About three weeks ago, a small team from the Raspberry Pi Foundation braved high temperatures and expensive coffees (and a scarcity of tea) to spend time with educators at the CSTA Annual Conference in Las Vegas.

With thousands of attendees from across the US and beyond participating in engaging workshops, thought-provoking talks, and visiting the fantastic expo hall, the CSTA conference was an excellent opportunity for us to connect with and learn from educators.

Meeting educators & sharing resources

Our hope for the conference week was to meet and learn from as many different educators as possible, and we weren’t disappointed. We spoke with a wide variety of teachers, school administrators, and thought leaders about the progress, successes, and challenges of delivering successful computer science (CS) programs in the US (more on this soon). We connected and reconnected with so many educators at our stand, gave away loads of stickers… and we even gave away a Raspberry Pi Pico to one lucky winner each day.

As well as learning from hundreds of educators throughout the week, we shared some of the ways in which the Foundation supports teachers to deliver effective CS education. Our team was on hand to answer questions about our wide range of free learning materials and programs to support educators and young people alike. We focused on sharing our projects site and all of the ways educators can use the site’s unique projects pathways in their classrooms. And of course we talked to educators about Code Club. It was awesome to hear from club leaders about the work their students accomplished, and many educators were eager to start a new club at their schools! 

Back in 2022 at the last in-person CSTA conference, we had donated a copy of our first special edition of Hello World magazine, The Big Book of Computing Pedagogy, for every attendee. This time around, we donated copies of our follow-up special edition, The Big Book of Computing Content. Where the first Big Book focuses on how to teach computing, the second Big Book delves deep into what we teach as the subject of computing, laying it out in 11 content strands.

• If you weren’t able to get your hands on a copy of The Big Book of Computing Content, you can download yours for free. 
• If you’d like to write about your teaching for Hello World, find out more and share your idea today.

Our talks about teaching (with) AI

One of the things that makes CSTA conferences so special is the fantastic range of talks, workshops, and other sessions running at and around the conference. We took the opportunity to share some of our work in flash talks and two full-length sessions.

One of the sessions was led by one of our Senior Learning Managers, Ben Garside, who gave a talk to a packed room on what we’ve learned from developing AI education resources for Experience AI. Ben shared insights we’ve gathered over the last two years and talked about the design principles behind the Experience AI resources.

Being in the room for Ben’s talk, I was struck by two key takeaways:

1. The issue of anthropomorphism, that is, projecting human-like characteristics onto artificial intelligence systems and other machines. This presents several risks and obstacles for young people trying to understand AI technology. In our teaching, we need to take care to avoid anthropomorphizing AI systems, and to help young people shift false conceptions they might bring into the classroom.
2. Teaching about AI requires fostering a shift in thinking. When we teach traditional programming, we show learners that this is a rules-based, deterministic approach; meanwhile, AI systems based on machine learning are driven by data and statistical patterns. These two approaches and their outcomes are distinct (but often combined), and we need to help learners develop their understanding of the significant differences.

Our second session was led by Diane Dowling, another Senior Learning Manager at the Foundation. She shared some of the development work behind Ada Computer Science, our free platform providing educators and learners with a vast set of questions and content to help understand CS.

Recently, we’ve been experimenting with the use of a large language model (LLM) on Ada to provide assessment feedback on long-form questions. This led to a great conversation between Diane and the audience about the practicalities, risks, and implications of such feature.

More on what we learned from CSTA coming soon

We had a fantastic time with the educators in Vegas and are grateful to CSTA and their sponsors for the opportunity to meet and learn from so many different people. We’ll be sharing some of what we learned from the educators we spoke to in a future blog post, so watch this space.

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The world of education is loud with discussions about the uses and risks of generative AI — tools for outputting human-seeming media content such as text, images, audio, and video. In answer, there’s a new practical guide on using generative AI aimed at Computing teachers (and others), written by a group of classroom teachers and researchers at the Raspberry Pi Computing Education Research Centre and Faculty of Education at the University of Cambridge.

Their new guide is a really useful overview for everyone who wants to:

• Understand the issues generative AI tools present in the context of education
• Find out how to help their schools and students navigate them
• Discover ideas on how to make use of generative AI tools in their teaching

Since generative AI tools have become publicly available, issues around data privacy and plagiarism are at the front of educators’ minds. At the same time, many educators are coming up with creative ways to use generative AI tools to enhance teaching and learning. The Research Centre’s guide describes the areas where generative AI touches on education, and lays out what schools and teachers can do to use the technology beneficially and help their learners do the same.

