Adaptive Micro Learning - Using Fragmented Time To Learn
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A01=Geng Sun
A01=Jiayin Lin
A01=Jun Shen
Adaptive Micro Open Learning
AI in Education
Author_Geng Sun
Author_Jiayin Lin
Author_Jun Shen
Category=UYQ
Cold Start Problem
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Learning Resource Recommendation
Micro Learning
Open Education Resources
Product details
- ISBN 9789811207457
- Publication Date: 09 Mar 2020
- Publisher: World Scientific Publishing Co Pte Ltd
- Publication City/Country: SG
- Product Form: Hardback
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This compendium introduces an artificial intelligence-supported solution to realize adaptive micro learning over open education resource (OER). The advantages of cloud computing and big data are leveraged to promote the categorization and customization of OERs micro learning context. For a micro-learning service, OERs are tailored into fragmented pieces to be consumed within shorter time frames.Firstly, the current status of mobile-learning, micro-learning, and OERs are described. Then, the significances and challenges of Micro Learning as a Service (MLaaS) are discussed. A framework of a service-oriented system is provided, which adopts both online and offline computation domain to work in conjunction to improve the performance of learning resource adaptation.In addition, a comprehensive learner model and a knowledge base is prepared to semantically profile the learners and learning resource. The novel delivery and access mode of OERs suffers from the cold start problem because of the shortage of already-known learner information versus the continuously released new micro OERs. This unique volume provides an excellent feasible algorithmic solution to overcome the cold start problem.
