Through the Hello World podcast, we help to connect computing educators around the world and share their experiences. In each episode, we expand on a topic from a recent Hello World magazine issue. After 5 seasons, and a break last year, we are back with season 6 today.
Episode 1: Do kids still need to learn how to code?
Joining my co-host Veronica and me are two computing educators: Pete Dring, Head of Computing at Fulford School in York, and Chris Coetzee, a computer science teacher for 24 years and currently a PhD student in Computer Science Education at Abertay Dundee. Given the recent developments in AI-based code generators, we talk about whether such tools will remove our learners’ need to learn to code or simply change what coding, and learning to code, looks like*.
What’s coming up in future episodes?
New episode of season 6 will come out every 2 weeks. In each episode we explore computing, coding, and digital making education by delving into an exciting topic together with our guests: experts, practitioners, and other members of the Hello World community.
Also in season 6, we’ll explore:
The role of computing communities
We discuss the value and importance of being connected to other computing educators through the many different teaching communities that exist around the world. What makes effective communities, and how do we build and sustain them?
Why is understanding cybersecurity so important?
From classroom lessons to challenges and competitions, there are lots of opportunities for learners to discover cybersecurity. There are also many pitfalls where learners’ online activities put them at risk of breaking the law. We discuss some of these pitfalls along with the many career opportunities in cybersecurity.
How to develop as a computing educator?
What is involved in becoming an effective computing educator? What knowledge, skills, and behaviours are needed, and how do we go about developing them? We sit down with teacher trainers and trainees to explore this topic.
What is the state of computing education and where is it heading?
Computing education has come a long way in the last decade in terms of practice and policy, as well as research. Together with our guests we discuss where computing education is today around the world, and we consider the lessons we can learn and the challenges ahead
What is the role of AI in your classroom?
AI continues to be a disruptive technology in many spaces, and the classroom is no exception. We hear examples of practices and approaches being explored by teachers in the classroom.
Listen and subscribe today
If you’ve not listened to the Hello World podcast yet, there are 5 whole seasons for you to discover. We talk about everything from ecology and quantum computing to philosophy, ethics, and inclusion, and our conversations always focus on the practicalities of teaching in the classroom.
Many of our podcast guests are Hello World authors, so if you’re an educator who wants to share your insights into how to teach young people about digital technology, please let us know. Your words could end up in the pages as well as on the airwaves of Hello World.
You’ll find the upcoming Hello World season and past episodes on your favourite podcast platform, including YouTube now, where you can also subscribe to never miss an episode. Alternatively, you can listen here via your browser.
How is computing taught around the globe? Our brand-new, free issue of Hello World, out today, paints a picture for you. It features stories from over 20 countries, where educators, researchers, and volunteers share their work and their personal challenges and joys in bringing computing education to their part of the world.
Global exchange in a worldwide community
In Hello World issue 23, you’ll hear about countries where computing is an official school subject and how it was set up that way, and you’ll hear about countries that are newer to computing education and working to fast-track their students’ learning.
Ethel Tshukudu’s article on her research using the CAPE framework is a fascinating comparison of computer science education in four African countries
Iliana Ramirez describes how volunteers are at the heart of Ciberistas, a technology training programme for young people in Mexico
Matthew Griffin’s article highlights how computing education works in Canada, a large country with two official languages
Dana Rensi’s article about a solar-powered Raspberry Pi computing lab in the middle of the Peruvian rainforest will surprise and delight you
Randal Rousseau, a librarian in Cape Town, South Africa, shares how he teaches children to code through unplugged activities
And there is lots more for you to discover in issue 23.
Sue Sentance, director of the Raspberry Pi Computing Education Research Centre at the University of Cambridge, says in her article:
“Our own experience of implementing computing education in England since 2014 has shown the importance of teachers supporting each other, and how various networks … are instrumental in bringing computing teachers together to share knowledge and experiences. With so many countries introducing computing education, and teachers around the globe facing similar challenges, maybe we need to extend this to a global teacher network, where teachers and policymakers can share good practice and learn from each other. “
Research highlights the importance of computing education to young people’s futures, whether or not they pursue a degree or career in the area. From teaching computing in schools where the electricity cuts out, to incorporating artificial intelligence into curricula in different countries, and to teaming up with local governments when there isn’t a national computing curriculum, educators are doing wonderful things around the globe to make sure the young people they support have the opportunity to learn. Read their stories today.
Also in issue 23:
Research on culturally adapted resources
How community building enhances computing education
Send us a message or tag us on social media to let us know which articles have made you think, and most importantly, which will help you with your teaching. And to hear monthly news about Hello World and the whole Raspberry Pi Foundation, sign up to the Hello World newsletter.
The use of generative AI tools (e.g. ChatGPT) in education is now common among young people (see data from the UK’s Ofcom regulator). As a computing educator or researcher, you might wonder what impact generative AI tools will have on how young people learn programming. In our latest research seminar, Barbara Ericson and Xinying Hou (University of Michigan) shared insights into this topic. They presented recent studies with university student participants on using generative AI tools based on large language models (LLMs) during programming tasks.
Using Parson’s Problems to scaffold student code-writing tasks
Barbara and Xinying started their seminar with an overview of their earlier research into using Parson’s Problems to scaffold university students as they learn to program. Parson’s Problems (PPs) are a type of code completion problem where learners are given all the correct code to solve the coding task, but the individual lines are broken up into blocks and shown in the wrong order (Parsons and Haden, 2006). Distractor blocks, which are incorrect versions of some or all of the lines of code (i.e. versions with syntax or semantic errors), can also be included. This means to solve a PP, learners need to select the correct blocks as well as place them in the correct order.
In one study, the research team asked whether PPs could support university students who are struggling to complete write-code tasks. In the tasks, the 11 study participants had the option to generate a PP when they encountered a challenge trying to write code from scratch, in order to help them arrive at the complete code solution. The PPs acted as scaffolding for participants who got stuck trying to write code. Solutions used in the generated PPs were derived from past student solutions collected during previous university courses. The study had promising results: participants said the PPs were helpful in completing the write-code problems, and 6 participants stated that the PPs lowered the difficulty of the problem and speeded up the problem-solving process, reducing their debugging time. Additionally, participants said that the PPs prompted them to think more deeply.
This study provided further evidence that PPs can be useful in supporting students and keeping them engaged when writing code. However, some participants still had difficulty arriving at the correct code solution, even when prompted with a PP as support. The research team thinks that a possible reason for this could be that only one solution was given to the PP, the same one for all participants. Therefore, participants with a different approach in mind would likely have experienced a higher cognitive demand and would not have found that particular PP useful.
Supporting students with varying self-efficacy using PPs
To understand the impact of using PPs with different learners, the team then undertook a follow-up study asking whether PPs could specifically support students with lower computer science self-efficacy. The results show that study participants with low self-efficacy who were scaffolded with PPs support showed significantly higher practice performance and higher problem-solving efficiency compared to participants who had no scaffolding. These findings provide evidence that PPs can create a more supportive environment, particularly for students who have lower self-efficacy or difficulty solving code writing problems. Another finding was that participants with low self-efficacy were more likely to completely solve the PPs, whereas participants with higher self-efficacy only scanned or partly solved the PPs, indicating that scaffolding in the form of PPs may be redundant for some students.
These two studies highlighted instances where PPs are more or less relevant depending on a student’s level of expertise or self-efficacy. In addition, the best PP to solve may differ from one student to another, and so having the same PP for all students to solve may be a limitation. This prompted the team to conduct their most recent study to ask how large language models (LLMs) can be leveraged to support students in code-writing practice without hindering their learning.
Generating personalised PPs using AI tools
This recent third study focused on the development of CodeTailor, a tool that uses LLMs to generate and evaluate code solutions before generating personalised PPs to scaffold students writing code. Students are encouraged to engage actively with solving problems as, unlike other AI-assisted coding tools that merely output a correct code correct solution, students must actively construct solutions using personalised PPs. The researchers were interested in whether CodeTailor could better support students to actively engage in code-writing.
In a study with 18 undergraduate students, they found that CodeTailor could generate correct solutions based on students’ incorrect code. The CodeTailor-generated solutions were more closely aligned with students’ incorrect code than common previous student solutions were. The researchers also found that most participants (88%) preferred CodeTailor to other AI-assisted coding tools when engaging with code-writing tasks. As the correct solution in CodeTailor is generated based on individual students’ existing strategy, this boosted students’ confidence in their current ideas and progress during their practice. However, some students still reported challenges around solution comprehension, potentially due to CodeTailor not providing sufficient explanation for the details in the individual code blocks of the solution to the PP. The researchers argue that text explanations could help students fully understand a program’s components, objectives, and structure.
In future studies, the team is keen to evaluate a design of CodeTailor that generates multiple levels of natural language explanations, i.e. provides personalised explanations accompanying the PPs. They also aim to investigate the use of LLM-based AI tools to generate a self-reflection question structure that students can fill in to extend their reasoning about the solution to the PP.
Barbara and Xinying’s seminar is available to watch here:
Find examples of PPs embedded in free interactive ebooks that Barbara and her team have developed over the years, including CSAwesome and Python for Everybody. You can also read more about the CodeTailor platform in Barbara and Xinying’s paper.
Join our next seminar
The focus of our ongoing seminar series is on teaching programming with or without AI.
For our next seminar on Tuesday12 March at 17:00–18:30 GMT, we’re joined by Yash Tadimalla and Prof. Mary Lou Maher (University of North Carolina at Charlotte). The two of them will share further insights into the impact of AI tools on the student experience in programming courses. To take part in the seminar, click the button below to sign up, and we will send you information about joining. We hope to see you there.
You can now access classroom resources created by us for the T Level in Digital Production, Design and Development. T Levels are a type of vocational qualification young people in England can gain after leaving school, and we are pleased to be able to support T Level teachers and students.
With our new resources, we aim to empower more young people to develop their digital skills and confidence while studying, meaning they can access more jobs and opportunities for further study once they finish their T Levels.
We worked collaboratively with the Gatsby Charitable Foundation on this pilot project as part of their Technical Education Networks Programme, the first time that we have created classroom resources for post-16 vocational education.
