Movement Computing Education for Middle Grades

Yoav Bergner, Deborah Damast, Allegra Romita, Anne Marie Robson Smock

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This paper takes a theoretical approach to movement computing education for young learners, with a focus on middle grades (grades 6-8, ages 11-14). This age group is targeted as a lower bound because, while some elements of computational thinking may be available to still younger learners, there are abstractions involved in movement computation that pre-require a certain amount of formal operation, in the Piagetian sense. We outline a parallel foundation of key ideas in movement (specifically dance) and key ideas in computing (specifically data representations) at this age-appropriate level. We describe how these foundations might be laid down together early on so that they can later be integrated via the introduction of sensing and feedback technology. Concepts in movement and choreography are studied using words and bodies, as in traditional dance education, and later using computer simulations and motion capture. Data concepts are introduced first by appeal to general questions and later by specification to the movement of individual and collective joints and bodies.

Original languageEnglish (US)
Title of host publicationProceedings of the 7th International Conference on Movement and Computing, MOCO 2020
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450375054
StatePublished - Jul 15 2020
Event7th International Conference on Movement and Computing, MOCO 2020 - Jersey City, Virtual, United States
Duration: Jul 15 2020Jul 17 2020

Publication series

NameACM International Conference Proceeding Series


Conference7th International Conference on Movement and Computing, MOCO 2020
Country/TerritoryUnited States
CityJersey City, Virtual


  • dance analytics
  • data science
  • education
  • motion capture

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications


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