Getting started in computer science education research can feel daunting. The field is broad, the community is large, and for those coming from a more technical background, it’s not always obvious where to begin – or how to build a network and a research identity in the area. For me, conference working groups – in particular Innovation and Technology in Computer Science Education (ITiCSE) working groups – played a huge role in helping me get started.
An ITiCSE Working Group consists of a small, international team of computing education researchers with varying levels of experience who collaborate over several months to explore an important topic in computer science education. Working groups begin before the ITiCSE conference, include an intensive in-person collaboration period, and typically produce a peer-reviewed report that helps shape research and practice in the field.
Benefits:
- Build International, Multi-Institutional Networks – working groups bring together researchers from diverse backgrounds and institutional contexts. For many early-career and experienced researchers alike, this is a rare opportunity to form meaningful collaborations that span continents and perspectives.
- Meaningful, Sustained Scholarship Over Time – a working group project unfolds across months – with regular virtual meetings, shared research tasks, and sustained writing. Members contribute to a goal that’s larger than any one individual’s project, gaining experience in co-creating research and scholarship.
- Produce High-Value Research Outputs – many ITiCSE Working Group reports are highly cited and widely regarded as influential contributions to the computing education literature. They tackle emerging and core issues in computing education – from GenAI use in computing education to studies that examine whether research findings replicate across different contexts, and efforts to develop shared terminology across the global community – and help shape the research agenda of the field.
- Sharpen Your Research & Collaboration Skills – working groups require active participation: literature reviews, data collection and analysis, methodological discussion, and co-writing. They’re intense and demanding, but also rewarding. Participants gain direct experience in international computer science education research collaboration that mirrors how many academic projects operate in practice.
Learning the Field by Doing: My Experience with ITiCSE Working Groups
My first experience as a member of an ITiCSE Working Group was as part of the “Data Systems Education: Curriculum Recommendations, Course Syllabi, and Industry Needs” working group [1] at ITiCSE 2024. This experience significantly strengthened my understanding of how the field frames problems, designs studies, analyses data, and positions contributions.
The experience gained from this initial working group led to me becoming a working group lead at ITiCSE 2025 for the “Extracting Notional Machines for Databases” Working Group [2]. This progression built on my existing experience, strengthening my understanding of computing education research and my ability to shape and guide research questions in the area. Leading a working group also made visible the collaborative nature of computing education research – balancing perspectives, methods, and institutional contexts to produce work that is meaningful across the community.
One of the biggest benefits of ITiCSE Working Groups is the network they create. Before joining a Working Group, I knew very few people working in data systems education. Through working groups, I connected with a community of data systems educators from different countries and institutions. For early-career researchers especially, working groups can be a powerful way to build a professional network and start to establish a presence in the computer science education community. These connections often grow into sustained collaborations, leading to continued research, further publications, and longer-term projects beyond the conference itself.
ITiCSE 2026 Working Groups
The ITiCSE Working Groups for this year have just been released. If you’re interested in CS education research but unsure where to begin, I’d strongly encourage taking a look. Getting involved in working groups made a huge difference for me, and I know it has done the same for many others. Find out more about the 2026 ITiCSE Working Groups and how to apply here: https://iticse.acm.org/2026/2026-working-group-proposals/
RIPPA
Research in Practice Project Activities (RIPPAs) [3] are similar to ITiCSE Working Groups but are associated with the United Kingdom and Ireland Computing Education Research (UKICER) conference. RIPPAs are multi-institutional, collaborative research projects that engage participants in joint research activities, workshops, and shared outputs, helping to build skills, networks, and impactful computing education research within the UKICER community.
I am currently co-leading a RIPPA titled “Dancing with Dilemma: Computer Science Education Research in Teaching-Focused Academics’ Promotion”. This RIPPA aims to study the specific promotion requirements of teaching-focused academics in Computer Science, the challenges that they face when going into Computer Science Education Research, and the support offered by their institutions, before providing recommendations. We are currently looking for teaching focussed academics to complete a survey and participate in interviews. For more information see: https://rippa.co.uk/dancingwithdilemma.
References
[1] Daphne Miedema, Toni Taipalus, Vangel V. Ajanovski, Abdussalam Alawini, Martin Goodfellow, Michael Liut, Svetlana Peltsverger, and Tiffany Young. 2025. Data Systems Education: Curriculum Recommendations, Course Syllabi, and Industry Needs. In 2024 Working Group Reports on Innovation and Technology in Computer Science Education (ITiCSE 2024). Association for Computing Machinery, New York, NY, USA, 95–123. https://doi.org/10.1145/3689187.3709609
[2] Daphne Miedema, George Fletcher, Fenia Aivaloglou, Leonard Busuttil, Laura Farinetti, Martin Goodfellow, Giovanna Guerrini, Georgiana Haldeman, Yuhan Pan, Sujeeth Goud Ramagoni, Chandrika Satyavolu, Raja Sooriamurthi, Xiaoying Tu, and Liviana Tudor. 2025. Extracting Notional Machines for Databases. In Proceedings of the 30th ACM Conference on Innovation and Technology in Computer Science Education V. 2 (ITiCSE 2025). Association for Computing Machinery, New York, NY, USA, 693–694. https://doi.org/10.1145/3724389.3731277