Play-to-Earn (P2E) crypto-games recently emerged as a gig opportunity despite the absence of regulations. As these platforms continue to grow, there is a need to understand the interactions involved to protect vulnerable stakeholders. This paper describes how an unintended social dynamic became a strategy guiding players to navigate an unregulated space. First, we inquired through surveys (N = 69) and interviews (N = 9) to understand stakeholder motivations and practices in this space. Second, we analyzed data and then conceptualized eight themes (e.g., Management, Social, Gaming). Then, we uncovered four types of relationships (e.g., Manager-Scholar, Manager-Investor-Scholar, Coach-Mentee, Scholar-Turned-Manager) that shaped the behaviours of the different users on the platform. Lastly, we present design implications and recommendations to guide the design of P2E crypto games and the gig-focused communities that thrive around them. Our results contribute to ongoing discussions in designing digital gig economies and crypto-based games.

Humans have been developing and playing musical instruments for millennia. With technological advancements, instruments were becoming ever more sophisticated. In recent decades computer-supported innovations have also been introduced in hardware design, usability, and aesthetics. One of the most commonly digitally augmented instruments is the piano. Besides electronic keyboards, several prototypes augmenting pianos with different projections providing various levels of interactivity on and around the keyboard have been implemented in order to support piano players. However, it is still unclear whether these solutions support the learning process. In this paper, we present a systematic review of augmented piano prototypes focusing on instrument learning based on the four themes derived from interviews with piano experts to understand better the problems of teaching the piano. These themes are (i) synchronised movement and body posture, (ii) sight-reading, (iii) ensuring motivation, and (iv) encouraging improvisation. We found that prototypes are saturated on the synchronisation themes, and there are opportunities for sight-reading, motivation, and improvisation themes. We conclude by presenting recommendations on augmenting piano systems towards enriching the piano learning experience as well as on possible directions to expand knowledge in the area.

Learning the piano is hard and many approaches including piano-roll visualisations have been explored in order to support novices and seasoned learners in this process. However, existing piano roll prototypes have not considered the spatiotemporal component (user’s ability to press on a moving target) when generating these visualisations and user modelling. In this PhD, we are going to look into two different approaches: (i) exploring whether existing techniques in single-target spatiotemporal modelling can be adapted to a multi-target scenario such as when learners use several fingers to press multiple moving targets when playing the piano, and (ii) exploring heuristics defined by experts marking various difficult parts of songs, and deciding on specific interventions needed for these marked parts. Using models and input from the experts we will design and build an adaptive piano roll training system. We will evaluate and compare these models in various user studies involving users trying to play piano pieces and develop their improvisation skills. We intend to uncover whether these adaptive visualisations will be helpful in the overall training of piano learners. Additionally, these models and adaptive visualisations will allow us to discover affordances that can potentially improve piano learning in general.