A Retrospective Autoethnography Documenting Dance Learning Through Data Physicalisations

Best Paper
Data PhysicalizationDigital Art Installations & Interactive PerformanceDance & Body Movement ComputingDancers & Performing ArtistsHCI ResearchersCognitive Scientists

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.

Quick Actions

Share

Share this page

ios_share

https://hci.top/en/papers/dis/164433/2024

AdRecommended

Learn AI Coding at CodeNow

At a Glance

Paper Snapshot

fact_check
dataset
Source
DIS
calendar_month
Year
2024
emoji_events
Award
Best Paper
group
Authors
5 authors
sell
Subtopics
Data Physicalization, Digital Art Installations & Interactive Performance, Dance & Body Movement Computing
work
Professions
Dancers & Performing Artists, HCI Researchers, Cognitive Scientists
article
Content Status
Abstract only
hub
Related Papers
0 related papers