Sustaining the Usefulness and Appeal of an Older Adult-led Makerspace through Developing and Adapting Resources.

Researchers are studying makerspaces as one way to support older adults in learning about and using new technologies and tools. In this paper, through a long-term (34 months), ethnographic approach, we study the ways that older adults arranged sociotechnical resources to sustain the community use of a makerspace. Our analysis identifies three interconnected resources that were developed: an adaptive staffing approach that could withstand constant personnel shifts and shortages; structured activities to draw interest and overcome challenges associated with learning to use the machines; and reference materials to support individuals in independent usage of the space. We describe the issues that arose as time went on with each of these resource types, and how individuals affiliated with the makerspace adapted the resources to address these issues. In the discussion, we extend best practices by reflecting on strategies that worked well in the makerspace, such as drawing interest through introductory classes, as well as different purposes for reference materials to support technology use.

Pushing boundaries of co-design by going online: Lessons learned and reflections from three perspectives.

The global COVID-19 pandemic made significant changes to our day-to-day lives, which impacted how we conduct research and design - including co-design. In this article, we present case studies from three different co-design groups that pushed the boundaries of traditional co-design, and conducted multiple co-design sessions (more than 150 total) over the last year and a half. The case studies for each team include: the transition to online co-design; the pros and cons of logistics and design tools utilized during the co-design sessions; and the advances, challenges, and surprises. We compare and contrast themes that emerged from the case studies and present additional dimensions that need to be addressed as researchers utilize online co-design and advance methods to conduct online co-design.

Exploring Machine Teaching with Children.

Iteratively building and testing machine learning models can help children develop creativity, flexibility, and comfort with machine learning and artificial intelligence. We explore how children use machine teaching interfaces with a team of 14 children (aged 7-13 years) and adult co-designers. Children trained image classifiers and tested each other's models for robustness. Our study illuminates how children reason about ML concepts, offering these insights for designing machine teaching experiences for children: (i) ML metrics (e.g. confidence scores) should be visible for experimentation; (ii) ML activities should enable children to exchange models for promoting reflection and pattern recognition; and (iii) the interface should allow quick data inspection (e.g. images vs. gestures).