Wuhan Vocational College of Software and Engineering (Wuhan Open University), Wuhan, Hubei, 430000, China.2School of Automotive Engineering, Wuhan University of Technology, Wuhan, Hubei, 430000, China.This study provides an in-depth research on the dynamic allocation of resources in higher education teaching and learning, especially in the application of edge intelligence architecture. In the study, the characteristics of edge intelligence and its application in smart mobile devices (SMDs) are first analyzed, highlighting the role of mobile edge computing (MEC) in reducing latency and improving the quality of user experience. Then, the study adopts a data acquisition method based on deep neural network (DNN) model to optimize the edge training model. The experimental results show that the efficiency of edge computing can be significantly improved by optimizing the allocation of computing resources and reducing the data transmission delay. Specifically, the total training delay and energy consumption of the edge server are reduced under different global iteration numbers in the experiment. In addition, the study also explores the integration of 5G networks and AR/VR technology in education. It proposes a teaching optimization model based on edge intelligence, improving interaction quality and learning efficiency in AR/VR safety education classrooms. The study shows that the teaching model performs well in reducing latency and increasing transmission rate, which is especially suitable for dual-teacher classroom scenarios and provides a new perspective for future higher education teaching.https://doi.org/10.2478/amns-2024-0619mobile edge computingdeep neural networkdynamic allocation of resourceseducational teaching methods97m50 |
| spellingShingle | Guo Jingjing Wei Xiaoxu Optimization of Higher Education Teaching Methodology System Based on Edge Intelligence Applied Mathematics and Nonlinear Sciences mobile edge computing deep neural network dynamic allocation of resources educational teaching methods 97m50 |
| title | Optimization of Higher Education Teaching Methodology System Based on Edge Intelligence |
| title_full | Optimization of Higher Education Teaching Methodology System Based on Edge Intelligence |
| title_fullStr | Optimization of Higher Education Teaching Methodology System Based on Edge Intelligence |
| title_full_unstemmed | Optimization of Higher Education Teaching Methodology System Based on Edge Intelligence |
| title_short | Optimization of Higher Education Teaching Methodology System Based on Edge Intelligence |
| title_sort | optimization of higher education teaching methodology system based on edge intelligence |
| topic | mobile edge computing deep neural network dynamic allocation of resources educational teaching methods 97m50 |
| url | https://doi.org/10.2478/amns-2024-0619 |
| work_keys_str_mv | AT guojingjing optimizationofhighereducationteachingmethodologysystembasedonedgeintelligence AT weixiaoxu optimizationofhighereducationteachingmethodologysystembasedonedgeintelligence |