Please use this identifier to cite or link to this item: https://repository.isls.org//handle/1/910
Title: Augmenting Qualitative Analyses of Collaborative Learning Groups Through Multi-Modal Sensing
Authors: Xie, Bin
Reilly, Joseph M
Dich, Yong Li
Schneider, Bertrand
Issue Date: Jul-2018
Publisher: International Society of the Learning Sciences, Inc. [ISLS].
Citation: Xie, B., Reilly, J. M., Dich, Y. L., & Schneider, B. (2018). Augmenting Qualitative Analyses of Collaborative Learning Groups Through Multi-Modal Sensing . In Kay, J. and Luckin, R. (Eds.) Rethinking Learning in the Digital Age: Making the Learning Sciences Count, 13th International Conference of the Learning Sciences (ICLS) 2018, Volume 1. London, UK: International Society of the Learning Sciences.
Abstract: In a previous study (N=84), we collected information about dyads who worked on an engineering task typical of makerspaces: programming a robot to solve mazes of increasing difficulty. We collected multimodal data using a variety of sensors, including mobile eye-trackers, galvanic skin response, motion sensors and audio / video streams. In this paper, we contrast two pairs that exhibited positive and negative learning gains. We first detail multimodal measures to compare differences and similarities across those groups, and then dive deeper into a qualitative analysis of their exchanges. We then describe how those measures could be used over the entire sample to capture productive interactions in small groups. We conclude by discussing how process data from sensors can augment traditional qualitative observations, and how it can create powerful synergies for better understanding collaborative interactions among learners in settings such as makerspaces.
URI: https://doi.dx.org/10.22318/cscl2018.608
https://repository.isls.org//handle/1/910
Appears in Collections:ICLS 2018

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