Please use this identifier to cite or link to this item: https://repository.isls.org//handle/1/571
Title: Multimodal Learning Analytics for the Qualitative Researcher
Authors: Worsley, Marcelo
Issue Date: Jul-2018
Publisher: International Society of the Learning Sciences, Inc. [ISLS].
Citation: Worsley, M. (2018). Multimodal Learning Analytics for the Qualitative Researcher. 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 2. London, UK: International Society of the Learning Sciences.
Abstract: The area of learning analytics is often viewed as a tool for supporting quantitative analysis. Based on previous research, this association between quantitative analysis and learning analytics does seem to be the trend. However, certain researchers have proposed the use of multimodal learning analytic techniques as a viable and valuable contribution to more qualitative research methodologies. This paper examines that idea by trying to use the output from an algorithm that learns discriminating features, as the starting point for video observations. Ultimately, the analysis suggests that there is utility in leaning on machine learning to help identify important patterns in the data, provided that those patterns are contextualized and studied using the original video data. Additionally, the work makes clear the need for better tools for conducting these types of multimodal analyses.
URI: https://doi.dx.org/10.22318/cscl2018.1109
https://repository.isls.org//handle/1/571
Appears in Collections:ICLS 2018

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