The Experts below are selected from a list of 142881 Experts worldwide ranked by ideXlab platform
Erik Duval - One of the best experts on this subject based on the ideXlab platform.
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alocom a generic content model for Learning Objects
International Journal on Digital Libraries, 2008Co-Authors: Katrien Verbert, Erik DuvalAbstract:e-Learning organizations are focusing heavily on Learning content reusability. The ultimate objective is a Learning object economy characterized by searchable digital libraries of reusable Learning Objects that can be exchanged and reused across various Learning systems. To enable such approach, basic questions of Learning content interoperability need to be addressed. This paper investigates the interoperation of Learning content defined according to different specifications. A number of content models are reviewed that define Learning Objects and their components. On the basis of a comparative analysis, the content models are mapped to a generic model for Learning Objects to address interoperability questions and to enable share and reuse on a global scale.
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towards a global component architecture for Learning Objects an ontology based approach
Lecture Notes in Computer Science, 2004Co-Authors: Katrien Verbert, Joris Klerkx, Michael Meire, Jehad Najjar, Erik DuvalAbstract:This paper investigates basic research issues that need to be addressed in order to reuse Learning Objects in a flexible way. We propose an ontology based approach. Our ontology for Learning Objects defines content structures and relationships between their components. A conceptual framework for structuring Learning Objects and their components is introduced. Architectures like Horn’s Information Blocks and the Darwin Information Typing Architecture are investigated as an approach to define Learning object component types.
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towards a global architecture for Learning Objects a comparative analysis of Learning object content models
Proceedings of the ED-MEDIA 2004 World Conference on Educational Multimedia Hypermedia and Telecommunications, 2004Co-Authors: Katrien Verbert, Erik DuvalAbstract:This paper investigates basic research issues that need to be addressed in order to reuse Learning Objects in a flexible way. We review a number of Learning object content models that define Learning Objects and their components in a more or less precise way. A comparative analysis is made of these models in order to address questions about repurposing Learning Objects in a different context. The content models are mapped on our general model for Learning Objects to facilitate the comparison.
Tzonei Wang - One of the best experts on this subject based on the ideXlab platform.
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a practical ontology query expansion algorithm for semantic aware Learning Objects retrieval
Computers in Education, 2008Co-Authors: Ming Che Lee, Kun Hua Tsai, Tzonei WangAbstract:Following the rapid development of Internet, particularly web page interaction technology, distant e-Learning has become increasingly realistic and popular. To solve the problems associated with sharing and reusing teaching materials in different e-Learning systems, several standard formats, including SCORM, IMS, LOM, and AICC, etc., recently have been proposed by several different international organizations. SCORM LOM, namely Learning object metadata, facilitates the indexing and searching of Learning Objects in a Learning object repository through extended sharing and searching features. However, LOM suffers a weakness in terms of semantic-awareness capability. Most information retrieval systems assume that users have cognitive ability regarding their needs. However, in e-Learning systems, users may have no idea of what they are looking for and the Learning object metadata. This study presents an ontological approach for semantic-aware Learning object retrieval. This approach has two significant novel features: a fully automatic ontology-based query expansion algorithm for inferring and aggregating user intention based on their original short query, and another ''ambiguity removal'' procedure for correcting inappropriate user query terms. This approach is sufficiently generic to be embedded to other LOM-based search mechanisms for semantic-aware Learning object retrieval. Focused on digital Learning material and contrasted to other traditional keyword-based search technologies, the proposed approach has experimentally demonstrated significantly improved retrieval precision and recall rate.
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personalized Learning Objects recommendation based on the semantic aware discovery and the learner preference pattern
Educational Technology & Society, 2007Co-Authors: Tzonei Wang, Ming Che Lee, Kun Hua Tsai, Ti Kai ChiuAbstract:With vigorous development of the Internet, especially the web page interaction technology, distant E-Learning has become more and more realistic and popular. Digital courses may consist of many Learning units or Learning Objects and, currently, many Learning Objects are created according to SCORM standard. It can be seen that, in the near future, a vast amount of SCORM-compliant Learning Objects will be published and distributed cross the Internet. Facing huge volumes of Learning Objects, learners may be lost in selecting suitable and favorite Learning Objects. In this paper, an adaptive personalized recommendation model is proposed in order to help recommend SCORM-compliant Learning Objects from repositories in the Internet. This model adopts an ontological approach to perform semantic discovery as well as both preference-based and correlation-based approaches to rank the degree of relevance of Learning Objects to a learner’s intension and preference. By implementing this model, a tutoring system is able to provide easily and efficiently suitable Learning Objects for active learners.
