Computing education faces a unique challenge when teaching debugging skills to teachers who do not have a formal computer science background but who will need to learn programming to teach CS courses. While most debugging research about post-secondary learners focuses on preparing students for the technology industry, teacher professional development (PD) programs often serve a different population: teachers who are simultaneously learning computing knowledge and the pedagogical skills to teach it effectively. Through semi-structured interviews with seven facilitators of computing PD programs, this study explores how experienced PD facilitators approach the concept of debugging and instruct teachers in the process of debugging. We use reflexive thematic analysis to show how teacher PD differs from debugging recommendations in post-secondary CS: Rather than focusing on understanding the root causes of errors, facilitators scaffold the teachers' process of identifying and locating bugs to aid teachers in more quickly producing working programs, which they see as important for supporting teachers' confidence. This practical approach acknowledges the time constraints of PD workshops. Finding and understanding bugs is the most difficult part of the debugging process and is something post-secondary students struggle with even after completing one or two semesters of CS courses. These insights challenge assumptions about debugging pedagogy and highlight the need to carefully consider when new CS teachers should learn error analysis skills.

Thematic analysis is an increasingly popular method in computing education research; however, widespread methodological confusion undermines its potential. For example, notions of objectivity do not make sense with reflexive approaches, and evidence of saturation is not required for thematic analysis. This position paper details how thematic analysis evolved from Braun and Clarke's influential 2006 work into an umbrella method encompassing three general approaches with corresponding epistemologies: coding reliability (positivist), reflexive (interpretivist), and codebook (hybrid) thematic analysis. Each has different goals and assumptions, but researchers often inadvertently mix incompatible elements. We then present our personal journeys of learning about thematic analysis and finally dissect common confusing claims in our field's publications and peer reviews. Our goal is for the field of computing education research to move towards a ''knowing'' practice. By clarifying thematic analysis approaches and providing guidance for authors and reviewers, we hope to help the field of computing education research better understand this popular method.

Emergent bilingual (EB) students are a growing demographic within the United States, with an increasing number enrolling in K-12 computing courses. Since programming languages are primarily grounded in English, K-12 computing teachers must balance and tailor their instruction to meet the needs of these students. Teachers reported a lack of sufficient computing education resources to guide their instruction for teaching computing to EB students. Through a thematic analysis of semi-structured interviews with eight K-12 computing teachers who have EB students in their classrooms, we identified some of the challenges they face and the strategies they use to support them. Our analysis revealed three challenges: (1) students experience cognitive overload from translating between English and their native language, (2) terminology has subtle differences across disciplines (e.g., 'variable' in Math vs. Science), and (3) educators' low computing self-efficacy. Teachers counter these challenges with two implemented strategies: (1) providing multiple ways for EB students to engage with content to prevent them from becoming overwhelmed, and (2) offering multiple modalities to help translate computing concepts. This study contributes to the ongoing discussion on inclusive computing education by offering insights into educators' needs and potential solutions for supporting EB students' learning in computing.

The goal of teaching everyone computing (explicitly including programming) predates the definition of the computer science (CS) major and even the prospect of a software development career. At the University of Michigan, we are creating courses for non-CS majors which are grounded in the computational practices of liberal arts and sciences faculty. These courses have no connection to the CS major curriculum or software development jobs. We focus here on two of the themes that those faculty valued (Computing for Expression and Computing for Justice) and the introductory courses that we designed around each theme. The courses emphasize gaining broad perspectives of computing, which serve the study of multiple disciplines. Student activities include readings, writing essays, classroom discussion, and open-ended programming homework assignments. This experience report describes our design process, the Creative Expression and Social Justice courses, and an initial evaluation of our design. Most of the programming assignments were written in the block-based programming language Snap!, with some in-class exercises using teaspoon languages. Several units ended with an ebook assignment to connect the Snap! programming to equivalent programs in Python, Processing, and SQL. Interview and survey findings suggest that students found this sequence and the courses useful, despite not counting toward a CS major or focusing on early software development skills. Students described usefulness in terms of developing general computing knowledge, preparation for a range of future careers, and introducing them to other course choices.

