The Multimodal Matrix as a Quantitative Ethnography Methodology

Sadly I missed the inaugural International Conference on Quantitative Ethnography for family reasons, but by all accounts it was a resounding success, with the 2020 conference already set for LA (Oct. 25-27).

Check out the #ICQE19 and #QuantitativeEthnography channels, and the fabulous keynotes from Jim Gee (Individuals and Discourses), Golnaz Arastoopur Irgens (Quantitative Ethnography Across Domains: Where we are and where we are going) and Dragan Gašević (Nurturing the Connections: The Role of Quantitative Ethnography in Learning Analytics).

The only silver lining of not making it in person was I wanted to make a screencast of our presentation, so here you go. We consider how QE principles have helped inform our efforts to model multimodal data streams from collocated nursing teamwork, in order to generate meaningful feedback.

Buckingham Shum, S., Echeverria, V. & Martinez-Maldonado, R. (2019). The Multimodal Matrix as a Quantitative Ethnography Methodology. In: Eagan B., Misfeldt, M. & Siebert-Evenstone, A. (Eds.), Advances in Quantitative Ethnography. Communications in Computer and Information Science, Vol. 1112. Springer: Cham, pp.26-40. DOI: [Paper][Slides]

Abstract: This paper seeks to contribute to the emerging field of Quantitative Ethnography (QE) by demonstrating its utility to solve a complex challenge in Learning Analytics: the provision of timely feedback to collocated teams and their coaches. We define two requirements that extend the QE concept in order to operationalise such a design process, namely, the use of co-design methodologies, and the availability of automated analytics workflow to close the feedback loop. We introduce the Multimodal Matrix as a data modelling approach that can integrate theoretical concepts about teamwork with contextual insights about specific work practices, enabling the analyst to map between higher order codes and low-level sensor data, with the option add the results of manually performed analyses. This is implemented in software as a workflow for rapid data modelling, analysis and interactive visualisation, demonstrated in the context of nursing teamwork simulations. We propose that this exemplifies how a QE methodology can underpin collocated activity analytics, at scale, with in-principle applications to embodied, collocated activities beyond our case study.

Keywords: multimodal, learning analytics, teamwork, CSCL, sense making

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