Modelling and identifying collaborative situations in a collocated multi-display groupware setting

Authors:
Roberto Martinez;James R. Wallace;Judy Kay;Kalina Yacef
Affiliations:
School of Information Technologies, University of Sydney, NSW, Australia;Department of Systems Design Engineering, University of Waterloo, ON, Canada;School of Information Technologies, University of Sydney, NSW, Australia;School of Information Technologies, University of Sydney, NSW, Australia
Venue:
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
Year:
2011

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Abstract

Detecting the presence or absence of collaboration during group work is important for providing help and feedback during sessions. We propose an approach which automatically distinguishes between the times when a co-located group of learners, using a problem solving computer-based environment, is engaged in collaborative, non-collaborative or somewhat collaborative behaviour. We exploit the available data, audio and application log traces, to automatically infer useful aspects of the group collaboration and propose a set of features to code them. We then use a set of classifiers and evaluate whether their results accurately match the observations made on videorecordings. Results show up to 69.4% accuracy (depending on the classifier) and that the error rate for extreme misclassification (e.g. when a collaborative episode is classified as non-collaborative, or vice-versa) is less than 7.6%. We argue that this technique can be used to show the teacher and the learners an overview of the extent of their collaboration so they can become aware of it.