2017

Conférence

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In technology-enhanced learning, semantic annotations have been employed to attach semantic metadata to learning materials in order to significantly enhance their accessibility by human users and machines as well. In this paper, we present an ontology-based multi-level semantic representation model that aims to enrich the description of learning objects with semantics regarding their subjects, competencies and instructional roles. More specifically, the proposed model uses three ontologies: a subject domain ontology describing the domain concepts and the relations that are covered by the subject matter being taught, a competency ontology describing the competency-related characteristics of learners and learning resources, and an instructional role ontology specifying the instructional role(s) a learning object can play in an instructional setting. To demonstrate the feasibility of our model, an illustrative example is given that explains how learning object semantics can be represented with different granularities.

@inproceedings{DBLP:conf/aiccsa/RezguiMG17,
  author       = {Kalthoum Rezgui and
                  H{\'{e}}dia Mhiri and
                  Khaled Gh{\'{e}}dira},
  title        = {An Ontology-Based Multi-level Semantic Representation Model for Learning
                  Objects Annotation},
  booktitle    = {14th {IEEE/ACS} International Conference on Computer Systems and Applications,
                  {AICCSA} 2017, Hammamet, Tunisia, October 30 - Nov. 3, 2017},
  pages        = {1391--1398},
  publisher    = {{IEEE} Computer Society},
  year         = {2017},
  url          = {https://doi.org/10.1109/AICCSA.2017.95},
  doi          = {10.1109/AICCSA.2017.95},
  timestamp    = {Sun, 06 Oct 2024 20:55:53 +0200},
  biburl       = {https://dblp.org/rec/conf/aiccsa/RezguiMG17.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}