Undergraduate students are taking computer science (CS) courses, even if they are not planning to become computer science majors. However, recommendations for post-secondary computing curriculum presume that students are learning computer science for a career goal such as software engineering. Prior work about non-majors in introductory programming courses have revealed additional reasons why students learn programming: such as to become conversational programmers or computational scientists. These purposes are not well served by programming education built around the creation of software professionals, which can lead to students with non-technological career goals feeling unwelcome or uninterested in CS courses. At my institution, we have created new computing courses designed to meet the needs of liberal arts and humanities majors by teaching discipline-specific programming skills within an elective course. I conducted an interview study of 24 undergraduate students who enrolled in these elective, general education (not CS credit bearing) courses about computing and programming. I seek to understand why students not required to study computer science are motivated to learn about computing and programming, and why they chose to learn about computing through a general education elective which could not aid CS or technical degree progress. I additionally investigate how students in this context approach programming and debugging within their open-ended assignments. My findings build upon prior work to highlight additional reasons why non-CS majors might want to study computing, and how students' computing-related goals affect their decision-making when creating and refining programs.

The capability of large language models (LLMs) to generate, debug, and explain code has sparked the interest of researchers and educators in undergraduate programming, with many anticipating their transformative potential in programming education. However, decisions about why and how to use LLMs in programming education may involve more than just the assessment of an LLM’s technical capabilities. Using the social shaping of technology theory as a guiding framework, our study explores how students’ social perceptions influence their own LLM usage. We then examine the correlation of self-reported LLM usage with students’ self-efficacy and midterm performances in an undergraduate programming course. Triangulating data from an anonymous end-of-course student survey (n = 158), a mid-course self-efficacy survey (n=158), student interviews (n = 10), self-reported LLM usage on homework, and midterm performances, we discovered that students’ use of LLMs was associated with their expectations for their future careers and their perceptions of peer usage. Additionally, early self-reported LLM usage in our context correlated with lower self-efficacy and lower midterm scores, while students’ perceived over-reliance on LLMs, rather than their usage itself, correlated with decreased self-efficacy later in the course.

Computing is everywhere, and it's here to stay. Computing is crucial in many disciplines and influences every discipline. It's unlikely we'll willingly return to a society unmediated by computing. How do our institutions proceed?

In this Birds of a Feather (BoF) session, we aim to foster a safe and open environment that allows CS education (CSEd) graduate students to share about their graduate experiences and learn from each other about graduate school, conducting research, and navigating the inherently multi-disciplinary space of CSEd. We have two main goals: (1) create specific networking opportunities between CSEd graduate students attending SIGCSE TS (hereafter just "SIGCSE") in order to foster a community of support, and (2) elicit discussions on the triumphs and tribulations that come as part of being a CSEd-focused graduate student. We plan for all attendees to leave our session with connections to fellow CSEd graduate students from other institutions (through email and a dedicated Slack) and a greater awareness of the community available to them within CS education research.

When undergraduate students studying disciplines other than computer science enroll in computing courses, they are likely seeking different skill sets than CS majors. One way to teach non-majors about computing in a way relevant to their fields of study is through contextualized computing courses where the programming instruction and activities focus on applications based in disciplines such as the humanities or arts. In an interview study of students in contextualized computing courses, I found that students wished to learn about computing for various professional and personal reasons, such as a desire to be conversational programmers or to create specific artifacts. One way that students are able to focus on their specific interest while in the courses is through creative, open-ended assignments where students can choose what they wish to create with programming. In my dissertation research, I will examine how students decide what to create in their assignments, how their plans for their final products change throughout their programming process, and in what ways students’ creations align with their reasons for enrolling in a computing course.

In this Birds of a Feather (BoF) session, we aim to create and foster a safe and open environment that allows graduate students share experiences and issues with graduate studies 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 from around the world, (2) to connect students with new opportunities and ways of thinking about CSEd, and (3) to cultivate mentorship among participants and send them off with potential collaborators, supporters, and advocates.

Social studies teachers regularly use digital tools to teach data literacy - the ability to understand, analyze and interpret data visualizations including charts, graphs, and timelines. These digital tools, such as websites for creating charts and graphs, aid in teaching data literacy as they allow students and teachers to visualize large numerical data sets that would be difficult to manipulate without the use of computational tools. However, the data visualization tools that are easily accessible to teachers are rarely built specifically for teaching and require methods for inputting or manipulating data with which teachers are unfamiliar. I propose that by examining how K-12 teachers understand data representations and building tools which scaffold that understanding, digital data visualization tools can be created that will be more usable by teachers and more likely to be adopted into classroom use.