File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

postgraduate thesis: Learning path optimization with incomplete learning object metadata

TitleLearning path optimization with incomplete learning object metadata
Authors
Advisors
Advisor(s):Lam, EYMTam, VWL
Issue Date2011
PublisherThe University of Hong Kong (Pokfulam, Hong Kong)
Citation
Fung, S. [馮思達]. (2011). Learning path optimization with incomplete learning object metadata. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b4716985
AbstractOne of the fundamental concerns of instructional design is pedagogical sequencing which is a practice of organizing course materials according to the underlying knowledge structure and concept dependency. In the conventional settings, like the secondary schools or tertiary institution, instructors are required to interpret learning materials by their own domain knowledge. But in many online learning systems, analyzing and interpreting learning materials are very challenging due to the lack of instructional contexts and pedagogical attributes of the learning units. The learning objects and learning object metadata (LOM) are learning technologies to formalize the concept of learning unit and standardizing the specification of learning object annotation framework. The learning object is aimed to provide a solution for reuse and sharing of learning materials, and to provide infrastructure for pedagogical design. The LOM has been widely adopted in various learning systems, methodologies and system frameworks proposed to solve instructional design problem based on the pedagogical information as provided in the LOM. However, an empirical study showed that most real-life learning objects do not provide necessary pedagogical information. Thus, it is not clear how the issue of incomplete metadata and hence incomplete pedagogical information will affect those LOM based methods. A new approach to reconstruct the underlying knowledge structure based on information extracted from LOM and data mining techniques is proposed. The main idea of the approach is to reconstruct knowledge structure by the context of learning materials. Intrinsically, the vector space model and the k-means clustering algorithm are applied to reconstruct the knowledge graph based on keyword extraction techniques, and concept dependency relations are extracted from the obtained knowledge graph. Then, the genetic algorithm is applied to optimize for a learning path that satisfies most of the obtained concept dependencies. Furthermore, the performance of applying different semantic interpreters and rule extraction methodology are carefully tested and compared. Experimental results revealed that learning paths generated by the proposed approach are very similar to learning paths designed by human instructors.
DegreeMaster of Philosophy
SubjectMetadata.
Computer-assisted instruction.
Educational technology.
Dept/ProgramElectrical and Electronic Engineering

 

DC FieldValueLanguage
dc.contributor.advisorLam, EYM-
dc.contributor.advisorTam, VWL-
dc.contributor.authorFung, Sze-tat.-
dc.contributor.author馮思達.-
dc.date.issued2011-
dc.identifier.citationFung, S. [馮思達]. (2011). Learning path optimization with incomplete learning object metadata. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b4716985-
dc.description.abstractOne of the fundamental concerns of instructional design is pedagogical sequencing which is a practice of organizing course materials according to the underlying knowledge structure and concept dependency. In the conventional settings, like the secondary schools or tertiary institution, instructors are required to interpret learning materials by their own domain knowledge. But in many online learning systems, analyzing and interpreting learning materials are very challenging due to the lack of instructional contexts and pedagogical attributes of the learning units. The learning objects and learning object metadata (LOM) are learning technologies to formalize the concept of learning unit and standardizing the specification of learning object annotation framework. The learning object is aimed to provide a solution for reuse and sharing of learning materials, and to provide infrastructure for pedagogical design. The LOM has been widely adopted in various learning systems, methodologies and system frameworks proposed to solve instructional design problem based on the pedagogical information as provided in the LOM. However, an empirical study showed that most real-life learning objects do not provide necessary pedagogical information. Thus, it is not clear how the issue of incomplete metadata and hence incomplete pedagogical information will affect those LOM based methods. A new approach to reconstruct the underlying knowledge structure based on information extracted from LOM and data mining techniques is proposed. The main idea of the approach is to reconstruct knowledge structure by the context of learning materials. Intrinsically, the vector space model and the k-means clustering algorithm are applied to reconstruct the knowledge graph based on keyword extraction techniques, and concept dependency relations are extracted from the obtained knowledge graph. Then, the genetic algorithm is applied to optimize for a learning path that satisfies most of the obtained concept dependencies. Furthermore, the performance of applying different semantic interpreters and rule extraction methodology are carefully tested and compared. Experimental results revealed that learning paths generated by the proposed approach are very similar to learning paths designed by human instructors.-
dc.languageeng-
dc.publisherThe University of Hong Kong (Pokfulam, Hong Kong)-
dc.relation.ispartofHKU Theses Online (HKUTO)-
dc.rightsThe author retains all proprietary rights, (such as patent rights) and the right to use in future works.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.source.urihttp://hub.hku.hk/bib/B47169850-
dc.subject.lcshMetadata.-
dc.subject.lcshComputer-assisted instruction.-
dc.subject.lcshEducational technology.-
dc.titleLearning path optimization with incomplete learning object metadata-
dc.typePG_Thesis-
dc.identifier.hkulb4716985-
dc.description.thesisnameMaster of Philosophy-
dc.description.thesislevelMaster-
dc.description.thesisdisciplineElectrical and Electronic Engineering-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.5353/th_b4716985-
dc.date.hkucongregation2012-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats