Do you remember a time before social media? Mobile phones? Email? We are surrounded by digital technology, and new applications impact our lives whether we engage with them or not. Issue 24 of Hello World, out today for free, gives you ideas for how to help your learners think openly and critically about technology.
Teaching about the impact of technology
For learners to become informed, empowered citizens, they need to understand the impact technology has on them as individuals, and on society as a whole. In our brand-new issue of Hello World, educators share insights from their work in and around classrooms that will help you engage your learners in learning about and discussing the impact of tech.
For example:
Jasmeen Kanwal and the team at Data Education in Schools share their resources for how young people can start to learn the skills they need to change the world with data
Julie York writes about how incorporating AI education into any classroom can help students prepare for future careers
Ben Hall discusses whether technology is divisive or inclusive, and how you can encourage students to think critically about it
This issue also includes stories on how educators use technology to create a positive impact for learners:
Yolanda Payne tells you how she’s using teaching experiences from the COVID-19 pandemic to bring better remote learning to communities in Georgia, USA, and in the US Virgin Islands
Mitchel Resnik and Natalie Rusk from Lifelong Kindergarten group at MIT Media Lab introduce their new free mobile app, OctoStudio, and how it helps learners and educators in underresourced areas get creative with code
And there is lots more for you to discover in issue 24.
The issue also covers how you can make time to teach about the impact of technology in an already packed curriculum. Sway Grantham, Senior Learning Manager at the Raspberry Pi Foundation, says in her article:
“As adults, it is easy for us to see the impact technology has had on society and on our lives. Yet when I tell pupils that, within my lifetime, it wasn’t always illegal to hold your mobile phone to your ear and have a call while driving, they are horrified. They are living in the now and don’t yet have the perspective to allow them to see the change that has happened. However, knowing the impact of technology allows us to learn from previous mistakes, to make decisions around ethical behaviour (such as using a phone while driving), and to critically engage in real-world issues.
As teachers, allocating some time to this topic throughout the year can seem challenging, but with a few small changes, the impact might be more than you can imagine.”
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With so many aspects of life impacted by technology, computing educators play a crucial role in supporting young people to become informed, empowered citizens. We hope you enjoy this issue of Hello World and find it useful in your teaching.
We are working with two partner organisations in Odisha, India, to develop and roll out the IT & Coding Curriculum (Kaushali), a computing curriculum for government high schools. Last year we launched the first part of the curriculum and rolled out teacher training. Read on to find out what we have learned from this work.
Supporting government schools in Odisha to teach computing
Previously we shared an insight into how we established Code Clubs in Odisha to bring computing education to young people. Now we are partnering with two Indian civil society organisations to develop high school curriculum resources for computing and support teachers to deliver this content.
With our two partners, we trained 311 master teachers during July and August 2023. The master teachers, most often mathematics or science teachers, were in turn tasked with training teachers from around 8000 government schools. The aim of the training was to enable the 8000 teachers to deliver the curriculum to grades 9 and 10 in the June 2023 – April 2024 academic year.
At the Foundation, we have been responsible for providing ongoing support to 1898 teachers from 10 districts throughout the academic year, including through webinars and other online and in-person support.
To evaluate the impact our work in Odisha is having, we gathered data using a mixed-methods approach that included gathering feedback from teachers via surveys and interviews, visiting schools, capturing reflections from our trainers, and reviewing a sample of students’ projects.
Positive impact on teachers and students
In our teacher survey, respondents were generally positive about the curriculum resources:
87% of the 385 respondents agreed that the curriculum resources were both high quality and useful for their teaching
91% agreed that they felt more confident to teach students IT & Coding as a result of the curriculum resources
Teachers also tended to agree that the initial training had helped improve their understanding and confidence, and they appreciated our ongoing support webinars.
“The curriculum resources are very useful for students.” – Teacher in Odisha
“The webinar is very useful to acquire practical knowledge regarding the specific topics.” – Teacher in Odisha
Teachers who responded to our survey observed a positive impact on students:
93% agreed their students’ digital literacy skills had improved
90% agreed that their students’ coding knowledge had improved
On school visits, our team observed that the teachers adopted and implemented the practical elements of the initial training quite well. However, survey responses and interviews showed that often teachers were not yet using all the elements of the curriculum as intended.
In their feedback, many teachers expressed a need for further regular training and support, and some reported additional challenges, such as other demands on their time and access to equipment.
When we observed training sessions master teachers delivered to teachers, we saw that, in some cases, information was lost within the training cascade (from our trainers, to master teachers, to teachers), including details about the intended pedagogical approach. It can be difficult to introduce experienced teachers to new pedagogical methods within a short training session, and teachers’ lack of computing knowledge also presents a challenge.
We will use all this data to shape how we support teachers going forward. Some teachers didn’t share feedback, and so in our further evaluation work, we will focus on making sure we hear a broad and representative range of teachers’ views and experiences.
What’s new this year?
In the current academic year, we are rolling out more advanced curriculum content for grade 10 students, including AI literacy resources developed at the Foundation. We’re currently training master teachers on this content, and they will pass on their knowledge to other teachers in the coming months. Based on teachers’ feedback, the grade 10 curriculum and the training also include a recap of some key points from the grade 9 curriculum.
A State Resource Group (SRG) has also been set up, consisting of 30 teachers who will support us with planning and providing ongoing support to master teachers and other teachers in Odisha. We have already trained the SRG members on the new curriculum content to enable them to best support teachers across the state. In addition to this, our local team in Odisha plans to conduct more visits and reach out directly to teachers more often.
Our plans for the future
The long-term vision for our work in India is to enable any school in India to teach students about computing and creating with digital technologies. A critical part of achieving this vision is the development of a comprehensive computing curriculum for grade 6 to 12, specifically tailored for government schools in India. Thanks to our work in Odisha, we are in a better position to understand the unique challenges and limitations of government schools. We’re designing our curriculum to address these challenges and ensure that every Indian student has the opportunity to thrive in the 21st century. If you would like to know more about our work and impact in India, please reach out to us via india@raspberrypi.org.
