Name
Title | ||
|---|---|---|
Taking advantage of metadata semantics: the case of learning-object-based lesson graphs |
Abstract | ||
|---|---|---|
Learning objects (LOs) are pieces of educational material characterized with a valuable amount of information about their content and usage. This additional information is defined as a set of metadata generally following the IEEE LOM specification. This specification also serves to characterize the relations existing between LOs. LOs whose relations are explicit are regarded as the nodes of a lesson graph. Link types and LO metadata constitute the lesson graph semantics. This article proposes to take advantage of lesson graph semantics using a context diffusion approach. It consists in diffusing the metadata-based processes along the edges of the lesson graph. This technique aims at coping with the metadata processing issues arising when some graph metadata are missing, incorrect, or incomplete. This article also presents a three-layer extensible framework for easing the use of context diffusion in a graph. As part of the framework, two original types of metadata processes are introduced. The first one takes advantage of the metadata attribute similarities between related LOs. The second one focuses on the lesson graph consistency. The framework and the application examples were implemented as an open-source Java library used in the lesson graph authoring tool LessonMapper2. During the lesson authoring process, we show that the framework can bring support not only for generating and validating metadata, but also for retrieving LOs. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/s10115-008-0181-z | Knowl. Inf. Syst. |
Keywords | Field | DocType |
learning-object-based lesson graph,related los,retrieving los,lo metadata,lesson graph semantics,lesson graph,metadata attribute similarity,metadata process,lesson graph consistency,graph metadata,metadata semantics,learning object metadata · lesson graph · metadata processing · metadata propagation · learning object retrieval · lesson authoring,metadata processing issue | Metadata,Metadata repository,Data mining,Computer science,Concept learning,Learning object,Artificial intelligence,Case-based reasoning,Java,Machine learning,Learning object metadata,Semantics | Journal |
Volume | Issue | ISSN |
20 | 3 | 0219-3116 |
Citations | PageRank | References |
5 | 0.48 | 13 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Olivier Motelet | 1 | 107 | 14.37 |
| Nelson Baloian | 2 | 271 | 46.73 |
| José A. Pino | 3 | 782 | 89.55 |