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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. 

This working group aims to identify available datasets within the context of computing education research. One particular area of interest is programming education, and the data in question may include students' steps, progress, or submissions in the form of program code. To achieve this goal, the working group will review well-known data resources and repositories (e.g., DataShop, GitHub, NSF Public Access Repository, and IEEE DataPort) and recent papers published within the SIGCSE community. As a result of the review process, the working group will create an overview of available datasets and characterize them while reflecting on current data practices, challenges, and the consequences of limited access to research data. Additionally, the group intends to propose a path for the community to become more open and move toward open data practices. This proposal highlights the importance of sharing research data within the computing education research community to make it stronger and more productive. 

Educational Data Mining in Computer Science Education (CSEDM) is an interdisciplinary research community that combines discipline-based computing education research (CER) with educational data-mining (EDM)  to advance knowledge in ways that go beyond what either research community could do on its own. The JEDM Special Issue on CSEDM received a total of 12 submissions. Each submission was reviewed by at least three reviewers, who brought expertise from both the EDM and CER communities, as well as one of special issue editors. Ultimately, three papers were accepted, for an acceptance rate of 25%.   These three papers cover a variety of important topics in CSEDM research. Edwards et al. discuss the challenges of collecting, sharing and analyzing programming data, and contribute two high-quality CS datasets. Gitinabard et al. contribute new approaches for analyzing data from pairs of students working on programs together, and show how such data can inform classroom instruction. Finally, Zhang et al. contribute a novel model for predicting students' programming performance based on their past performance. Together, these papers showcase the complexities of data, analytics and modeling in the domain of CS, and contribute to our understanding of how students learn in CS classrooms.