The web is inaccessible. According to a 2024 review of 1 million websites, 95.9% of home pages online had one or more accessibility failures—and many had over fifty automatically-detected issues [2]. Despite years of work analyzing accessibility failures and paths towards improvement, comparatively little work at ASSETS focuses on the labor of doing web accessibility. Drawing from a duoethnography of working on accessibility, this position paper explores the practical work of accessibility in academic and professional environments and makes concrete recommendations for web accessibility practitioners. We argue that accessibility is not primarily an issue of awareness or an overreliance on compliance: we show that accessibility is much better understood as a complex resource management problem that requires multifaceted and ongoing effort to maintain.

Researchers in the CSCW community have long problematized the separation of social and ethical considerations from design work. Despite increasing attention to tech ethics and ethics education, however, computer scientists' sense of ethical responsibility remains of concern. This paper offers insights on how this boundary between tech and ethics is maintained and reinforced for students as they develop their identities as computer scientists. Drawing on interviews with eight undergraduate computer science (CS) students at McGill University, we explore the role that ethics play in the legitimate peripheral participation of students inside and outside their formal education. We found that while individual opinions on the importance of ethics varied, students agreed that ethics are not valued or rewarded in their education, extracurriculars, or future work prospects. We describe how placing ethics outside the boundary of computing acts as a form of occupational closure, excluding both important multidisciplinary work and marginalized bodies. We argue that in order to promote ethical practice in the design of CSCW systems, we must make it in the interest of future designers to learn socially grounded ethics. This requires that designers, researchers, and future employers actively reshape the boundaries of computing by asserting social and ethical considerations as values of computing and design.

To enact social justice in the computer science classroom, we need to go beyond adding token ethics modules to Computer Science (CS) curricula and to rethink the power structures in our pedagogical practices. Critical pedagogy (CP) is a long-standing pedagogical tradition that aims to re-envision power structures in the classroom, but has been relatively underutilized in computing education. To go beyond theoretical ideas of what CP should be in CS, we interviewed 13 computing educators who identified as being influenced by critical pedagogy. We asked participants about their teaching practices, and how they apply CP ideals in their classrooms. To illustrate themes from our interviews and to give a rich description of what a CP-influenced classroom looks like, we present three vignettes highlighting a contrast of approaches to critical CS education: raising students' critical consciousness to see structures of oppression, helping students learn technology that supports their activism, and changing what it means to do computer science by integrating social and political forces. By providing tangible, concrete examples we hope to provide educators with inspiration for their own practice.

Machine learning (ML) has become an important topic for students across disciplines to understand because of its useful applications and its societal impacts. At the same time, there is little existing work on ML education, particularly about teaching ML to non-majors. This paper presents an exploration of the pedagogical content knowledge (PCK) for teaching ML to non-majors. Through ten interviews with instructors of ML courses for non-majors, we inquired about student preconceptions as well as what students find easy or difficult about learning ML. We identified PCK in the form of three preconceptions and five barriers faced by students, and six pedagogical tactics adopted by instructors. The preconceptions were found to concern themselves more with ML's reputation rather than its inner workings. Student barriers included underestimating human decision in ML and conflating human thinking with computer processing. Pedagogical tactics for teaching ML included strategically choosing datasets, walking through problems by hand, and customizing to the domain(s) of students. As we consider the lessons from these findings, we hope that this will serve as a first step toward improving the teaching of ML to non-majors.

Given its societal impacts and applications to numerous fields, machine learning (ML) is an important topic to understand for many students outside of computer science and statistics. However, machine-learning education research is nascent, and research on this subject for non-majors thus far has only focused on curricula and courseware. We interviewed 10 instructors of ML courses for non-majors, inquiring as to what their students find both easy and difficult about machine learning. While ML has a reputation for having algorithms that are difficult to understand, in practice our participating instructors reported that it was not the algorithms that were difficult to teach, but the higher-level design decisions. We found that the learning goals that participants described as hard to teach were consistent with higher levels of the Structure of Observed Learning Outcomes (SOLO) taxonomy, such as making design decisions and comparing/contrasting models. We also found that the learning goals that were described as easy to teach, such as following the steps of particular algorithms, were consistent with the lower levels of the SOLO taxonomy. Realizing that higher-SOLO learning goals are more difficult to teach is useful for informing course design, public outreach, and the design of educational tools for teaching ML.

