Collaboration and abstract representations: towards predictive models based on raw speech and eye-tracking data

This study aims to explore the possibility of using machine learning techniques to build predictive models of performance in collaborative induction tasks. More specifically, we explored how signal-level data, like eye-gaze data and raw speech may be used to build such models. The results show that such low level features have effectively some potential to predict performance in such tasks. Implications for future applications design are shortly discussed.


Published in:
CSCL '09: Proceedings of the 2009 conference on Computer support for collaborative learning
Presented at:
Computer Support for Collaborative Learning (CSCL) 2009, Rhodes, June 8-13
Year:
2009
Publisher:
International Society of the Learning Sciences
Note:
Invited Paper
Laboratories:




 Record created 2009-08-04, last modified 2018-03-18

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