Knowledge extraction and representation of collaborative activity through ontology-based and Social Network Analysis technologies

Authors:
Luis Casillas;Thanasis Daradoumis
Affiliations:
Department of Computer Sciences, University of Guadalajara, Av. Revolucion, 1500, 44840 Guadalajara, Mexico.;Department of Informatics, Multimedia and Telecommunications, Open University of Catalonia, Rambla Poblenou 156, 08018 Barcelona, Spain
Venue:
International Journal of Business Intelligence and Data Mining
Year:
2009

Citing 6
Cited 1

Computational Modeling and Analysis of Knowledge Sharing in Collaborative Distance Learning

User Modeling and User-Adapted Interaction
A layered framework for evaluating on-line collaborative learning interactions

International Journal of Human-Computer Studies
Awareness and teamwork in computer-supported collaborations

Interacting with Computers
Multi-dimensional tracking in virtual learning teams: an exploratory study

CSCL '02 Proceedings of the Conference on Computer Support for Collaborative Learning: Foundations for a CSCL Community
A neural approach for modeling the inference of awareness in computer-supported collaboration

EC-TEL'06 Proceedings of the First European conference on Technology Enhanced Learning: innovative Approaches for Learning and Knowledge Sharing
Implementing temporal logics: tools for execution and proof

CLIMA'05 Proceedings of the 6th international conference on Computational Logic in Multi-Agent Systems

Modelling symmetry of activity as an indicator of collocated group collaboration

UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper describes an approach for extracting and representing the knowledge generated from collaborative interaction of small learning teams that work together in distance to carry out a software project or a case study. Our approach is based on ontology, Social Network Analysis (SNA) and fuzzy classification, which has been initially developed as a mechanism to analyse and understand behavioural patterns from collaboration performed in different scenarios. Through the SNA we are able to define an ontological profile that provides a deep knowledge about the participants' roles, intentions and effects; hence a fuzzy model can perform inferences over individual indicators.