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Leveraging interest-driven embodied practices to build quantitative literacies: A case study using motion and audio capture from dance

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Abstract

We report on an exploratory effort to design an interest-based learning experience for high school (step) dancers to engage with concepts in mathematics and data science. We hypothesized that generating and analyzing data from their own dance movement, through motion and audio capture, would (a) enable learners to form analogies between off-line embodied experiences and new abstract concepts and (b) support motivation to learn due to perceived relevance and usefulness of data science to dance practice. Based on initial interviews to understand the specific needs and interests of the steppers, we developed some early prototypes for visual and acoustic analysis, concentrating on pose precision, tempo, and spectral characteristics (timbre) for. Teacher and student reactions to the tools demonstrated support for our hypotheses that off-line embodied cognition would help with new knowledge acquisition and that the perceived usefulness of data science would motivate learning. Several promising future directions remain to develop an interest-based and embodied data science curriculum.

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Authors and Affiliations

  1. Department of Administration, Leadership, and Technology, New York University, 82 Washington Square East, Pless Hall 7th Floor, New York, NY, 10003, USA

    Yoav Bergner

  2. New York University, New York, USA

    Shiri Mund, Ofer Chen & Willie Payne

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  1. Yoav Bergner
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  2. Shiri Mund
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Correspondence to Yoav Bergner.

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This study has been approved by the University Committee on Activities Involving Human Subjects (UCAIHS) at New York University and has been performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.

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Bergner, Y., Mund, S., Chen, O. et al. Leveraging interest-driven embodied practices to build quantitative literacies: A case study using motion and audio capture from dance. Education Tech Research Dev 69, 2013–2036 (2021). https://doi.org/10.1007/s11423-020-09804-2

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