Abstract
Tabletops offer a new form of interaction and create new possibilities for small groups of people to collaborate and discuss tasks aided by the shared use of digital materials and tools. The collaborative affordances of tabletops make them suitable for many uses in public spaces as well as in more restricted environments such as workplaces and learning settings. This creates new opportunities for improving collaboration, particularly by capturing data that can be used to model the nature of the interactions and to present this model to the users in a form that will facilitate improved collaboration. It is timely to establish principles for designing tabletop-based systems in a manner that can facilitate such modelling. These principles should support effective use of data mining tools to create group collaboration models. In this paper, we outline theoretical design principles based on a careful analysis of the nature of tabletop datasets and collaboration.
Original language | English |
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Title of host publication | International Workshop on Adaptive Support for Team Collaboration 2011, ASTC 2011 |
Publisher | Rheinisch-Westfaelische Technische Hochschule Aachen |
Pages | 47-51 |
Number of pages | 5 |
Publication status | Published - 2011 |
Externally published | Yes |
Event | International Workshop on Adaptive Support for Team Collaboration 2011, ASTC 2011 - Held in Conjunction with the 19th International Conference on User Modeling, Adaptation and Personalization, UMAP 2011 - Girona, Spain Duration: 15 Jul 2011 → 15 Jul 2011 |
Conference
Conference | International Workshop on Adaptive Support for Team Collaboration 2011, ASTC 2011 - Held in Conjunction with the 19th International Conference on User Modeling, Adaptation and Personalization, UMAP 2011 |
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Country/Territory | Spain |
City | Girona |
Period | 15/07/11 → 15/07/11 |
Keywords
- Collaborative learning
- Collocated collaboration
- Group modelling
- Interactive tabletops
- Machine learning