Teaching students about generative AI tools

It’s widely accepted that AI tools can bring benefits but can also be used in unhelpful or harmful ways. Basic knowledge of how AI and machine learning works is key to being able to get the best from them. The Research Centre’s guide shares recommended educational resources for teaching learners about AI.

One of the recommendations is Experience AI, a set of free classroom resources we’re creating. It includes a set of 6 lessons for providing 11- to 14-year-olds with a foundational understanding of AI systems, as well as a standalone lesson specifically for teaching about large language model-based AI tools, such as ChatGPT and Google Gemini. These materials are for teachers of any specialism, not just for Computing teachers.

You’ll find that even a brief introduction to how large language models work is likely to make students’ ideas about using these tools to do all their homework much less appealing. The guide outlines creative ways you can help students see some of generative AI’s pitfalls, such as asking students to generate outputs and compare them, paying particular attention to inaccuracies in the outputs.

Generative AI tools and teaching computing

We’re still learning about what the best ways to teach programming to novice learners are. Generative AI has the potential to change how young people learn text-based programming, as AI functionality is now integrated into many of the major programming environments, generating example solutions or helping to spot errors.

The Research Centre’s guide acknowledges that there’s more work to be done to understand how and when to support learners with programming tasks through generative AI tools. (You can follow our ongoing seminar series on the topic.) In the meantime, you may choose to support established programming pedagogies with generative AI tools, such as prompting an AI chatbot to generate a PRIMM activity on a particular programming concept.

As ethics and the impact of technology play an important part in any good Computing curriculum, the guide also shares ways to use generative AI tools as a focus for your classroom discussions about topics such as bias and inequality.

Using generative AI tools to support teaching and learning

Teachers have been using generative AI applications as productivity tools to support their teaching, and the Research Centre’s guide gives several examples you can try out yourself. Examples include creating summaries of textual materials for students, and creating sets of questions on particular topics. As the guide points out, when you use generative AI tools like this, it’s important to always check the accuracy of the generated materials before you give any of them to your students.

Putting a school-wide policy in place

Importantly, the Research Centre’s guide highlights the need for a school-wide acceptable use policy (AUP) that informs teachers, other school staff, and students on how they may use generative AI tools. This section of the guide suggests websites that offer sample AUPs that can be used as a starting point for your school. Your AUP should aim to keep users safe, covering e-safety, privacy, and security issues as well as offering guidance on being transparent about the use of generative tools.

It’s not uncommon that schools look to specialist Computing teachers to act as the experts on questions around use of digital tools. However, for developing trust in how generative AI tools are used in the school, it’s important to encourage as wide a range of stakeholders as possible to be consulted in the process of creating an AUP.

A source of support for teachers and schools

As the Research Centre’s guide recognises, the landscape of AI and our thinking about it might change. In this uncertain context, the document offers a sensible and detailed overview of where we are now in understanding the current impact of generative AI on Computing as a subject, and on education more broadly. The example use cases and thought-provoking next steps on how this technology can be used and what its known risks and concerns are should be helpful for all interested educators and schools.

I recommend that all Computing teachers read this new guide, and I hope you feel inspired about the key role that you can play in shaping the future of education affected by AI.

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Developed by us and Google DeepMind, Experience AI provides teachers with free resources to help them confidently deliver lessons that inspire and educate young people about artificial intelligence (AI) and the role it could play in their lives.

Tracy Mayhead is a computer science teacher at Arthur Mellows Village College in Cambridgeshire. She recently taught Experience AI to her KS3 pupils. In this blog post, she shares 4 key learnings from this experience.

1. Preparation saves time

The Experience AI lesson plans provided a clear guide on how to structure our lessons.

Each lesson includes teacher-facing intro videos, a lesson plan, a slide deck, activity worksheets, and student-facing videos that help to introduce each new AI concept. 

It was handy to know in advance which websites needed unblocking so students could access them. 

You can find a unit overview on the Experience AI website to get an idea of what is included in each lesson.