Post-16 vocational training and T Levels
T Levels are Technical Levels, 2-year courses for 16- to 18-year-old school leavers. Launched in England in September 2020, T Levels cover a range of subjects and have been developed in collaboration with employers, education providers, and other organisations. The aim is for T Levels to specifically prepare young people for entry into skilled employment, an apprenticeship, or related technical study in further or higher education.
For us, this T Level pilot project follows on from work we did in 2022 to learn more about post-16 vocational training and identify gaps where we could make a difference.
Something interesting we found was the relatively low number of school-age young people who started apprenticeships in the UK in 2019/20. For example, a 2021 Worldskills UK report stated that only 18% of apprentices were young people aged 19 and under. 39% were aged 19-24, and the remaining 43% were people aged 25 and over.
To hear from young people about their thoughts directly, we spoke to a group of year 10 students (ages 14 to 15) at Gladesmore School in Tottenham. Two thirds of these students said that digital skills were ‘very important’ to them, and that they would consider applying for a digital apprenticeship or T Level. When we asked them why, one of the key reasons they gave was the opportunity to work and earn money, rather than moving into further study in higher education and paying tuition fees. One student’s answer was for example, “It’s a good way to learn new skills while getting paid, and also gives effective work experience.”
T Level curriculum materials and project brief
To support teachers in delivering the Digital Production, Design and Development T Level qualification, we created a new set of resources: curriculum materials as well a project brief with examples to support the Occupational Specialism component of the qualification.
The curriculum materials on the topic ‘Digital environments’ cover content related to computer systems including hardware, software, networks, and cloud environments. They are designed for teachers to use in the classroom and consist of a complete unit of work: lesson plans, slide decks, activities, a progression chart, and assessment materials. The materials are designed in line with our computing content framework and pedagogy principles, on which the whole of our Computing Curriculum is based.
The project brief is a real-world scenario related to our work and gives students the opportunity to problem-solve as though they are working in an industry job.
Our thanks to the Gatsby Foundation for collaborating with us on this work to empower more young people to fulfil their potential through the power of computing and digital technologies.
Everyone who has taught children before will know the excited gleam in their eyes when the lessons include something to interact with physically. Whether it’s printed and painstakingly laminated flashcards, laser-cut models, or robots, learners’ motivation to engage with the topic will increase along with the noise levels in the classroom.
However, these hands-on activities are often seen as merely a technique to raise interest, or a nice extra project for children to do before the ‘actual learning’ can begin. But what if this is the wrong way to think about this type of activity?
How do children learn?
In our 2023 online research seminar series, focused on computing education for primary-aged (K–5) learners, we delved into the most recent research aimed at enhancing learning experiences for students in the earliest stages of education. From a deep dive into teaching variables to exploring the integration of computational thinking, our series has looked at the most effective ways to engage young minds in the subject of computing.
It’s only fitting that in our final seminar in the series, Anaclara Gerosa from the University of Glasgow tackled one of the most fundamental questions in education: how do children actually learn? Beyond the conventional methods, emerging research has been shedding light on a fascinating approach — the concept of grounded cognition. This theory suggests that children don’t merely passively absorb knowledge; they physically interact with it, quite literally ‘grasping’ concepts in the process.
Grounded cognition, also known in variations as embodied and situated cognition, offers a new perspective on how we absorb and process information. At its core, this theory suggests that all cognitive processes, including language and thought, are rooted in the body’s dynamic interactions with the environment. This notion challenges the conventional view of learning as a purely cognitive activity and highlights the impact of action and simulation.
There is evidence from many studies in psychology and pedagogy that using hands-on activities can enhance comprehension and abstraction. For instance, finger counting has been found to be essential in understanding numerical systems and mathematical concepts. A recent study in this field has shown that children who are taught basic computing concepts with unplugged methods can grasp abstract ideas from as young as 3. There is therefore an urgent need to understand exactly how we could use grounded cognition methods to teach children computing — which is arguably one of the most abstract subjects in formal education.
A recent study in this field has shown that children who are taught basic computing concepts with unplugged methods can grasp abstract ideas from as young as 3.
A new framework for teaching computing
Anaclara is part of a group of researchers at the University of Glasgow who are currently developing a new approach to structuring computing education. Their EIFFEL (Enacted Instrumented Formal Framework for Early Learning in Computing) model suggests a progression from enacted to formal activities.
Following this model, in the early years of computing education, learners would primarily engage with activities that allow them to work with tangible 3D objects or manipulate intangible objects, for instance in Scratch. Increasingly, students will be able to perform actions in an instrumented or virtual environment which will require the knowledge of abstract symbols but will not yet require the knowledge of programming languages. Eventually, students will have developed the knowledge and skills to engage in fully formal environments, such as writing advanced code.
In a recent literature review, Anaclara and her colleagues looked at existing research into using grounded cognition theory in computing education. Although several studies report the use of grounded approaches, for instance by using block-based programming, robots, toys, or construction kits, the focus is generally on looking at how concrete objects can be used in unplugged activities due to specific contexts, such as a limited availability of computing devices.
The next steps in this area are looking at how activities that specifically follow the EIFFEL framework can enhance children’s learning.
Research into grounded cognition activities in computer science is ongoing, but we encourage you to try incorporating more hands-on activities when teaching younger learners and observing the effects yourself. Here are a few ideas on how to get started:
Explore the ‘Teach Data Literacy’ guide, developed by the Data Education in Schools team, which offers some practical activities to support young learners to develop their data literacy skills. You can find out more about the Data Education in Schools initiative in Kate Farrell and Judy Robertson’s seminar on teaching primary learners how to be data citizens from May 2023.
Check out Barefoot Computing, which offers a range of resources for early years education that involve physical manipulation and simulation.
Join us at our next seminar
In 2024, we are exploring different ways to teach and learn programming, with and without AI tools. In our next seminar, on 13 February at 17:00 GMT, Majeed Kazemi from the University of Toronto will be joining us to discuss whether AI-powered code generators can help K–12 students learn to program in Python. All of our online seminars are free and open to everyone. Sign up and we’ll send you the link to join on the day.
“Computational thinking is really about thinking, and sometimes about computing.” – Aman Yadav, Michigan State University
Computational thinking is a vital skill if you want to use a computer to solve problems that matter to you. That’s why we consider computational thinking (CT) carefully when creating learning resources here at the Raspberry Pi Foundation. However, educators are increasingly realising that CT skills don’t just apply to writing computer programs, and that CT is a fundamental approach to problem-solving that can be extended into other subject areas. To discuss how CT can be integrated beyond the computing classroom and help introduce the fundamentals of computing to primary school learners, we invited Dr Aman Yadav from Michigan State University to deliver the penultimate presentation in our seminar series on computing education for primary-aged children.
In his presentation, Aman gave a concise tour of CT practices for teachers, and shared his findings from recent projects around how teachers perceive and integrate CT into their lessons.
Research in context
Aman began his talk by placing his team’s work within the wider context of computing education in the US. The computing education landscape Aman described is dominated by the National Science Foundation’s ambitious goal, set in 2008, to train 10,000 computer science teachers. This objective has led to various initiatives designed to support computer science education at the K–12 level. However, despite some progress, only 57% of US high schools offer foundational computer science courses, only 5.8% of students enrol in these courses, and just 31% of the enrolled students are female. As a result, Aman and his team have worked in close partnership with teachers to address questions that explore ways to more meaningfully integrate CT ideas and practices into formal education, such as:
What kinds of experiences do students need to learn computing concepts, to be confident to pursue computing?
What kinds of knowledge do teachers need to have to facilitate these learning experiences?
What kinds of experiences do teachers need to develop these kinds of knowledge?
The CT4EDU project
At the primary education level, the CT4EDU project posed the question “What does computational thinking actually look like in elementary classrooms, especially in the context of maths and science classes?” This project involved collaboration with teachers, curriculum designers, and coaches to help them conceptualise and implement CT in their core instruction.
During professional development workshops using both plugged and unplugged tasks, the researchers supported educators to connect their day-to-day teaching practice to four foundational CT constructs:
Debugging
Abstraction
Decomposition
Patterns
An emerging aspect of the research team’s work has been the important relationship between vocabulary, belonging, and identity-building, with implications for equity. Actively incorporating CT vocabulary in lesson planning and classroom implementation helps students familiarise themselves with CT ideas: “If young people are using the language, they see themselves belonging in computing spaces”.
A main finding from the study is that teachers used CT ideas to explicitly engage students in metacognitive thinking processes, and to help them be aware of their thinking as they solve problems. Rather than teachers using CT solely to introduce their students to computing, they used CT as a way to support their students in whatever they were learning. This constituted a fundamental shift in the research team’s thinking and future work, which is detailed further in a conceptual article.
The Smithsonian Science for Computational Thinking project
The work conducted for the CT4EDU project guided the approach taken in the Smithsonian Science for Computational Thinking project. This project entailed the development of a curriculum for grades 3 and 5 that integrates CT into science lessons.
Part of the project included surveying teachers about the value they place on CT, both before and after participating in professional development workshops focused on CT. The researchers found that even before the workshops, teachers make connections between CT and the rest of the curriculum. After the workshops, an overwhelming majority agreed that CT has value (see image below). From this survey, it seems that CT ties things together for teachers in ways not possible or not achieved with other methods they’ve tried previously.
Despite teachers valuing the CT approach, asking them to integrate coding into their practices from the start remains a big ask (see image below). Many teachers lack knowledge or experience of coding, and they may not be curriculum designers, which means that we need to develop resources that allow teachers to integrate CT and coding in natural ways. Aman proposes that this requires a longitudinal approach, working with teachers over several years, using plugged and unplugged activities, and working closely with schools’ STEAM or specialist technology teachers where applicable to facilitate more computationally rich learning experiences in classrooms.
Integrated computational thinking
Aman’s team is also engaged in a research project to integrate CT at middle school level for students aged 11 to 14. This project focuses on the question “What does CT look like in the context of social studies, English language, and art classrooms?”
For this project, the team conducted three Delphi studies, and consequently created learning pathways for each subject, which teachers can use to bring CT into their classrooms. The pathways specify practices and sub-practices to engage students with CT, and are available on the project website. The image below exemplifies the CT integration pathways developed for the arts subject, where the relationship between art and data is explored from both directions: by using CT and data to understand and create art, and using art and artistic principles to represent and communicate data.
