A Knowledge Diffusion Model in Autonomous Learning Under Multiple Networks for Personalized Educational Resource Allocation

Learners' autonomous learning is at the heart of modern education, and the convenient network brings new opportunities for it. We notice that learners mainly use the combination of online and offline learning methods to complete the entire autonomous learning process, but most of the existing m...

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Vydané v:IEEE transactions on learning technologies Ročník 14; číslo 4; s. 430 - 444
Hlavní autori: Wan, Pengfei, Wang, Xiaoming, Lin, Yaguang, Pang, Guangyao
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.08.2021
Institute of Electrical and Electronics Engineers, Inc
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Autonomous learning
Blended Learning
Complexity theory
Conventional Instruction
Customization
Diffusion
Distance learning
Education
Educational Resources
Efficiency
Electronic Learning
Independent Study
Internet
knowledge diffusion
Knowledge engineering
knowledge sharing
Learning Processes
Learning Strategies
Learning systems
Machine learning
Mathematics
Networks
Personal Autonomy
personalized e-learning
Resource allocation
resource allocation algorithm
Resource management
Social networking (online)
Student Needs
ISSN:1939-1382, 2372-0050
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Shrnutí:Learners' autonomous learning is at the heart of modern education, and the convenient network brings new opportunities for it. We notice that learners mainly use the combination of online and offline learning methods to complete the entire autonomous learning process, but most of the existing models cannot effectively describe the complex process of knowledge diffusion under the multinetworks framework. By analyzing the relationship between online learning and offline learning in the autonomous learning, we develop a novel formal model to characterize the dynamic process of knowledge diffusion in the autonomous learning under multiple networks. To guide learners to learn independently and effectively expand the scope of knowledge through hybrid online learning, we then introduce the personalized needs of learners and the guidance of educators and further propose an effective algorithm (ERAA) to allocate educational resources. Through the experiments, we verify the effectiveness of the model and analyze the efficiency of the proposed algorithm. Then, we compare the proposed algorithm with the existing four algorithms and the results show that the proposed algorithm improves performance by 38%. This article provides highly realistic significance for education administrators to analyze the practice, develop autonomous learning strategy and put online educational resources.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1939-1382
2372-0050
DOI:10.1109/TLT.2021.3103006