Most software in the world is not accessible to people with disabilities, in part because most people who create software do not know how to make it accessible. Using the new free online book Teaching Accessible Computing, participants in this special session will learn how they can incorporate accessibility topics into their favorite CS courses, like introductory programming, data structures, or artificial intelligence courses. The editors of the book will lead the special session, and several authors of the book's 17 chapters will talk about their chapters and lead topic groups to go into more depth about how they incorporated accessibility into their CS courses. Participants will leave this session with increased enthusiasm and concrete resources to support teaching accessibility in their CS topic courses, establishing connections to the broader community of CS educators and researchers interested in teaching accessibility.

Early exposure to ethical reasoning and diversity, equity, and inclusion (DEI) concepts is critical for preparing socially responsible computer scientists. To support this goal, we designed and implemented a required non-programming first-year undergraduate computer science course that is taken concurrently with an introductory programming course. This discussion-based course integrates DEI and ethics as foundational themes. The new course adopts a breadth-first structure delivered over a 10-week quarter, with 5 of the 18 sessions focused on DEI and ethics. Over three consecutive academic years, we collected pre- and post-course survey data from 451 students enrolled in the new course. Results show an %statistically significant increase in student's recognition of the importance of DEI and ethics and a substantial increase in understanding how these dimensions intersect with technical practice. In this experience report, we describe the course design, instructional methods, and survey instruments; present key findings; and reflect on lessons learned. This work contributes a model for embedding DEI and ethics into the early undergraduate computing curriculum.

With the ubiquity of computing technologies, adolescents are increasingly affected by algorithmic biases. While previous work provides insight into adolescents’ perceptions of algorithmic bias, few provide guidance on how to engage adolescents in discourse on algorithmic bias that prioritizes both their agency and safety. To address this, we developed and conducted group discussions and design activities based on three scenarios of algorithmic bias with 15 adolescents of color (ages 15-17) in a summer academic program in the United States targeted at students from families with low-income backgrounds or who would be the first in their family to pursue post-secondary education. When sensemaking, all participants considered factors beyond the scenarios, using their situated knowledge to contextualize perceptions of unfairness. They also considered sources of bias and impacts of unfairness at different levels of individuals, communities, and society. However, when designing solutions, they tended to design for hypothetical “average users” instead of considering nuances of user populations. We offer insights for algorithmic fairness learning experiences that support situated reasoning in adolescents.

Technology should be accessible and inclusive, so designers should learn to consider the needs of different users. Toward this end, we created the theoretically-grounded CIDER assumption elicitation technique, an educational analytical design evaluation method to teach inclusive design skills. CIDER ( Critique , Imagine , Design , Expand , Repeat ) helps designers recognize and respond to bias using the critical lens of assumptions about users . Through an 11-week mixed-method case study in an interaction design course with 40 undergraduate students and follow-up interviews, we found that activities based on the CIDER technique may have helped students identify increasingly many types of design bias over time and reflect on their unconscious biases about users. The activities also had lasting impacts, encouraging some students to adopt more inclusive approaches in subsequent design work. We discuss the implications of these findings, namely that educational techniques like CIDER can help designers learn to create equitable technology designs.

Computing students should learn to think critically about the software they design, but prior work in computing and human-computer interaction (HCI) education suggests that developing these skills can be a difficult task. To address some of these challenges, we developed the CIDER (Critique, Imagine, Design, Expand, Repeat) assumption elicitation technique. CIDER is an educational analytical design evaluation method to help computing students recognize and respond to exclusionary design biases in software user interfaces (UIs), helping them critically consider how their design decisions could differentially impact various user groups and how they might design more inclusively. The CIDER technique is grounded in prior HCI and computing education work and was specifically designed to target learning challenges computing students face in HCI design courses. We hope to evaluate the efficacy of CIDER in a variety of learning contexts, contributing to HCI and computing education foundations for teaching and learning critical software design skills.

