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Automatic Adaptive Retrieval and Composition of Learning Objects Based on Multidimensional Learner Charcteristics

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dc.contributor.advisor vilas Wuwongse (Chairman) en_US
dc.contributor.author Burasakorn Yoosooka en_US
dc.contributor.other Teerapat Sanguankotchakorn (Member) en_US
dc.contributor.other B.H.W. Hadikusumo (Member) en_US
dc.contributor.other Macro Ronchetti (External Examiner) en_US
dc.date.accessioned 2015-01-12T10:42:19Z
dc.date.available 2015-01-12T10:42:19Z
dc.date.issued 2012-08 en_US
dc.identifier.other AIT Diss no.IM-12-01 en_US
dc.identifier.uri http://www.cs.ait.ac.th/xmlui/handle/123456789/419
dc.description.abstract This dissertation aims to propose a new approach to automatic retrieval and composition of Learning Objects (LOs) in an Adaptive Educational Hypermedia System (AEHS) using multidimensional learner characteristics to enhance learning effectiveness. The approach focuses on adaptive techniques in four components of AEHS: Learning Paths, LO Retrieval, LO Sequencing, and Examination Difficulty Levels. This approach has been designed to enable the adaptation of rules which are represented by XML Declarative Description (XDD) to become generic. Hence, the application to various domains is possible. The approach dynamically selects, sequences, and composes LOs into an individual learning package based on the use of domain ontology, learner profiles, and LO metadata. The ontologies are represented by Web Ontology Language (OWL). The Sharable Content Object Reference Model (SCORM) is employed to represent LO metadata and learning packages in order to support LO sharing. The IMS Learner Information Package Specification (IMS LIP) is used to represent learner profiles. Both standards are represented by means of Extensible Markup Language (XML). Thus, the information can be exchangeable and interoperable with other systems. Moreover, a new method to automatic retrieval of Learning Objects (LOs) from local or external LO repositories via Linked Open Data (LOD) principles is extended to the approach. This method dynamically selects the most appropriate LOs for an individual learning package in an adaptive e-Learning system based on the use of LO metadata, learner profiles, ontologies, and LOD principles. The method has been designed to interlink the domain ontology with external open knowledge in the LOD cloud. SPARQL endpoints for datasets in the LOD cloud are also provided for instructors and learners to discover their desired LOs. The commonly known vocabularies such as Dublin Core (DC), IEEE Learning Object Metadata (IEEE LOM), Web Ontology Language (OWL), and Resource Description Framework (RDF) are employed to represent metadata and to link it with external LO repositories as well as DBpedia, the central hub of the LOD cloud. By using these techniques, the LOs and external knowledge can be exchangeable, shareable, and interoperable, resulting in an enhanced access to better learning resources. Based on the proposed approach, a prototype system has been developed and evaluated. It has been discovered that the system has yielded positive effects in terms of the learners’ satisfaction. en_US
dc.description.sponsorship Rajamangala University of Technology Thanyaburi en_US
dc.language.iso eng en_US
dc.subject.lcsh Others en_US
dc.title Automatic Adaptive Retrieval and Composition of Learning Objects Based on Multidimensional Learner Charcteristics en_US
dc.type Dissertation en_US

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