Drawing on the long debate about whether computer science (CS) and computational thinking skills are innate or learnable, this working group is based on the following hypothesis: The apparent innate ability of some CS learners who succeed in CS courses despite no prior exposure to computing is a manifestation of early childhood experiences and learning outside formal education.

Large volumes of Computer Science Educational (CS Ed) material are published every year but it is apparent that equally large volumes of this are not being read or having much impact on practice, or even available to the practitioners who could use it. How can we distribute CS Ed materials and information more effectively and in potentially innovative ways? This BOF will provide a platform for discussion on a selection of techniques that encourage discussion and dissemination of CS Ed techniques in the community. Is traditional publishing still a good approach or is it just part of a wider group of techniques?

Since the 1990s, a great deal of effort has been toward improving female participation in computing. Yet the numbers in North America haven't budged: women continue to make up 18\% of CS majors. Current efforts and research focus on societal, cultural, and psychological reasons for this underrepresentation. I argue that the political dimension also needs to be considered both in terms of why women are underrepresented and how to change it. I have found that admissions policies have a profound effect on how many women study undergraduate CS. I've also observed that diversity is not being considered in mainstream CS department policymaking, and ``women's issues'' are expected to be solved by women's groups. And in the women's spaces, I've observed a focus on individual career advancement (``Lean In'') rather than a push for political, collective action.

It is commonly thought that CS grades are bimodal. We statistically analyzed 778 distributions of final course grades from a large research university, and found only 5.8% of the distributions passed tests of multimodality. We then devised a psychology experiment to understand why CS educators believe their grades to be bimodal. We showed 53 CS professors a series of histograms displaying ambiguous distributions and asked them to categorize the distributions. A random half of participants were primed to think about the fact that CS grades are commonly thought to be bimodal; these participants were more likely to label ambiguous distributions as "bimodal". Participants were also more likely to label distributions as bimodal if they believed that some students are innately predisposed to do better at CS. These results suggest that bimodal grades are instructional folklore in CS, caused by confirmation bias and instructor beliefs about their students.

How to increase diversity in computer science is an important open question in CS education. A number of best practices have been suggested based on case studies; however, for scaling these efforts up in a sustainable fashion, it remains unclear which types of initiatives are most effective in which contexts. We examine gender diversity initiatives in CS education from a policy analysis perspective, adapting McDonnell and Elmore's 1987 notion of policy instruments, wherein the initiative is the unit of analysis. We present a conceptual framework for categorizing the different policy instruments by a cross of 'leverage' and 'targetedness', and discuss how different types of initiatives will scale. We argue that universally-targeted, high-leverage initiatives are most important for scaling up diversity initiatives in CS education, with medium-leverage being a stepping stone to high leverage change.

Numpy (Numerical Python) and Scipy (Scientific Python) are Python libraries for doing numerical/scientific work that are popular with research scientists, as they allow for matrix-based computation in Python. I report on my initial experiences teaching a CS1 in Python to natural/social science students using a "numpy-first" approach. Students were taught about numpy arrays and matrix manipulations before learning lists and loops. I found this approach helped this audience appreciate the relevance of CS to their own fields, and possibly better learn topics such as logic and file I/O.

In the past three decades, a great deal of effort has been put into trying to improve female participation in computer science. Yet, the numbers in North America haven't budged: women continue to make up 18% of CS majors [3]. Could it be that these well-intentioned efforts to increase diversity are not having the effects we want - or worse, are having subtle, counterproductive effects? Sociologists and social psychologists have documented several ways in which diversity initiatives can be undermined - or even have counterproductive effects. Are these phenomena present in women-in-CS initiatives? In my research, I am interested in evaluating how we as a community are working to increase diversity in CS.

We present a history of female participation in North American CS, with a focus on the social forces involved. For educators to understand the status quo, and how to change it, we must understand the historical forces that have led us here. We begin with the female ''computers'' of the 19th century, then cover the rise of computing machines, establishment of CS, and a history of CS education with regard to gender. In our discussion of academic CS, we contemplate academic generations of female computer scientists and describe their differential experiences.

