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

Authors:
Marc-Antoine Nüssli;Patrick Jermann;Mirweis Sangin;Pierre Dillenbourg
Affiliations:
Ecole Polytechnique Fédérale de Lausanne, CRAFT, Lausanne, Switzerland;Ecole Polytechnique Fédérale de Lausanne, CRAFT, Lausanne, Switzerland;Ecole Polytechnique Fédérale de Lausanne, CRAFT, Lausanne, Switzerland;Ecole Polytechnique Fédérale de Lausanne, CRAFT, Lausanne, Switzerland
Venue:
CSCL'09 Proceedings of the 9th international conference on Computer supported collaborative learning - Volume 1
Year:
2009

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Abstract

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.