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a Learning Objects recommendation model based on the preference and ontological approaches
International Conference on Advanced Learning Technologies, 2006Co-Authors: Kun Hua Tsai, Ming Che Lee, Ti Kai Chiu, Tzonei WangAbstract:With vigorous development of Internet, especially the web page interaction technology, distant e-Learning has become more and more realistic and popular. Digital courses may consist of many Learning units or Learning Objects and, currently, many Learning Objects are created according to SCORM standard. It can be seen that, in the near future, a vast amount of SCORM-compliant Learning Objects will be published and distributed cross the Internet. Facing the huge volume of Learning Objects, learners will be lost in selecting suitable and favorite Learning Objects. In this paper an adaptive personalized ranking mechanism is proposed to help recommend SCORM-compliant Learning Objects from repositories in the Internet. The mechanism uses both preference-based and neighbor-interest-based approaches in ranking the degree of relevance of Learning Objects to a users intension. By this model, a tutoring system is able to provide easily and efficiently for active learners suitable Learning Objects.
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a service based framework for personalized Learning Objects retrieval and recommendation
Lecture Notes in Computer Science, 2006Co-Authors: Ming Che Lee, Kun Hua Tsai, Tzonei WangAbstract:With vigorous development of Internet, especially the web page interaction technology, distant e-Learning has become more and more realistic and popular. To solve the problems of sharing and reusing teaching materials in different e-Learning systems, presently several standard formats, including SCORM, IMS, LOM, and AICC, etc., have been proposed by several different international organizations. SCORM LOM, i.e. the Learning Object Metadata, enables the indexing and searching of Learning Objects in a Learning object repository by extended sharing and searching features. However, LOM is deficient in semantic-awareness operations in spite of its multifarious fields in describing a Learning Object. It is difficult for a learner, even for advanced learners, to completely specify so many terms when they are searching. This paper proposes a service-based framework for personalized Learning Objects retrieval and recommendation. The work of personalization harnesses the power of probabilistic semantic inference for query keywords, LOM-based user preference logging, and other users' feedback for recommendation weighting to retrieve the most suitable Learning object for users. An ontology-based query expansion algorithm and an integrated Learning Objects recommendation algorithm are also proposed.
Katrien Verbert - One of the best experts on this subject based on the ideXlab platform.
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alocom a generic content model for Learning Objects
International Journal on Digital Libraries, 2008Co-Authors: Katrien Verbert, Erik DuvalAbstract:e-Learning organizations are focusing heavily on Learning content reusability. The ultimate objective is a Learning object economy characterized by searchable digital libraries of reusable Learning Objects that can be exchanged and reused across various Learning systems. To enable such approach, basic questions of Learning content interoperability need to be addressed. This paper investigates the interoperation of Learning content defined according to different specifications. A number of content models are reviewed that define Learning Objects and their components. On the basis of a comparative analysis, the content models are mapped to a generic model for Learning Objects to address interoperability questions and to enable share and reuse on a global scale.
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towards a global component architecture for Learning Objects an ontology based approach
Lecture Notes in Computer Science, 2004Co-Authors: Katrien Verbert, Joris Klerkx, Michael Meire, Jehad Najjar, Erik DuvalAbstract:This paper investigates basic research issues that need to be addressed in order to reuse Learning Objects in a flexible way. We propose an ontology based approach. Our ontology for Learning Objects defines content structures and relationships between their components. A conceptual framework for structuring Learning Objects and their components is introduced. Architectures like Horn’s Information Blocks and the Darwin Information Typing Architecture are investigated as an approach to define Learning object component types.
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towards a global architecture for Learning Objects a comparative analysis of Learning object content models
Proceedings of the ED-MEDIA 2004 World Conference on Educational Multimedia Hypermedia and Telecommunications, 2004Co-Authors: Katrien Verbert, Erik DuvalAbstract:This paper investigates basic research issues that need to be addressed in order to reuse Learning Objects in a flexible way. We review a number of Learning object content models that define Learning Objects and their components in a more or less precise way. A comparative analysis is made of these models in order to address questions about repurposing Learning Objects in a different context. The content models are mapped on our general model for Learning Objects to facilitate the comparison.
Kun Hua Tsai - One of the best experts on this subject based on the ideXlab platform.
