Touchpad Mapper

Ather Sharif, Venkatesh Potluri, Jazz Rui Xia Ang, Jacob O. Wobbrock, Jennifer Mankoff: Touchpad Mapper: Examining Information Consumption From 2D Digital Content Using Touchpads by Screen-Reader Users: ASSETS ’24 (best poster!) and W4A ’24 (open access)

Touchpads are common, but they are not very useful for people who use screen readers. We created and tested a tool called Touchpad Mapper to let Blind and visually impaired people make better use of touchpads. Touchpad Mapper lets screen-reader users use touchpads to interact with digital content like images and videos.

Touchpad mapping could be used in many apps. We built two examples:

  1. Users can use the touchpad to identify where things are in an image.
  2. Users can control a video’s progress with the touchpad, including rewinding and fast-forwarding.

We tested Touchpad Mapper with three people who use screen readers. They said they got information more quickly with our tool than with a regular keyboard.

Bespoke Slides for Fluctuating Access Needs

Kelly Avery Mack, Kate S. Glazko, Jamil Islam, Megan Hofmann, Jennifer Mankoff: “It’s like Goldilocks: ” Bespoke Slides for Fluctuating Audience Access Needs. ASSETS 2024: 71:1-71:15

Slide deck accessibility is usually thought to mainly impact people who are blind or visually impaired. However, many other people might need modifications to access a slide deck.

We talked with 17 people who have disabilities and use slide decks and learned their needs did not always overlap. For some people, their own needs even changed at times. For example, some needed lower contrast colors at night.

Next, we explored how a tool could help change a presentation to fit their needs. We tested this tool with 14 of the people we talked to earlier. Then, we interviewed four people who make and present slide decks to get their thoughts on this tool.

Finally, we tried to make a working version of this tool. It has some of the features that the people we talked to wanted, but we learned that when apps are not designed for access, and not open source, they make full access hard to add.

Stacy Hsueh

Stacy is an Asian woman with black hair, wearing a black turtle neck, smiling with head slightly turned to the side.

Stacy Hsueh is a postdoctoral researcher at UW’s Center for Research and Education on Accessible Technology and Experiences (CREATE), working with Jen Mankoff and Anat Caspi.

Her work uses critical theories to interrogate computing norms and employs design methods to explore disability-led design. Her current research focuses on understanding experiences of precarity in underserved communities and examining the role of technology plays in challenging or reinforcing structural inequities. 

Miles Stanley

A young white, cisgender man standing outdoors, smiling. He has medium-length brown hair and wears a blue North Face jacket with a hood. The background is blurred and features a cityscape taken from an elevated viewpoint.

Miles is a third-year computer science undergraduate at the University of Washington. He is interested in new applications of generative AI, HCI, and programming languages. In his free time, he enjoys running and brunch with friends. Miles is currently working on the Accessible Flowcharts Project.

Runxin Shi

A young man with short black hair smiles at the camera while seated at a wooden table in a café. He is wearing a bright green button-up shirt and holding a fork in one hand and a spoon in the other. On the table in front of him is a tray holding a plate of spaghetti and a small bowl of fresh salad. Behind him, there are wooden shelves, and a window.

Runxin Shi is a third-year undergraduate studying Computer Science at the Paul G. Allen School of Computer Science and Engineering at the University of Washington. He is passionate about using modern technologies, such as wearable devices, to enhance accessibility and unlock new possibilities for people with disabilities. With experience in backend development, qualitative research, user research, and interaction design, Runxin takes a multidisciplinary approach to problem-solving. Beyond academics, he has a deep appreciation for art and enjoys visiting museums and creating interactive art installations in his free time.

Varun Narayanswamy

[An Indian male] wearing a gray hoodie, sitting in a striped multicolor chair. In his arms are two kittens, both orange and white, the one on the left pressing its face into his arm while the other looks to the side. The boy is looking down at the cat on the right.

Varun Narayanswamy is a student in the Master’s from Human Computer Interaction and Design (MHCI+D). His research interests include HCI, data visualization, frontend development, mobile development, and education technology.

