Abstract
This paper describes a promising methodology for studying co-located groups: mobile eye-trackers. We provide a comprehensive description of our data collection and analysis processes so that other researchers can take advantage of this cutting-edge technology. Data were collected in a controlled experiment where 27 student dyads (N = 54) interacted with a Tangible User Interface. They first had to define some design principles for optimizing a warehouse layout by analyzing a set of Contrasting Cases, and build a small-scale layout based on those principles. The contributions of this paper are that: 1) we replicated prior research showing that levels of Joint Visual Attention (JVA) are correlated with collaboration quality across all groups; 2) we then qualitatively analyzed two dyads with high levels of JVA and show that it can hide a free-rider effect (Salomon and Globerson 1989); 3) in conducting this analysis, we additionally developed a new visualization (augmented cross-recurrence graphs) that allows researchers to distinguish between high JVA groups that have balanced and unbalanced levels of participations; 4) finally, we generalized this effect to the entire sample and found a significant negative correlation between dyads’ learning gains and unbalanced levels of participation (as computed from the eye-tracking data). We conclude by discussing implications for automatically analyzing students’ interactions using dual eye-trackers.
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Acknowledgments
We gratefully acknowledge support from the National Science Foundation for this work from the LIFE Center (NSF #0835854) as well as the Leading House Technologies for Vocation Education, funded by the Swiss State Secretariat for Education, Research and Innovation. Finally, we would like to thank SMI (SensoMotoric Instruments) for their eye-tracking technology and Jacques Kurzo for his deep involvement in this project.
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Schneider, B., Sharma, K., Cuendet, S. et al. Leveraging mobile eye-trackers to capture joint visual attention in co-located collaborative learning groups. Intern. J. Comput.-Support. Collab. Learn 13, 241–261 (2018). https://doi.org/10.1007/s11412-018-9281-2
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DOI: https://doi.org/10.1007/s11412-018-9281-2