Personalized E-Learning through Environment Design and Collaborative Activities
Identifieur interne : 000702 ( Main/Exploration ); précédent : 000701; suivant : 000703Personalized E-Learning through Environment Design and Collaborative Activities
Auteurs : Felix Mödritscher [Autriche] ; Fridolin Wild [Autriche]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2008.
English descriptors
- Teeft :
- Action statements, Activity pages, Activity pattern, Activity patterns, Adaptable courseware, Adaptation effects, Adaptive, Adaptive behavior, Application mashup, Application mashups, Artifact, Best practice, Cognitive traits, Collaborative, Collaborative activities, Communication channels, Content area, Control widgets, Creation facilities, Different applications, Domain experts, Educational technology society, Elearning, Environment design, Facilitator, Feed networks, First prototypic implementation, Further personalization strategies, Future work, High degree, Higher education, Interactive media, International conference, International journal, Learner, Learner interaction model, Learner interaction scripting language, Learner interactions, Lisl, Lisl code, Lisl interpreter, Lisl script, Long tail, Management systems, Mashup, Mashup space, Modeling, Mupple, Mupple approach, Mupple page, Necessary models, Network effects, Networked communities, Other actors, Other hand, Other learners, Personalization, Real world, Regulation facilities, Research project adele, Scripting, Specific system, Subject domain, Systemic behavior, Technological infrastructure, Tool videowiki, Traditional approaches, User, User modeling, Various tools, Videowiki, World conference, Xomashup component.
Abstract
Abstract: Over the last century, many theoretical frameworks and technological solutions for personalized e-learning have emerged. The underlying models, however, are often based on the practice that domain experts develop an adaptation strategy to personalize content or parts of a learning platform, which leads to different problematic aspects decreasing the feasibility or utility of such approaches. After giving a brief overview of the historical development and basic concepts of personalized e-learning, we outline the shortcomings of the traditional ‘top-down, ex ante’ models and present an alternative approach which deals with personal learning environments, web application mashups, learning activities and learner interactions, as well as pattern-based best practice sharing. Furthermore, a prototypic implementation for our ‘learner-driven, bottom-up’ approach to personalized e-learning, namely the ‘Mash-UP Personal Learning Environment’ (MUPPLE), is presented and discussed on the basis of a concrete scenario.
Url:
DOI: 10.1007/978-3-540-89350-9_26
Affiliations:
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Le document en format XML
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<front><div type="abstract" xml:lang="en">Abstract: Over the last century, many theoretical frameworks and technological solutions for personalized e-learning have emerged. The underlying models, however, are often based on the practice that domain experts develop an adaptation strategy to personalize content or parts of a learning platform, which leads to different problematic aspects decreasing the feasibility or utility of such approaches. After giving a brief overview of the historical development and basic concepts of personalized e-learning, we outline the shortcomings of the traditional ‘top-down, ex ante’ models and present an alternative approach which deals with personal learning environments, web application mashups, learning activities and learner interactions, as well as pattern-based best practice sharing. Furthermore, a prototypic implementation for our ‘learner-driven, bottom-up’ approach to personalized e-learning, namely the ‘Mash-UP Personal Learning Environment’ (MUPPLE), is presented and discussed on the basis of a concrete scenario.</div>
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