Constructing a Guidance Network with Learning Navigation Patterns
Save to My Collections
Tsoi, K.K. & Ng, V. (2007). Constructing a Guidance Network with Learning Navigation Patterns. In C. Montgomerie & J. Seale (Eds.), Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications 2007 (pp. 3795-3803). Chesapeake, VA: AACE.
Retrieved from http://www.editlib.org/p/25924.
World Conference on Educational Multimedia, Hypermedia and Telecommunications (EDMEDIA) 2007
June 25, 2007
Craig Montgomerie & Jane Seale
More Information on EDMEDIA
Table of Contents
A single predefined learning path may not be ideal when learners' backgrounds are diverse and unpredictable during the development stage of the courseware. Very often, learners would prefer different learning sequences according to their needs and past experience. Our research focuses on the discovery of effective learning paths. Frequent sequences are extracted from previous successful learners' logs and be used to build a learning flow model of which the relationships between content materials are formulated by the use of conditional probability. Our experiment shows that learner performance would improve when such recommendations are available. Also, it is found that recommendation in the form of learning flow or network is better than recommendation of any single learning object.
- A Learning Object Life Cycle
- Adaptive Website Chunking: What You See is What You Need
- Teachiing with Technology: A constructivist/cognitivist model
- Applying Cognitive Learning Theory to Design a Hypermedia-enhanced Learning Environment
- Theories for Instructional System Design: A Critical Review
- Learning Objects in Context
- Learning Theory and Instruction Design Using Learning Objects
- Combining Instructional Models and Enabling Technologies to Embed Best Practices in Course Instructional Design
- From Pebbles to Boulders: Information Chunking in Educational Websites
- The Design and Development of Second Generation Learning Objects
Comments & Discussion
Comment on the paper above. You must be registered to participate. Registration is free.