There is a widespread desire to improve the numbers of women in CS, and as a result, a large number of well-intentioned initiatives targeting the issue. However, explicit interventions -- interventions which call attention to a minority being a minority -- can backfire, and actually harm attempts to improve diversity. In my work; I plan to study what unintentional, negative effects women-in-CS initiatives can have; and to give suggestions for mitigating them. I am in an early stage of my PhD, having had my topic approved by my committee and working toward implementing empirical studies.

Comparing and contrasting different solution approaches is known in math education and cognitive science to increase student learning -- what about CS? In this experiment, we replicated work from Rittle-Johnson and Star, using a pretest--intervention--posttest--follow-up design (n=241). Our intervention was an in-class workbook in CS2. A randomized half of students received questions in a compare-and-contrast style, seeing different code for different algorithms in parallel. The other half saw the same code questions sequentially, and evaluated them one at a time. Students in the former group performed better with regard to procedural knowledge (code reading & writing), and flexibility (generating, recognizing & evaluating multiple ways to solve a problem). The two groups performed equally on conceptual knowledge. Our results agree with those of Rittle-Johnson and Star, indicating that the existing work in this area generalizes to CS education.

We demonstrate "Dr. Horrible's Fork Bomb", a CS2 lab assignment we have developed for teaching security issues in C/C++ as well as practice in using gdb. Students must determine/discover two command-line arguments that will prevent the provided executable from reaching a fork bomb. The two arguments are obscured through a combination of a stack buffer overflow, an integer overflow, and an unseeded call to rand. Students report the activity as motivating for learning about security, memory organization, and integer representation.

No abstract available.

We present a highly reusable "inverted" project in which students learn asymptotic and practical behaviour of dictionary data structures--linked-lists, arrays, balanced trees, and hash tables--in an atmosphere of mild competition. Much like David Levine's Nifty Assignment "Sort Detective", rather than implementing the dictionaries, students' programs generate input to our (unlabeled) implementations, and students use timing data to label the implementations. Much like Bryant and O'Halloran's computer architecture labs, students also compete to "convince" a web-based, automated system that their input generators distinguish the dictionaries based on trend-line behaviour. Initial assessment results suggest the project makes substantially improves students' understanding of practical performance of various dictionary data structures, particularly hash tables. UBC has used the project in three terms, and we plan to use it at UBC and U Toronto in coming terms.

From an ongoing research project on teaching hash tables using worked examples, we present four preliminary observations. First, that rather than there being a small set of common misconceptions, student misconceptions are diverse and often unique to the student. Second, that students' naive language about hash tables when given a pretest is influenced by words from the Internet (e.g. "hashtag"). Third, we observed that students' language on concept questions evolves with repeated testing, becoming more conceptually accurate but technically less precise. And finally, that students' code code correctness is not correlated to code style, but is correlated to how students performed on the concept questions.

Teaching assistants (TAs) play an integral role in teaching computer science undergraduates in North America. We report lessons in lab TA management, based on a case study which identified environmental factors affecting TAs' job satisfaction. These factors were identified through a series of semi-structured interviews about 23 lab sections taught at the University of British Columbia. We corroborated this with observational sampling of eight different TAs. Identified physical factors affecting job satisfaction include the layout and lighting of the lab rooms. Temporal factors include the intensity and length of the lab sessions. Having a positive social environment (in particular, support from team teaching and staff meetings) was also found to improve job satisfaction.

This presentation describes our approach to teaching pseudorandom number generation (PRNG) in CS labs. We use PRNG at two universities as an example of an application of sequential circuitry in our digital logic courses. Our goal is for our students to have meaningful assignments, and to relate digital logic not only to the larger CS curriculum, but to the students' lives. This also us a motivation to discuss "what is randomness?", security issues relating to seeding and encryption, and why and how we use randomness in computing.

We report on best practices we have established to teach first-year computer science students in closed laboratories, founded on over three years of action research in a large introductory discrete mathematics and digital logic course. Our practices have resulted in statistically significant improvements in student and teaching assistant perception of the labs. Specifically, we discuss our practices of streamlining labs to reduce load on students that is extraneous to the lab's learning goals; establishing a positive first impression for students and TAs in the early weeks of the term; and effectively managing the teaching staff, including weekly preparation meetings for TAs using and a gradual, iterative curriculum development cycle that engages all stakeholders in the course.