Browsing by Author "Hickey, Daniel"

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    A Model of Project Continuation in Game Jams and Hackathons
    (2024-08) Faas, Travis Byron; Miller, Andrew; Dombrowski, Lynn; Brady, Erin; Hickey, Daniel
    Game jams and hackathons are events where individuals design and build new technology prototypes in a short timeframe. Prototypes made at hackathons are often abandoned after the event and are never finished or used by their intended audiences. Though continued work on prototypes is not the only goal of hackathons, many expect that some hackathon projects will continue to be developed to fulfill the civic, educational, or entrepreneurial goals of hackathon organizers and attendees. To assist hackathon organizers in running hackathons that produce continued projects, I present in this document a model of project continuation in online hackathons and a tool that directs conversations that develops the necessary components of continuation. This model was developed through three studies: a design study that generated the design for a bot to be used in an online game jam that directs users in breaking the boundedness of their game concept, a deployment study where the bot was deployed and used in an online game jam, and a longitudinal study that followed the continuation practices of individuals who used the bot during the jam. In the presented continuation model, I highlight how recent personal interests generate an extended development context that reduces the boundedness of game jams and show how regular sharing and discussion of progress creates social investment in the success of projects that contributes to continuation intention and support. This continuation model requires a resting period post-hackathon, which sometimes generates conceptual continuation where a project is abandoned but the major project concepts are explored in later projects. Taking this idea of concept continuation further, I offer suggestions on how to gain continuation in hackathons by reducing their time-boundedness and making the events more permeable to allow for prior-existing projects to be accepted and further developed at these events.
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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.