Anzeige
Erschienen in:
2017 | Supplement | Buchkapitel
An Ontology for Describing Scenarios of Multi-players Learning Games: Toward an Automatic Detection of Group Interactions
verfasst von : Mathieu Guinebert, Amel Yessad, Mathieu Muratet, Vanda Luengo
Erschienen in: Data Driven Approaches in Digital Education
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Abstract
Multi-players learning games (MPLG) tend to foster learners’ engagement and immersion in learning games’ activities. They are an interesting way to organize learning situations in which learners interact with each other and solve challenges.
In this context, teachers need to orchestrate MPLGs’ scenarios to arouse desired interactions such as cooperation, collaboration or competition. Do the intended interactions by the teachers occur when learners really play the scenario?
Developing an automatic system to help teachers to detect interactions between learners in a MPLG’s scenario or to help them to build a multi-players scenario according to their objectives in term of interactions is a challenging task. This is largely due to the lack of formal and shared model that describes MPLG’s scenario accurately.
In this paper, we present an ontology that formalizes MPLG’s scenarios and their identified requirements: the modularity, the multiple roles, the resource management and the final state. The ontology was built iteratively by using the methodology METHONTOLOGY and used to model the knowledge of four different MPLGs’ scenario. A study was carried out in order to check the completeness of the ontology and the effectiveness of the knowledge modeling process.