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https://repository.isls.org//handle/1/4433
Title: | Unpacking Collaborative Learning Processes During Hands-on Activities Using Mobile Eye-Trackers |
Authors: | Schneider, Bertrand |
Issue Date: | Jun-2019 |
Publisher: | International Society of the Learning Sciences (ISLS) |
Citation: | Schneider, B. (2019). Unpacking Collaborative Learning Processes During Hands-on Activities Using Mobile Eye-Trackers. In Lund, K., Niccolai, G. P., Lavoué, E., Hmelo-Silver, C., Gweon, G., & Baker, M. (Eds.), A Wide Lens: Combining Embodied, Enactive, Extended, and Embedded Learning in Collaborative Settings, 13th International Conference on Computer Supported Collaborative Learning (CSCL) 2019, Volume 1 (pp. 41-48). Lyon, France: International Society of the Learning Sciences. |
Abstract: | This paper describes a multimodal dataset captured during a collaborative learning activity typical of makerspaces. Participants were introduced to computational thinking concepts using a block-based environment: they had to program a robot to solve a variety of mazes. Mobile eye-trackers, physiological wristbands and motion sensors captured their behavior and social interactions. In this paper, I leverage prior work on joint visual attention (Tomasello, 1995) and analyze the eye-tracking data collected during the study. This paper provides three contributions: 1) I use an emerging methodology to capture joint visual attention in a co-located setting using mobile eye-trackers (Schneider & al., 2018); 2) I then replicate findings showing that levels of joint visual attention are positively correlated with collaboration quality; 3) finally, I present a new measure that captures cycles of collaborative / individual work, which is positively associated with learning gains (but not with collaboration quality). I discuss these results and conclude with implications for capturing students' interactions in co- located spaces using Multimodal Learning Analytics. |
URI: | https://doi.dx.org/10.22318/cscl2019.41 https://repository.isls.org//handle/1/4433 |
Appears in Collections: | CSCL 2019 |
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