Embodiment is a concept that has gained widespread usage within the Human-Computer Interaction (HCI) community in recent years. In a general sense, embodiment is the notion that cognition arises not just from the mind, but also through our bodies‘ physical and social interactions with the world around us. HCI has employed this body-centric approach to the design of technology in a variety of domains, including interaction design, robotics, music systems, and education. However, due to the broad number of academic domains that define and operationalize embodiment within HCI (e.g., cognitive science, social science, learning science, neuroscience, AI, robotics, and so forth), it has become a remarkably fuzzy term with little understood about what designs result in desired outcomes. Essentially, HCI researchers and practitioners often employ a black box of design decisions when creating their embodied systems.
Notably, the inconsistent framing and application of embodiment within HCI is a substantial drawback when trying to design embodied technology to support particular use cases such as learning, where understanding the 'why' of outcomes is essential. In this dissertation, I contribute work towards opening up the black box of embodied design to develop a more precise understanding of its proper application for the development of learning technology. This was done through the creation of a taxonomical design framework that outlines key methods for incorporating embodiment into the design of educational games and simulations. In order to create the design framework, I collected over 60 exemplars of embodied learning games and simulations, followed by the application of a bottom up, open coding method to distill seven core design dimensions. I then demonstrated the design framework‘s importance for a variety of HCI use cases including 1) categorizing existing embodied educational technologies, 2) identifying problematic design spaces, and 3) identifying design gaps for the generation of novel embodied learning systems.
I also further employed the design framework to develop my own embodied learning system, Bots & (Main)Frames, which teaches basic programming and computational thinking skills through the use of tangibles. In order to better understand when and how embodied tangible technology can aid learning, I built two versions of Bots & (Main)Frames that only differed in input method (non-embodied mouse vs. embodied tangible programming blocks), while keeping all other game mechanics, aesthetics, and so forth identical. I then conducted two controlled experimental studies to compare differences between the two versions of Bots & (Main)Frames. My results show that an embodied tangible design had far greater positive impact for a number of key learning factors including programming self-beliefs, situational interest, enjoyment, and overall learning/performance outcomes. The quantitative and qualitative findings from these studies make key advances toward understanding when and how embodied tangible technology can aid in learning computational thinking skills.