Computational thinking in the primary classroom
Aman’s work highlights the broad value of CT in education. However, to meaningfully integrate CT into the classroom, Aman suggests that we have to take a longitudinal view of the time and methods required to build teachers’ understanding and confidence with the fundamentals of CT, in a way that is aligned with their values and objectives. Aman argues that CT is really about thinking, and sometimes about computing, to support disciplinary learning in primary classrooms. Therefore, rather than focusing on integrating coding into the classroom, he proposes that we should instead talk about using CT practices as the building blocks that provide the foundation for incorporating computationally rich experiences in the classroom.
Our 2024 seminar series is on the theme of teaching programming, with or without AI. In this series, we explore the latest research on how teachers can best support school-age learners to develop their programming skills.
In the rapidly evolving digital landscape, students are increasingly interacting with AI-powered applications when listening to music, writing assignments, and shopping online. As educators, it’s our responsibility to equip them with the skills to critically evaluate these technologies.
A key aspect of this is understanding ‘explainability’ in AI and machine learning (ML) systems. The explainability of a model is how easy it is to ‘explain’ how a particular output was generated. Imagine having a job application rejected by an AI model, or facial recognition technology failing to recognise you — you would want to know why.
Establishing standards for explainability is crucial. Otherwise we risk creating a world where decisions impacting our lives are made by opaque systems we don’t understand. Learning about explainability is key for students to develop digital literacy, enabling them to navigate the digital world with informed awareness and critical thinking.
Why AI explainability is important
AI models can have a significant impact on people’s lives in various ways. For instance, if a model determines a child’s exam results, parents and teachers would want to understand the reasoning behind it.
Artists might want to know if their creative works have been used to train a model and could be at risk of plagiarism. Likewise, coders will want to know if their code is being generated and used by others without their knowledge or consent. If you came across an AI-generated artwork that features a face resembling yours, it’s natural to want to understand how a photo of you was incorporated into the training data.
Explainability is about accountability, transparency, and fairness, which are vital lessons for children as they grow up in an increasingly digital world.
There will also be instances where a model seems to be working for some people but is inaccurate for a certain demographic of users. This happened with Twitter’s (now X’s) face detection model in photos; the model didn’t work as well for people with darker skin tones, who found that it could not detect their faces as effectively as their lighter-skinned friends and family. Explainability allows us not only to understand but also to challenge the outputs of a model if they are found to be unfair.
In essence, explainability is about accountability, transparency, and fairness, which are vital lessons for children as they grow up in an increasingly digital world.
Routes to AI explainability
Some models, like decision trees, regression curves, and clustering, have an in-built level of explainability. There is a visual way to represent these models, so we can pretty accurately follow the logic implemented by the model to arrive at a particular output.
By teaching students about AI explainability, we are not only educating them about the workings of these technologies, but also teaching them to expect transparency as they grow to be future consumers or even developers of AI technology.
A decision tree works like a flowchart, and you can follow the conditions used to arrive at a prediction. Regression curves can be shown on a graph to understand why a particular piece of data was treated the way it was, although this wouldn’t give us insight into exactly why the curve was placed at that point. Clustering is a way of collecting similar pieces of data together to create groups (or clusters) with which we can interrogate the model to determine which characteristics were used to create the groupings.
A decision tree that classifies animals based on their characteristics; you can follow these models like a flowchart
However, the more powerful the model, the less explainable it tends to be. Neural networks, for instance, are notoriously hard to understand — even for their developers. The networks used to generate images or text can contain millions of nodes spread across thousands of layers. Trying to work out what any individual node or layer is doing to the data is extremely difficult.
Regardless of the complexity, it is still vital that developers find a way of providing essential information to anyone looking to use their models in an application or to a consumer who might be negatively impacted by the use of their model.
Model cards for AI models
One suggested strategy to add transparency to these models is using model cards. When you buy an item of food in a supermarket, you can look at the packaging and find all sorts of nutritional information, such as the ingredients, macronutrients, allergens they may contain, and recommended serving sizes. This information is there to help inform consumers about the choices they are making.
Model cards attempt to do the same thing for ML models, providing essential information to developers and users of a model so they can make informed choices about whether or not they want to use it.
Model cards include details such as the developer of the model, the training data used, the accuracy across diverse groups of people, and any limitations the developers uncovered in testing.
Model cards should be accessible to as many people as possible.
A real-world example of a model card is Google’s Face Detection model card. This details the model’s purpose, architecture, performance across various demographics, and any known limitations of their model. This information helps developers who might want to use the model to assess whether it is fit for their purpose.
Transparency and accountability in AI
As the world settles into the new reality of having the amazing power of AI models at our disposal for almost any task, we must teach young people about the importance of transparency and responsibility.
As a society, we need to have hard discussions about where and when we are comfortable implementing models and the consequences they might have for different groups of people. By teaching students about explainability, we are not only educating them about the workings of these technologies, but also teaching them to expect transparency as they grow to be future consumers or even developers of AI technology.
Most importantly, model cards should be accessible to as many people as possible — taking this information and presenting it in a clear and understandable way. Model cards are a great way for you to show your students what information is important for people to know about an AI model and why they might want to know it. Model cards can help students understand the importance of transparency and accountability in AI.
Today’s blog is written by Dr Alex Hadwen-Bennett, who we worked with to find out primary school learners’ experiences of engaging with culturally relevant Computing lessons. Alex is a Lecturer in Computing Education at King’s College London, where he undertakes research focusing on inclusive computing education and the pedagogy of making.
For this reason, a particular focus of the Raspberry Pi Foundation’s academic research programme is to support Computing teachers in the use of culturally relevant pedagogy. This pedagogy involves developing learning experiences that deliberately aim to enable all learners to engage with and succeed in Computing, including by bringing their culture and interests into the classroom.
At the beginning of this study, teachers adapted two units of work that cover digital literacy skills
Conducting the focus groups
For the focus groups, the Foundation team asked teachers from three schools to each choose four learners to take part. All children in the three focus groups had taken part in all the lessons involving the culturally adapted resources. The children were both boys and girls, and came from diverse cultural backgrounds where possible.
The questions for the focus groups were prepared in advance and covered:
Perceptions of Computing as a subject
Reflections of their experiences of the engaging with culturally adapted resources
Perceptions of who does Computing
Outcomes from the focus groups
“I feel happy that I see myself represented in some way.”
“It was nice to do something that actually represented you in many different ways, like your culture and your background.”
– Statements of learners who participated in the focus groups
When the learners were asked about what they did in their Computing lessons, most of them made references to working with and manipulating graphics; fewer made references to programming and algorithms. This emphasis on graphics is likely related to this being the most recent topic the learners engaged with. The learners were also asked about their reflections on the culturally adapted graphics unit that they had recently completed. Many of them felt that the unit gave them the freedom to incorporate things that related to their interests or culture. The learners’ responses also suggested that they felt represented in the work they completed during the unit. Most of them indicated that their interests were acknowledged, whereas fewer mentioned that they felt their cultural backgrounds were highlighted.
“Anyone can be good at computing if they have the passion to do it.”
– Statement by a learner who participated in a focus group
When considering who does computing, the learners made multiple references to people who keep trying or do not give up. Whereas only a couple of learners said that computer scientists need to be clever or intelligent to do computing. A couple of learners suggested that they believed that anyone can do computing. It is encouraging that the learners seemed to associate being good at computing with effort rather than with ability. However, it is unclear whether this is associated with the learners engaging with the culturally adapted resources.
Reflections and next steps
While this was a small-scale study, the focus groups findings do suggest that engaging with culturally adapted resources can make primary learners feel more represented in their Computing lessons. In particular, engaging with an adapted unit led learners to feel that their interests were recognised as well as, to a lesser extent, their cultural backgrounds. This suggests that primary-aged learners may identify their practical interests as the most important part of their background, and want to share this in class.
Finally, the responses of the learners suggest that they feel that perseverance is a more important quality than intelligence for success in computing and that anyone can do it. While it is not possible to say whether this is directly related to their engagement with a culturally adapted unit, it would be an interesting area for further research.
The Foundation would like to extend thanks to Cognizant for funding this research, and to the primary computing teachers and learners who participated in the project.
Underrepresentation in computing is a widely known issue, in industry and in education. To cite some statistics from the UK: a Black British Voices report from August 2023 noted that 95% of respondents believe the UK curriculum neglects black lives and experiences; fewer students from working class backgrounds study GCSE Computer Science; when they leave formal education, fewer female, BAME, and white working class people are employed in the field of computer science (Kemp 2021); only 21% of GCSE Computer Science students, 15% at A level, and 22% at undergraduate level are female (JCQ 2020, Ofqual 2020, UCAS 2020); students with additional needs are also underrepresented.
Such statistics have been the status quo for too long. Many Computing teachers already endeavour to bring about positive change where they can and engage learners by including their interests in the lessons they deliver, so how can we support them to do this more effectively? Extending the reach of computing so that it is accessible to all also means that we need to consider what formal and informal values predominate in the field of computing. What is the ‘hidden’ curriculum in computing that might be excluding some learners? Who is and who isn’t represented?
Katharine Childs (Raspberry Pi Foundation)
In a recent research seminar, Katharine Childs from our team outlined a research project we conducted, which included a professional development workshop to increase primary teachers’ awareness of and confidence in culturally relevant pedagogy. In the workshop, teachers considered how to effectively adapt curriculum materials to make them culturally relevant and engaging for the learners in their classrooms. Katharine described the practical steps teachers took to adapt two graphics-related units, and invited seminar participants to apply their learning to a graphics activity themselves.
What is culturally relevant pedagogy?
Culturally relevant pedagogy is a teaching framework which values students’ identities, backgrounds, knowledge, and ways of learning. By drawing on students’ own interests, experiences and cultural knowledge educators can increase the likelihood that the curriculum they deliver is more relevant, engaging and accessible to all.
The idea of culturally relevant pedagogy was first introduced in the US in the 1990s by African-American academic Gloria Ladson-Billings (Ladson-Billings 1995). Its aim was threefold: to raise students’ academic achievement, to develop students’ cultural competence and to promote students’ critical consciousness. The idea of culturally responsive teaching was later advanced by Geneva Gay (2000) and more recently brought into focus in US computer science education by Kimberly Scott and colleagues (2015). The approach has been localised for England by Hayley Leonard and Sue Sentance (2021) in work they undertook here at the Foundation.
