Motion Capture as an Instrument in Multimodal Collaborative Learning Analytics

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Abstract

In this paper, we describe an exploratory study where we investigate the possibilities of motion capture system as an instrument to consider in multimodal analyses of face-to-face collaborative learning scenarios. The goal is to understand to what extent motion capture can facilitate certain measurements leading to collaborative learning indicators that are currently time-consuming to achieve with other instruments. We focus on the simultaneous measurement of known physical collaboration indicators such as gaze direction, the distance between learners and the speed of movement/reactions. The study considers a lab setting simulating a classroom scenario based on the Jigsaw collaborative learning flow pattern, which proposes a sequence of activities with changes in group size and formation. Preliminary results indicate a high degree of applicability of the system in measuring these indicators, with certain limitations for gaze direction measurements. With appropriate marker position on the participants, the system is able to automatically provide desired measurements with satisfactory precision. Additionally, with a small number of additional markers, we were able to determine the way students used working surfaces (shared desks).

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Vujovic, M., Tassani, S., & Hernández-Leo, D. (2019). Motion Capture as an Instrument in Multimodal Collaborative Learning Analytics. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11722 LNCS, pp. 604–608). Springer Verlag. https://doi.org/10.1007/978-3-030-29736-7_49

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