Computing for all
We aim to understand and work to remove the barriers to computing education, including the factors obstructing young people’s engagement and progression in computing as a subject and career.
Gender Balance in Computing programme
We have recently conducted the largest ever set of trials on interventions that aim to overcome barriers for girls engaging with computing in school. For example, we have investigated how the use of storytelling and pair programming helps improve engagement and interest in computing for girls. Read more about gender in computing in our series of blog posts and in our paper on the factors impacting gender balance in computing.
Culturally responsive computing teaching
We are currently working on a Google-funded project to understand culturally responsive teaching in computing. Making computing culturally relevant means that learners with a range of cultural identities are able to identify with the examples chosen to illustrate computing concepts, to engage effectively with the teaching methods, and to feel empowered to use computing to address problems that are meaningful to them and their communities. This work builds on our work in 2021 to develop guidelines for educators to support them with culturally relevant pedagogy and culturally responsive teaching. You can find more information on our culturally relevant pedagogy for computing education page.
Culturally adapted resources for primary schools
In England, there has been limited research to investigate the engagement of primary-aged students from different backgrounds in computing lessons. We are grateful to the Cognizant Foundation for supporting us to investigate how to create culturally adapted computing resources for primary classroom use and to study the impact such resources might have. If you’d like to know more, read our blog post about the initial recruitment phase of the project.
Teaching and learning computing
We research computing education in formal and non-formal settings, investigating processes of learning and teaching, as well as teacher professional development.
Computer science for learners aged 14 to 18
Aligned with our work on the Ada Computer Science platform, we are collaborating with the Department of Computer Science and Technology at the University of Cambridge to research ways in which young people aged 14 to 18 learn computer science. You can read more about this topic in our collaborative report on the development and impact of the platform's first version (Isaac Computer Science).
Non-formal computing education
The informal learning team and research team worked together throughout 2021 to write a review of research literature relating to non-formal learning. As we work extensively to support young people in clubs and extracurricular activities we felt that being able to understand the state of current research was really important as a baseline to support any future research projects in this area. Read up on our results in this paper we presented at the 2022 ICER conference.
Computing education worldwide
We have recently published a study that investigated the capacity for delivering computing education in Botswana, Kenya, Nigeria and Uganda. More research is needed to investigate the dependencies between policy and vision, infrastructure, curriculum implementation, and teacher professional development, and thus support and facilitate the development of global computing education.
UK & Ireland computing teacher survey
In March 2022, we conducted a large survey of computing teachers across England, Wales, Scotland, Northern Ireland and the Republic of Ireland. We are still in the early stages of analysing the impact that different educational systems, curricula and policy priorities have on the experiences of teachers. Read about our first insights in this blog article
Classroom talk in programming
Following on from previous research on Predict-Run-Investigate-Modify-Make (PRIMM), we have investigated the role of language in the programming classroom and published a paper about teachers’ perspectives on classroom talk.
AI and data science education
We are investigating how best to learn and teach the fundamentals of artificial intelligence, machine learning, and data science.
AI and ML categorisation framework
We have analysed more than 500 learning resources about AI and machine learning (ML). To do this, we developed and used a framework called SEAME. The framework can be used to categorise or design resources using four levels: Social & Ethical, Application, Model, Engine.
Developing classroom resources for AI education
In collaboration with Google DeepMind, an interdisciplinary team at the Raspberry Pi Foundation has produced a unit of six lessons and further classroom activities as part of the Experience AI learning programme. These materials help teachers introduce AI and ML to students aged 11 to 14 (England Key Stage 3, US Grades 6–8). To create the materials, design principles were decided, such as avoiding anthropomorphisation, developing key explanations, and applying our SEAME framework.
Using LLMs to explain programming error messages
We have conducted a small study with expert computing educators to explore the use of large language models (LLMs) to generate explanations of programming error message within integrated development environments (IDEs). We interviewed educators on their use of LLM-generated error messages and on their views about how students might be impacted by these messages when learning to code.
Literature review of classroom interventions related to AI and ML
We have conducted and published a systematic literature review of K–12 classroom interventions related to AI and ML.
Teachers’ views about learning and teaching of AI and ML
In a small pilot study, we have investigated teachers’ motivations for learning and teaching about AI and ML.
Seminar series about the teaching and learning of AI and ML in schools
In 2021/2022, we hosted an online seminar series on AI and ML teaching and learning in K–12, in partnership with The Alan Turing Institute. Leading international researchers who investigate the teaching and learning of AI and ML in schools presented their research and wrote chapters in our seminar proceedings detailing their work in this area.