Here are details about some of the projects I've worked on recently.

Building blocks of computational thinking: Young children's developing capacities for problem decomposition

Griffin Dietz, James Landay, Hyowon Gweon. CogSci 2019.

Computational thinking refers to a range of problem-solving skills applicable to computer science and everyday life. Although recent research in developmental cognitive science suggests mental capacities relevant to CT may emerge quite early in life, research on CT, and computer science education more generally, has made little contact with this literature. As a way to better bridge these fields, we explore the development of problem decomposition, a critical feature of CT, in the spatial domain. We ask whether young children can break a complex spatial problem down into subcomponents that can be reassembled to solve the overarching problem. Across two experiments (Exp. 1: 4- to 7-year-olds; Exp. 2: 3- to 5-year-olds) that involve constructing block structures, we demonstrate that some of the key capacities underlying problem decomposition begin to emerge in preschool years and develop throughout early childhood. Although preschool-aged children struggle to solve an open-ended decomposition problem that requires generation and execution of decomposition plans, even 4-year-olds can successfully evaluate the viability of these plans. These results suggest that experimental methods in developmental cognitive science can inform CS education research that focuses on promoting CT; by identifying when and how CT concepts emerge in early childhood, we can better create age-appropriate educational tools.

Giggle Gauge: A Self-Report Instrument for Evaluating Children's Engagement with Technology

Griffin Dietz, Zachary Pease, Brenna McNally, Elizabeth Foss. IDC 2020.

As robots become ubiquitous in our everyday environment, we start seeing them used in groups, rather than individually, to complete tasks. We present a study aimed at understanding how different movement patterns impact humans’ perceptions of groups of small tabletop robots. To understand this, we focus on the effects of changing the robots’ speed, smoothness, and synchronization on perceived valence, arousal, and dominance. We find that speed had the strongest correlation to these factors. With regard to human emotional response to the robots, we align with and build on prior work dealing with individual robots that correlates speed to valence and smoothness to arousal. In addition, participants noted an increase in positive affect in response to synchronized motion, though synchronization had no significant impact on measured perception. Based on our quantitative and qualitative results, we suggest design implications for swarm robot motion.