Capturing Movement Decomposition to Support Learning and Teaching in Contemporary Dance

Abstract : Our goal is to understand how dancers learn complex dance phrases. We ran three workshops where dancers learned dance fragments from videos. In workshop 1, we analyzed how dancers structure their learning strategies by decomposing movements. In workshop 2, we introduced MoveOn, a technology probe that lets dancers decompose video into short, repeatable clips to support their learning. This served as an effective analysis tool for identifying the changes in focus and understanding their decomposition and recomposition processes. In workshop 3, we compared the teacher's and dancers' decomposition strategies, and how dancers learn on their own compared to teacher-created decompositions. We found that they all ungroup and regroup dance fragments, but with different foci of attention, which suggests that teacher-imposed decomposition is more effective for introductory dance students, whereas personal decomposition is more suitable for expert dancers. We discuss the implications for designing technology to support analysis, learning and teaching of dance through movement decomposition.
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Jean-Philippe Rivière, Sarah Alaoui, Baptiste Caramiaux, Wendy Mackay. Capturing Movement Decomposition to Support Learning and Teaching in Contemporary Dance. Proceedings of the ACM on Human-Computer Interaction , Association for Computing Machinery (ACM), 2019, Proceedings of the ACM on Human-Computer Interaction, 3 (CSCW), pp.1-22. ⟨10.1145/3359188⟩. ⟨hal-02378487⟩

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