We take evaluation of our work seriously and are always looking to understand how we can improve and increase the impact we have on the lives of young people. To find out more about our approach to impact, you can read about our recently updated theory of change, which supports how we evaluate what we do.
Today, Laura James, Head of Computing and ICT at King Edward’s School in Bath, UK, shares how Experience AI has transformed how she teaches her students about artificial intelligence. This article will also appear in issue 24 of Hello World magazine, which will be available for free from 1 July and focuses on the impact of technology.
I recently delivered Experience AI lessons to three Year 9 (ages 13–14) classes of about 20 students each with a ratio of approximately 2:3 girls to boys. They are groups of keen pupils who have elected to study computing as an option. The Experience AI lessons are an excellent set of resources.
Everything you need
Part of the Experience AI resources is a series of six lessons that introduce the concepts behind machine learning and artificial intelligence (AI). There are full lesson plans with timings, clear PowerPoint presentations, and activity sheets. There is also an end-of-topic multiple choice assessment provided.
Accompanying these are interesting, well-produced videos that underpin the concepts, all explained by real people who work in the AI industry. Plus, there are helpful videos for the educators, which explain certain parts of the scheme of work — particularly useful for parts that might have been seen as difficult for non-specialist teachers, for example, setting up a project using the Machine Learning for Kids website.
Confidence delivering lessons
The clear and detailed resources meant I felt mostly confident in delivering lessons. The suggested timings were a good guideline, although in some lessons, this did not always go to plan. For example, when the pupils were enjoying investigating websites that produce images generated by a text prompt, they were keen to spend more time on this than was allocated in the lesson plan. In this case, I modified the timings on the fly and set the final task of this lesson as a homework task.
Learning about AI sparked the students’ curiosity, and it triggered a few questions that I could not answer immediately. However, I admitted this was a new area for me, and with some investigation, found answers to many of their extra questions. This shows that the topic of AI is such an inspiring and important one for the next generation, and how important it is to add this to the curriculum now before students make their own, potentially biased, opinions about it.
“I’ve enjoyed actually learning about what AI is and how it works because before I thought it was just a scary computer that thinks like a human.” – Student, King Edward’s School, UK
Impact on learners
The pupils’ feedback from the series of lessons was unerringly positive. I felt the lessons on bias in data were particularly important. The lesson where they trained their own algorithm recognising tomatoes and apples was a key one as it gave students an immediate sense of how a flawed training data set created bias and can impact the answers from a supposedly intelligent AI tool. I hope this has changed their outlook on AI-generated results and reinforced their critical thinking skills.
Many students are now seeing the influence of AI appearing in more and more tools around them and have mentioned that a career in AI is now something they are interested in.
“I have enjoyed learning about how AI is actually programmed rather than just hearing about how impactful and great it could be.” – Student, King Edward’s School, UK
Clearly this topic is incredibly important, and the Experience AI series of lessons is an excellent introduction to this for key stage 3 students (ages 11–14). My tips for other educators would be:
I delivered these to bright Year 9s and added a few more coding activities from the Machine Learning for Kids website. As these lessons stand, they could be delivered to Year 8s (ages 12–13), but perhaps Year 7s (ages 11–12) might struggle with some of the more esoteric concepts.
Before each lesson, ensure you read the content and familiarise yourself with the lesson resources and tools used. The Machine Learning for Kids website can take a little getting used to, but it is a powerful tool that brings to life how machine learning works, and many pupils said this was their favourite part of the lessons.
Before the lesson, ensure that the websites that you need to access are unblocked by your school’s firewall!
I tried to add a hands-on activity each lesson, e.g. for Lesson 1, I showed the students Google’s Quick, Draw! game, which they enjoyed and has a good section on the training data used to train the AI tool to recognise the drawings.
We also spent an extra lesson using the brilliant Machine Learning for Kids website and followed the ‘Shoot the bug’ worksheet, which allowed pupils to train an algorithm to learn how to play a simple video game.
I also needed to have a weekly homework task, so I would either use part of the activity from the lesson or quickly devise something (e.g. research another use for AI we haven’t discussed/what ethical issues might occur with a certain use of AI). Next year, our department will formalise these to help other teachers who might deliver these lessons to set these tasks more easily.
Equally, I needed to have a summative assessment at the end of the topic. I used some of the multiple choice questions that were provided but added some longer-answer questions and made an online assessment to allow me to mark students’ answers more efficiently.
“I have always been fascinated by AI applications and finally finding out how they work and make the decisions they do has been a really cool experience.” – Student, King Edward’s School, UK
From comments I have had from the students, they really engaged with the lessons and appreciated the opportunity to discuss and explore the topic, which is often associated with ‘deception’ within school. It allowed them to understand the benefits and the risks of AI and, most importantly, to begin to understand how it works ‘under the hood’, rather than see AI as a magical, anthropomorphised entity that is guessing their next move.
“The best part about learning about AI was knowing the dangers and benefits associated and how we can safely use it in our day-to-day life.” – Student, King Edward’s School, UK
As for my perspective, I really enjoyed teaching this topic, and it has earned its place in the Year 9 scheme of work for next year.
If you’re interested in teaching the Experience AI Lessons to your students, download the resources for free today at experience-ai.org.
I’m excited to announce that we’re developing a new set of Code Editor features to help school teachers run text-based coding lessons with their students.
New Code Editor features for teaching
Last year we released our free Code Editor and made it available as an open source project. Right now we’re developing a new set of features to help schools use the Editor to run text-based coding lessons online and in-person.
The new features will enable educators to create coding activities in the Code Editor, share them with their students, and leave feedback directly on each student’s work. In a simple and easy-to-use interface, educators will be able to give students access, group them into classes within a school account, and quickly help with resetting forgotten passwords.
Example Code Editor feedback screen from an early prototype
We’re adding these teaching features to the Code Editor because one of the key problems we’ve seen educators face over the last few months has been the lack of an ideal tool to teach text-based coding in the classroom. There are some options available, but they can be cost-prohibitive for schools and educators. Our mission is to support young people to realise their full potential through the power of computing, and we believe that to tackle educational disadvantage, we need to offer high-quality tools and make them as accessible as possible. This is why we’ll offer the Code Editor and all its features to educators and students for free, forever.
