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http://hdl.handle.net/10609/109855
Title: Enhancing knowledge management in online collaborative learning
Author: Caballé Llobet, Santi  
Daradoumis Haralabus, Atanasi  
Xhafa, Fatos
Conesa Caralt, Jordi  
Others: Universitat Oberta de Catalunya. Internet Interdisciplinary Institute (IN3)
Universitat Politècnica de Catalunya
Keywords: collaborative learning
knowledge management
ontology
awareness
feedback
scaffolding
group monitoring
Issue Date: 30-Jun-2010
Publisher: International Journal of Software Engineering and Knowledge Engineering
Citation: Caballé, S., Daradoumis, T., Xhafa, F. & Conesa, J. (2010). Enhancing Knowledge Management in Online Collaborative Learning. International Journal of Software Engineering and Knowledge Engineering, 20(4), 485-497. doi: 10.1142/S0218194010004839
Project identifier: info:eu-repo/grantAgreement/257639
Also see: https://doi.org/10.1142/S0218194010004839
Abstract: This study aims to explore two crucial aspects of collaborative work and learning: on the one hand, the importance of enabling collaborative learning applications to capture and structure the information generated by group activity and, on the other hand, to extract the relevant knowledge in order to provide learners and tutors with efficient awareness, feedback and support as regards group performance and collaboration. To this end, in this paper we first propose a conceptual model for data analysis and management that identifies and classifies the many kinds of indicators that describe collaboration and learning into high-level aspects of collaboration. Then, we provide a computational platform that, at a first step, collects and classifies both the event information generated asynchronously from the users' actions and the labeled dialogues from the synchronous collaboration according to these indicators. This information is then analyzed in next steps to eventually extract and present to participants the relevant knowledge about the collaboration. The ultimate aim of this platform is to efficiently embed information and knowledge into collaborative learning applications. We eventually suggest a generalization of our approach to be used in diverse collaborative learning situations and domains.
Language: English
URI: http://hdl.handle.net/10609/109855
ISSN: 0218-1940MIAR
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