Alternative (alt) text is a description of a digital images so that someone who is blind or low vision or otherwise uses a screen reader to understand image content. Little work examines what it is like to write alt text for an image. We created interface designs to support writing and providing feedback about alt text and tested them with people who write alt text and people who use alt text. Our results suggest that authoring interfaces that support authors in choosing what to include in their descriptions result in higher quality alt text. The feedback interfaces highlighted considerable diferences in the perceptions of authors and SRUs regarding “high-quality” alt text. We discuss the implications of these results on applications that support alt text.
Designing Tools for High-Quality Alt Text Authoring. Kelly Mack, Edward Cutrell, Bongshin Lee, and Meredith Ringel Morris. In The 23rd International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS ’21). Association for Computing Machinery, New York, NY, USA, Article 23, 1–14.
The COVID-19 pandemic forced many people to convert their daily work lives to a “virtual” format where everyone connected remotely from their home, which affected the accessibility of work environments. We the authors, full time and intern members of an accessibility-focused team at Microsoft Research, reflect on our virtual work experiences as a team consisting of members with a variety of abilities, positions, and seniority during the summer intern season. We reflect on our summer experiences, noting the successful strategies we used to promote access and the areas in which we could have further improved access.
In order for “human-centered research” to include all humans, we need to make sure that research practices are accessible for both participants and researchers with disabilities. Yet, people rarely discuss how to make common methods accessible. We interviewed 17 accessibility experts who were researchers or community organizers about their practices. Our findings emphasize the importance of considering accessibility at all stages of the research process and across different dimensions of studies like communication, materials, time, and space. We explore how technology or processes could reflect a norm of accessibility and offer a practical structure for planning accessible research.
Passive mobile sensing for the purpose of human state modeling is a fast-growing area. It has been applied to solve a wide range of behavior-related problems, including physical and mental health monitoring, affective computing, activity recognition, routine modeling, etc. However, in spite of the emerging literature that has investigated a wide range of application scenarios, there is little work focusing on the lessons learned by researchers, and on guidance for researchers to this approach. How do researchers conduct these types of research studies? Is there any established common practice when applying mobile sensing across different application areas? What are the pain points and needs that they frequently encounter? Answering these questions is an important step in the maturing of this growing sub-field of ubiquitous computing, and can benefit a wide range of audiences. It can serve to educate researchers who have growing interests in this area but have little to no previous experience. Intermediate researchers may also find the results interesting and helpful for reference to improve their skills. Moreover, it can further shed light on the design guidelines for a future toolkit that could facilitate research processes being used. In this paper, we fill this gap and answer these questions by conducting semi-structured interviews with ten experienced researchers from four countries to understand their practices and pain points when conducting their research. Our results reveal a common pipeline that researchers have adopted, and identify major challenges that do not appear in published work but that researchers often encounter. Based on the results of our interviews, we discuss practical suggestions for novice researchers and high-level design principles for a toolkit that can accelerate passive mobile sensing research.
Mental health of UW students during Spring 2020 varied tremendously: the challenges of online learning during the pandemic were entwined with social isolation, family demands and socioeconomic pressures. In this context, individual differences in coping mechanisms had a big impact. The findings of this paper underline the need for interventions oriented towards problem-focused coping and suggest opportunities for peer role modeling.
Heterogeneity in individuals’ levels of anxiety (reported in ESM). Individual trajectories of anxiety are shown in different line types and colors (dotted versus solid lines represent different participants). Although the mean level of anxiety is 1 on a scale of 0–4, the significant variation in responses invites examination of individuals and subgroups.
This mixed-method study examined the experiences of college students during the COVID-19 pandemic through surveys, experience sampling data collected over two academic quarters (Spring 2019 n1 = 253; Spring 2020 n2 = 147), and semi-structured interviews with 27 undergraduate students.
There were no marked changes in mean levels of depressive symptoms, anxiety, stress, or loneliness between 2019 and 2020, or over the course of the Spring 2020 term. Students in both the 2019 and 2020 cohort who indicated psychosocial vulnerability at the initial assessment showed worse psychosocial functioning throughout the entire Spring term relative to other students. However, rates of distress increased faster in 2020 than in 2019 for these individuals. Across individuals, homogeneity of variance tests and multi-level models revealed significant heterogeneity, suggesting the need to examine not just means but the variations in individuals’ experiences.
Thematic analysis of interviews characterizes these varied experiences, describing the contexts for students’ challenges and strategies. This analysis highlights the interweaving of psychosocial and academic distress: Challenges such as isolation from peers, lack of interactivity with instructors, and difficulty adjusting to family needs had both an emotional and academic toll. Strategies for adjusting to this new context included initiating remote study and hangout sessions with peers, as well as self-learning. In these and other strategies, students used technologies in different ways and for different purposes than they had previously. Supporting qualitative insight about adaptive responses were quantitative findings that students who used more problem-focused forms of coping reported fewer mental health symptoms over the course of the pandemic, even though they perceived their stress as more severe.