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a practical ontology query expansion algorithm for semantic aware Learning Objects retrieval
Computers in Education, 2008Co-Authors: Ming Che Lee, Kun Hua Tsai, Tzonei WangAbstract:Following the rapid development of Internet, particularly web page interaction technology, distant e-Learning has become increasingly realistic and popular. To solve the problems associated with sharing and reusing teaching materials in different e-Learning systems, several standard formats, including SCORM, IMS, LOM, and AICC, etc., recently have been proposed by several different international organizations. SCORM LOM, namely Learning object metadata, facilitates the indexing and searching of Learning Objects in a Learning object repository through extended sharing and searching features. However, LOM suffers a weakness in terms of semantic-awareness capability. Most information retrieval systems assume that users have cognitive ability regarding their needs. However, in e-Learning systems, users may have no idea of what they are looking for and the Learning object metadata. This study presents an ontological approach for semantic-aware Learning object retrieval. This approach has two significant novel features: a fully automatic ontology-based query expansion algorithm for inferring and aggregating user intention based on their original short query, and another ''ambiguity removal'' procedure for correcting inappropriate user query terms. This approach is sufficiently generic to be embedded to other LOM-based search mechanisms for semantic-aware Learning object retrieval. Focused on digital Learning material and contrasted to other traditional keyword-based search technologies, the proposed approach has experimentally demonstrated significantly improved retrieval precision and recall rate.
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personalized Learning Objects recommendation based on the semantic aware discovery and the learner preference pattern
Educational Technology & Society, 2007Co-Authors: Tzonei Wang, Ming Che Lee, Kun Hua Tsai, Ti Kai ChiuAbstract:With vigorous development of the Internet, especially the web page interaction technology, distant E-Learning has become more and more realistic and popular. Digital courses may consist of many Learning units or Learning Objects and, currently, many Learning Objects are created according to SCORM standard. It can be seen that, in the near future, a vast amount of SCORM-compliant Learning Objects will be published and distributed cross the Internet. Facing huge volumes of Learning Objects, learners may be lost in selecting suitable and favorite Learning Objects. In this paper, an adaptive personalized recommendation model is proposed in order to help recommend SCORM-compliant Learning Objects from repositories in the Internet. This model adopts an ontological approach to perform semantic discovery as well as both preference-based and correlation-based approaches to rank the degree of relevance of Learning Objects to a learner’s intension and preference. By implementing this model, a tutoring system is able to provide easily and efficiently suitable Learning Objects for active learners.
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a Learning Objects recommendation model based on the preference and ontological approaches
International Conference on Advanced Learning Technologies, 2006Co-Authors: Kun Hua Tsai, Ming Che Lee, Ti Kai Chiu, Tzonei WangAbstract:With vigorous development of Internet, especially the web page interaction technology, distant e-Learning has become more and more realistic and popular. Digital courses may consist of many Learning units or Learning Objects and, currently, many Learning Objects are created according to SCORM standard. It can be seen that, in the near future, a vast amount of SCORM-compliant Learning Objects will be published and distributed cross the Internet. Facing the huge volume of Learning Objects, learners will be lost in selecting suitable and favorite Learning Objects. In this paper an adaptive personalized ranking mechanism is proposed to help recommend SCORM-compliant Learning Objects from repositories in the Internet. The mechanism uses both preference-based and neighbor-interest-based approaches in ranking the degree of relevance of Learning Objects to a users intension. By this model, a tutoring system is able to provide easily and efficiently for active learners suitable Learning Objects.
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a service based framework for personalized Learning Objects retrieval and recommendation
Lecture Notes in Computer Science, 2006Co-Authors: Ming Che Lee, Kun Hua Tsai, Tzonei WangAbstract:With vigorous development of Internet, especially the web page interaction technology, distant e-Learning has become more and more realistic and popular. To solve the problems of sharing and reusing teaching materials in different e-Learning systems, presently several standard formats, including SCORM, IMS, LOM, and AICC, etc., have been proposed by several different international organizations. SCORM LOM, i.e. the Learning Object Metadata, enables the indexing and searching of Learning Objects in a Learning object repository by extended sharing and searching features. However, LOM is deficient in semantic-awareness operations in spite of its multifarious fields in describing a Learning Object. It is difficult for a learner, even for advanced learners, to completely specify so many terms when they are searching. This paper proposes a service-based framework for personalized Learning Objects retrieval and recommendation. The work of personalization harnesses the power of probabilistic semantic inference for query keywords, LOM-based user preference logging, and other users' feedback for recommendation weighting to retrieve the most suitable Learning object for users. An ontology-based query expansion algorithm and an integrated Learning Objects recommendation algorithm are also proposed.
Ming Che Lee - One of the best experts on this subject based on the ideXlab platform.
