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aiDance: A Non-Invasive Approach in Designing AI-Based Feedback for Ballet Assessment and LearningTrajkova, Milka 12 1900 (has links)
Indiana University-Purdue University Indianapolis (IUPUI) / 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. / 2023-12-28
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