UMSI researchers recognized with Best Paper and honorable mention awards at 2023 CHI conference
University of Michigan School of Information researchers have earned a Best Paper and four Honorable Mention designations at the 2023 ACM CHI Conference on Human Factors in Computing Systems.
Best Paper awards go to the top one percent of accepted papers at ACM CHI, the premiere international conference on Human-Computer Interaction. Honorable mentions are awarded to the top five percent of accepted papers.
This year’s conference will take place in Hamburg, Germany from April 23-28. To see a full list of accepted papers and workshop presentations by UMSI researchers, check out our CHI research roundup.
Best Paper Award
“Infrastructuring Care: How Trans and Non-Binary People Meet Health and Well-Being Needs through Technology”
Lauren Wilcox, Renee Shelby, Rajesh Veeraraghavan, Oliver Haimson, Gabi Erickson, Michael Turken, Beka Gulotta
We present a cross-cultural diary study with 64 transgender (trans) and non-binary adults in Mexico, the U.S., and India, to understand experiences keeping track of and managing aspects of personal health and well-being. Based on a reflexive thematic analysis of diary data, we highlight sociotechnical interactions that shape how trans and non-binary people track and manage aspects of their health and well-being. Specifically, we surface the ways in which trans and non-binary people infrastructure forms of care, by assembling together elements of informal social ecologies, formalized knowledge sources, and self-reflective media. We examine the forms of precarity that interact with care infrastructure and shape management of health and well-being, including management of gender identity transitions. We discuss the ways in which our findings extend knowledge at the intersection of technology and marginalized health needs, and conclude by arguing for the importance of a research agenda to move toward TGNB-inclusive design.
Honorable Mention Awards
“Trauma-Informed Social Media: Towards Solutions for Reducing and Healing Online Harm”
Social media platforms exacerbate trauma, and many users experience various forms of trauma unique to them (e.g., doxxing and swatting). Trauma is the psychological and physical response to experiencing a deeply disturbing event. Platforms' failures to address trauma threaten users' well-being globally, especially amongst minoritized groups. Platform policies also expose moderators and designers to trauma through content they must engage with as part of their jobs (e.g., child sexual abuse). We consider how a trauma-informed approach might help address or decrease the likelihood of (re)experiencing trauma online. A trauma-informed approach to social media recognizes that everyone likely has a trauma history and that trauma is experienced at the individual, secondary, collective, and cultural levels. This paper proceeds by detailing trauma and its impacts. We then describe how the six trauma-informed principles can be applied to social media design, content moderation, and companies. We conclude by offering recommendations that balance platform responsibility and accountability with well-being and healing for all.
Colaroid: A Literate Programming Approach for Authoring Explorable Multi-Stage Tutorials
Multi-stage programming tutorials are key learning resources for programmers, using progressive incremental steps to teach them how to build larger software systems. A good multi-stage tutorial describes the code clearly, explains the rationale and code changes for each step, and allows readers to experiment as they work through the tutorial. In practice, it is time-consuming for authors to create tutorials with these attributes. In this paper, we introduce Colaroid, an interactive authoring tool for creating high quality multi-stage tutorials. Colaroid tutorials are augmented computational notebooks, where snippets and outputs represent a snapshot of a project, with source code differences highlighted, complete source code context for each snippet, and the ability to load and tinker with any stage of the project in a linked IDE. In two laboratory studies, we found Colaroid makes it easy to create multi-stage tutorials, while offering advantages to readers compared to video and web-based tutorials.
ReadingQuizMaker: A Human-NLP Collaborative System that Supports Instructors to Design High-Quality Reading Quiz Questions
Xinyi Lu, Simin Fan, Jessica Houghton, Lu Wang, Xu Wang
Despite that reading assignments are prevalent, methods to encourage students to actively read are limited. We propose a system ReadingQuizMaker that supports instructors to conveniently design high-quality questions to help students comprehend readings. ReadingQuizMaker adapts to instructors’ natural workflows of creating questions, while providing NLP-based process-oriented support. ReadingQuizMaker enables instructors to decide when and which NLP models to use, select the input to the models, and edit the outcomes. In an evaluation study, instructors found the resulting questions to be comparable to their previously designed quizzes. Instructors praised ReadingQuizMaker for its ease of use, and considered the NLP suggestions to be satisfying and helpful. We compared ReadingQuizMaker with a control condition where instructors were given automatically generated questions to edit. Instructors showed a strong preference for the human-AI teaming approach provided by ReadingQuizMaker. Our findings suggest the importance of giving users control and showing an immediate preview of AI outcomes when providing AI support.
“VizProg: Identifying Misunderstandings By Visualizing Students’ Coding Progress”
Programming instructors often conduct in-class exercises to help them identify students that are falling behind and surface students’ misconceptions. However, as we found in interviews with programming instructors, monitoring students’ progress during exercises is difficult, particularly for large classes. We present VizProg, a system that allows instructors to monitor and inspect students’ coding progress in real-time during in-class exercises. VizProg represents students’ statuses as a 2D Euclidean spatial map that encodes the students’ problem-solving approaches and progress in real-time. VizProg allows instructors to navigate the temporal and structural evolution of students’ code, understand relationships between code, and determine when to provide feedback. A comparison experiment showed that VizProg helped to identify more students’ problems than a baseline system. VizProg also provides richer and more comprehensive information for identifying important student behavior. By managing students’ activities at scale, this work presents a new paradigm for improving the quality of live learning.
More information is available on CHI 2023.
— Noor Hindi, UMSI public relations specialist