Browsing by Subject "Dance technology"

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    aiDance: A Non-Invasive Approach in Designing AI-Based Feedback for Ballet Assessment and Learning
    (2021-12) Trajkova, Milka; Cafaro, Francesco; Bolchini, Davide; Dombrowski, Lynn; Fusco, Judi; Hickey, Daniel; Magerko, Brian; Toenjes, John
    Since its codified genesis in the 18th century, ballet training has largely been unchanged: it relies on tools that lack adequate support for both dancers and teachers. In particular, providing effective augmented feedback remains challenging as it can be limited, not always provided at the proper time, and highly subjective as it depends on the visual experience of an instructor. Designing a ballet assessment and learning tool with the aim of achieving a meaningful educational experience is an interdisciplinary challenge due to the fine motor movements and patterns of the art form. My work examines how we can effectively augment ballet learning in three phases using mixedmethod approaches. First, through my past professional experience as a ballet dancer, I explore how the design and in-lab evaluation of augmented visual and verbal feedback can improve the technical performance for novices and experts via remote learning. Second, I investigate the learning and teaching challenges that currently exist in traditional in-person training environments for dancers and teachers. Furthermore, I study the current technology use, reasons for non-use, and derive design requirements for future use. Lastly, I focus on how we can design aiDance, an AI-based feedback tool that attempts to represent an affordable and non-invasive approach that augments teachers’ abilities to facilitate assessment in the 21st century and pirouette towards the enhancement of learning. With this empirical work, I present insights that inform the HCI community at the intersection of dance and design in addressing the first steps towards the standardization of motor learning feedback.
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    Current Use, Non-Use, and Future Use of Ballet Learning Technologies
    (ACM, 2021-06) Trajkova, Milka; Cafaro, Francesco; Human-Centered Computing, School of Informatics and Computing
    Learning ballet is a complex motor task that can be effectively enhanced by technology. Learning technologies, however, are not typically used for the assessment of ballet technique due to a lack of adequate and non-invasive tools that can be pragmatically adopted. We conducted an interview-based qualitative study with seven expert ballet teachers and six pre-professional dancers to examine their current and future technology use in a ballet technique class. Through inductive and deductive analysis, we identified reasons for technology non-use and derived seven requirements that can inform the design and implementation of ballet assessment technologies including designing for: adaptation to multi-skill/multi-method environments, teacher/dancer skill augmentation, agency, non-invasive design, feedback for gross/fine movements, trust, and proprioception by supporting transformative assessment. We discuss barriers for technology acceptance and unintended consequences that should be considered when designing future technologies for ballet.