Extending eductional metadata schemas to describe adaptive learning resources

Conlan, Owen; Hockemeyer, Cord; Lefrère, Paul; Wade, Vincent; Albert, Dietrich

doi:10.1145/504216.504258

Cited by 15 publications

(11 citation statements)

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“…The extension consisted of the addition of a sub-section called Adaptivity to the Education section of the schema. This sub-section caters for user definable adaptivity types allowing the metadata creator to develop complex relationships and dependencies within the metadata description of the service [5].…”

Section: Content Model For the Adaptive Service -The Content Modelmentioning

confidence: 99%

Multi-model, Metadata Driven Approach to Adaptive Hypermedia Services for Personalized eLearning

Conlan

Wade

Bruen

et al. 2002

Lecture Notes in Computer Science

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Abstract.One of the major obstacles in developing quality eLearning content is the substantial development costs involved and development time required [12]. Educational providers, such as those in the university sector and corporate learning, are under increasing pressure to enhance the pedagogical quality and technical richness of their course offerings while at the same time achieving improved return on investment. One means of enhancing the educational impact of eLearning courses, while still optimizing the return on investment, is to facilitate the personalization and repurposing of learning objects across multiple related courses. However, eLearning courses typically differ strongly in ethos, learning goals and pedagogical approach whilst learners, even within the same course, may have different personal learning goals, motivations, prior knowledge and learning style preferences. This paper proposes an innovative multi-model approach to the dynamic composition and delivery of personalized learning utilizing reusable learning objects. The paper describes an adaptive metadata driven engine that composes, at runtime, tailored educational experiences across a single content base. This paper presents the theoretical models, design and implementation of the adaptive hypermedia educational service. This service is currently being successfully used for the delivery of undergraduate degree courses in Trinity College, Dublin as well as being used as part of a major EU research trial.

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Section: Content Model For the Adaptive Service -The Content Modelmentioning

confidence: 99%

Multi-model, Metadata Driven Approach to Adaptive Hypermedia Services for Personalized eLearning

Conlan

Wade

Bruen

et al. 2002

Lecture Notes in Computer Science

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“…Such a distinction supports the development of adaptive e-Learning systems with highly reusable content including the structure information through metadata usage [4,8].…”

Section: 3: Knowledge Space Theorymentioning

confidence: 99%

“…Whenever the documents in a course change (including the addition of new and the elimination of existing documents), prerequisite information for other, unchanged documents may also change. This makes it quite difficult to maintain a RATH course [4].…”

Section: 2: Collaborative Competence-based Rath (C²rath)mentioning

confidence: 99%

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Applying competence structures for peer tutor recommendations in CSCL environments

Heller

Hockemeyer

Albert

IEEE International Conference on Advanced Learning Technologies, 2004. Proceedings.

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“…APeLS attempts to stay faithful to the SCORM and AICC approach by providing a necessary extension to the content API (O. Conlan, Hockemeyer, Lefrere, Wade, & Albert, 2001). …”

Section: Related Workmentioning

confidence: 99%

From Learning Objects to Adaptive Content Services for E-Learning

Brusilovsky¹,

Wade²,

Conlan³

Architecture Solutions for E-Learning Systems

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This paper argues that a new generation of powerful E-learning systems could start on the crossroads of two emerging fields: courseware re-use and adaptive educational systems. We argue for a new distributed architecture for E-learning systems based on the idea of adaptive reusable content services. This paper discusses problems that have to be solved on the way to the new organization of E-learning and reviews existing approaches and tools that are paving the way to next generation E-learning systems. It also presents two pioneer systems -APeLS and KnowledgeTree that have attempted to develop a new service-based architecture for adaptive Elearning.

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Extending eductional metadata schemas to describe adaptive learning resources

Cited by 15 publications

References 1 publication

Multi-model, Metadata Driven Approach to Adaptive Hypermedia Services for Personalized eLearning

Multi-model, Metadata Driven Approach to Adaptive Hypermedia Services for Personalized eLearning

Applying competence structures for peer tutor recommendations in CSCL environments

From Learning Objects to Adaptive Content Services for E-Learning

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