Capturing Movement Decomposition to Support Learning and Teaching in Contemporary Dance

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.

[1]  Balakrishnan Ramadoss,et al.  SEMI-AUTOMATED ANNOTATION AND RETRIEVAL OF DANCE MEDIA OBJECTS , 2007, Cybern. Syst..

[2]  Luis Molina-Tanco,et al.  The Delay Mirror: a Technological Innovation Specific to the Dance Studio , 2017, MOCO.

[3]  R. Schmidt,et al.  VARIABILITY OF PRACTICE AND IMPLICIT MOTOR LEARNING , 1997 .

[4]  Rafael Kuffner dos Anjos,et al.  3D Annotation in Contemporary Dance: Enhancing the Creation-Tool Video Annotator , 2016, MOCO.

[5]  David Kirsh Knowledge, Explicit vs Implicit , 2009 .

[6]  Baptiste Caramiaux,et al.  Dissociable effects of practice variability on learning motor and timing skills , 2018, PloS one.

[7]  Wendy E. Mackay,et al.  Knotation: Exploring and Documenting Choreographic Processes , 2018, CHI.

[8]  Kjeld Schmidt,et al.  CSCW: Four Characters in Search of a Context , 1989, ECSCW.

[9]  Michel Beaudouin-Lafon,et al.  Instrumental interaction: an interaction model for designing post-WIMP user interfaces , 2000, CHI.

[10]  Norbert Schnell,et al.  Gesture capture: Paradigms in interactive music/dance systems , 2011 .

[11]  Peter Goodyear,et al.  Creating shareable representations of practice , 1998 .

[12]  Roberto Martínez Maldonado,et al.  Exploring video annotation as a tool to support dance teaching , 2018, OZCHI.

[13]  J. Diedrichsen,et al.  Motor skill learning between selection and execution , 2015, Trends in Cognitive Sciences.

[14]  Celine Latulipe,et al.  Bodies in critique: a technological intervention in the dance production process , 2012, CSCW.

[15]  Brian Dorn,et al.  Piloting TrACE: Exploring Spatiotemporal Anchored Collaboration in Asynchronous Learning , 2015, CSCW.

[16]  Reed Stevens,et al.  Using a digital video annotation tool to teach dance composition , 2003 .

[17]  David Kirsh,et al.  Embodied cognition and the magical future of interaction design , 2013, TCHI.

[18]  Wendy E. Mackay,et al.  How Do Dancers Learn To Dance?: A first-person perspective of dance acquisition by expert contemporary dancers , 2018, MOCO.

[19]  Celine Latulipe,et al.  The choreographer's notebook: a video annotation system for dancers and choreographers , 2011, C&C '11.

[20]  Kristina Höök,et al.  Embracing First-Person Perspectives in Soma-Based Design , 2018, Informatics.

[21]  Tovi Grossman,et al.  YouMove: enhancing movement training with an augmented reality mirror , 2013, UIST.

[22]  Christian Jacquemin,et al.  Interactive Visuals as Metaphors for Dance Movement Qualities , 2015, ACM Trans. Interact. Intell. Syst..

[23]  Wendy E. Mackay,et al.  Structured observation with polyphony: a multifaceted tool for studying music composition , 2014, Conference on Designing Interactive Systems.

[24]  R. Bjork,et al.  Self-regulated learning: beliefs, techniques, and illusions. , 2013, Annual review of psychology.

[25]  J. Pine,et al.  Chunking mechanisms in human learning , 2001, Trends in Cognitive Sciences.

[26]  Marilyn Chandler McEntyre What's in a Phrase? , 2014 .

[27]  Wendy E. Mackay,et al.  Reification, polymorphism and reuse: three principles for designing visual interfaces , 2000, AVI '00.

[28]  Akrivi Katifori,et al.  A Web-based system for annotation of dance multimodal recordings by dance practitioners and experts , 2018, MOCO.

[29]  R. Magill,et al.  A REVIEW OF THE CONTEXTUAL INTERFERENCE EFFECT IN MOTOR SKILL ACQUISITION , 1990 .

[30]  Akrivi Katifori,et al.  Exploring Visualizations in Real-time Motion Capture for Dance Education , 2016, PCI.

[31]  Robert J. Crutcher,et al.  The role of deliberate practice in the acquisition of expert performance. , 1993 .

[32]  J. Shea,et al.  Contextual interference effects on the acquisition, retention, and transfer of a motor skill. , 1979 .

[33]  R. Bjork,et al.  Learning Concepts and Categories , 2008, Psychological science.

[34]  Jessica A. Grahn,et al.  Optimizing Music Learning: Exploring How Blocked and Interleaved Practice Schedules Affect Advanced Performance , 2016, Front. Psychol..

[35]  Diogo Cabral,et al.  Multimodal video annotation for contemporary dance creation , 2011, CHI Extended Abstracts.

[36]  Anu Sööt,et al.  Contemporary Approaches to Dance Pedagogy – The Challenges of the 21st Century , 2014 .

[37]  Allison Druin,et al.  Technology probes: inspiring design for and with families , 2003, CHI '03.

[38]  Marianela Ciolfi Felice Supporting expert creative practice , 2018 .

[39]  Kathleen M. MacQueen,et al.  Applied Thematic Analysis , 2011 .

[40]  Claire O'Malley,et al.  Computer Supported Collaborative Learning , 1995, NATO ASI Series.

[41]  A. Travlos,et al.  Specificity and Variability of Practice, and Contextual Interference in Acquisition and Transfer of an Underhand Volleyball Serve , 2010, Perceptual and motor skills.

[42]  Marcos Serrano,et al.  Movement qualities as interaction modality , 2012, DIS '12.