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Adaptivity with Multidimensional Learning Objects
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Memmel, M. (2005). Adaptivity with Multidimensional Learning Objects. In G. Richards (Ed.), Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2005 (pp. 2221-2236). Chesapeake, VA: AACE.
Retrieved from http://www.editlib.org/p/21526.
World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (ELEARN) 2005
E-Learn 2005--World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education
More Information on ELEARN
Table of Contents
One of the most important goals and tasks of many E-learning systems is adaptivity, i.e., to automatically create individual learning environments taking into account a learner's special needs, interests, goals and preferences, be it in a corporate, government, healthcare, or higher education environment. An adaptive E-learning system must be able to capture information about the learner, to interpret this information and thus to determine which content will be presented. On the one hand, this requires a lot of skills from the field of user modeling. On the other hand, an appropriate learning objects repository is required. What kind of learning objects is required to enable a system's adaptivity? Which types of learning objects can be distinguished, and why? This will be discussed in this work, and a new architecture enabling different levels of adaptivity by the use of multidimensional, atomic learning objects is presented and discussed.
- Usage Analysis in Learning Systems
- Supporting E-Learning with Technologies for Electronic Documents
- Learning Objects in Context
- Toward a Quality Assurance Approach to E-Learning Courses
- The Validation of an Instrument Measuring TPACK
- Online Practice Test: Problems of Writing Questions using Bloom’s Taxonomy
- A REVIEW OF WEB-BASED LEARNING SYSTEMS FOR PROGRAMMING
- Interactive Multimedia and Problem Based Learning: Experiencing Project Failure
- The Connected Learning Space
- Cognitive Issues for Learning and Performance from Multimedia Interfaces: Implications for Design
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