Provide opportunities for open-ended and problem solving activities
Promote collaboration and structured group discussion
Promote student agency through choice
Review the learning environment
Review related policies, processes, and training in your school and department
At first glance it is easy to think that you do most of those things already, or to disregard some items as irrelevant to the computing curriculum. What would your own cultural identity (see AO2) have to do with computing, you might wonder. But taking a less complacent perspective might lead you to consider all the different facets that make up your identity and then to think about the same for the students you teach. You may discover that there are many areas which you have left untapped in your lesson planning.
Katharine explained how this is where the professional development workshop showed itself as beneficial for the participants. It gave teachers the opportunity to reflect on how their cultural identity impacted on their teaching practices — as a starting point to learning more about other aspects of the culturally relevant pedagogy approach.
Our researchers were interested in how they could work alongside teachers to adapt two computing units to make them more culturally relevant for teachers’ specific contexts. They used the Computing Curriculum units on Photo Editing (Year 4) and Vector Graphics (Year 5).
Katharine illustrated some of the adaptations teachers and researchers working together had made to the emoji activity above, and which areas of opportunity (AO) had been addressed; this aspect of the research will be reported in later publications.
Results after the workshop
Although the numbers of participants in this pilot study was small, the findings show that the professional development workshop significantly increased teachers’ awareness of culturally relevant pedagogy and their confidence in adapting resources to take account of local contexts:
After the workshop, 10/13 teachers felt more confident to adapt resources to be culturally relevant for their own contexts, and 8/13 felt more confident in adapting resources for others.
Before the workshop, 5/13 teachers strongly agreed that it was an important part of being a computing teacher to examine one’s own attitudes and beliefs about race, gender, disabilities, sexual orientation. After the workshop, the number in agreement rose to 12/13.
After the workshop, 13/13 strongly agreed that part of a computing teacher’s responsibility is to challenge teaching practices which maintain social inequities (compared to 7/13 previously).
Before the workshop, 4/13 teachers strongly agreed that it is important to allow student choice when designing computing activities; this increased to 9/13 after the workshop.
These quantitative shifts in perspective indicate a positive effect of the professional development pilot.
Katharine described that in our qualitative interviews with the participating teachers, they expressed feeling that their understanding of culturally relevant pedagogy had increased and they recognized the many benefits to learners of the approach. They valued the opportunity to discuss their contexts and to adapt materials they currently used with other teachers, because it made it a more ‘authentic’ and practical professional development experience.
The seminar ended with breakout sessions inviting viewers to consider possible adaptations that could be made to the graphics activities which had been the focus of the workshop.
In the breakout sessions, attendees also discussed specific examples of culturally relevant teaching practices that had been successful in their own classrooms, and they considered how schools and computing educational initiatives could support teachers in their efforts to integrate culturally relevant pedagogy into their practice. Some attendees observed that it was not always possible to change schemes of work without a ‘whole-school’ approach, senior leadership team support, and commitment to a research-based professional development programme.
Where do you see opportunities for your teaching?
The seminar reminds us that the education system is not culture neutral and that teachers generally transmit the dominant culture (which may be very different from their students’) in their settings (Vrieler et al, 2022). Culturally relevant pedagogy is an attempt to address the inequities and biases that exist, which result in many students feeling marginalised, disenfranchised, or underachieving. It urges us to incorporate learners’ cultures and experiences in our endeavours to create a more inclusive computing curriculum; to adopt an intersectional lens so that all can thrive.
As a pilot study, the workshop was offered to a small cohort of 13, yet the findings show that the intervention significantly increased participants’ awareness of culturally relevant pedagogy and their confidence in adapting resources to take account of local contexts.
Of course there are many ways in which teachers already adapt resources to make them interesting and accessible to their pupils. Further examples of the sort of adaptations you might make using these areas of opportunity include:
AO1: You could find out to what extent learners feel like they ‘belong’ or are included in a particular computing-related career. This is sure to yield valuable insights into learners’ knowledge and/or preconceptions of computing-related careers.
AO3: You could introduce topics such as the ethics of AI, data bias, investigations of accessibility and user interface design.
AO4: You might change the context of a unit of work on the use of conditional statements in programming, from creating a quiz about ‘Vikings’ to focus on, for example, aspects of youth culture which are more engaging to some learners such as football or computer games, or to focus on religious celebrations, which may be more meaningful to others.
AO5: You could experiment with a particular pedagogical approach to maximise the accessibility of a unit of work. For example, you could structure a programming unit by using the PRIMM model, or follow the Universal Design for Learning framework to differentiate for diversity.
AO6/7: You could offer more open-ended and collaborative activities once in a while, to promote engagement and to allow learners to express themselves autonomously.
AO8: By allowing learners to choose topics which are relevant or familiar to their individual contexts and identities, you can increase their feeling of agency.
AO9: You could review both your learning materials and your classroom to ensure that all your students are fully represented.
AO10: You can bring colleagues on board too; the whole enterprise of embedding culturally relevant pedagogy will be more successful when school- as well as department-level policies are reviewed and prioritised.
Can you see an opportunity for integrating culturally relevant pedagogy in your classroom? We would love to hear about examples of culturally relevant teaching practices that you have found successful. Let us know your thoughts or questions in the comments below.
To get a practical overview of culturally relevant pedagogy, read our 2-page Quick Read on the topic and download the guidelines we created with a group of teachers and academic specialists.
Tomorrow we’ll be sharing a blog about how the learners who engaged with the culturally adapted units found the experience, and how it affected their views of computing. Follow us on social media to not miss it!
Join our upcoming seminars live
On 12 December we’ll host the last seminar session in our series on primary (K-5) computing. Anaclara Gerosa will share her work on how to design and structure early computing activities that promote and scaffold students’ conceptual understanding. As always, the seminar is free and takes place online at 17:00–18:30 GMT / 12:00–13:30 ET / 9:00–10:30 PT / 18:00–19:30 CET. Sign up and we’ll send you the link to join on the day.
How do you best teach programming in school? It’s one of the core questions for primary and secondary computing teachers. That’s why we’re making it the focus of our free online seminars in 2024. You’re invited to attend and hear about the newest research about the teaching and learning of programming, with or without AI tools.
Building on the success and the friendly, accessible session format of our previous seminars, this coming year we will delve into the latest trends and innovative approaches to programming education in school.
Our online seminars are for everyone interested in computing education
Our monthly online seminars are not only for computing educators but also for everyone else who is passionate about teaching young people to program computers. The seminar participants are a diverse community of teachers, technology enthusiasts, industry professionals, coding club volunteers, and researchers.
With the seminars we aim to bridge the gap between the newest research and practical teaching. Whether you are an educator in a traditional classroom setting or a mentor guiding learners in a CoderDojo or Code Club, you will gain insights from leading researchers about how school-age learners engage with programming.
What to expect from the seminars
Each online seminar begins with an expert presenter delivering their latest research findings in an accessible way. We then move into small groups to encourage discussion and idea exchange. Finally, we come back together for a Q&A session with the presenter.
Here’s what attendees had to say about our previous seminars:
“As a first-time attendee of your seminars, I was impressed by the welcoming atmosphere.”
“[…] several seminars (including this one) provided valuable insights into different approaches to teaching computing and technology.”
“I plan to use what I have learned in the creation of curriculum […] and will pass on what I learned to my team.”
“I enjoyed the fact that there were people from different countries and we had a chance to see what happens elsewhere and how that may be similar and different to what we do here.”
January seminar: AI-generated Parson’s Problems
Computing teachers know that, for some students, learning about the syntax of programming languages is very challenging. Working through Parson’s Problem activities can be a way for students to learn to make sense of the order of lines of code and how syntax is organised. But for teachers it can be hard to precisely diagnose their students’ misunderstandings, which in turn makes it hard to create activities that address these misunderstandings.
At our first 2024 seminar on 9 January, Dr Barbara Ericson and Xinying Hou (University of Michigan) will present a promising new approach to helping teachers solve this difficulty. In one of their studies, they combined Parsons Problems and generative AI to create targeted activities for students based on the errors students had made in previous tasks. Thus they were able to provide personalised activities that directly addressed gaps in the students’ learning.
Sign up now to join our seminars
All our seminars start at 17:00 UK time (18:00 CET / 12:00 noon ET / 9:00 PT) and are held online on Zoom. To ensure you don’t miss out, sign up now to receive calendar invitations, and access links for each seminar on the day.
Since launching our free online courses about computing on the edX platform back in August, we’ve been training course facilitators and analysing the needs of educators around the world. We want every course participant to have a great experience learning with us — read on to find out what we’re doing right now and into 2024 to ensure this.
Online courses for all adults who support young people
Educators of all kinds are key for supporting children and young people to engage with computing technology and develop digital skills. You might be a professional teacher, or a parent, volunteer, youth worker, librarian… there are so many roles in which people share knowledge with young learners.
That’s why our online courses are designed to support any kind of educator to:
Understand the full breadth of topics within computing
Discover how to introduce computing to young people in clear and exciting ways that are grounded in the latest research
We are constantly improving our online courses based on your feedback, the latest education research, and the insights our team members gain through supporting you on your course learning journeys. Three principles guide these improvements: accessibility, scalability, and sustainability.
Making our courses more relevant and accessible
Our online courses are used by people who live around the world and bring various knowledge and experiences. Some participants are classroom teachers, others have computing experience from their job and want to volunteer at a kids’ coding club, and some may be parents who want to support their children. It’s important to us that our courses are relevant and accessible to all kinds of adult learners.
We’re currently working to:
Simplify the English in the courses for participants who speak it as a second language
Adapt the course activities for specific settings where participants help young people learn so that e.g. teachers see how the activities work in the classroom, and volunteers who run coding clubs see how they work in club sessions
Ensure our course facilitators have experience in a range of different settings including coding clubs, and in a variety of different contexts around the world
Making our courses useful for more groups of people
When we think about the scalability of our courses, we think about how to best support as many educators around the world as possible. If we can make the jobs of all educators easier, whatever their setting is like, then we are making the right choices.
