Abstract
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 language | English |
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Title of host publication | Artificial Intelligence in Education - 16th International Conference, AIED 2013, Proceedings |
Publisher | Springer |
Pages | 101-110 |
Number of pages | 10 |
ISBN (Print) | 9783642391118 |
DOIs | |
Publication status | Published - 2013 |
Externally published | Yes |
Event | International Conference on Artificial Intelligence in Education 2013 - Memphis, United States of America Duration: 9 Jul 2013 → 13 Jul 2013 Conference number: 16th https://link.springer.com/book/10.1007/978-3-642-39112-5 (Proceedings) |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer |
Volume | 7926 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | International Conference on Artificial Intelligence in Education 2013 |
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Abbreviated title | AIED 2013 |
Country/Territory | United States of America |
City | Memphis |
Period | 9/07/13 → 13/07/13 |
Internet address |
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Keywords
- CSCL
- Data Mining
- Face-to-face Collaboration
- Tabletops