Knowledge Visualization for Self-Regulated Learning
Faculty of Education, The University of Hong Kong, Hong Kong // email@example.com
Faculty of Education, The University of Hong Kong, Hong Kong // firstname.lastname@example.org
Faculty of Education, The University of Hong Kong, Hong Kong // email@example.com
Faculty of Education, The University of Hong Kong, Hong Kong // College of Science, Nanjing University of Aeronautics and Astronautics, China // firstname.lastname@example.org
Faculty of Education, The University of Hong Kong, Hong Kong // email@example.com
ABSTRACT: The Web allows self-regulated learning through interaction with large amounts of learning resources. While enjoying the flexibility of learning, learners may suffer from cognitive overload and conceptual and navigational disorientation when faced with various information resources under disparate topics and complex knowledge structures. This study proposed a knowledge visualization (KV) approach to this problem in an online course. The investigation involved the design, development, and evaluation of an enhanced learning system for the course using the proposed approach. The focus was on visualization of domain knowledge structure and integrating the structure with curriculum design, learning resources, learning assessment, intellectual process, and social learning. Survey and interviews with students demonstrated high user satisfaction and acceptance with the developed system and its functions for KV. These findings lay the foundation for further exploration with the system to determine its impact on reducing cognitive load and improving the learning process.
Keywords: Knowledge Visualization, Online Learning, Knowledge Structure, Self-Regulation, E-Learning