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

Roberto Martinez, James R. Wallace, Judy Kay, Kalina Yacef

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

23 Citations (Scopus)

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 video-recordings. 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.

Original languageEnglish
Title of host publicationArtificial Intelligence in Education - 15th International Conference, AIED 2011
PublisherSpringer
Pages196-204
Number of pages9
ISBN (Print)9783642218682
DOIs
Publication statusPublished - 2011
Externally publishedYes
EventInternational Conference on Artificial Intelligence in Education 2011 - Auckland, New Zealand
Duration: 28 Jun 20111 Jul 2011
Conference number: 15th
https://link.springer.com/book/10.1007%2F978-3-642-21869-9 (Proceedings)

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6738 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Artificial Intelligence in Education 2011
Abbreviated titleAIED 2011
CountryNew Zealand
CityAuckland
Period28/06/111/07/11
Internet address

Keywords

  • Collaborative Learning
  • Data Mining
  • Group Modelling

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