We’re currently working to:
Talk with the global network of educators we’re a part of to better understand what works for them so we can reflect that in the courses
Include a wider range of examples for settings beyond the classroom in the courses
Adapt our courses so they are relevant to participants with various needs while sustaining the high quality of the overall learning experience
Making the learning from our courses sustainable
The educators who take our courses work to achieve amazing things, and this means they are often busy. That they take the time to complete one of our courses to learn new things is a commitment we want to make sure is rewarded. The learning you get from participating in our online courses should continue to benefit you far beyond the time you spend completing it. This is what we mean by sustainability.
We’re currently working to:
Lay out clear learning pathways so you can build on the knowledge you gain in one course in the next course
Offer course resources that are easy to access after you’ve completed the course
Explore ways to build communities around our courses where you can share successes and learning outcomes with your fellow participants
Learn with us, and help us design better courses for you
Our work to improve the accessibility, scalability, and sustainability of our courses will continue into 2024, and these three principles will likely be part of our online training strategy for the following year too.
If you’d like to support young people in your life to learn about computing and digital technologies, take one of our free courses now and learn something new. We have twenty courses available right now and they are totally free.
We are also looking for adult testers for new course content. So if you’re any kind of educator and would like to test upcoming online course content and share your feedback and experiences, please send us a message with the subject ‘Educator training’.
We all know that learning to program, and specifically learning how to debug or fix code, can be frustrating and leave beginners overwhelmed and disheartened. In a recent blog article, our PhD student Lauria at the Raspberry Pi Computing Education Research Centre highlighted the pivotal role that teachers play in shaping students’ attitudes towards debugging. But what about teachers who are coding novices themselves?
In many countries, primary school teachers are holistic educators and often find themselves teaching computing despite having little or no experience in the field. In a recent seminar of our series on computing education for primary-aged children, Luisa Greifenstein told attendees that struggling with debugging and negative attitudes towards programming were among the top ten challenges mentioned by teachers.
Luisa is a researcher at the University of Passau, Germany, and has been working closely with both teacher trainees and experienced primary school teachers in Germany. She’s found that giving feedback to students can be difficult for primary school teachers, and especially for teacher trainees, as programming is still new to them. Luisa’s seminar introduced a tool to help.
A unique approach: Visualising debugging with LitterBox
To address this issue, the University of Passau has initiated the primary::programming project. One of its flagship tools, LitterBox, offers a unique solution to debugging and is specifically designed for Scratch, a beginners’ programming language widely used in primary schools.
You can upload Scratch program files to LitterBox to analyse them. Click to enlarge.
LitterBox serves as a static code debugging tool that transforms code examination into an engaging experience. With a nod to the Scratch cat, the tool visualises the debugging of Scratch code as checking the ‘litterbox’, categorising issues into ‘bugs’ and ‘smells’:
Bugs represent code patterns that have gone wrong, such as missing loops or specific blocks
Smells indicate that the code couldn’t be processed correctly because of duplications or unnecessary elements
The code patterns LitterBox recognises. Click to enlarge.
What sets LitterBox apart is that it also rewards correct code by displaying ‘perfumes’. For instance, it will praise correct broadcasting or the use of custom blocks. For every identified problem or achievement, the tool provides short and direct feedback.
LitterBox also identifies good programming practice. Click to enlarge.
Luisa and her team conducted a study to gauge the effectiveness of LitterBox. In the study, teachers were given fictitious student code with bugs and were asked to first debug the code themselves and then explain in a manner appropriate to a student how to do the debugging.
The results were promising: teachers using LitterBox outperformed a control group with no access to the tool. However, the team also found that not all hints proved equally helpful. When hints lacked direct relevance to the code at hand, teachers found them confusing, which highlighted the importance of refining the tool’s feedback mechanisms.
Despite its limitations, LitterBox proved helpful in another important aspect of the teachers’ work: coding task creation. Novice students require structured tasks and help sheets when learning to code, and teachers often invest substantial time in developing these resources. While LitterBox does not guide educators in generating new tasks or adapting them to their students’ needs, in a second study conducted by Luisa’s team, teachers who had access to LitterBox not only received support in debugging their own code but also provided more scaffolding in task instructions they created for their students compared to teachers without LitterBox.
How to maximise the impact of new tools: use existing frameworks and materials
One important realisation that we had in the Q&A phase of Luisa’s seminar was that many different research teams are working on solutions for similar challenges, and that the impact of this research can be maximised by integrating new findings and resources. For instance, what the LitterBox tool cannot offer could be filled by:
Pedagogical frameworks to enhance teachers’ lessons and feedback structures. Frameworks such as PRIMM (Predict, Run, Investigate, Modify, and Make) or TIPP&SEE for Scratch projects (Title, Instructions, Purpose, Play & Sprites, Events, Explore) can serve as valuable resources. These frameworks provide a structured approach to lesson design and teaching methodologies, making it easier for teachers to create engaging and effective programming tasks. Additionally, by adopting semantic waves in the feedback for teachers and students, a deeper understanding of programming concepts can be fostered.
Existing courses and materials to aid task creation and adaptation. Our expert educators at the Raspberry Pi Foundation have not only created free lesson plans and courses for teachers and educators, but also dedicated non-formal learning paths for Scratch, Python, Unity, web design, and physical computing that can serve as a starting point for classroom tasks.
Exploring innovative ideas in computing education
As we navigate the evolving landscape of programming education, it’s clear that innovative tools like LitterBox can make a significant difference in the journey of both educators and students. By equipping educators with effective debugging and task creation solutions, we can create a more positive and engaging learning experience for students.
If you’re an educator, consider exploring how such tools can enhance your teaching and empower your students in their coding endeavours.
You can watch the recording of Luisa’s seminar here:
Sign up now to join our next seminar
If you’re interested in the latest developments in computing education, join us at one of our free, monthly seminars. In these sessions, researchers from all over the world share their innovative ideas and are eager to discuss them with educators and students. In our December seminar, Anaclara Gerosa (University of Edinburgh) will share her findings about how to design and structure early-years computing activities.
This will be the final seminar in our series about primary computing education. Look out for news about the theme of our 2024 seminar series, which are coming soon.
I am delighted to announce that the Raspberry Pi Foundation and Google DeepMind are building a global network of educational organisations to bring AI literacy to teachers and students all over the world, starting with Canada, Kenya, and Romania.
Learners around the world will gain AI literacy skills through Experience AI.
Experience AI
We launched Experience AI in September 2022 to help teachers and students learn about AI technologies and how they are changing the world.
Developed by the Raspberry Pi Foundation and Google DeepMind, Experience AI provides everything that teachers need to confidently deliver engaging lessons that will inspire and educate young people about AI and the role that it could play in their lives.
Experience AI is designed to inspire learners about AI through real-world contexts.
We provide lesson plans, classroom resources, worksheets, hands-on activities, and videos that introduce a wide range of AI applications and the underlying technologies that make them work. The materials are designed to be relatable to young people and can be taught by any teacher, whether or not they have a technical background. Alongside the classroom resources, we provide teacher professional development, including an online course that provides an introduction to machine learning and AI.
Part of Experience AI are video interviews with AI developers at Google DeepMind.
The materials are grounded in real-world contexts and emphasise the potential for young people to positively change the world through a mastery of AI technologies.
Since launching the first resources, we have seen significant demand from teachers and students all over the world, with over 200,000 students already learning with Experience AI.
Experience AI network
Building on that initial success and in response to huge demand, we are now building a global network of educational organisations to expand the reach and impact of Experience AI by translating and localising the materials, promoting them to schools, and supporting teacher professional development.
Obum Ekeke OBE, Head of Education Partnerships at Google DeepMind, says:
“We have been blown away by the interest we have seen in Experience AI since its launch and are thrilled to be working with the Raspberry Pi Foundation and local partners to expand the reach of the programme. AI literacy is a critical skill in today’s world, but not every young person currently has access to relevant education and resources. By making AI education more inclusive, we can help young people make more informed decisions about using AI applications in their daily lives, and encourage safe and responsible use of the technology.”
Experience AI helps learners understand how they might use AI to positively change the world.
Today we are announcing the first three organisations that we are working with, each of which is already doing fantastic work to democratise digital skills in their part of the world. All three are already working in partnership with the Raspberry Pi Foundation and we are excited to be deepening and expanding our collaboration to include AI literacy.
Digital Moment, Canada
Digital Moment is a Montreal-based nonprofit focused on empowering young changemakers through digital skills. Founded in 2013, Digital Moment has a track record of supporting teachers and students across Canada to learn about computing, coding, and AI literacy, including through supporting one of the world’s largest networks of Code Clubs.
“We’re excited to be working with the Raspberry Pi Foundation and Google DeepMind to bring Experience AI to teachers across Canada. Since 2018, Digital Moment has been introducing rich training experiences and educational resources to make sure that Canadian teachers have the support to navigate the impacts of AI in education for their students. Through this partnership, we will be able to reach more teachers and with more resources, to keep up with the incredible pace and disruption of AI.”
Indra Kubicek, President, Digital Moment
Tech Kidz Africa, Kenya
Tech Kidz Africa is a Mobasa-based social enterprise that nurtures creativity in young people across Kenya through digital skills including coding, robotics, app and web development, and creative design thinking.
“With the retooling of teachers as a key objective of Tech Kidz Africa, working with Google DeepMind and the Raspberry Pi Foundation will enable us to build the capacity of educators to empower the 21st century learner, enhancing the teaching and learning experience to encourage innovation and prepare the next generation for the future of work.”
Grace Irungu, CEO, Tech Kidz Africa
Asociația Techsoup, Romania
Asociația Techsoup works with teachers and students across Romania and Moldova, training Computer Science, ICT, and primary school teachers to build their competencies around coding and technology. A longstanding partner of the Raspberry Pi Foundation, they foster a vibrant community of CoderDojos and support young people to participate in Coolest Projects and the European Astro Pi Challenge.
“We are enthusiastic about participating in this global partnership to bring high-quality AI education to all students, regardless of their background. Given the current exponential growth of AI tools and instruments in our daily lives, it is crucial to ensure that students and teachers everywhere comprehend and effectively utilise these tools to enhance their human, civic, and professional potential. Experience AI is the best available method for AI education for middle school students. We couldn’t be more thrilled to work with the Raspberry Pi Foundation and Google DeepMind to make it accessible in Romanian for teachers in Romania and the Republic of Moldova, and to assist teachers in fully integrating it into their classes.”
