Rachael Zehrung

Rachael Zehrung, Lily Huang, Bongshin Lee et al.

Wearable devices hold great potential for promoting children's health and well-being. However, research on kids' wearables is sparse and often focuses on their use in the context of parental surveillance. To gain insight into the current landscape of kids' wearables, we surveyed 47 wearable devices marketed for children. We collected rich data on the functionality of these devices and assessed how different features satisfy parents' information needs, and identified opportunities for wearables to support children's needs and interests. We found that many kids' wearables are technologically sophisticated devices that focus on parents' ability to communicate with their children and keep them safe, as well as encourage physical activity and nurture good habits. We discuss how our findings could inform the design of wearables that serve as more than monitoring devices, and instead support children and parents as equal stakeholders, providing implications for kids' agency, long-term development, and overall well-being. Finally, we identify future research efforts related to designing for kids' self-tracking and collaborative tracking with parents.

Rachael Zehrung, Astha Singhal, Michael Correll et al.

More visualization systems are simplifying the data analysis process by automatically suggesting relevant visualizations. However, little work has been done to understand if users trust these automated recommendations. In this paper, we present the results of a crowd-sourced study exploring preferences and perceived quality of recommendations that have been positioned as either human-curated or algorithmically generated. We observe that while participants initially prefer human recommenders, their actions suggest an indifference for recommendation source when evaluating visualization recommendations. The relevance of presented information (e.g., the presence of certain data fields) was the most critical factor, followed by a belief in the recommender's ability to create accurate visualizations. Our findings suggest a general indifference towards the provenance of recommendations, and point to idiosyncratic definitions of visualization quality and trustworthiness that may not be captured by simple measures. We suggest that recommendation systems should be tailored to the information-foraging strategies of specific users.