Alongside the new classroom management features, we’re also working on improved Python library support for the Code Editor, so that you and your students can get more creative and use the Editor for more advanced topics. We continue to support HTML, CSS, and JavaScript in the Editor too, so you can set website development tasks in the classroom.
Educators have already been incredibly generous in their time and feedback to help us design these new Code Editor features, and they’ve told us they’re excited to see the upcoming developments. Pete Dring, Head of Computing at Fulford School, participated in our user research and said on LinkedIn: “The class management and feedback features they’re working on at the moment look really promising.” Lee Willis, Head of ICT and Computing at Newcastle High School for Girls, also commented on the Code Editor: “We have used it and love it, the fact that it is both for HTML/CSS and then Python is great as the students have a one-stop shop for IDEs.”
Our commitment to you
Free forever: We will always provide the Code Editor and all of its features to educators and students for free.
A safe environment: Accounts for education are designed to be safe for students aged 9 and up, with safeguarding front and centre.
Privacy first: Student data collection is minimised and all collected data is handled with the utmost care, in compliance with GDPR and the ICO Children’s Code.
Best-practice pedagogy: We’ll always build with education and learning in mind, backed by our leading computing education research.
Community-led: We value and seek out feedback from the computing education community so that we can continue working to make the Code Editor even better for teachers and students.
Get started
We’re working to have the Code Editor’s new teaching features ready later this year. We’ll launch the setup journey sooner, so that you can pre-register for your school account as we continue to work on these features.
Before then, you can complete this short form to keep up to date with progress on these new features or to get involved in user testing.
The Code Editor is already being used by thousands of people each month. If you’d like to try it, you can get started writing code right in your browser today, with zero setup.
It’s been nearly two years since the launch of the Raspberry Pi Computing Education Research Centre. Today, the Centre’s Director Dr Sue Sentance shares an update about the Centre’s work.
The Raspberry Pi Computing Education Research Centre (RPCERC) is unique for two reasons: we are a joint initiative between the University of Cambridge and the Raspberry Pi Foundation, with a team that spans both; and we focus exclusively on the teaching and learning of computing to young people, from their early years to the end of formal education.
As the name implies, our work is focused on research into computing education and all our research projects align to one of the following themes:
AI education
Broadening participation in computing
Computing around the world
Pedagogy and the teaching of computing
Physical computing
Programming education
These themes encompass substantial research questions, so it’s clear we have a lot to do! We have only been established for a few years, but we’ve made a good start and are grateful to those who have funded additional projects that we are working on.
In our work, we endeavour to maintain two key principles that are hugely important to us: sharing our work widely and working collaboratively. We strive to engage in the highest quality rigorous research, and to publish in academic venues. However, we make sure these are available openly for those outside academia. We also favour research that is participatory and collaborative, so we work closely with teachers and other stakeholders.
Within our six themes we are running a number of projects, and I’ll outline a few of these here.
Exploring physical computing in primary schools
Physical computing is more engaging than simply learning programming and computing skills on screen because children can build interactive and tangible artefacts that exist in the real world. But does this kind of engagement have any lasting impact? Do positive experiences with technology lead to more confidence and creativity later on? These are just some of the questions we aim to answer.
We are delighted to be starting a new longitudinal project investigating the experience of young people who have engaged with the BBC micro:bit and other physical computing devices. We aim to develop insights into changes in attitudes, agency, and creativity at key points as students progress from primary through to secondary education in the UK.
To do this, we will be following a cohort of children over the course of five years — as they transition from primary school to secondary school — to give us deeper insights into the longer-term impact of working with physical computing than has been possible previously with shorter projects. This longer-term project has been made possible through a generous donation from the Micro:bit Educational Foundation, the BBC, and Nominet.
We are conducting a range of projects in the general area of artificial intelligence (AI), looking both at how to teach and learn AI, and how to learn programming with the help of AI. In our work, we often use the SEAME framework to simplify and categorise aspects of the teaching and learning of AI. However, for many teachers, it’s the use of AI that has generated the most interest for them, both for general productivity and for innovative ways of teaching and learning.
In one of our AI-related projects, we have been working with a group of computing teachers and the Faculty of Education to develop guidance for schools on how generative AI can be useful in the context of computing teaching. Computing teachers are at the forefront of this potential revolution for school education, so we’ve enjoyed the opportunity to set up this researcher–teacher working group to investigate these issues. We hope to be publishing our guidance in June — again watch this space!
Culturally responsive computing teaching
We’ve carried out a few different projects in the last few years around culturally responsive computing teaching in schools, which to our knowledge are unique for the UK setting. Much of the work on culturally responsive teaching and culturally relevant pedagogy (which stem from different theoretical bases) has been conducted in the USA, and we believe we are the only research team in the UK working on the implications of culturally relevant pedagogy research for computing teaching here.
In one of our studies, we worked with a group of teachers in secondary and primary schools to explore ways in which they could develop and reflect on the meaning of culturally responsive computing teaching in their context. We’ve published on this work, and also produced a technical report describing the whole project.
In another project, we worked with primary teachers to explore how existing resources could be adapted to be appropriate for their specific context and children. These projects have been funded by Cognizant and Google.
‘Core’ projects
As well as research that is externally funded, it’s important that we work on more long-term projects that build on our research expertise and where we feel we can make a contribution to the wider community.
We have four projects that I would put into this category:
Teacher research projects This year, we’ve been running a project called Teaching Inquiry in Computing Education, which supports teachers to carry out their own research in the classroom.
Computing around the world Following on from our survey of UK and Ireland computing teachers and earlier work on surveying teachers in Africa and globally, we are developing a broader picture of how computing education in school is growing around the world. Watch this space for more details.
PRIMM We devised the Predict–Run–Investigate–Modify–Make lesson structure for programming a few years ago and continue to research in this area.
LCT semantic wave theory Together with universities in London and Australia, we are exploring ways in which computing education can draw on legitimation code theory (LCT).