Elena Coman, Director of Development, Asociația Techsoup
Get involved
These are the first of what will become a global network of organisations supporting tens of thousands of teachers to equip millions of students with a foundational understanding of AI technologies through Experience AI. If you want to get involved in inspiring the next generation of AI leaders, we would love to hear from you.
On September 29 2023, amidst much excitement and enthusiasm, a significant event took place at a unique school in Moinabad, Telangana: the teams of the Raspberry Pi Foundation and Telangana Social Welfare Residential Educational Institutions Society (TSWREIS) gathered to celebrate our partnership on the esteemed Coding Academy of TSWREIS.
This event marked a special project for us where we are piloting a distinctive, progression-based computing curriculum in a government school and a degree college in India.
Partnering with TSWREIS to bring computing education to Telangana
At the Foundation, our goal is to work closely with schools, tailoring our offerings to their contexts. Our objective is to design and evaluate unique learning experiences by integrating content from our diverse range of high-quality educational products. Through these efforts, we aim to drive significant advancements in education and technology, benefiting both students and education systems across the world.
TSWREIS manages 268 residential educational institutions in Telangana, with a primary focus on delivering quality education to under-resourced young people, particularly children from scheduled castes and tribes in rural areas. Among these institutions is the Coding Academy school, located in Moinabad, which operates as a fully residential co-ed school for grades 6 to 12, accommodating around 800 students. Additionally, TSWREIS oversees another centre of excellence, the Coding Academy degree college in Shamirpet catering to 600 undergraduate female students.
We joined forces with TSWREIS to form a collaborative partnership with their Coding Academy units at both high school and college. We’re committed to sharing our expertise in computing and coding curriculum for students from Grade 6 to intermediate at the school, and across all courses at the college.
The exciting model for our partnership with TSWREIS
We took on the challenge of directly delivering a comprehensive curriculum at the Coding Academy school and college through our own educators, exclusively hired and trained for this project. This is an exciting new approach for us, because up to this point, we have never directly delivered a curriculum anywhere in the world. However, we know we have created a world-class computing curriculum for educators in formal (and non-formal) settings, and we have many years’ experience of training teachers, so we are well-prepared to face this project and its potential challenges head-on and make it a success.
To begin the project, our team members based in India conducted a thorough study of the Coding Academy students’ interests and learning levels. Based on this, our Curriculum team in the UK and India customised and localised the content in our curriculum. We will be observing the curriculum’s delivery in classrooms and collecting students’ responses, and based on this data we’ll further refine the localised curriculum.
Throughout the project’s lifespan, we’ll measure the effectiveness of our curriculum and the impact of learning on the students. To do this, we’ll collect data from classroom observations, periodic assessments, and focused group discussions with students and educators.
Starting from the second year of the project, we will build capacity within the system. In collaboration with TSWREIS, we’ll select teachers from within the organisation based on their interest and competence, and initiate their training. Our objective is that by the project’s fifth year, TSWREIS will have achieved self-sufficiency in delivering computing education to students at the Coding Academy as well as other institutions in its purview.
The promise of this project for our work in India
We began delivering lessons at the Coding Academy college and school in July, and it’s worth mentioning that it’s been a rollercoaster ride so far. We’ve been working closely with the TSWREIS team to equip both the academic units with the resources needed for seamless implementation of the project. Our India-based team has been able to ensure continuity in the project’s momentum and plug every gap, and is working tirelessly to make this big, challenging, and exciting project blossom and succeed. When it comes to the students’ energy, enthusiasm, and the sparkle in their eyes for their learning, it’s unmatched, and everyone feels proud of their achievements so far.
This work with TSWREIS holds immense importance for us, representing our dedication to shaping a brighter educational landscape especially for young people from under-resourced communities. We hope to replicate similar initiatives across various regions in India, enabling widespread access to quality education. We also aspire to take forward our initiatives in much larger dimensions for the entirety of India.
In addition to our partnership with TSWREIS, we are actively engaged in several other impactful projects in India, such as our partnership with Mo School Abhiyan in Odisha to serve the government’s schools across Odisha state, and our collaboration with Pratham Foundation, which is helping us reach under-resourced communities and furthering our commitment to enhancing educational experiences.
We look towards the future
In reflection, the voices at the launch event on September 29 echoed the anticipation and optimism that filled the air on that memorable day. Chief guests who graciously attended the event were Shri. E Naveen Nicholas, IAS, Secretary at TSWREIS & TTWREIS, and Rachel Bennett, our Managing Director at the Raspberry Pi Foundation. Heartfelt gratitude to them for their presence and blessings. We also extend our thanks to our funding partner in this work, Ezrah Charitable Trust, and our delivery partners for their invaluable support.
The energy felt on the event day continues to drive our determination to do the work that lies ahead. As we look forward to the future, our hope and the hope of both the Coding Academy team and students are aligned: hope for a brighter, technologically empowered future, where education becomes a beacon of opportunity for all.
Recent developments in artificial intelligence are changing how the world sees computing and challenging computing educators to rethink their approach to teaching. In the brand-new issue of Hello World, out today for free, we tackle some big questions about AI and computing education. We also get practical with resources for your classroom.
Teaching and AI
In their articles for issue 22, educators explore a range of topics related to teaching and AI, including what is AI literacy and how do we teach it; gender bias in AI and what we can do about it; how to speak to young children about AI; and why anthropomorphism hinders learners’ understanding of AI.
Our feature articles also include a research digest on AI ethics for children, and of course hands-on examples of AI lessons for your learners.
A snapshot of AI education
Hello World issue 22 is a comprehensive snapshot of the current landscape of AI education. Ben Garside, Learning Manager for our Experience AI programme and guest editor of this issue, says:
“When I was teaching in the classroom, I used to enjoy getting to grips with new technological advances and finding ways in which I could bring them into school and excite the students I taught. Occasionally, during the busiest of times, I’d also look longingly at other subjects and be jealous that their curriculum appeared to be more static than ours (probably a huge misconception on my behalf).”
It’s inspiring for me to see how the education community is reacting to the opportunities that AI can provide.
Ben Garside
“It’s inspiring for me to see how the education community is reacting to the opportunities that AI can provide. Of course, there are elements of AI where we need to tread carefully and be very cautious in our approach, but what you’ll see in this magazine is educators who are thinking creatively in this space.”
Download Hello World issue 22 for free
AI is a topic we’ve addressed before in Hello World, and we’ll keep covering this rapidly evolving area in future. We hope this issue gives you plenty of ideas to take away and build upon.
Also in issue 22:
Vocational training for young people
Making the most of online educator training
News about BBC micro:bit
An insight into the WiPSCE 2023 conference for teachers and educators
And much, much more
You can download your free PDF issue now, or purchase a print copy from our store. UK-based subscribers for a free print edition can expect their copies to arrive in the mail this week.
Send us a message or tag us on social media to let us know which articles have made you think and, most importantly, which will help you with your teaching.
It’s been less than a year since ChatGPT catapulted generative artificial intelligence (AI) into mainstream public consciousness, reigniting the debate about the role that these powerful new technologies will play in all of our futures.
‘Will AI save or destroy humanity?’ might seem like an extreme title for a podcast, particularly if you’ve played with these products and enjoyed some of their obvious limitations. The reality is that we are still at the foothills of what AI technology can achieve (think World Wide Web in the 1990s), and lots of credible people are predicting an astonishing pace of progress over the next few years, promising the radical transformation of almost every aspect of our lives. Comparisons with the Industrial Revolution abound.
At the same time, there are those saying it’s all moving too fast; that regulation isn’t keeping pace with innovation. One of the UK’s leading AI entrepreneurs, Mustafa Suleyman, said recently: “If you don’t start from a position of fear, you probably aren’t paying attention.”
What is AI literacy for young people?
What does all this mean for education, and particularly for computing education? Is there any point trying to teach children about AI when it is all changing so fast? Does anyone need to learn to code anymore? Will teachers be replaced by chatbots? Is assessment as we know it broken?
If we’re going to seriously engage with these questions, we need to understand that we’re talking about three different things:
AI literacy: What it is and how we teach it
Rethinking computer science (and possibly some other subjects)
Enhancing teaching and learning through AI-powered technologies
AI literacy: What it is and how we teach it
For young people to thrive in a world that is being transformed by AI systems, they need to understand these technologies and the role they could play in their lives.
Our SEAME model articulates the concepts, knowledge, and skills that are essential ingredients of any AI literacy curriculum.
The first problem is defining what AI literacy actually means. What are the concepts, knowledge, and skills that it would be useful for a young person to learn?
The reality is that — with a few notable exceptions — the vast majority of AI literacy resources available today are probably doing more harm than good.
In the past couple of years there has been a huge explosion in resources that claim to help young people develop AI literacy. Our research team mapped and categorised over 500 resources, and undertaken a systematic literature review to understand what research has been done on K–12 AI classroom interventions (spoiler: not much).
The reality is that — with a few notable exceptions — the vast majority of AI literacy resources available today are probably doing more harm than good. For example, in an attempt to be accessible and fun, many materials anthropomorphise AI systems, using human terms to describe them and their functions and thereby perpetuating misconceptions about what AI systems are and how they work.
What emerged from this work at the Raspberry Pi Foundation is the SEAME model, which articulates the concepts, knowledge, and skills that are essential ingredients of any AI literacy curriculum. It separates out the social and ethical, application, model, and engine levels of AI systems — all of which are important — and gets specific about age-appropriate learning outcomes for each.
This research has formed the basis of Experience AI (experience-ai.org), a suite of resources, lessons plans, videos, and interactive learning experiences created by the Raspberry Pi Foundation in partnership with Google DeepMind, which is already being used in thousands of classrooms.
If we’re serious about AI literacy for young people, we have to get serious about AI literacy for teachers.
Defining AI literacy and developing resources is part of the challenge, but that doesn’t solve the problem of how we get them into the hands and minds of every young person. This will require policy change. We need governments and education system leaders to grasp that a foundational understanding of AI technologies is essential for creating economic opportunity, ensuring that young people have the mindsets to engage positively with technological change, and avoiding a widening of the digital divide. We’ve messed this up before with digital skills. Let’s not do it again.
