2020: strengthening the Quantitative Ethnography community
2020 will be remembered for many things… but amidst the disruption, it’s been a year of consolidation for the exciting, emerging field of Quantitative Ethnography (the book by David Williamson Shaffer; my review for Jnl. Learning Analytics).
The newly launched International Society for QE has been coordinating virtual events to strengthen professional ties across the globe, upskill researchers in the new tools and techniques, and the 2nd international conference is in Feb 2021. ISQE are to be congratulated on this progress, and in particular, the Epistemic Analytics Lab at U. Wisconsin-Madison are doing an awesome job in generously sharing their expertise, and making their work available through free analytical tools.
I was honoured to be asked to help design and chair the monthly webinar series which is building a library of examples how QE methods can be applied in diverse contexts. That’s proven to be a fascinating experience, and our own work (based on Vanessa Echeverria’s PhD) wrapped this up earlier this month (more coming in 2021!).
Abstract: Collocated, face-to-face teamwork remains a pervasive mode of working and learning, which is hard to replicate online. In team-based situations, learners’ embodied, multimodal interaction with each other and with digital and material resources has been studied by researchers, but due to its complexity, has remained opaque to automated analysis. The ready availability of sensors makes it increasingly affordable to instrument work spaces to automatically capture activity traces to study teamwork and groupwork. Yet, a key challenge is the enrichment of these multiple and intertwined quantitative data streams with the qualitative insights needed to make sense of them. In this seminar, we will discuss our inroads into giving meaning to multimodal group data. We have followed a human-centred approach to design meaningful end-user interfaces that convert multimodal data into data stories. Based on Quantitative Ethnography principles, we developed a modelling technique, termed the Multimodal Matrix, to grounding quantitative data in the semantics derived from a qualitative interpretation of the context from which it arises. We will present practical examples in the context of high-fidelity clinical simulations in which multimodal data (physiological, positioning, and logged actions) have been transformed into learning analytics interfaces that support teachers’ and learners’ reflection.
Video: Transcript
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Presentation: Slides
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