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Enhancing Interoperability in Smart Learning Spaces through LOM Application Profiles

This document discusses the development of Learning Object Metadata (LOM) Application Profiles aimed at improving interoperability between learning repositories in smart learning environments. It highlights the need for effective sorting of learning materials and activities, delineating the distinct roles they play in educational contexts. Through the example of the Elena Smart Space for Learning, we propose a structured approach for creating adaptable profiles that refine LOM elements, specify restrictions, and align with common data models, ultimately supporting personalized learning experiences.

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Enhancing Interoperability in Smart Learning Spaces through LOM Application Profiles

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Presentation Transcript

  1. Outline • Introduction • LOM Application Profiles • The ELENA Smart Space for Learning • A LOM Application Profile for a Smart Space for Learning • Conclusions

  2. Introduction • What do we aim? • Overcome present lack of interoperability between learning repositories.

  3. Application profiles and ontologies • LOM Well known standard • App. ProfileAdaptation or combination of schemas • LOM+AP Good starting point to interoperate

  4. What makes a LOM application profile? • Selects LOM elements • Specifies value restrictions • Refines LOM semantics • Specifies multiplicity constraints • Introduces required extensions • Refines the learning object notion • Possibly aim backwards compatibility

  5. The Need for Interoperability in Smart Spaces for Learning • LOMAP initiated by Elena and extended by ProLearn. • Elena’s Smart Space for Learning: • Network of learning repositories, which supports the personalized mediation of heterogeneous learning objects. • Corporate learners are being offered heterogeneous set of learning objects. • Shortcomings in decision effectiveness, process administration, IT infrastructure. • Need for a global view of integrated internal and external repositories. • Search based on an ontology or common data model shared by several systems.

  6. Differentiating Learning Material from Learning Activity • LM: Consumed independently of time and location. Physical or digital good. • LA: Live events. Synchronous service. • IMS LD and SCORM already use this concept. • Here an LA is an autonomous entity. • The AP is conditioned by the selection criteria and the results description.

  7. Application Profile Design • Refinement of the Learning Object Notion • Distinguish two types of learning objects and be expressive enough for contractual purposes. • Selection of LOM elements • Mainly for learning material. • Specification of value restrictions • For LOM value spaces and vCard elements. • Semantics refinement • To apply it in the Smart Space for Learning. • Specification of multiplicity constraints • Also for each vocabulary entry. • Introduction of extensions • Both in vocabulary and elements.

  8. Overview of Selected Elements and Mappings • Categories (LOM, Profile): • Rights • General • Metametadata • Technical • Classification • Lifecycle • Educational • Relation and annotation • Mappings to Dublin Core and Open-Qcat. Brokerage aspects Identification LM technical aspects Versatility LA concepts LO classification Notused

  9. Conclusion • LOM Application Profile for building Smart Spaces of Learning. • Generic process for designing LOM Application Profiles. • Distinction between Learning Material and Learning Activities. • Learning objects described for brokerage purposes.

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