Teacher professional development is key to AI literacy for young people.
More than anything, we need to invest in teachers and their professional development. While there are some fantastic computing teachers with computer science qualifications, the reality is that most of the computing lessons taught anywhere on the planet are taught by a non-specialist teacher. That is even more so the case for anything related to AI. If we’re serious about AI literacy for young people, we have to get serious about AI literacy for teachers.
Rethinking computer science
Alongside introducing AI literacy, we also need to take a hard look at computer science. At the very least, we need to make sure that computer science curricula include machine learning models, explaining how they constitute a new paradigm for computing, and give more emphasis to the role that data will play in the future of computing. Adding anything new to an already packed computer science curriculum means tough choices about what to deprioritise to make space.
One of our Experience AI Lessons revolves around the us of AI technology to study the Serengeti ecosystem.
And, while we’re reviewing curricula, what about biology, geography, or any of the other subjects that are just as likely to be revolutionised by big data and AI? As part of Experience AI, we are launching some of the first lessons focusing on ecosystems and AI, which we think should be at the heart of any modern biology curriculum.
Some are saying young people don’t need to learn how to code. It’s an easy political soundbite, but it just doesn’t stand up to serious scrutiny.
There is already a lively debate about the extent to which the new generation of AI technologies will make programming as we know it obsolete. In January, the prestigious ACM journal ran an opinion piece from Matt Welsh, founder of an AI-powered programming start-up, in which he said: “I believe the conventional idea of ‘writing a program’ is headed for extinction, and indeed, for all but very specialised applications, most software, as we know it, will be replaced by AI systems that are trained rather than programmed.”
Writing computer programs is an essential part of learning how to analyse problems in computational terms.
With GitHub (now part of Microsoft) claiming that their pair programming technology, Copilot, is now writing 46 percent of developers’ code, it’s perhaps not surprising that some are saying young people don’t need to learn how to code. It’s an easy political soundbite, but it just doesn’t stand up to serious scrutiny.
Even if AI systems can improve to the point where they generate consistently reliable code, it seems to me that it is just as likely that this will increase the demand for more complex software, leading to greater demand for more programmers. There is historical precedent for this: the invention of abstract programming languages such as Python dramatically simplified the act of humans providing instructions to computers, leading to more complex software and a much greater demand for developers.
Learning to program will help young people understand how the world around them is being transformed by AI systems.
However these AI-powered tools develop, it will still be essential for young people to learn the fundamentals of programming and to get hands-on experience of writing code as part of any credible computer science course. Practical experience of writing computer programs is an essential part of learning how to analyse problems in computational terms; it brings the subject to life; it will help young people understand how the world around them is being transformed by AI systems; and it will ensure that they are able to shape that future, rather than it being something that is done to them.
Enhancing teaching and learning through AI-powered technologies
Technology has already transformed learning. YouTube is probably the most important educational innovation of the past 20 years, democratising both the creation and consumption of learning resources. Khan Academy, meanwhile, integrated video instruction into a learning experience that gamified formative assessment. Our own edtech platform, Ada Computer Science, combines comprehensive instructional materials, a huge bank of questions designed to help learning, and automated marking and feedback to make computer science easier to teach and learn. Brilliant though these are, none of them have even begun to harness the potential of AI systems like large language models (LLMs).
The challenge for all of us working in education is how we ensure that ethics and privacy are at the centre of the development of [AI-powered edtech].
One area where I think we’ll see huge progress is feedback. It’s well-established that good-quality feedback makes a huge difference to learning, but a teacher’s ability to provide feedback is limited by their time. No one is seriously claiming that chatbots will replace teachers, but — if we can get the quality right — LLM applications could provide every child with unlimited, on-demand feedback. AI-powered feedback — not giving students the answers, but coaching, suggesting, and encouraging in the way that great teachers already do — could be transformational.
The challenge for all of us working in education is how we ensure that ethics and privacy are at the centre of the development of AI-powered edtech.
We are already seeing edtech companies racing to bring new products and features to market that leverage LLMs, and my prediction is that the pace of that innovation is going to increase exponentially over the coming years. The challenge for all of us working in education is how we ensure that ethics and privacy are at the centre of the development of these technologies. That’s important for all applications of AI, but especially so in education, where these systems will be unleashed directly on young people. How much data from students will an AI system need to access? Can that data — aggregated from millions of students — be used to train new models? How can we communicate transparently the limitations of the information provided back to students?
Ultimately, we need to think about how parents, teachers, and education systems (the purchasers of edtech products) will be able to make informed choices about what to put in front of students. Standards will have an important role to play here, and I think we should be exploring ideas such as an AI kitemark for edtech products that communicate whether they meet a set of standards around bias, transparency, and privacy.
Realising potential in a brave new world
We may very well be entering an era in which AI systems dramatically enhance the creativity and productivity of humanity as a species. Whether the reality lives up to the hype or not, AI systems are undoubtedly going to be a big part of all of our futures, and we urgently need to figure out what that means for education, and what skills, knowledge, and mindsets young people need to develop in order to realise their full potential in that brave new world.
That’s the work we’re engaged in at the Raspberry Pi Foundation, working in partnership with individuals and organisations from across industry, government, education, and civil society.
If you have ideas and want to get involved in shaping the future of computing education, we’d love to hear from you.
This article will also appear in issue 22 of Hello World magazine, which focuses on teaching and AI. We are publishing this new issue on Monday 23 October. Sign up for a free digital subscription to get the PDF straight to your inbox on the day.
Block-based programming applications like Scratch and ScratchJr provide millions of children with an introduction to programming; they are a fun and accessible way for beginners to explore programming concepts and start making with code. ScratchJr, in particular, is designed specifically for children between the ages of 5 and 7, enabling them to create their own interactive stories and games. So it’s no surprise that they are popular tools for primary-level (K–5) computing teachers and learners. But how can teachers assess coding projects built in ScratchJr, where the possibilities are many and children are invited to follow their imagination?
Aim Unahalekhala
In the latest seminar of our series on computing education for primary-aged children, attendees heard about two research studies that explore the use of ScratchJr in K–2 education. The speaker, Apittha (Aim) Unahalekhala, is a graduate researcher at the DevTech Research Group at Tufts University. The two studies looked at assessing young children’s ScratchJr coding projects and understanding how they create projects. Both of the studies were part of the Coding as Another Language project, which sees computer science as a new literacy for the 21st century, and is developing a literacy-based coding curriculum for K–2.
How to evaluate children’s ScratchJr projects
ScratchJr offers children 28 blocks to choose from when creating a coding project. Some of these are simple, such as blocks that determine the look of a character or setting, while others are more complex, such as messaging blocks and loops. Children can combine the blocks in many different ways to create projects of different levels of complexity.
Selecting blocks for a ScratchJr project
At the start of her presentation, Aim described a rubric that she and her colleagues at DevTech have developed to assess three key aspects of a ScratchJr coding project. These aspects are coding concepts, project design, and purposefulness.
Coding concepts in ScratchJr are sequencing, repeats, events, parallelism, coordination, and the number parameter
Project design includes elaboration (number of settings and characters, use of speech bubbles) and originality (character and background customisation, animated looks, sounds)
The rubric lets educators or researchers:
Assess learners’ ability to use their coding knowledge to create purposeful and creative ScratchJr projects
Identify the level of mastery of each of the three key aspects demonstrated within the project
Identify where learners might need more guidance and support
The elements covered by the ScratchJr project evaluation rubric. Click to enlarge.
As part of the study, Aim and her colleagues collected coding projects from two schools at the start, middle, and end of a curriculum unit. They used the rubric to evaluate the coding projects and found that project scores increased over the course of the unit.
They also found that, overall, the scores for the project design elements were higher than those for coding concepts: many learners enjoyed spending lots of time designing their characters and settings, but made less use of other features. However, the two scores were correlated, meaning that learners who devoted a lot of time to the design of their project also got higher scores on coding concepts.
The rubric is a useful tool for any teachers using ScratchJr with their students. If you want to try it in your classroom, the validated rubric is free to download from the DevTech research group’s website.
How do young children create a project?
The rubric assesses the output created by a learner using ScratchJr. But learning is a process, not just an end outcome, and the final project might not always be an accurate reflection of a child’s understanding.
By understanding more about how young children create coding projects, we can improve teaching and curriculum design for early childhood computing education.
In the second study Aim presented, she set out to explore this question. She conducted a qualitative observation of children as they created coding projects at different stages of a curriculum unit, and used Google Analytics data to conduct a quantitative analysis of the steps the children took.
A project creation process involving iteration
Her findings highlighted the importance of encouraging young learners to explore the full variety of blocks available, both by guiding them in how to find and use different blocks, and by giving them the time and tools they need to explore on their own.
She also found that different teaching strategies are needed at different stages of the curriculum unit to support learners. This helps them to develop their understanding of both basic and advanced blocks, and to explore, customise, and iterate their projects.
Early-unit strategy:
Encourage free play to self-discover different functions, especially basic blocks
Mid-unit strategy:
Set plans on how long children will need on customising vs coding
More guidance on the advanced blocks, then let children explore
End-of-unit strategy:
Provide multiple sessions to work
Promote iteration by encouraging children to keep improving code and adding details
Teaching strategies for different stages of the curriculum
Join our next seminar on primary computing education
At our next seminar, we welcome Aman Yadav (Michigan State University), who will present research on computational thinking in primary school. The session will take place online on Tuesday 7 November at 17:00 UK time. Don’t miss out and sign up now:
From 27 to 29 September 2023, we and the University of Cambridge are hosting the WiPSCE International Workshop on Primary and Secondary Computing Education Research for educators and researchers. This year, this annual conference will take place at Robinson College in Cambridge. We’re inviting all UK-based teachers of computing subjects to apply for one of five ‘all expenses paid’ places at this well-regarded annual event.
You could attend WiPSCE with all expenses paid
WiPSCE is where teachers and researchers discuss research that’s relevant to teaching and learning in primary and secondary computing education, to teacher training, and to related topics. You can find more information about the conference, including the preliminary programme, at wipsce.org.
As a teacher at the conference, you will:
Engage with high-quality international research in the field where you teach
Learn ways to use that research to develop your own classroom practice
Find out how to become an advocate in your professional community for research-informed approaches to the teaching of computing.