Yusuf Mohammed

Yusuf is a second-year undergraduate at the University of Washington majoring in Computer Science. He has prior experience with full stack web development and databases. He is interested in how generative AI can be used to improve accessibility. He is also interested in Machine Learning and Systems Programming. In his free time, he enjoys playing spikeball and watching football. Currently, he is working on the Text Simplification Project in the Make4All lab.

KnitScript: A Domain-Specific Scripting Language for Advanced Machine Knitting

Knitting machines can fabricate complex fabric structures using robust industrial fabrication machines. However, machine knitting’s full capabilities are only available through low-level programming languages that operate on individual machine operations. We present KnitScript, a domain-specific machine knitting scripting language that supports computationally driven knitting designs. KnitScript provides a comprehensive virtual model of knitting machines, giving access to machine-level capabilities as they are needed while automating a variety of tedious and error-prone details. Programmers can extend KnitScript with Python programs to create more complex programs and user interfaces. We evaluate the expressivity of KnitScript through a user study where nine machine knitters used KnitScript code to modify knitting patterns. We demonstrate the capabilities of KnitScript through three demonstrations where we create: a program for generating knitted figures of randomized trees, a parameterized hat template that can be modified with accessibility features, and a pattern for a parametric mixed-material lampshade. KnitScript advances the state of machine-knitting research by providing a platform to develop and share complex knitting algorithms, design tools, and patterns.

Megan Hofmann, Lea Albaugh, Tongyan Wang, Jennifer Mankoff, Scott E. Hudson: KnitScript: A Domain-Specific Scripting Language for Advanced Machine Knitting. UIST 2023: 21:1-21:21

https://youtube.com/watch?v=emV3xNVlg-0%3Fsi%3DLOM1EWRCmhcnzQJY

Domain Specific Metaheuristic Optimization

For non-technical domain experts and designers it can be a substantial challenge to create designs that meet domain specific goals. This presents an opportunity to create specialized tools that produce optimized designs in the domain. However, implementing domain specific optimization methods requires a rare combination of programming and domain expertise. Creating flexible design tools with re-configurable optimizers that can tackle a variety of problems in a domain requires even more domain and programming expertise. We present OPTIMISM, a toolkit which enables programmers and domain experts to collaboratively implement an optimization component of design tools. OPTIMISM supports the implementation of metaheuristic optimization methods by factoring them into easy to implement and reuse components: objectives that measure desirable qualities in the domain, modifiers which make useful changes to designs, design and modifier selectors which determine how the optimizer steps through the search space, and stopping criteria that determine when to return results. Implementing optimizers with OPTIMISM shifts the burden of domain expertise from programmers to domain experts.

Megan Hofmann, Nayha Auradkar, Jessica Birchfield, Jerry Cao, Autumn G. Hughes, Gene S.-H. Kim, Shriya Kurpad, Kathryn J. Lum, Kelly Mack, Anisha Nilakantan, Margaret Ellen Seehorn, Emily Warnock, Jennifer Mankoff, Scott E. Hudson: OPTIMISM: Enabling Collaborative Implementation of Domain Specific Metaheuristic Optimization. CHI 2023: 709:1-709:19

https://youtube.com/watch?v=wjQrFeLbOiw%3Fsi%3DkMTxEkEBjoUrQDJ3

A Multi-StakeholderAnalysis of Accessibility in Higher Education

People with disabilities face extra hardship in institutions of higher education because of accessibility barriers built into the educational system. While prior work investigates the needs of individual stakeholders, this work ofers insights into the communication and collaboration between key stakeholders in creating access in institutions of higher education. The authors present refectionsfrom their experiences working with disability service ofces to meet their access needs and the results from interviewing 6 professors and 6 other disabled students about their experience in achieving access. Our results indicate that there are rich opportunities for technological solutions to support these stakeholders in communicating about and creating access

Kelly Avery Mack, Natasha A Sidik, Aashaka Desai, Emma J. McDonnell, Kunal Mehta, Christina Zhang, Jennifer Mankoff: Maintaining the Accessibility Ecosystem: a Multi-Stakeholder Analysis of Accessibility in Higher Education. ASSETS 2023: 100:1-100:6