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We present an algorithm that canonicalizes the algebraic representations of the topological semantics of machine knitting programs. Machine knitting is a staple technology of modern textile production where hundreds of mechanical needles are manipulated to form yarn into interlocking loop structures. Our semantics are defined using a variant of a monoidal category, and they closely correspond to string diagrams. We formulate our canonicalization as an Abstract Rewriting System (ARS) over words in our category, and prove that our algorithm is correct and runs in polynomial time. 

There is a growing community of researchers at the intersection- tion of data mining, AI, and computing education research. The objective of the CSEDM workshop is to facilitate a dis- Discussion among this research community, with a focus on how data mining can be uniquely applied in computing ed- ucation research. For example, what new techniques are needed to analyze program code and CS log data? How do results from CS education inform our analysis of this data? The workshop is meant to be an interdisciplinary event at the intersection of EDM and Computing Education Research. Researchers, faculty, and students are encouraged to share their AI- and data-driven approaches, methodological- gies, and experiences where data transforms how students learn Computer Science (CS) skills. This full-day workshop will feature paper presentations and discussions to promote collaboration. 

When instructors want to design programming assignments to motivate their students, a common design choice is to have those students write code to make an artifact (e.g. apps, games, music, or images). The goal of this study is to understand the impacts of including artifact creation in a programming assignment on students’ motivation, time on task, and cognitive load. To do so, we conducted a controlled lab study with seventy-three students from an introductory engineering course. The experimental group created a simulation they could interact with – thus having the full experience of artifact creation – while the control group wrote the exact same code, but evaluated it only with test cases. We hypothesized that students who could interact with the simulation they were programming would be more motivated to complete the assignment and report higher intrinsic motivation. However, we found no significant difference in motivation or cognitive load between the groups. Additionally, the experimental group spent more time completing the assignment than the control group. Our results suggest that artifact creation may not be necessary for motivating students in all contexts, and that artifact creation may have other effects such as increased time on task. Additionally, instructors and researchers should consider when, and in what contexts, artifact creation is beneficial and when it may not be 

Background and Context. Research software in the Computing Education Research (CER) domain frequently encounters issues with scalability and sustained adoption, which limits its educational impact. Despite the development of numerous CER programming (CER-P) tools designed to enhance learning and instruction, many fail to see widespread use or remain relevant over time. Previous research has primarily examined the challenges educators face in adopting and reusing CER tools, with few focusing on understanding the barriers to scaling and adoption practices from the tool developers’ perspective. Objectives. To address this, we conducted semi-structured interviews with 16 tool developers within the computing education community, focusing on the challenges they encounter and the practices they employ in scaling their CER-P tools. Method. Our study employs thematic analysis of the semi-structured interviews conducted with developers of CER-P tools. Findings. Our analysis revealed several barriers to scaling highlighted by participants, including funding issues, maintenance burdens, and the challenge of ensuring tool interoperability for a broader user base. Despite these challenges, developers shared various practices and strategies that facilitated some degree of success in scaling their tools. These strategies include the development of teaching materials and units of curriculum, active marketing within the academic community, and the adoption of flexible design principles to facilitate easier adaptation and use by educators and students. Implications. Our findings lay the foundation for further discussion on potential community action initiatives, such as the repository of CS tools and the community of tool developers, to allow educators to discover and integrate tools more easily in their classrooms and support tool developers by exchanging design practices to build high-quality education tools. Furthermore, our study suggests the potential benefits of exploring alternative funding models. 

There is a growing community of researchers at the intersection of data mining, AI, and computing education research. The objective of the CSEDM workshop is to facilitate a discussion among this research community, with a focus on how data mining can be uniquely applied in computing education research. For example, what new techniques are needed to analyze program code and CS log data? How do results from CS education inform our analysis of this data? The workshop is meant to be an interdisciplinary event at the intersection of EDM and Computing Education Research. Researchers, faculty, and students are encouraged to share their AI- and data-driven approaches, methodologies, and experiences where data transforms how students learn Computer Science (CS) skills. This full-day hybrid workshop will feature paper presentations and discussions to promote collaboration. 

In computing classrooms, building an open-ended programming project engages students in the process of designing and implementing an idea of their own choice. An explicit planning process has been shown to help students build more complex and ambitious open-ended projects. However, novices encounter difficulties in exploring and creatively expressing ideas during planning. We present Idea Builder, a storyboarding-based planning system to help novices visually express their ideas. Idea Builder includes three features: 1) storyboards to help students express a variety of ideas that map easily to programming code, 2) animated example mechanics with example actors to help students explore the space of possible ideas supported by the programming environments, and 3) synthesized starter code to help students easily transition from planning to programming. Through two studies with high school coding workshops, we found that students self-reported as feeling creative and feeling easy to communicate ideas; having access to animated example mechanics of an actor help students to build those actors in their plans and projects; and that most students perceived the synthesized starter code from Idea Builder as helpful and time-saving. 

Many students struggle when they are first learning to program. Without help, these students can lose confidence and negatively assess their programming ability, which can ultimately lead to dropouts. However, detecting the exact moment of student struggle is still an open question in computing education. In this work, we conducted a think-aloud study with five high-school students to investigate the automatic detection of progressing and struggling moments using a detector algorithm (SPD). SPD classifies student trace logs into moments of struggle and progress based on their similarity to prior students' correct solutions. We explored the extent to which the SPD-identified moments of struggle aligned with expert-identified moments based on novices' verbalized thoughts and programming actions. Our analysis results suggest that SPD can catch students' struggling and progressing moments with a 72.5% F1-score, but room remains for improvement in detecting struggle. Moreover, we conducted an in-depth examination to discover why discrepancies arose between expert-identified and detector-identified struggle moments. We conclude with recommendations for future data-driven struggle detection systems. 

Many students struggle with decomposition and planning despite the necessity of these skills in computing education. Hence, more tools are needed to scaffold these processes. In this paper, we present Jigsaw, a standalone visual planning tool to help students practice decomposition and planning before writing code. Jigsaw allows students to compose a solution to a new problem based on previously seen “patterns,” such as the accumulator pattern for summing values or the filter pattern for conditional input selection. Students can connect these patterns together to see how data flows between them and define a solution plan. Jigsaw’s goal is to scaffold students’ planning processes by presenting relevant patterns for a given problem. Using a within-subjects design, we evaluated Jigsaw by observing 17 undergraduate students as they planned for and implemented two programming assignments. The experimental task included Jigsaw, and the control task did not. This design aimed to understand how the tool impacted students’ planning and programming process. Subsequently, we conducted interviews with these students regarding their planning and programming experiences with and without Jigsaw. Many students explicitly mentioned they would employ Jigsaw for planning and appreciated the scaffolding it provided. Students also admired the Jigsaw’s novelty in visualizing programming problems. We conclude with our design takeaways and recommendations for future work.