Example quotes:
“I like to build things and stuff like that. I like to see it in person and feel it. So the fact that everything was online…. I’m just basically reading all the time. I just couldn’t learn that way”
Insomnia has been pretty hard for me . . . I would spend a lot of time lying in bed not doing anything when I had a lot of homework to do the next day. So then I would become stressed about whether I’ll be able to finish that homework or not.”
“It was challenging … being independent and then being pushed back home. It’s a huge change because now you have more rules again”
“For a few of my classes I feel like actually [I] was self-learning because sometimes it’s hard to sit through hours of lectures and watch it.”
“I would initiate… we have a study group chat and every day I would be like ‘Hey I’m going to be on at this time starting at this time.’ So then I gave them time to all have the room open for Zoom and stuff. Okay and then any time after that they can join and then said I [would] wait like maybe 30 minutes or even an hour…. And then people join and then we work maybe … till midnight, a little bit past midnight”
Megan is a Phd Student at the Human Computer Interaction Institute at Carnegie Mellon Unviversity. She is advised by Prof. Jennifer Mankoff of the University of Washington and and Prof. Scott E. Hudson. She completed her bachelors in Computer Science at Colorado State University in 2017. She is an NSF Fellow, and a Center for Machine Learning and Health Fellow. During her Undergraduate degree Megan’s research was adviced by Dr. Jaime Ruiz and Prof. Amy Hurst.
Her research focuses on creating computer aided design and fabrication tools that expand the digital fabrication process with new materials. She uses participatory observation and participatory design methods to study assistive technology and digital fabrication among many stakeholder (people with disabilities, caregivers, and clinicians).
The onset of COVID-19 led many makers to dive deeply into the potential applications of their work to help with the pandemic. Our group’s efforts on this front, all of which were collaborations with a variety of people from multiple universities, led me to this reflective talk about the additional work that is needed for us to take the next step towards democratizing fabrication.
Natasha Ann Sidik is a Senior at the University of Washington majoring in Psychology with a Minor in Informatics. As an advocate for inclusivity, she centers most of her work on learning, normalizing, and sharing best practices around accessibility. Growing up in Indonesia and the US as a non-traditional student gave her many perspectives and allowed her to network with diverse groups of people. Under the make4all Lab, Natasha is currently working on research to help improve the experiences of students with disabilities at the University of Washington. Find more of her work at https://sidiknatasha.github.io/portfolio/.
Sylvia Janicki, Matt Ziegler, Jennifer Mankoff: Navigating Illness, Finding Place: Enhancing the Experience of Place for People Living with Chronic Illness. COMPASS 2021: 173-187
When chronic illness, such as Lyme disease, is viewed through a disability lens, equitable access to public spaces becomes an important area for consideration. Yet chronic illness is often viewed solely through an individualistic, medical model lens. We contribute to this field of study in four consecutive steps using Lyme disease as a case study: (1) we highlight urban design and planning literature to make the case for its relevance to chronic illness; (2) we explore the place-related impacts of living with chronic illness through an analysis of interviews with fourteen individuals living with Lyme disease; (3) we derive a set of design guidelines from our literature review and interviews that serve to support populations living with chronic illness; and (4) we present an interactive mapping prototype that applies our design guidelines to support individuals living with chronic illness in experiencing and navigating public and outdoor spaces.
Visual semantics provide spatial information like size, shape, and position, which are necessary to understand and efficiently use interfaces and documents. Yet little is known about whether blind and low-vision (BLV) technology users want to interact with visual affordances, and, if so, for which task scenarios. In this work, through semi-structured and task-based interviews, we explore preferences, interest levels, and use of visual semantics among BLV technology users across two device platforms (smartphones and laptops), and information seeking and interactions common in apps and web browsing. Findings show that participants could benefit from access to visual semantics for collaboration, navigation, and design. To learn this information, our participants used trial and error, sighted assistance, and features in existing screen reading technology like touch exploration. Finally, we found that missing information and inconsistent screen reader representations of user interfaces hinder learning. We discuss potential applications and future work to equip BLV users with necessary information to engage with visual semantics.
Megan is a Phd Student at the Human Computer Interaction Institute at Carnegie Mellon Unviversity. She is advised by Prof. Jennifer Mankoff of the University of Washington and and Prof. Scott E. Hudson. She completed her bachelors in Computer Science at Colorado State University in 2017. She is an NSF Fellow, and a Center for Machine Learning and Health Fellow. During her Undergraduate degree Megan’s research was adviced by Dr. Jaime Ruiz and Prof. Amy Hurst.
