Multiple software systems unintentionally exclude users, making the early integration of inclusive design training into software design education essential. In this study, we integrated critical pedagogy into an undergraduate software design course through (1) the CIDER assumption elicitation technique and (2) the exposure of values reflected in technology. Students' pre/post responses to the Critical Computing Index reveal that students' personal effectiveness increased after taking the software design course. Our initial analyses suggest that classwork encouraged critical reflection and agency, especially for improvements focused on accessibility, while interviews indicated that students grew from viewing marginalized perspectives as abstract ideals to actively integrating them into their design practices.

In this session, we aim to foster a community among computer science faculty across 2-year and 4-year primarily undergraduate institutions (PUIs). We will foster an inclusive environment through activities that allow for multiple forms of engagement (e.g. shows of hands, small group introductions, and full group discussion). We will facilitate discussions about ways computer science is changing at PUIs, differences seen across institutions, and ways that our community can be sustained outside of the SIGCSE TS. In prior years, discussions have focused on topics such as availability of course staff and tutors, availability of conference funding, organizational infrastructure such as sponsored program offices, and requirements for promotion.

Integrated Computer Science (CS) learning fosters the learning of CS concepts and skills within the context of a non-CS discipline, offering an avenue to expand equitable access to high-quality K-8 CS education. However, prior research has identified a lack of curriculum as a barrier to integrated CS implementation and in existing curriculum, gaps in CS concept coverage. This indicates a need for both refinement of existing and the creation of new integrated CS learning experiences.

Background: The growing complexity of the impacts of computing technologies on adolescents’ lives requires them to make similarly complex decisions around technology, fueling a rise in education efforts to look at the ethical implications of these advancements with young people. Though prior computing ethics education efforts integrate ethical perspectives, they have rarely drawn from scholarship on how to teach ethics and philosophy.

With the growing awareness of emerging technologies' impacts on teens' lives, families, and communities, rethinking the ways in which we educate and talk about these innovations and their moral and ethical complexities are gaining steam. We present a novel pedagogical intervention that blends techniques from Philosophy for Children (P4C), a pedagogical approach that teaches youth reasoning and argumentative skills, with Youth as Philosophers of Technology, a framework for computing education that foregrounds learning how to decode and unmake tech's relationship with power through artistic, moral and humanistic inquiry, without devaluing core computing practices, such as design, making, coding, and tinkering. We studied this intervention in a summer elective class with 12 students ages 14--18 in the US. Our ongoing data analysis revealed two categories of themes: (1) 'launchpads for ethical sensemaking', namely instances when we observed ethical sensemaking around technology, and (2) 'expressions of ethical sensemaking', namely what students' ethical sensemaking looked like when discussing the ethical implications of technology. We hope to catalyze discussions for both researchers on characterizations of and growth around ethical sensemaking of technology, as well as practitioners on implementations of Youth as Philosophers of Technology and P4C ideas in their classrooms.

Prior research has investigated children’s perceptions of algorithmic bias, but provides little guidance on engaging children in conversations on algorithmic bias that center their agency and well-being. To address this, we developed discussions and design activities based on three scenarios of algorithmic (un)fairness. We conducted these discussions and activities with 16 children (ages 8-12) in the US, and examined our data using qualitative thematic analysis. Grounded in lived experiences and situated knowledge, participants were capable of reasoning around both explicit and implicit effects of algorithmic bias. Participants also expressed distrust of technology, doubting technology’s abilities and preferring human approaches to resolve unfairness. This work contributes (1) a more nuanced understanding of children’s situated reasoning of technology, suggesting their potential for critical engagement and (2) a blueprint for engaging children in scaffolded yet open-ended sensemaking around algorithmic fairness, informing the design of tools, curricula, and other learning experiences for children.

Motivation. All students studying Computer Science (CS) deserve to feel a sense of belonging. In a post-secondary CS class, undergraduate Teaching Assistants (TAs) have the majority of student contact hours, making student-TA interactions, such as those during office hours, important in shaping student belonging. Therefore, we sought to understand student and TA conceptions of belonging, their narratives about their journeys of belonging in CS, and how TAs influence student sense of belonging through office hour interactions.

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.

The worldwide push for computing education at younger ages requires that teachers are prepared to deliver instruction that supports all learners. Other discipline-based education research fields offer a wealth of instructional scaffolds worthy of exploration in computing. One such scaffold drawn from math education is diagramming. While diagrams are frequently employed in university computing, little is known about its applications in K-8 (ages 6-14) computing.

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.

With the global movement to incorporate computer science instruction into elementary education, learners are being introduced to computer science and computational thinking (CS/CT) ideas at increasingly younger ages. At these early ages, young learners are developing cognitive abilities foundational to their education. While other discipline-based education fields, such as math, science, and reading, have long studied the role of cognitive abilities, such as short-term working memory and long-term retrieval, in their respective fields, similar research in computer science education is relatively sparse.