We are currently looking for a research associate to lead on one or more of these core projects, so if you’re interested, get in touch.
Developing new computing education researchers
One of our most important goals is to support new researchers in computing education, and this involves recruiting and training PhD students. During 2022–2023, we welcomed our very first PhD students, Laurie Gale and Salomey Afua Addo, and we will be saying hello to two more in October 2024. PhD students are an integral part of RPCERC, and make a great contribution across the team, as well as focusing on their own particular area of interest in depth. Laurie and Salomey have also been out and about visiting local schools too.
Laurie GaleSalomey Afua Addo
Laurie’s PhD study focuses on debugging, a key element of programming education. He is looking at lower secondary school students’ attitudes to debugging, their debugging behaviour, and how to teach debugging. If you’d like to take part in Laurie’s research, you can contact us at rpcerc-enquiries@cst.cam.ac.uk.
Salomey’s work is in the area of AI education in K–12 and spans the UK and Ghana. Her first study considered the motivation of teachers in the UK to teach AI and she has spent some weeks in Ghana conducting a case study on the way in which Ghana implemented AI into the curriculum in 2020.
Thanks!
We are very grateful to the Raspberry Pi Foundation for providing a donation which established the RPCERC and has given us financial security for the next few years. We’d also like to express our thanks for other donations and project funding we’ve received from Google, Google DeepMind, the Micro:bit Educational Foundation, BBC, and Nominet. If you would like to work with us, please drop us a line at rpcerc-enquiries@cst.cam.ac.uk.
Today’s blog is from Aimy Lee, Chief Operating Officer at Penang Science Cluster, part of our global partner network for Experience AI.
Artificial intelligence (AI) is transforming the world at an incredible pace, and at Penang Science Cluster, we are determined to be at the forefront of this fast-changing landscape.
The Malaysian government is actively promoting AI literacy among citizens, demonstrating a commitment to the nation’s technological advancement. This dedication is further demonstrated by the Ministry of Education’s recent announcement to introduce AI basics into the primary school curriculum, starting in 2027.
Why we chose Experience AI
At Penang Science Cluster, we firmly believe that AI is already an essential part of everybody’s future, especially for young people, for whom technologies such as search engines, AI chatbots, image generation, and facial recognition are already deeply ingrained in their daily experiences. It is vital that we equip young people with the knowledge to understand, harness, and even create AI solutions, rather than view AI with trepidation.
With this in mind, we’re excited to be one of the first of many organisations to join the Experience AI global partner network. Experience AI is a free educational programme offering cutting-edge resources on artificial intelligence and machine learning for teachers and students. Developed in collaboration between the Raspberry Pi Foundation and Google DeepMind, as a global partner we hope the programme will bring AI literacy to thousands of students across Malaysia.
Our goal is to demystify AI and highlight its potential for positive change. The Experience AI programme resonated with our mission to provide accessible and engaging resources tailored for our beneficiaries, making it a natural fit for our efforts.
Experience AI pilot: Results and student voices
At the start of this year, we ran an Experience AI pilot with 56 students to discover how the programme resonated with young people. The positive feedback we received was incredibly encouraging! Students expressed excitement and a genuine shift in their understanding of AI.
Their comments, such as discovering the fun of learning about AI and seeing how AI can lead to diverse career paths, validated the effectiveness of the programme’s approach.
One student’s changed perspective — from fearing AI to recognising its potential — underscores the importance of addressing misconceptions. Providing accessible AI education empowers students to develop a balanced and informed outlook.
“I learnt new things and it changed my mindset that AI is not going to take over the world.” – Student who took part in the Experience AI pilot
Launching Experience AI in Malaysia
The successful pilot paved the way for our official Experience AI launch in early April. Students who participated in the pilot were proud to be a part of the launch event, sharing their AI knowledge and experience with esteemed guests, including the Chief Minister of Penang, the Deputy Finance Minister of Malaysia, and the Director of the Penang State Education Department. The presence of these leaders highlights the growing recognition of the significance of AI education.
Experience AI launch event in Malaysia
Building a vibrant AI education community
Following the launch, our immediate focus has shifted to empowering teachers. With the help of the Raspberry Pi Foundation, we’ll conduct teacher workshops to equip them with the knowledge and tools to bring Experience AI into their classrooms. Collaborating with education departments in Penang, Kedah, Perlis, Perak, and Selangor will be vital in teacher recruitment and building a vibrant AI education community.
Inspiring the next generation of AI creators
Experience AI marks an exciting start to integrating AI education within Malaysia, for both students and teachers. Our hope is to inspire a generation of young people empowered to shape the future of AI — not merely as consumers of the technology, but as active creators and innovators.
We envision a future where AI education is as fundamental as mathematics education, providing students with the tools they need to thrive in an AI-driven world. The journey of AI exploration in Malaysia has only just begun, and we’re thrilled to play a part in shaping its trajectory.
If you’re interested in partnering with us to bring Experience AI to students and teachers in your country, you can register your interest here.
We work with mission-aligned educational organisations all over the world to support young people’s computing education. In 2023 we established four partnerships in Kenya and South Africa with organisations Coder:LevelUp, Blue Roof, Oasis Mathare, and Tech Kidz Africa, which support young people in underserved communities. Our shared goal is to support educators to establish and sustain extracurricular Code Clubs and CoderDojos in schools and community organisations. Here we share insights into the impact the partnerships are having.
Evaluating the impact of the training
In the partnerships we used a ‘train the trainer’ model, which focuses on equipping our partners with the knowledge and skills to train and support educators and learners. This meant that we trained a group of educators from each partner, enabling them to then run their own training sessions for other educators so they can set up coding clubs and run coding sessions. These coding sessions aim to increase young people’s skills and confidence in computing and programming.
We also conducted an evaluation of the impact of our work in these partnerships. We shared two surveys with educators (one shortly after they completed their initial training, a second for when they were running coding sessions), and another survey for young people to fill in during their coding sessions. In two of the partnerships, we also conducted interviews and focus groups with educators and young people.
Although we received lots of valuable feedback, only a low proportion of participants responded to our surveys, so the data may not be representative of the experience of all participating educators.
