Museums have embraced embodied interaction: its novelty generates buzz and excitement among their patrons, and it has enormous educational potential. Human-Data Interaction (HDI) is a class of embodied interactions that enables people to explore large sets of data using interactive visualizations that users control with gestures and body movements. In museums, however, HDI installations have no utility if visitors do not engage with them. In this paper, we present a quasi-experimental study that investigates how different ways of representing the user ("mode type") next-to a data visualization alters the way in which people engage with a HDI system. We consider four mode types: avatar, skeleton, camera overlay, and control. Our findings indicate that the mode type impacts the number of visitors that interact with the installation, the gestures that people do, and the amount of time that visitors spend observing the data on display and interacting with the system.

Embodied interaction is particularly useful in museums because it allows to leverage findings from embodied cognition to support the learning of STEM concepts and thinking skills. In this paper, we focus on Human-Data Interaction (HDI), a class of embodied interactions that investigates the design of interactive data visualizations that users control with gestures and body movements. We describe an HDI system that we iteratively designed, implemented, and observed at a science museum, and that allows visitors to explore large sets of data on two 3D globe maps. We present and discuss design strategies and optimization that we implemented to mitigate two sets of design challenges: (1) Dealing with display, interaction, and affordance blindness; and, (2) Supporting multiple functionalities and collaboration.