Worldwide, national initiatives have led to many school districts implementing computing curricula at the primary level. At that age, students are learning the foundational skills of reading and math. It is important to understand how computing can influence the development of these skills. While some argue that learning computing sharpens problem-solving skills that are applicable to other subjects, evidence supporting this belief is thin.

In this Birds of a Feather (BoF) session, we aim to create and foster a safe and open environment that allows participants to get feedback on research-in-progress and share experiences and issues with being a graduate student in CSEd research. We invite genuine and honest conversations about the challenges facing rising academics and professionals in this area. We have three goals: (1) to foster a community of support among CSEd grad students, (2) to connect students with new opportunities and ways of thinking about CSEd, and (3) to cultivate mentorship among participants to send them off with potential collaborators, supporters and advocates.

With the growth of Computer Science (CS) and Computational Thinking (CT) instruction in the primary/elementary domain, it is important that such instruction supports diverse learners. Four categories of students -- students in poverty, multi-lingual students, students with disabilities, and students who have below-grade-level proficiency in reading and math, may face academic challenges that can hinder their learning in CS/CT curricula. However, little is known about how to support these students in CS/CT instruction, especially at this young age. TIPP&SEE, a meta-cognitive strategy that scaffolds learning by proceduralizing engagement through example code, may offer some support. A quasi-experimental study revealed that the gaps between students with and without academic challenges narrowed when using the TIPP&SEE strategy, indicating its promise in providing equitable learning opportunities in CS/CT.

With the rise of Computational Thinking (CT) instruction at the elementary level, it is imperative for elementary computing instruction to support a variety of learners. TIPP&SEE is a meta-cognitive learning strategy that scaffolds student learning when learning from example code. Results from a previous study show statistically-significant performance differences favoring students using the TIPP&SEE strategy on a written assessment. In this work, our goal is gain insight as to it why such dramatic learning differences may have occurred. We analyze the students' computational artifacts and TIPP&SEE worksheets. Artifact analysis reveals that students in the TIPP&SEE group are more thorough in their work, completing more elements of the required tasks. In addition, they build open-ended projects with longer scripts that utilize more learned blocks. Worksheet analysis shows that students were highly accurate on some types of questions but largely skipped others. Despite these positive behaviors, there was little statistical correlation between student worksheet correctness, project completion, and written assessment performance. Therefore, while students in the TIPP&SEE group performed actions we believe lead to more success, no individual actions directly explain the results. Like other meta-cognitive strategies, the value of TIPP&SEE may lie in cognitive processes not directly observable, and may vary based upon individual student differences.

Worldwide, many countries are implementing Computer Science (CS) and Computational Thinking (CT) instruction at the primary and secondary school level. This push for CS/CT instruction in younger ages increases the need for pedagogical practices, such as learning strategies, that are effective for a wide array of learners. However, research into strategies for young learners is relatively sparse, especially compared with strategies at the university level. This study aims to investigate a commonly used practice at the university level, diagramming, as a potential strategy for students in grades 4-8 (ages 10-15). Our hope is that diagramming can be added to a suite of strategies to support program comprehension in younger learners.

The computer science community has struggled to assess student learning via Scratch programming at the primary school level (ages 7-12). Prior work has relied most heavily on artifact (student code/projects) analysis, with some attempts at one-on-one interviews and written assessments. In this paper, we explore the relationship between artifact analysis and written assessments. Through this study of a large-scale introductory computing implementation, we found that for students who had code in their projects, student performance on specific questions on the written assessments is only very weakly correlated to specific attributes of final projects typically used in artifact analysis as well as attributes we use to define candidate code (r < 0.2, p < 0.05). In particular, the correlation is not nearly strong enough to serve as a proxy for understanding.

As quantum computation (QC) comes closer to reality, questions arise as to what elements to teach, how to teach it, and to what depth. QC instruction typically depends heavily on advanced math and/or physics. Our interdisciplinary science / computer science / education team co-created quantum computing (QC) learning trajectories (LT), zines, and activities, for young learners. We present the LT's (Superposition, Quantum State, Entanglement, Measurement, and Reversibility) and the iterative process that created them.

With many school districts nationwide integrating Computer Science (CS) and Computational Thinking (CT) instruction at the K-8 level, it is crucial researchers closely inspect the relationship between program expression and student understandings. In this study, we propose and report on our use of Scratch Charades, a game in which students act out Scratch scripts while others build them. The purpose of Scratch Charades is to familiarize students with scripts and blocks without the cognitive overhead of the complex user interface. However, in this study, we also used it to elicit student understandings about Scratch blocks and scripts to design mnemonics to help students debug their code. We propose two building and/or debugging strategies based on our observations.

