An automatic approach for mining patterns of collaboration around an interactive tabletop

Roberto Martinez-Maldonado, Judy Kay, Kalina Yacef

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

17 Citations (Scopus)


Learning to collaborate is important. But how does one learn to collaborate face-to-face? What are the actions and strategies to follow for a group of students who start a task? We analyse aspects of students' collaboration when working around a multi-touch tabletop enriched with sensors for identifying users, their actions and their verbal interactions. We provide a technological infrastructure to help understand how highly collaborative groups work compared to less collaborative ones. The contributions of this paper are (1) an automatic approach to distinguish, discover and distil salient common patterns of interaction within groups, by mining the logs of students' tabletop touches and detected speech; and (2) the instantiation of this approach in a particular study. We use three data mining techniques: a classification model, sequence mining, and hierarchical clustering. We validated our approach in a study of 20 triads building solutions to a posed question at an interactive tabletop. We demonstrate that our approach can be used to discover patterns that may be associated with strategies that differentiate high and low collaboration groups.

Original languageEnglish
Title of host publicationArtificial Intelligence in Education - 16th International Conference, AIED 2013, Proceedings
Number of pages10
ISBN (Print)9783642391118
Publication statusPublished - 2013
Externally publishedYes
EventInternational Conference on Artificial Intelligence in Education 2013 - Memphis, United States of America
Duration: 9 Jul 201313 Jul 2013
Conference number: 16th

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Artificial Intelligence in Education 2013
Abbreviated titleAIED 2013
CountryUnited States of America


  • CSCL
  • Data Mining
  • Face-to-face Collaboration
  • Tabletops

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