New opportunities to learn to code
Following our training, our partners themselves trained 332 educators across Kenya and South Africa to work directly in schools and communities running coding sessions. This led to the setup of nearly 250 Code Clubs and CoderDojos and additional coding sessions in schools and communities, reaching more than 11,500 young people.
As a result, access to coding and programming has increased in areas where this provision would otherwise not be available. One educator told us:
“We found it extremely beneficial, because a lot of our children come from areas in the community where they barely know how to read and write, let alone know how to use a computer… [It provides] the foundation, creating a fun way of approaching the computer as opposed to it being daunting.”
Curiosity, excitement and increased confidence
We found encouraging signs of the impact of this work on young people.
Nearly 90% of educators reported seeing an increase in young people’s computing skills, with over half of educators reporting that this increase was large. Over three quarters of young people who filled in our survey reported feeling confident in coding and computer programming.
The young people spoke enthusiastically about what they had learned and the programs they had created. They told us they felt inspired to keep learning, linking their interests to what they wanted to do in coding sessions. Interests included making dolls, games, cartoons, robots, cars, and stories.
When we spoke with educators and young people, a key theme that emerged was the enthusiasm and curiosity of the young people to learn more. Educators described how motivated they felt by the excitement of the young people. Young people particularly enjoyed finding out the role of programming in the world around them, from understanding traffic lights to knowing more about the games they play on their phones.
One educator told us:
“…students who knew nothing about technology are getting empowered.”
This confidence is particularly encouraging given that educators reported a low level of computer literacy among young people at the start of the coding sessions. One educator described how coding sessions provided an engaging hook to support teaching basic IT skills, such as mouse skills and computer-related terms, alongside coding.
Addressing real-world problems
One educator gave an example of young people using what they are learning in their coding club to solve real-world problems, saying:
“It’s life-changing because some of those kids and the youths that you are teaching… they’re using them to automate things in their houses.”
Many of these young people live in informal settlements where there are frequent fires, and have started using skills they learned in the coding sessions to automate things in their homes, reducing the risk of fires. For example, they are programming a device that controls fans so that they switch on when the temperature gets too high, and ways to switch appliances such as light bulbs on and off by clapping.
Continuing to improve our support
From the gathered feedback, we also learned some useful lessons to help improve the quality of our offer and support to our partners. For example, educators faced challenges including lack of devices for young people, and low internet connectivity. As we continue to develop these partnerships, we will work with partners to make use of our unplugged activities that work offline, removing the barriers created by low connectivity.
We are continuing to develop the training we offer and making sure that educators are able to access our other training and resources. We are also using the feedback they have given us to consider where additional training and support may be needed. Future evaluations will further strengthen our evidence and provide us with the insights we need to continue developing our work and support more educators and young people.
Our thanks to our partners at Coder:LevelUp, Blue Roof, Oasis Mathare, and Tech Kidz Africa for sharing our mission to enable young people to realise their full potential through the power of computing and digital technologies. As we continue to build partnerships to support Code Clubs and CoderDojos across South Africa and Kenya, it is heartening to hear first-hand accounts of the positive impact this work has on young people.
Sri Yash Tadimalla from the University of North Carolina and Dr Mary Lou Maher, Director of Research Community Initiatives at the Computing Research Association, are exploring how student identities affect their interaction with AI tools and their perceptions of the use of AI tools. They presented findings from two of their research projects in our March seminar.
How students interact with AI tools
A common approach in research is to begin with a preliminary study involving a small group of participants in order to test a hypothesis, ways of collecting data from participants, and an intervention. Yash explained that this was the approach they took with a group of 25 undergraduate students on an introductory Java programming course. The research observed the students as they performed a set of programming tasks using an AI chatbot tool (ChatGPT) or an AI code generator tool (GitHub Copilot).
Highly confident students rely heavily on AI tools and are confident about the quality of the code generated by the tool without verifying it
Cautious students are careful in their use of AI tools and verify the accuracy of the code produced
Curious students are interested in exploring the capabilities of the AI tool and are likely to experiment with different prompts
Frustrated students struggle with using the AI tool to complete the task and are likely to give up
Innovative students use the AI tool in creative ways, for example to generate code for other programming tasks
Whether these attitudes are common for other and larger groups of students requires more research. However, these preliminary groupings may be useful for educators who want to understand their students and how to support them with targeted instructional techniques. For example, highly confident students may need encouragement to check the accuracy of AI-generated code, while frustrated students may need assistance to use the AI tools to complete programming tasks.
An intersectional approach to investigating student attitudes
Yash and Mary Lou explained that their next research study took an intersectional approach to student identity. Intersectionality is a way of exploring identity using more than one defining characteristic, such as ethnicity and gender, or education and class. Intersectional approaches acknowledge that a person’s experiences are shaped by the combination of their identity characteristics, which can sometimes confer multiple privileges or lead to multiple disadvantages.
In the second research study, 50 undergraduate students participated in programming tasks and their approaches and attitudes were observed. The gathered data was analysed using intersectional groupings, such as:
Students who were from the first generation in their family to attend university and female
Students who were from an underrepresented ethnic group and female
Although the researchers observed differences amongst the groups of students, there was not enough data to determine whether these differences were statistically significant.
Who thinks using AI tools should be considered cheating?
Participating students were also asked about their views on using AI tools, such as “Did having AI help you in the process of programming?” and “Does your experience with using this AI tool motivate you to continue learning more about programming?”
The same intersectional approach was taken towards analysing students’ answers. One surprising finding stood out: when asked whether using AI tools to help with programming tasks should be considered cheating, students from more privileged backgrounds agreed that this was true, whilst students with less privilege disagreed and said it was not cheating.
This finding is only with a very small group of students at a single university, but Yash and Mary Lou called for other researchers to replicate this study with other groups of students to investigate further.
Acknowledging differences to prevent deepening divides
As researchers and educators, we often hear that we should educate students about the importance of making AI ethical, fair, and accessible to everyone. However, simply hearing this message isn’t the same as truly believing it. If students’ identities influence how they view the use of AI tools, it could affect how they engage with these tools for learning. Without recognising these differences, we risk continuing to create wider and deeper digital divides.
