LM-DTM: An Environment for XML-Based, LIP/PAPI-Compliant Deployment, Transformation and Matching of Learner Models

Our shared belief is that learning, like other human activities, cannot and will not be confined within rigidly defined course systems or learning repositories, inclosing learning resources which cannot be tailored to the different learner´s needs, skills, interests, preferences, goals, etc. Therefore, a learning environment, beside supporting communication between knowledge providers and consumers, has to be organized in a flexible manner based on different learner profiles. Learner modeling has become a highly challenging task to provide personalized, adaptive and context-based learning. The work presented in this paper address this issue by providing a meta-level solution for description, transformation and matching of learner models, based on standards such as IMS LIP, IEEE PAPI, XML to foster the reuse and exchange of learner models between learning platforms, both by universities and corporations.

• Type: Conference
• Year: 2005
• Where: ICALT
• Authors: Mohamed Amine Chatti, Ralf Klamma, Christoph Quix, David Kensche
• Keywords: ICALT 2005, Learner, Learner Modeling, Learner Models, Machine Learning