Computing education research (CER) has used demographic data to understand learners’ identities, backgrounds, and contexts for efforts such as culturally-responsive computing. Prior work indicates that failing to elucidate and critically engage with the implicit assumptions of a field can unintentionally reinforce power structures that further marginalize people from non-dominant groups. The goal of this paper is two-fold: to understand what populations CER researchers have studied, and to surface implicit assumptions about how researchers have collected, reported, and used demographic data on these populations. We conducted a content analysis of 510 peer-reviewed papers published in 12 CER venues from 2012 to 2021. We found that (1) 60% of papers studied older learners in formal contexts (i.e. post-secondary education); (2) 68% of papers left unclear how researchers collected demographic data; and (3) while 94% of papers were single-site studies, only 14% addressed the limitations of their contexts. We also identified hegemonic norms through ambiguous aggregate term usage (e.g. underrepresented, diverse) in 23% of papers, and through incomplete reporting of demographics (i.e. leaving out demographics for some participants in their sample) in 35% of papers. We discuss the implications of these findings for the CER field, raising considerations for CER researchers to keep in mind when collecting, reporting, and using demographic data.

As computing courses become larger, students of minoritized groups continue to disproportionately face challenges that hinder their academic and professional success (e.g. implicit bias, microaggressions, lack of resources, assumptions of preparatory privilege). This can impact career aspirations and sense of belonging in computing communities. Instructors have the power to make immediate changes to support more equitable learning, but they are often unaware of students' challenges. To help both instructors and students understand the inequities in their classes, we developed StudentAmp, an interactive system that uses student feedback and self-reported demographic information (e.g. gender, ethnicity, disability, educational background) to show challenges and how they affect students differently. To help instructors make sense of feedback, StudentAmp ranks challenges by student-perceived disruptiveness. We conducted formative evaluations with five large college computing courses (150 - 750 students) being taught remotely during the COVID-19 pandemic. We found that students shared challenges beyond the scope of the course, perceived sharing information about who they were as useful but potentially dangerous, and that teaching teams were able to use this information to consider the positionality of students sharing challenges. Our findings relate to a central design tension of supporting equity by sharing contextualized information about students while also ensuring their privacy and well-being.

Design is a distinct discipline with its own practices, tools, professions, and areas of scholarship. However, practitioners from other fields often leverage aspects of design in their own work, leading to subfields like engineering design and architecture design that are neither wholly design nor wholly the intersecting discipline. Similarly, design and computing are known to intersect in educational contexts. Unfortunately, we do not yet have a clear understanding of how to characterize the kinds of design that may accompany computing topics, resulting in challenges to teaching and learning. This gap is particularly prevalent in K-12 computing education, where design is often used to promote student engagement but rarely studied as its own disciplinary phenomenon. Toward the goal of better understanding the nature and role of design in computing education, this article motivates and describes two qualitative, exploratory analyses of how design skills manifest in popular K-12 computing education curricula and activities. We find evidence to suggest two types of design within existing computing education curricula and standards: nondisciplinary problem-space design , which deals with defining software requirements, and disciplinary program-space design , which deals with choosing how best to meet those requirements. We find that these two types of computing design may exist independently, but they often overlap, creating an intriguing intersection of discipline-specific computing design educational activity. Finally, we discuss the practical implications of proceeding with research and educational practice in light of these results, highlighting the need for further exploration into the unique overlap of design and computing education.

Software developers often make interface design decisions and work with designers. Therefore, computing students who seek to become developers need some education about interface design. While prior work has studied difficulties that educators face when teaching design to computing students, there is comparatively little work on the difficulties computing students face when learning HCI design skills. To uncover these difficulties, we conducted two qualitative studies consisting of surveys and interviews with (1) computing students and (2) educators who teach interface design to computing students. Qualitative analysis of their responses revealed 18 types of learning difficulties students might experience in HCI design education, including difficulties around the mechanics of design work, project management skills, the wicked nature of design problems, and distorted perspectives on design.