The CS community has struggled to assess student learning at the K-8 level, with techniques ranging from one-on-one interviews to written assessments. While scalable, automated techniques exist for analyzing student code, a scalable method for assessing student comprehension of their own code has remained elusive. This study is a first step in bridging the gap between the knowledge gained from interviews and the time efficiency and scalability of written assessments and automated analysis. The goal of this study is to understand how student answers on various types of questions differ depending on whether they are being asked about their own code or generic code. We find that while there were no statistically-significant differences in overall scores, questions about generic and personalized code of comparable complexity are far from equivalent. Our qualitative analyses revealed interesting patterns in student responses, inviting further research into this assessment technique. In particular, students answered differently from students with generic code when presented with individual blocks from their code taken out of context and placed into different code snippets, and students answered in a way that demonstrates a functional, instead of structural, understanding on Explain in Plain English (EiPE) questions.

CSEd graduate students face a variety of unique issues. First, within a CS or information science program, this research area is not considered to be highly popular with respect to the quantity of CSEd faculty, graduate courses, and professionals immersed in this research area. Already, this presents a disadvantage compared to other peers in popular areas who have resources and/or a research community at their institutions to support them. Further, graduate students in this field are frequently one of the few, if not the only, CSEd researchers at their institutions. As a result, many graduate students have limited avenues to get consistent, relevant, critical feedback on their work, especially research-in-progress, i.e. research that has not already been published. In this Birds of a Feather (BoF) session, we aim to create and foster a safe and open environment that allows participants to get feedback on research-in-progress and share experiences and issues with being a graduate student in CSEd research. We invite genuine and honest conversations about the challenges facing rising academics and professionals in this area. Though the focus will be on CSEd research graduate students, any attendee who would like to hear about our experiences is welcome. We have two goals: (1) to cultivate mentorship relationships among participants to send them off with potential collaborators, supporters and advocates, and (2) to brainstorm ways to sustain these discussions in the ACM CSEd community.

With the rise of Computational Thinking (CT) instruction at the elementary level, it is imperative that elementary computing instruction support a variety of learners. A popular pedagogical approach for this age group is Use-->Modify-->Create, which introduces a concept through a more scaffolded, guided instruction before culminating in a more open-ended project for student engagement. Yet, there is little research on student learning during the Use-->Modify step, nor strategies to promote learning in this step. This paper introduces TIPP&SEE, a metacognitive learning strategy that further scaffolds student learning during this step. Results from an experimental study show statistically-significant performance gains from students using the TIPP&SEE strategy on nearly all assessment questions of moderate and hard difficulty, suggesting its potential as an effective CS/CT learning strategy.

As many school districts nationwide continue to incorporate Computer Science (CS) and Computational Thinking (CT) instruction at the K-8 level, it is crucial that we understand the factors and skills, such as reading and math proficiency, that contribute to the success of younger learners in a computing curriculum and are typically developed at this age. Yet, little is known about the relationship between reading and math proficiency, and the learning of key CS concepts at the elementary level. This study focused on 4th-grade students (ages 9-10) who were taught events, sequence, and repetition through an adaptation of the Creative Computing Curriculum. While all students benefited from access to such a curriculum, there were statistically-significant differences in learning outcomes, especially between students whose reading and math proficiency are below grade-level, and students whose proficiency are at or above grade-level. This performance gap suggests the need for curricular improvement and learning strategies that are CS specific for students who struggle with reading and math.

The computer science community has struggled to assess student learning, especially at the early elementary level. Prior work has included one-on-one interviews, written assessments, and artifact analysis, each with their own benefits and drawbacks. Through our Personalized Assessment Worksheets for Scratch (PAWS) tool, we explore personalized assessments as an assessment technique that lies in between interviews, written assessments, and artifact analysis. PAWS creates personalized written assessments that integrates code from student Scratch projects. We hope that our PAWS tool, and more generally personalized assessments, will lead to an assessment technique that is both more accurate than written assessments and artifact analysis, and less time-consuming than interviews.

Major metropolitan school districts around the United States are implementing computer science in elementary school classrooms as part of the CS for All (CS4All) initiative. Little is known, however, about the success of such a large-scale rollout, especially in terms of equity. In this study we analyze the performance of 4th grade classrooms completing three modules of an introductory computational thinking curriculum, looking at not only overall results but also the variance in performance between high-, mid-, and low-performing schools (as identified by their school report cards). We find that all classrooms are benefiting from the computational thinking (CT) curriculum, making great strides in providing equitable access to CT education. However, statistically-significant differences in performance are present, especially between the high- and low-performing schools, showing that there is still room for improvement in developing strategies and curricula for struggling learners.

A new wave of collaborative robots designed to work alongside humans is bringing the automation historically seen in large-scale industrial settings to new, diverse contexts. However, the ability to program these machines often requires years of training, making them inaccessible or impractical for many. This paper rethinks what robot programming interfaces could be in order to make them accessible and intuitive for adult novice programmers. We created a block-based interface for programming a one-armed industrial robot and conducted a study with 67 adult novices comparing it to two programming approaches in widespread use in industry. The results show participants using the block-based interface successfully implemented robot programs faster with no loss in accuracy while reporting higher scores for usability, learnability, and overall satisfaction. The contribution of this work is showing the potential for using block-based programming to make powerful technologies accessible to a wider audience.