Prompting Assessment and Quality Classification in e-learning Recommender Systems
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Feiman, H. (2008). Prompting Assessment and Quality Classification in e-learning Recommender Systems. In J. Luca & E. Weippl (Eds.), Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications 2008 (pp. 1018-1025). Chesapeake, VA: AACE.
Retrieved from http://www.editlib.org/p/28517.
World Conference on Educational Multimedia, Hypermedia and Telecommunications (EDMEDIA) 2008
June 30, 2008
Joseph Luca & Edgar R. Weippl
More Information on EDMEDIA
Table of Contents
Newsday, for solved the difficult e-learning system that problem is teaching material can’t share and useful. But large learning active will be the learning track tree very complex and don’t elasticity that will be the learning track tree can’t manage and useful. So how to use application theory to improve the learning track structure become the useful teaching resources also the important research problem. This paper proposes the use learning Agent’s characteristic to record user learning process and the data what use the senior teacher to experience Quality the classification evaluate system classification evaluate processing. That evaluate data will to provide the teacher and appraises message that learning type to be possible to enable the learner to obtain the most suitable study result, the teacher elasticity’s appraisal method can be teaches students in accordance with adaptive asynchronous learning that is goal.
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- The International Handbook Summit Call to Action for Learning with Technology in the 21st Century
- Learning Objects in Context
- Implementing Embedded Assessment to Provide Feedback to Student and Instructor.
- SITE's Digital Fabrication Initative
- Usable But Not Entertaining e-Learning Material
- The “Corporate University” as Technological and Scientific Support of the Virtual Education in Latin America
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