We are delighted that, thanks to generous funding from a funder, we can offer five free places to UK computing teachers, covering:
The registration fee
Two nights’ accommodation at Robinson College
Up to £500 supply costs paid to your school to cover your teaching
You need to be a currently practising, UK-based teacher of Computing (England), Computing Science (Scotland), ICT or Digital Technologies (N. Ireland), or Computer Science (Wales)
Your headteacher needs to be able to provide written confirmation that they are happy for you to attend WiPSCE
You need to be available to attend the whole conference from Wednesday lunchtime to Friday afternoon
You need to be willing to share what you learn from the conference with your colleagues at school and with your broader teaching community, including through writing an article about your experience and its relevance to your teaching for this blog or Hello World magazine
The application form will ask your for:
Your name and contact details
Demographic and school information
Your teaching experience
A statement of up to 500 words on why you’re applying and how you think your teaching practice, your school and your colleagues will benefit from your attendance at WiPSCE (500 words is the maximum, feel free to be concise)
After the 19 July deadline, we’re aiming to inform you of the outcome of your application on Friday 21 July.
Use the information you share in your form, particularly in your statement
Select applicants from a mix of primary and secondary schools, with a mix of years of computing teaching experience, and from a mix of geographic areas
Join us in strengthening research-informed computing classroom practice
We’d be delighted to receive your application. Being able to facilitate teachers’ attendance at the conference is very much aligned with our approach to research. Both at the Foundation and the Raspberry Pi Computing Education Research Centre, we’re committed to conducting research that’s directly relevant to schools and teachers, and to working in close collaboration with teachers.
We hope you are interested in attending WiPSCE and becoming an advocate for research-informed computing education practice. If your application is unsuccessful, we hope you consider coming along anyway. We’re looking forward to meeting you there. In the meantime, you can keep up with WiPSCE news on Twitter.
Learning materials, teaching approaches, and the curriculum as a whole are three areas where culturally relevance is important.
In our latest research study, funded by Cognizant, we worked with 13 primary school teachers in England on adapting computing lessons to incorporate culturally relevant and responsive principles and practices. Here’s an insight into the workshop we ran with them, and what the teachers and we have taken away from it.
Adapting lesson materials based on culturally relevant pedagogy
In the group of 13 England-based primary school Computing teachers we worked with for this study:
One third were specialist primary Computing teachers, and the other two thirds were class teachers who taught a range of subjects
Some acted as Computing subject lead or coordinator at their school
Most had taught Computing for between three and five years
The majority worked in urban areas of England, at schools with culturally diverse catchment areas
In November 2022, we held a one-day workshop with the teachers to introduce culturally relevant pedagogy and explore how to adapt two six-week units of computing resources.
An example of a collaborative activity from the workshop
The first part of the workshop was a collaborative, discussion-based professional development session exploring what culturally relevant pedagogy is. This type of pedagogy uses equitable teaching practices to:
Draw on the breadth of learners’ experiences and cultural knowledge
Facilitate projects that have personal meaning for learners
Develop learners’ critical consciousness
The rest of the workshop day was spent putting this learning into practice while planning how to adapt two units of computing lessons to make them culturally relevant for the teachers’ particular settings. We used a design-based approach for this part of the workshop, meaning researchers and teachers worked collaboratively as equal stakeholders to decide on plans for how to alter the units.
We worked in four groups, each with three or four teachers and one or two researchers, focusing on one of two units of work from The Computing Curriculum for teaching digital skills: a unit on photo editing for Year 4 (ages 8–9), and a unit about vector graphics for Year 5 (ages 9–10).
We based the workshop around two Computing Curriculum units that cover digital literacy skills.
In order to plan how the resources in these units of work could be made culturally relevant for the participating teachers’ contexts, the groups used a checklist of ten areas of opportunity. This checklist is a result of one of our previous research projects on culturally relevant pedagogy. Each group used the list to identify a variety of ways in which the units’ learning objectives, activities, learning materials, and slides could be adapted. Teachers noted down their ideas and then discussed them with their group to jointly agree a plan for adapting the unit.
By the end of the day, the groups had designed four really creative plans for:
A Year 4 unit on photo editing that included creating an animal to represent cultural identity
A Year 4 unit on photo editing that included creating a collage all about yourself
A Year 5 unit on vector graphics that guided learners to create their own metaverse and then add it to the class multiverse
A Year 5 unit on vector graphics that contextualised the digital skills by using them in online activities and in video games
Outcomes from the workshop
Before and after the workshop, we asked the teachers to fill in a survey about themselves, their experiences of creating computing resources, and their views about culturally relevant resources. We then compared the two sets of data to see whether anything had changed over the course of the workshop.
The workshop was a positive experience for the teachers.
After teachers had attended the workshop, they reported a statistically significant increase in their confidence levels to adapt resources to be culturally relevant for both themselves and others.
Teachers explained that the workshop had increased their understanding of culturally relevant pedagogy and of how it could impact on learners. For example, one teacher said:
“The workshop has developed my understanding of how culturally adapted resources can support pupil progress and engagement. It has also highlighted how contextual appropriateness of resources can help children to access resources.” – Participating teacher
Some teachers also highlighted how important it had been to talk to teachers from other schools during the workshop, and how they could put their new knowledge into practice in the classroom:
“The dedicated time and value added from peer discourse helped make this authentic and not just token activities to check a box.” – Participating teacher
“I can’t wait to take some of the work back and apply it to other areas and subjects I teach.” – Participating teacher
What you can expect to see next from this project
After our research team made the adaptations to the units set out in the four plans made during the workshop, the adapted units were delivered by the teachers to more than 500 Year 4 and 5 pupils. We visited some of the teachers’ schools to see the units being taught, and we have interviewed all the teachers about their experience of delivering the adapted materials. This observational and interview data, together with additional survey responses, will be analysed by us, and we’ll share the results over the coming months.
As part of the project, we observed teachers delivering the adapted units to their learners.
In our next blog post about this work, we will delve into the fascinating realm of parental attitudes to culturally relevant computing, and we’ll explore how embracing diversity in the digital landscape is shaping the future for both children and their families.
We’ve also written about this professional development activity in more detail in a paper to be published at the UKICER conference in September, and we’ll share the paper once it’s available.
Finally, we are grateful to Cognizant for funding this academic research, and to our cohort of primary computing teachers for their enthusiasm, energy, and creativity, and their commitment to this project.
We love hearing from members of the community and how they use their passion for computing and digital making to inspire others. Our community stories series takes you on a tour of the globe to meet educators and young tech creators from the USA, Iraq, Romania, and more.
For our latest story, we are in the UK with Spencer, a Computer Science teacher at King Edward VI Sheldon Heath Academy (KESH), Birmingham. After 24 years as a science teacher, Spencer decided to turn his personal passion for digital making into a career and transitioned to teaching Computer Science.
Meet Spencer
From the moment he printed his name on the screen of an Acorn Electron computer at age ten, Spencer was hooked on digital making. He’s remained a member of the digital making community throughout his life, continuing to push himself with his creations and learn new skills whenever possible. Wanting to spread his knowledge and make sure the students at his school had access to computer science, he began running a weekly Code Club in his science lab:
“Code Club was a really nice vehicle for me to get students into programming and digital making, before computer science was an option at the school. So Code Club originally ran in my science lab around the Bunsen burners and all the science equipment, and we do some programming on a Friday afternoon making LEDs flash and a little bit of Minecraft. And from that, the students really got an exciting sense of what programming and digital making could be.”
– Spencer
While running his Code Club, Spencer really embedded himself in the Raspberry Pi community, attending Raspberry Jams, engaging with like-minded people on Twitter, and continuing to rely on our free training to upskill.
When leadership at KESH began to explore introducing Computer Science to the curriculum, Spencer knew he was the right person for the job, and just where to look to make sure he had the right support:
“So when I decided to change from being a science teacher to a computer science teacher, there were loads of course options you could find online, and a lot of them required some really specific prior knowledge and skills. The Foundation’s resources take you from a complete novice, complete beginner — my very first LED flashing on and off — to being able to teach computational thinking and algorithms. So it was a really clear progression from using the Foundation resources that helped take me from a Physics teacher, who could use electricity to light and LED on, to a programmer who could teach how to use this in our digital making for our students.”
– Spencer
Thanks to the support from KESH and Spencer’s compelling can-do attitude, he was soon heading up a brand-new Computer Science department. This was met with great enthusiasm from the learners at KESH, with a willing cohort eagerly signing up for the new subject.
“It’s really exciting to see how students have embraced Computer Science as a brand-new subject at school. The take-up for our first year at GCSE was fantastic with 25 students, and this year I’ve really got students asking about, ‘Is there an option for next year, and how can I get on to it?’ Students are almost blown away by the resources now.”
– Spencer
Supporting all students
Spencer has a mission to make sure all of KESH’s learners can learn about computing, and making his lessons accessible to all means he’s become a firm favourite amongst the students for his collaborative teaching approach.
“Mr Organ teaches you, and then he just puts you in. If you do need help, you can ask people around you, or him, but he lets you make your own mistakes and learn from there. He will then give you help so you don’t make those mistakes the next time.”
Spencer’s work is shaped by his awareness that many of the learners at KESH come from under-resourced areas of Birmingham and backgrounds that are underrepresented in computing. He knows that many of them have previously had limited opportunities to use digital tools. This is something he is driven to change.
“I want my young students here, regardless of their background, regardless of their area they’ve been brought up in, to have the same experiences as all other students in the country. And the work I do with Raspberry Pi, and the work I do with Code Club, is a way of opening those doors for our young people.”
– Spencer
Share Spencer’s story and inspire other educators
As a passionate member of the Raspberry Pi Foundation community, Spencer has been counted on as a friendly face for many years, sharing his enthusiasm on training courses, at Foundation events, and as a part of discussions on Twitter. With the goal to introduce Computer Science at A level shortly, and an ever-growing collection of digital makes housed in his makerspace, Spencer shows no signs of slowing down.
If you are interested in changing your teaching path to focus on Computer Science, take a look at the free resources we have available to support you on your journey.
Help us celebrate Spencer and his dedication to opening doors for his learners by sharing his story on Twitter, LinkedIn, and Facebook.
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