A Retrospective Autoethnography Documenting Dance Learning Through Data Physicalisations

We present a retrospective autoethnography grounded in data-driven design. The first author collected her movement data and subjective experience of learning the dance repertoire of modern dance pioneer Isadora Duncan, which together were encoded into the design of a set of plaster artefacts physicalising her embodied dance learning progression. The artefacts reflect the first author’s bodily transformation, mirroring her transition from discomfort to ease, and changes in her expressive capabilities. Our method offers an alternative to documentation of embodied learning through design. Throughout our design process we leverage on the movement data, the field notes and the first author’s memory of her journey, all of which constitute entangled and complementary input into her experience of dance learning. We show that the data physicalisations provided a gateway into the intangible experience and allowed for a deep and reflexive understanding of our dataset

A Retrospective Autoethnography Documenting Dance Learning Through Data Physicalisations

International audienceWe present a retrospective autoethnography grounded in data-driven design. The first author collected her movement data and subjective experience of learning the dance repertoire of modern dance pioneer Isadora Duncan, which together were encoded into the design of a set of plaster artefacts physicalising her embodied dance learning progression. The artefacts reflect the first author's bodily transformation, mirroring her transition from discomfort to ease, and changes in her expressive capabilities. Our method offers an alternative to documentation of embodied learning through design. Throughout our design process we leverage on the movement data, the field notes and the first author's memory of her journey, all of which constitute entangled and complementary input into her experience of dance learning. We show that the data physicalisations provided a gateway into the intangible experience and allowed for a deep and reflexive understanding of our dataset