Adaptive Micro Learning: Using Fragmented Time to Learn
- Length: 152 pages
- Edition: 1
- Language: English
- Publisher: WSPC
- Publication Date: 2020-02-11
- ISBN-10: 9811207453
- ISBN-13: 9789811207457
- Sales Rank: #0 (See Top 100 Books)
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.
Cover Halftitle Series Editors Title Copyright Contents Chapter 1: Introduction 1.1 Background 1.2 Research Objectives 1.3 Contribution of the Book 1.4 Outline of the Book Chapter 2: Literature Review 2.1 Mobile Learning and Micro Learning 2.2 Open Learning and Open Educational Resources 2.3 Micro Open Learning 2.4 Adaptive Learning: Learner Modelling and EDM & LA Chapter 3: Research Design 3.1 Research Background 3.2 Research Motivation 3.3 Research Challenges 3.4 Research Purpose 3.5 System Framework Chapter 4: Comprehensive Learner Model for Micro Open Learning and Micro Open Learning Content 4.1 Comprehensive Learner Model 4.2 Micro Open Learning Content Chapter 5: Semantic Knowledge Base Construction: Education Data Mining and Learning Analytics Strategy 5.1 Conceptual Graph-Based Ontology Construction for Micro Open Learning and Proposed Data Processing Strategy 5.2 EDM and LA Strategy Chapter 6: Online Computation for MLaaS 6.1 Lightweight Learner-Micro OER Profile for Cold Start Recommendation 6.2 Online Computation Process for Cold Start Problem 6.3 Real-Time Complementary Mechanisms Chapter 7: Implementation and Empirical Evaluation 7.1 System Implementation 7.2 Empirical Evaluation Chapter 8: Conclusion 8.1 Summary 8.2 Recommendation for Future Work Index
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