The Multimodal Matrix as a Quantitative Ethnography Methodology

Written by Simon Buckingham Shum, Vanessa Echeverria, Roberto Martinez-Maldonado – Part of the International Conference on Quantitative Ethnography


“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 operationalize it such a design process, namely, the use of codesign methodologies, and the availability of automated analytics workflow to close the feedback loop. We introduce the Multimodal Matrix as a data modeling 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 modeling, analysis, and interactive visualization, 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