• DocumentCode
    3656340
  • Title

    Indexing Learning Scenarios by the Most Adapted Contexts: An approach Based on the Observation of Scenario Progress in Session

  • Author

    Mariem Chaabouni;Claudine Piau-Toffolon;Mona Laroussi;Christophe Choquet;Henda Ben Ghezala

  • Author_Institution
    LIUM, Maine Univ., Le Mans, France
  • fYear
    2015
  • fDate
    7/1/2015 12:00:00 AM
  • Firstpage
    39
  • Lastpage
    43
  • Abstract
    The BASAR project offers a repository of blended learning scenarios. This project aims to reuse and capitalize good teaching practices. A teacher-designer would have the ability to choose a scenario that matches his needs, to be modified, used and refined. Specializing the scenario to a given context often improves the learning quality. On the other hand, it increases the difficulty to reuse it in a different context. Knowing the appropriate contexts for a scenario is essential for better reusing a part of this scenario or all of it (granularity). So, how can we characterize the learning scenarios with their most appropriate contexts based on the observation of the learning sessions progress in order to enhance scenario retrieval? This paper proposes a multi-faceted approach to index learning scenarios using the context trees formalism. The main objective of this indexing is to facilitate the learning scenarios design by and for reuse.
  • Keywords
    "Context","Context modeling","Indexing","Collaboration","Unified modeling language","Conferences","Prototypes"
  • Publisher
    ieee
  • Conference_Titel
    Advanced Learning Technologies (ICALT), 2015 IEEE 15th International Conference on
  • Type

    conf

  • DOI
    10.1109/ICALT.2015.138
  • Filename
    7265256