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a practical ontology query expansion algorithm for semantic aware Learning Objects retrieval
Computers in Education, 2008Co-Authors: Ming Che Lee, Kun Hua Tsai, Tzonei WangAbstract:Following the rapid development of Internet, particularly web page interaction technology, distant e-Learning has become increasingly realistic and popular. To solve the problems associated with sharing and reusing teaching materials in different e-Learning systems, several standard formats, including SCORM, IMS, LOM, and AICC, etc., recently have been proposed by several different international organizations. SCORM LOM, namely Learning object metadata, facilitates the indexing and searching of Learning Objects in a Learning object repository through extended sharing and searching features. However, LOM suffers a weakness in terms of semantic-awareness capability. Most information retrieval systems assume that users have cognitive ability regarding their needs. However, in e-Learning systems, users may have no idea of what they are looking for and the Learning object metadata. This study presents an ontological approach for semantic-aware Learning object retrieval. This approach has two significant novel features: a fully automatic ontology-based query expansion algorithm for inferring and aggregating user intention based on their original short query, and another ''ambiguity removal'' procedure for correcting inappropriate user query terms. This approach is sufficiently generic to be embedded to other LOM-based search mechanisms for semantic-aware Learning object retrieval. Focused on digital Learning material and contrasted to other traditional keyword-based search technologies, the proposed approach has experimentally demonstrated significantly improved retrieval precision and recall rate.
-
personalized Learning Objects recommendation based on the semantic aware discovery and the learner preference pattern
Educational Technology & Society, 2007Co-Authors: Tzonei Wang, Ming Che Lee, Kun Hua Tsai, Ti Kai ChiuAbstract:With vigorous development of the Internet, especially the web page interaction technology, distant E-Learning has become more and more realistic and popular. Digital courses may consist of many Learning units or Learning Objects and, currently, many Learning Objects are created according to SCORM standard. It can be seen that, in the near future, a vast amount of SCORM-compliant Learning Objects will be published and distributed cross the Internet. Facing huge volumes of Learning Objects, learners may be lost in selecting suitable and favorite Learning Objects. In this paper, an adaptive personalized recommendation model is proposed in order to help recommend SCORM-compliant Learning Objects from repositories in the Internet. This model adopts an ontological approach to perform semantic discovery as well as both preference-based and correlation-based approaches to rank the degree of relevance of Learning Objects to a learner’s intension and preference. By implementing this model, a tutoring system is able to provide easily and efficiently suitable Learning Objects for active learners.
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a Learning Objects recommendation model based on the preference and ontological approaches
International Conference on Advanced Learning Technologies, 2006Co-Authors: Kun Hua Tsai, Ming Che Lee, Ti Kai Chiu, Tzonei WangAbstract:With vigorous development of Internet, especially the web page interaction technology, distant e-Learning has become more and more realistic and popular. Digital courses may consist of many Learning units or Learning Objects and, currently, many Learning Objects are created according to SCORM standard. It can be seen that, in the near future, a vast amount of SCORM-compliant Learning Objects will be published and distributed cross the Internet. Facing the huge volume of Learning Objects, learners will be lost in selecting suitable and favorite Learning Objects. In this paper an adaptive personalized ranking mechanism is proposed to help recommend SCORM-compliant Learning Objects from repositories in the Internet. The mechanism uses both preference-based and neighbor-interest-based approaches in ranking the degree of relevance of Learning Objects to a users intension. By this model, a tutoring system is able to provide easily and efficiently for active learners suitable Learning Objects.
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a service based framework for personalized Learning Objects retrieval and recommendation
Lecture Notes in Computer Science, 2006Co-Authors: Ming Che Lee, Kun Hua Tsai, Tzonei WangAbstract:With vigorous development of Internet, especially the web page interaction technology, distant e-Learning has become more and more realistic and popular. To solve the problems of sharing and reusing teaching materials in different e-Learning systems, presently several standard formats, including SCORM, IMS, LOM, and AICC, etc., have been proposed by several different international organizations. SCORM LOM, i.e. the Learning Object Metadata, enables the indexing and searching of Learning Objects in a Learning object repository by extended sharing and searching features. However, LOM is deficient in semantic-awareness operations in spite of its multifarious fields in describing a Learning Object. It is difficult for a learner, even for advanced learners, to completely specify so many terms when they are searching. This paper proposes a service-based framework for personalized Learning Objects retrieval and recommendation. The work of personalization harnesses the power of probabilistic semantic inference for query keywords, LOM-based user preference logging, and other users' feedback for recommendation weighting to retrieve the most suitable Learning object for users. An ontology-based query expansion algorithm and an integrated Learning Objects recommendation algorithm are also proposed.