For our next seminar on Tuesday 16 April at 17:00 to 18:30 GMT, we’re joined by Brett A. Becker (University College Dublin), who will talk about how generative AI can be used effectively in secondary school programming education and how it can be leveraged so that students can be best prepared for continuing their education or beginning their careers. To take part in the seminar, click the button below to sign up, and we will send you information about how to join. We hope to see you there.
Here at the Raspberry Pi Foundation, we believe that it’s important that our academic research has a practical application. An important area of research we are engaged in is broadening participation in computing education by investigating how the subject can be made more culturally relevant — we have published several studies in this area.
Licensed under the Open Government Licence.
However, we know that busy teachers do not have time to keep abreast of all the latest research. This is where our Pedagogy Quick Reads come in. They show teachers how an area of current research either has been or could be applied in practice.
Our new Pedagogy Quick Reads summarises the central tenets of culturally relevant pedagogy (the theory) and then lays out 10 areas of opportunity as concrete ways for you to put the theory into practice.
Why is culturally relevant pedagogy necessary?
Computing remains an area where many groups of people are underrepresented, including those marginalised because of their gender, ethnicity, socio-economic background, additional educational needs, or age. For example, recent stats in the BCS’ Annual Diversity Report 2023 record that in the UK, the proportion of women working in tech was 20% in 2021, and Black women made up only 0.7% of tech specialists. Beyond gender and ethnicity, pupils who have fewer social and economic opportunities ‘don’t see Computing as a subject for somebody like them’, a recent report from Teach First found.
The fact that in the UK, 94% of girls and 79% of boys drop Computing at age 14 should be of particular concern for Computing educators. This last statistic makes it painfully clear that there is much work to be done to broaden the appeal of Computing in schools. One approach to make the subject more inclusive and attractive to young people is to make it more culturally relevant.
As part of our research to help teachers effectively adapt their curriculum materials to make them culturally relevant and engaging for their learners, we’ve identified 10 areas of opportunity — areas where teachers can choose to take actions to bring the latest research on culturally relevant pedagogy into their classrooms, right here, right now.
Applying the areas of opportunity in your classroom
The Pedagogy Quick Read gives teachers ideas for how they can use the areas of opportunity (AOs) to begin to review their own curriculum, teaching materials, and practices. We recommend picking one area initially, and focusing on that perhaps for a term. This helps you avoid being overwhelmed, and is particularly useful if you are trying to reach a particular group, for example, Year 9 girls, or low-attaining boys, or learners who lack confidence or motivation.
For example, one simple intervention is AO1 ‘Finding out more about our learners’. It’s all too easy for teachers to assume that they know what their students’ interests are. And getting to know your students can be especially tricky at secondary level, when teachers might only see a class once a fortnight or in a carousel.
However, finding out about your learners can be easily achieved in an online survey homework task, set at the beginning of a new academic year or term or unit of work. Using their interests, along with considerations of their backgrounds, families, and identities as inputs in curriculum planning can have tangible benefits: students may begin to feel an increased sense of belonging when they see their interests or identities reflected in the material later used.
How we’re using the AOs
The Quick Read presents two practical case studies of how we’ve used the 10 AO to adapt and assess different lesson materials to increase their relevance for learners.
Case study 1: Teachers in UK primary school adapt resources
As we’ve shared before, we implemented culturally relevant pedagogy as part of UK primary school teachers’ professional development in a recent research project. The Quick Read provides details of how we supported teachers to use the AOs to adapt teaching material to make it more culturally relevant to learners in their own contexts. Links to the resources used to review 2 units of work, lesson by lesson, to adapt tasks, learning material, and outcomes are included in the Quick Read.
Extract from the booklet used in a teacher professional development workshop to frame possible adaptations to lesson activities.
Case study 2: Reflecting on the adaption of resources for a vocational course for young adults in a Kenyan refugee camp
In a different project, we used the AOs to reflect on our adaptation of classroom materials from The Computing Curriculum, which we had designed for schools in England originally. Partnering with Amala Education, we adapted Computing Curriculum materials to create a 100-hour course for young adults at Kakuma refugee camp in Kenya who wanted to develop vocational digital literacy skills.
The diagram below shows our ratings of the importance of applying each AO while adapting materials for this particular context. In this case, the most important areas for making adaptations were to make the context more culturally relevant, and to improve the materials’ accessibility in terms of readability and output formats (text, animation, video, etc.).
Importance of the areas of opportunity to a course adaptation.
You can use this method of reflection as a way to evaluate your progress in addressing different AOs in a unit of work, across the materials for a whole year group, or even for your school’s whole approach. This may be useful for highlighting those areas which have, perhaps, been overlooked.
Applying research to practice with the AOs
The ‘Areas of opportunity’ Pedagogy Quick Read aims to help teachers apply research to their practice by summarising current research and giving practical examples of evidence-based teaching interventions and resources they can use.
The set of AOs was developed as part of a wider research project, and each one is itself research-informed. The Quick Read includes references to that research for everyone who wants to know more about culturally relevant pedagogy. This supporting evidence will be useful to teachers who want to address the topic of culturally relevant pedagogy with senior or subject leaders in their school, who often need to know that new initiatives are evidence-based.
Our goal for the Quick Read is to raise awareness of tried and tested pedagogies that increase accessibility and broaden the appeal of Computing education, so that all of our students can develop a sense of belonging and enjoyment of Computing.
Let us know if you have a story to tell about how you have applied one of the areas of opportunity in your classroom.
To date, our research in the field of culturally relevant pedagogy has been generously supported by funders including Cognizant and Google. We are very grateful to our partners for enabling us to learn more about how to make computing education inclusive for all.
One of the Raspberry Pi Foundation’s core values is our focus on impact. This means that we are committed to learning from the best available evidence, and to being rigorous and transparent about the difference we’re making.
Like many charities, an important part of our approach to achieving and measuring our impact is our theory of change. We are excited to launch a newly refreshed theory of change that reflects our mission and strategy to ensure that young people can realise their full potential through the power of computing and digital technologies.