“My favourite bit was making my own model, and choosing the training data. I enjoyed seeing how the amount of data affected the accuracy of the AI and testing the model.” – Student, Arthur Mellows Village College, UK 

2. The lessons can be adapted to meet student’s needs 

It was clear from the start that I could adapt the lessons to make them work for myself and my students.

Having estimated times and corresponding slides for activities was beneficial for adjusting the lesson duration. The balance between learning and hands-on tasks was just right.

I felt fairly comfortable with my understanding of AI basics. However, teaching it was a learning experience, especially in tailoring the lessons to cater to students with varying knowledge. Their misconceptions sometimes caught me off guard, like their belief that AI is never wrong. Adapting to their needs and expectations was a learning curve. 

“It has definitely changed my outlook on AI. I went from knowing nothing about it to understanding how it works, why it acts in certain ways, and how to actually create my own AI models and what data I would need for that.” – Student, Arthur Mellows Village College, UK 

3. Young people are curious about AI and how it works

My students enjoyed the practical aspects of the lessons, like categorising apples and tomatoes. They found it intriguing how AI could sometimes misidentify objects, sparking discussions on its limitations. They also expressed concerns about AI bias, which these lessons helped raise awareness about. I didn’t always have all the answers, but it was clear they were curious about AI’s implications for their future.

It’s important to acknowledge that as a teacher you won’t always have all the answers especially when teaching AI literacy, which is such a new area. This is something that can be explored in a class alongside students.

There is an online course you can use that can help get you started teaching about AI if you are at all nervous.

“I learned a lot about AI and the possibilities it holds to better our futures as well as how to train it and problems that may arise when training it.” – Student, Arthur Mellows Village College, UK

4. Engaging young people with AI is important

Students are fascinated by AI and they recognise its significance in their future. It is important to equip them with the knowledge and skills to fully engage with AI.

Experience AI provides a valuable opportunity to explore these concepts and empower students to shape and question the technology that will undoubtedly impact their lives.

“It has changed my outlook on AI because I now understand it better and feel better equipped to work with AI in my working life.” – Student, Arthur Mellows Village College, UK 

What is your experience of teaching Experience AI lessons?

We completely agree with Tracy. AI literacy empowers people to critically evaluate AI applications and how they are being used. Our Experience AI resources help to foster critical thinking skills, allowing learners to use AI tools to address challenges they are passionate about. 

We’re also really interested to learn what misconceptions students have about AI and how teachers are addressing them. If you come across misconceptions that surprise you while you’re teaching with the Experience AI lesson materials, please let us know via the feedback form linked in the final lesson of the six-lesson unit.

If you would like to teach Experience AI lessons to your students, download the free resources from experience-ai.org

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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 Resnick 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.”

Share your thoughts & subscribe to Hello World

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.

• Share your thoughts and ideas about the new issue and the topic on social media by tagging the Hello World Twitter/X or Facebook accounts
• Find out how you can write for the magazine
• Subscribe to Hello World for free to never miss an issue

The post Hello World #24 out now: Impact of tech appeared first on Raspberry Pi Foundation.

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

Students’ skills were also demonstrated by the Scratch projects we reviewed. And students from Odisha shared 314 projects in Coolest Projects — our online technology showcase for young people — including the project ‘We’ll build a new Odisha’ and an apple catching game.

Feedback and observations about teacher training

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.

The post Introducing a computing curriculum in Odisha appeared first on Raspberry Pi Foundation.

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 

Tips for other teachers

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.

The post A teacher’s guide to teaching Experience AI lessons appeared first on Raspberry Pi Foundation.

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.

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.

The post Introducing classroom management to the Code Editor appeared first on Raspberry Pi Foundation.

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.

We’ve been very busy at the RPCERC since we held our formal launch event in July 2022. We would love everyone who follows the Raspberry Pi Foundation’s work to keep an eye on what we are up to too: you can do that by checking out our website and signing up to our termly newsletter. 

What does the RPCERC do?

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. 

Do follow our research to see what we find out!

Generative AI for computing teachers

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:

1. 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.
2. 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.
3. PRIMM
   We devised the Predict–Run–Investigate–Modify–Make lesson structure for programming a few years ago and continue to research in this area.
4. 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’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.

The post An update from the Raspberry Pi Computing Education Research Centre appeared first on Raspberry Pi Foundation.

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.

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.

The post Teaching a generation of AI innovators in Malaysia with Experience AI appeared first on Raspberry Pi Foundation.

