Semantic gap detection in metadata of adaptive learning environments

Sergey Sosnovsky, Isaac Alpizar Chacon

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

1 Cita (Scopus)

Resumen

Quality of learning objects metadata, in many respects, defines the quality of an adaptive learning environment presenting these learning objects to a student. Metadata inconsistencies and gaps may be the cause of various problems: from a system malfunction to ineffective learning experiences. In this paper, we propose an intelligent and rigorous mechanism for detecting metadata gaps in collections of learning content. The mechanism converts learning objects metadata into an OWL2 ontology, detects logical conflicts using Semantic Web reasoning techniques and generates human-readable explanations for an author to resolve the gaps. The evaluation of the developed semantic gap detection tool with real learning content collections demonstrates its effectiveness.

Idioma originalInglés
Título de la publicación alojadaProceedings - IEEE 14th International Conference on Advanced Learning Technologies, ICALT 2014
EditoresDemetrios G. Sampson, Michael J. Spector, Nian-Shing Chen, Ronghuai Huang, Kinshuk
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas548-552
Número de páginas5
ISBN (versión digital)9781479940387
DOI
EstadoPublicada - 17 sept 2014
Evento14th IEEE International Conference on Advanced Learning Technologies, ICALT 2014 - Athens, Grecia
Duración: 7 jul 20149 jul 2014

Serie de la publicación

NombreProceedings - IEEE 14th International Conference on Advanced Learning Technologies, ICALT 2014

Conferencia

Conferencia14th IEEE International Conference on Advanced Learning Technologies, ICALT 2014
País/TerritorioGrecia
CiudadAthens
Período7/07/149/07/14

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