What is a theory of change?
A theory of change describes the difference an organisation aims to make in the world, the actions it takes to achieve this, and the underlying assumptions about how its actions will create change.
It’s like a good cake recipe. It describes the ingredients and tools that are required, how these are combined, and what the results should be. But a theory of change goes further: it also addresses why you need the cake in the first place, and the reasons why the recipe will produce such a good cake if you follow it correctly!
What is the change we want to make?
Our theory of change begins with a statement of the problem that needs solving: too many young people are missing out on the enormous opportunities from digital technologies, and access to opportunities to learn depends too much on who you are and where you were born.
We want to see a world where young people can take advantage of the opportunities that computers and digital technologies offer to transform their own lives and communities, to contribute to society, and to help address the world’s challenges.
To help us empower young people to do this, we have identified three broad sets of outcomes that we should target, measure, and hold ourselves accountable for. These map roughly to the COM-B model of behaviour change. This model suggests that for change to be achieved, people need a combination of capabilities, opportunities, and motivation.
Our identified outcomes are that we support young people to:
Build knowledge and skills in computing
Understand the opportunities and risks associated with new technologies
Develop the mindsets to confidently engage with technological change
We also support teachers, youth workers, volunteers, and parents to develop their skills and knowledge, and equip them to inspire young people and help them learn. For example, The Computing Curriculum provides a complete bank of free lesson plans and other resources, and Experience AI is our educational programme that includes everything teachers need to deliver lessons on artificial intelligence and machine learning in secondary schools.
Finally, we aim to elevate the state of computing education globally by advocating for policy and systems change, and undertaking our own original research to deepen our understanding of how young people learn about computing.
How will we use our theory of change?
Our theory of change is an important part of our approach to evaluating the impact of our resources and programmes, and it informs all our monitoring and evaluation plans. These plans identify the questions we want to answer, key metrics to monitor, and the data sources we use to understand the impact we’re having and to gather feedback to improve our impact in future.
The theory of change also informs a shared outcomes framework that we are applying consistently across all of our products. This framework supports planning and helps keep us focused as we consider new opportunities to further our mission.
A final role our theory of change plays is to help communicate our mission to other stakeholders, and explain how we can work with our partners and communities to achieve change.
You can read our new theory of change here and if you have any questions or feedback on it, please do get in touch.
We are pleased to announce that we are renewing our partnership with Oak National Academy in England to provide an updated high-quality Computing curriculum and lesson materials for Key Stages 1 to 4.
New curriculum and materials for the classroom
In 2021 we partnered with Oak National Academy to offer content for schools in England that supported young people to learn Computing at home while schools were closed as a result of the coronavirus pandemic.
In our renewed partnership, we will create new and updated materials for primary and secondary teachers to use in the classroom. These classroom units will be available for free on the Oak platform and will include everything a teacher needs to deliver engaging lessons, including slide decks, worksheets, quizzes, and accompanying videos for over 550 lessons. The units will cover both the general national Computing curriculum and the Computer Science GCSE, supporting teachers to provide a high-quality Computing offering to all students aged 5 to 16.
These new resources will update the very successful Computing Curriculum and will be rigorously tested by a Computing subject expert group.
“I am delighted that we are continuing our partnership with Oak National Academy to support all teachers in England with world-leading resources for teaching Computing and Computer Science. This means that all teachers in England will have access to free, rigorous and tested classroom resources that they can adapt to suit their context and students.” – Philip Colligan, CEO
All our materials on the Oak platform will be free and openly available, and can be accessed by educators worldwide.
Research-informed, time-saving, and adaptable resources
The materials will bring teachers the added benefit of saving valuable time, and schools can choose to adapt and use the resources in the way that works best for their students
Supporting schools in England and worldwide
We have already started work and will begin releasing units of lessons in autumn 2024. All units across Key Stages 1 to 4 will be available by autumn 2025.
We’re excited to continue our partnership with Oak National Academy to provide support to teachers and students in England.
We offer Ada Computer Science as a platform to support educators and learners alike. But we don’t take its usefulness for granted: as part of our commitment to impact, we regularly gather user feedback and evaluate all of our products, and Ada is no exception. In this blog, we share some of the feedback we’ve gathered from surveys and interviews with the people using Ada.
What’s new on Ada?
Ada Computer Science is our online learning platform designed for teachers, students, and anyone interested in learning about computer science. If you’re teaching or studying a computer science qualification at school, you can use Ada Computer Science for classwork, homework, and revision.
Launched last year as a partnership between us and the University of Cambridge, Ada’s comprehensive resources cover topics like algorithms, data structures, computational thinking, and cybersecurity. It also includes 1,000 self-marking questions, which both teachers and students can use to assess their knowledge and understanding.
Throughout 2023, we continued to develop the support Ada offers. For example, we:
Added over 100 new questions
Expanded code specimens to cover Java and Visual Basic as well as Python and C#
Added an integrated way of learning about databases through writing and executing SQL
Incorporated a beta version of an embedded Python editor with the ability to run code and compare the output with correct solutions
A few weeks ago we launched two all-new topics about artificial intelligence (AI) and machine learning.
So far, all the content on Ada Computer Science is mapped to GCSE and A level exam boards in England, and we’ve just released new resources for the Scottish Qualification Authority’s Computer Systems area of study to support students in Scotland with their National 5 and Higher qualifications.
Who is using Ada?
Ada is being used by a wide variety of users, from at least 127 countries all across the globe. Countries where Ada is most popular include the UK, US, Canada, Australia, Brazil, India, China, Nigeria, Ghana, Kenya, China, Myanmar, and Indonesia.
Just over half of students using Ada are completing work set by their teacher. However, there are also substantial numbers of young people benefitting from using Ada for their own independent learning. So far, over half a million question attempts have been made on the platform.
How are people using Ada?
Students use Ada for a wide variety of purposes. The most common response in our survey was for revision, but students also use it to complete work set by teachers, to learn new concepts, and to check their understanding of computer science concepts.
Teachers also use Ada for a combination of their own learning, in the classroom with their students, and for setting work outside of lessons. They told us that they value Ada as a source of pre-made questions.
