Abstract
Individuals interacting in a computer supported collaborative learning (CSCL) environment produce a variety of information elements during their participation; these information elements usually have a complex structure and semantics, which make it rather difficult to find out the behavioral attitudes and profiles of the users involved. This work provides a model that can be used to discover awareness information lying underneath multi-user interaction. This information is initially captured in log files and then is represented in a specific form in events-databases. By using data mining techniques, it is possible to infer both the users’ behavioral profiles and the relationships that occur in a CSCL environment. In this work we combine different data mining strategies and a neural-based approach in order to construct a multi-layer model that provides a mechanism for inferring different types of awareness information from group activity and presenting it to the interested parties.
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Daradoumis, T., Casillas, L.A. (2006). A Neural Approach for Modeling the Inference of Awareness in Computer-Supported Collaboration. In: Nejdl, W., Tochtermann, K. (eds) Innovative Approaches for Learning and Knowledge Sharing. EC-TEL 2006. Lecture Notes in Computer Science, vol 4227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11876663_37
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DOI: https://doi.org/10.1007/11876663_37
Publisher Name: Springer, Berlin, Heidelberg
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