Towards flexible learning object metadata

Brooks, Christopher; McCalla, Gordon I.

doi:10.1504/ijceell.2006.008917

Cited by 44 publications

(27 citation statements)

References 12 publications

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“…In ALOCOM [9] ontologies have been introduced successfully to enhance reusability, whereas in another system the LOM was extended with ontology-based semantic annotations for meaningful interactions with learning objects [8]. Semantic technologies were also used to promote a process-oriented approach to metadata; reasoning over learner-LO interactions was seen as a basis for adaptive environments [1].…”

Section: Metadata and Semantic Technologiesmentioning

confidence: 99%

“…It has now become evident that the effective deployment of LOs is dependent on the availability of relevant and effective metadata categories. As a result, most of the recent efforts in LO management point to the need for common structures for representing different types of metadata, including metadata related to learning experiences [1]. One significant feature of the LO lifecycle is that the transitions between its stages are facilitated by the use and generation of metadata.…”

Section: Introductionmentioning

confidence: 99%

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The Learning Object Triangle

Anane

2014

2014 IEEE 14th International Conference on Advanced Learning Technologies

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Section: Metadata and Semantic Technologiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Learning Object Triangle

Anane

2014

2014 IEEE 14th International Conference on Advanced Learning Technologies

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“…Variable time length learning objects were used. A total time of instruction of 20,000 time units was used and each learning object ranged from 30 time units to 480 time units 10 …”

Section: Simulationmentioning

confidence: 99%

Peer-Based Intelligent Tutoring Systems: A Corpus-Oriented Approach

Champaign

Cohen

2010

Intelligent Tutoring Systems

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In this thesis, we present an artificial intelligence approach for tutoring students in environments where there is a large repository of possible learning objects (e.g. texts, videos). In particular, we advocate that students learn on the basis of past experiences of peers. This aligns with McCalla's proposed ecological approach for intelligent tutoring, where a learning object's history-of-use is retained and leveraged to instruct future students. We offer three distinct models that serve to deliver the required intelligent tutoring: (i) a curriculum sequencing algorithm selecting which learning objects to present to students based on benefits to knowledge obtained by similar peers (ii) a framework for peers to provide commentary on the learning objects they've experienced (annotations) together with an algorithm for reasoning about which annotations to present to students that incorporates modeling trust in annotators (i.e. their reputation) and ratings provided by students (votes for and against) for the annotations they have been shown (iii) an opportunity for peers to guide the growth of the corpus by proposing divisions of current objects, together with an algorithm for reasoning about which of these new objects should be offered to students in order to enhance their learning. All three components are validated as beneficial in improving the learning of students. This is first of all achieved through a novel approach of simulated student learning, designed to enable the tracking of the experiences of a very large number of peers with an extensive repository of objects, through the effective modeling of knowledge gains. This is also coupled with a preliminary study with human participants that confirms the value of our framework. In all, we offer a rich and varied role for peers in guiding the learning of students in intelligent tutoring environments, made possible by careful modeling of the students who are being taught and of the potential benefits to learning that would be derived with the selection of appropriate tutorial content.iii Acknowledgements First and foremost I would like to thank my supervisor, Professor Robin Cohen, for guiding me through the completion of this dissertation and my PhD studies. Robin overwhelmed me with the amount of time and effort she put in to helping me complete my doctoral work. My committee members, Professor Edward Lank and Professor Peter van Beek, have provided detailed and helpful feedback throughout the process, which I greatly appreciate. Thanks as well to Professor Gord McCalla for his helpful advice. My appreciation goes out to the anonymous reviewers who considered earlier versions of this work and provided feedback during conference and journal submissions over the last 4 years.I would like to acknowledge Ahmed Ataullah, Simina Brânzei, John Doucette and Lachlan Dufton for being wonderful officemates: always willing to talk through issues I was working on, go for coffee and share a laugh. I greatly enjoyed working in the AI Group at the University of ...

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“…1 illustrates TANGRAM's architecture and depicts the ontologies it uses. These ontologies are concisely described in the following subsections 1 . Additionally, to annotate content units in TANGRAM, we defined a profile of the IEEE LOM RDF Binding specification 2 .…”

Section: Ontologies For Dynamic Assembly Of Personalized Contentmentioning

confidence: 99%

“…A recent study by Brooks et al [1] has shown that current e-learning standards and specifications (such as the IEEE LOM standard) are rather restrictive in terms of the variety of metadata they capture and imprecise in expressing the structure of such metadata. Moreover, few of the metadata fields proposed by such specifications are actually used in learning object repositories (LORs) to annotate the LOs, which reduces the possibility for agents to retrieve the LOs.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Assembly of Personalized Learning Content on the Semantic Web

Jovanović

Gašević

Devedžić

2006

Lecture Notes in Computer Science

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Abstract. This paper presents an ontology-based approach for automatic decomposition of learning objects (LOs) into reusable content units, and dynamic reassembly of such units into personalized learning content. To test our approach we developed TANGRAM, an integrated learning environment for the domain of Intelligent Information Systems. Relying on a number of ontologies, TANGRAM allows decomposition of LOs into smaller content units, which can be later assembled into new LOs personalized to the user's domain knowledge, preferences, and learning styles. The focus of the presentation is on the ontologies themselves, in the context of user modeling and personalization. Furthermore, the paper presents the algorithm we apply to dynamically assemble content units into personalized learning content. We also discuss our experiences with dynamic content generation and point out directions for future work.

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Towards flexible learning object metadata

Cited by 44 publications

References 12 publications

The Learning Object Triangle

The Learning Object Triangle

Peer-Based Intelligent Tutoring Systems: A Corpus-Oriented Approach

Dynamic Assembly of Personalized Learning Content on the Semantic Web

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