“I like having a bank of questions as a teacher. It’s tiring to create more. I like that I can use the finder and create questions very quickly.” — Computer science teacher, A level
“I like the structure of how it [Ada] is put together. [Resources] are really easy to find and being able to sort by exam board makes it really useful because… at A level there is a huge difference between exam boards.” — GCSE and A level teacher
What feedback are people giving about Ada?
Students and teachers alike were very positive about the quality and usefulness of Ada Computer Science. Overall, 89% of students responding to our survey agreed that Ada is useful for helping them to learn about computer science, and 93% of teachers agreed that it is high quality.
“The impact for me was just having a resource that I felt I always could trust.” — Head of Computer Science
Most teachers also reported that using Ada reduces their workload, saving an average of 3 hours per week.
“[Quizzes] are the most useful because it’s the biggest time saving…especially having them nicely self-marked as well.” — GCSE and A level computer science teacher
Even more encouragingly, Ada users report a positive impact on their knowledge, skills, and attitudes to computer science. Teachers report that, as a result of using Ada, their computer science subject knowledge and their confidence in teaching has increased, and report similar benefits for their students.
“They can easily…recap and see how they’ve been getting on with the different topic areas.” — GCSE and A level computer science teacher
“I see they’re answering the questions and learning things without really realising it, which is quite nice.” — GCSE and A level computer science teacher
How do we use people’s feedback to improve the platform?
Our content team is made up of experienced computer science teachers, and we’re always updating the site in response to feedback from the teachers and students who use our resources. We receive feedback through support tickets, and we have a monthly meeting where we comb through every wrong answer that students entered to help us identify new misconceptions. We then use all of this to improve the content, and the feedback we give students on the platform.
We’d love to hear from you
We’ll be conducting another round of surveys later this year, so when you see the link, please fill in the form. In the meantime, if you have any feedback or suggestions for improvements, please get in touch.
And if you’ve not signed up to Ada yet as a teacher or student, you can take a look right now over at adacomputerscience.org
AI models for general-purpose programming, such as OpenAI Codex, which powers the AI pair programming tool GitHub Copilot, have the potential to significantly impact how we teach and learn programming.
The basis of these tools is a ‘natural language to code’ approach, also called natural language programming. This allows users to generate code using a simple text-based prompt, such as “Write a simple Python script for a number guessing game”. Programming-specific AI models are trained on vast quantities of text data, including GitHub repositories, to enable users to quickly solve coding problems using natural language.
As a computing educator, you might ask what the potential is for using these tools in your classroom. In our latest research seminar, Majeed Kazemitabaar (University of Toronto) shared his work in developing AI-assisted coding tools to support students during Python programming tasks.
Evaluating the benefits of natural language programming
Majeed argued that natural language programming can enable students to focus on the problem-solving aspects of computing, and support them in fixing and debugging their code. However, he cautioned that students might become overdependent on the use of ‘AI assistants’ and that they might not understand what code is being outputted. Nonetheless, Majeed and colleagues were interested in exploring the impact of these code generators on students who are starting to learn programming.
Using AI code generators to support novice programmers
In one study, the team Majeed works in investigated whether students’ task and learning performance was affected by an AI code generator. They split 69 students (aged 10–17) into two groups: one group used a code generator in an environment, Coding Steps, that enabled log data to be captured, and the other group did not use the code generator.
Learners who used the code generator completed significantly more authoring tasks — where students manually write all of the code — and spent less time completing them, as well as generating significantly more correct solutions. In multiple choice questions and modifying tasks — where students were asked to modify a working program — students performed similarly whether they had access to the code generator or not.
A test was administered a week later to check the groups’ performance, and both groups did similarly well. However, the ‘code generator’ group made significantly more errors in authoring tasks where no starter code was given.
Majeed’s team concluded that using the code generator significantly increased the completion rate of tasks and student performance (i.e. correctness) when authoring code, and that using code generators did not lead to decreased performance when manually modifying code.
Finally, students in the code generator group reported feeling less stressed and more eager to continue programming at the end of the study.
Student perceptions when (not) using AI code generators
Understanding how novices use AI code generators
In a related study, Majeed and his colleagues investigated how novice programmers used the code generator and whether this usage impacted their learning. Working with data from 33 learners (aged 11–17), they analysed 45 tasks completed by students to understand:
The context in which the code generator was used
What learners asked for
How prompts were written
The nature of the outputted code
How learners used the outputted code
Their analysis found that students used the code generator for the majority of task attempts (74% of cases) with far fewer tasks attempted without the code generator (26%). Of the task attempts made using the code generator, 61% involved a single prompt while only 8% involved decomposition of the task into multiple prompts for the code generator to solve subgoals; 25% used a hybrid approach — that is, some subgoal solutions being AI-generated and others manually written.
In a comparison of students against their post-test evaluation scores, there were positive though not statistically significant trends for students who used a hybrid approach (see the image below). Conversely, negative though not statistically significant trends were found for students who used a single prompt approach.
A positive correlation between hybrid programming and post-test scores
Though not statistically significant, these results suggest that the students who actively engaged with tasks — i.e. generating some subgoal solutions, manually writing others, and debugging their own written code — performed better in coding tasks.
Majeed concluded that while the data showed evidence of self-regulation, such as students writing code manually or adding to AI-generated code, students frequently used the output from single prompts in their solutions, indicating an over-reliance on the output of AI code generators.
He suggested that teachers should support novice programmers to write better quality prompts to produce better code.
If you want to learn more, you can watch Majeed’s seminar:
The focus of our ongoing seminar series is on teaching programming with or without AI.
For our next seminar on Tuesday16 April at 17:00–18:30 GMT, we’re joined by Brett Becker (University College Dublin), who will discuss how generative AI may be effectively utilised in secondary school programming education and how it can be leveraged so that students can be best prepared for whatever lies ahead. 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.
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.
* If you want to dive into the newest research on programming education with and without AI, check out our current seminar series.
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
Tips for hosting a STEM event in school
And much, much more.
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?
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.
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:
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.
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