Although exploring alternatives is fundamental to creating better interface designs, current processes for creating alternatives are generally manual, limiting the alternatives a designer can explore. We present Scout, a system that helps designers rapidly explore alternatives through mixed-initiative interaction with high-level constraints and design feedback. Prior constraint-based layout systems use low-level spatial constraints and generally produce a single design. Tosupport designer exploration of alternatives, Scout introduces high-level constraints based on design concepts (e.g.,~semantic structure, emphasis, order) and formalizes them into low-level spatial constraints that a solver uses to generate potential layouts. In an evaluation with 18 interface designers, we found that Scout: (1) helps designers create more spatially diverse layouts with similar quality to those created with a baseline tool and (2) can help designers avoid a linear design process and quickly ideate layouts they do not believe they would have thought of on their own.

Inclusive design is important in today's software industry, but there is little research about how to teach it. In collaboration with 9 teacher-researchers across 8 U.S. universities and more than 400 computer and information science students, we embarked upon an Action Research investigation to gather insights into the pedagogical content knowledge (PCK) that teachers need to teach a particular inclusive design method called GenderMag. Analysis of the teachers' observations and experiences, the materials they used, direct observations of students' behaviors, and multiple data on the students' own reflections on their learning revealed 11 components of inclusive design PCK. These include strategies for anticipating and addressing resistance to the topic of inclusion, strategies for modeling and scaffolding perspective taking, and strategies for tailoring instruction to students' prior beliefs and biases.

How can we support software professionals who want to build human-adaptive sociotechnical systems? Building such systems requires skills some developers may lack, such as applying human-centric concepts to the software they develop and/or mentally modeling other people. Effective socio-technical methods exist to help, but most are manual and cognitively burdensome. In this paper, we investigate ways semi-automating a socio-technical method might help, using as our lens GenderMag, a method that requires people to mentally model people with genders different from their own. Toward this end, we created the GenderMag Recorder's Assistant, a semi-automated visual tool, and conducted a small field study and a 92-participant controlled study. Results of our investigation revealed ways the tool helped with cognitive load and ways it did not; unforeseen advantages of the tool in increasing participants' engagement with the method; and a few unforeseen advantages of the manual approach as well.

Personas often aim to improve product designers' ability to "see through the eyes of" target users through the empathy personas can inspire - but personas are also known to promote stereotyping. This tension can be particularly problematic when personas (who, of course as "people" have genders) are used to promote gender inclusiveness - because reinforcing stereotypical perceptions can run counter to gender inclusiveness. In this paper we explicitly investigate this tension through a new approach to personas: one that includes multiple photos (of males and females) for a single persona. We compared this approach to an identical persona with only one photo using a controlled laboratory study and an eye-tracking study. Our goal was to answer the following question: is it possible for personas to encourage product designers to engage with personas while at the same avoiding promoting gender stereotyping? Our results are encouraging about the use of personas with multiple pictures as a way to expand participants' consideration of multiple genders without reducing their engagement with the persona.

More people are learning to code than ever, but most learning opportunities do not explicitly teach the problem solving skills necessary to succeed at open-ended programming problems. In this paper, we present a new approach to impart these skills, consisting of: 1) explicit instruction on programming problem solving, which frames coding as a process of translating mental representations of problems and solutions into source code, 2) a method of visualizing and monitoring progression through six problem solving stages, 3) explicit, on-demand prompts for learners to reflect on their strategies when seeking help from instructors, and 4) context-sensitive help embedded in a code editor that reinforces the problem solving instruction. We experimentally evaluated the effects of our intervention across two 2-week web development summer camps with 48 high school students, finding that the intervention increased productivity, independence, programming self-efficacy, metacognitive awareness, and growth mindset. We discuss the implications of these results on learning technologies and classroom instruction.

Recent research has reported numerous studies bringing into question the gender inclusiveness of many kinds of software. Inclusiveness of software (gender or otherwise) matters because supporting diversity matters - it is well-known that the more diverse a group of problem-solvers, the higher the quality of the solution. To help software creators identify features within their software that are not gender-inclusive, we recently created a method known as GenderMag. In this paper, we investigate the experience of teams of software professionals using GenderMag to find problems with software they are building. Our results show a high engagement with GenderMag personas - more than twice that of other personas research - and a very high degree of accuracy (93%) most of the time. Finally, our results pinpointed situations that we term “detours” that were especially prone to errors, with teams 6 times more likely to make errors in detours than they did otherwise.