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Title: Methods for Analyzing Temporally Entangled Multimodal Data
Authors: Sung, Hanall
Swart, Michael I.
Nathan, Mitchell J.
Keywords: CSCL
Issue Date: 2022
Publisher: International Society of the Learning Sciences
Citation: Sung, H., Swart, M. I., & Nathan, M. J. (2022). Methods for analyzing temporally entangled multimodal data. In Weinberger, A. Chen, W., Hernández-Leo, D., & Chen, B. (Eds.), Proceedings of the 15th International Conference on Computer-Supported Collaborative Learning - CSCL 2022 (pp. 242-249). International Society of the Learning Sciences.
Abstract: While the challenges of collecting multimodal data are becoming surmountable with the help of the rapid development of sensor technologies, the challenges of analyzing multimodality remain substantial. It is imperative that researchers explore ways to successfully integrate theoretical and methodological frameworks for analyzing multimodal interactions in CSCL contexts. We identified two main approaches to analyzing multimodal data in CSCL settings—triangulating and interleaving—and highlighted the remaining challenges to unfolding the dynamic interplay between different modes with the consideration of temporality. To tackle these challenges, we presented an empirical example of multimodal learning analysis that practically employed the multimodal matrix and ENA for operationalizing and visualizing temporally entangled multimodal interactions. This paper (1) extends the theoretical underpinnings of temporality in studies of learning processes in CSCL settings, and (2) provides empirical evidence that indicates the potential of the interleaving approach to capture the core of complex meaning-making processes.
Description: Long Paper
Appears in Collections:ISLS Annual Meeting 2022

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