Educators around the world are grappling with the problem of whether to use artificial intelligence (AI) tools in the classroom. As more and more teachers start exploring the ways to use these tools for teaching and learning computing, there is an urgent need to understand the impact of their use to make sure they do not exacerbate the digital divide and leave some students behind.

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). 

The data analysis uncovered five emergent attitudes of students using AI tools to complete programming tasks: 

• 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. 

You can watch the full seminar here:

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. 

Join our next seminar

The focus of our ongoing seminar series is on teaching programming with or without AI. 

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.

The schedule of our upcoming seminars is online. You can catch up on past seminars on our blog and on the previous seminars and recordings page.

The post Insights into students’ attitudes to using AI tools in programming education appeared first on Raspberry Pi Foundation.

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. 

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. 

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.). 

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.

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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:

1. Build knowledge and skills in computing
2. Understand the opportunities and risks associated with new technologies
3. Develop the mindsets to confidently engage with technological change

How do we make a difference?

We work at multiple levels throughout education systems and society, which together will achieve deep and long-lasting change for young people. We design learning experiences and initiatives that are fun and engaging, including hundreds of free coding and computing projects, the Coolest Projects showcase for young tech creators, and the European Astro Pi Challenge, which gives young people the chance to run their computer programs in space.

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.

The post Our new theory of change appeared first on Raspberry Pi Foundation.

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

As we did with The Computing Curriculum, we’ll design these teaching resources to model best practice, and they will be informed by leading research into pedagogy and computing education, as well as by user testing and feedback. 

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. 

And as always, our comprehensive classroom resources can be downloaded for free, by anyone in the world, from our website.

The post Supporting Computing in England through our renewed partnership with Oak National Academy appeared first on Raspberry Pi Foundation.

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

The post How we’re creating more impact with Ada Computer Science appeared first on Raspberry Pi Foundation.

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.

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.

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:

1. The context in which the code generator was used
2. What learners asked for
3. How prompts were written
4. The nature of the outputted code
5. 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.

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:

You can read more about Majeed’s work on his personal website. You can also download and use the code generator Coding Steps yourself.

Join our next seminar

The focus of our ongoing seminar series is on teaching programming with or without AI. 

For our next seminar on Tuesday 16 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.

The schedule of our upcoming seminars is online. You can catch up on past seminars on our previous seminars and recordings page.

The post Using an AI code generator with school-age beginner programmers appeared first on Raspberry Pi Foundation.

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. “

We aim for Hello World magazine to be one of the places where this sharing, exchange, and learning can take place. Subscribe for free to never miss an issue, and find out how you can write for the magazine.

Download Hello World issue 23 for free

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 post Hello World #23 out now: Global exchange of computing education ideas appeared first on Raspberry Pi Foundation.

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 Tuesday 12 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.

The schedule of our upcoming seminars is online. You can catch up on past seminars on our previous seminars and recordings page.

The post Supporting learners with programming tasks through AI-generated Parson’s Problems appeared first on Raspberry Pi Foundation.

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.

Access the T Level resources

The T Level project brief materials are available for download now, with the T Level classroom materials coming in the next few weeks.

We hope T Level teachers and students find the resources useful and interesting — if you’re using them, please let us know your thoughts and feedback.

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.

The post Our T Level resources to support vocational education in England appeared first on Raspberry Pi Foundation.

Everyone who has taught children before will know the excited gleam in their eyes when the lessons include something to interact with physically. Whether it’s printed and painstakingly laminated flashcards, laser-cut models, or robots, learners’ motivation to engage with the topic will increase along with the noise levels in the classroom.

However, these hands-on activities are often seen as merely a technique to raise interest, or a nice extra project for children to do before the ‘actual learning’ can begin. But what if this is the wrong way to think about this type of activity? 

How do children learn?

In our 2023 online research seminar series, focused on computing education for primary-aged (K–5) learners, we delved into the most recent research aimed at enhancing learning experiences for students in the earliest stages of education. From a deep dive into teaching variables to exploring the integration of computational thinking, our series has looked at the most effective ways to engage young minds in the subject of computing.

It’s only fitting that in our final seminar in the series, Anaclara Gerosa from the University of Glasgow tackled one of the most fundamental questions in education: how do children actually learn? Beyond the conventional methods, emerging research has been shedding light on a fascinating approach — the concept of grounded cognition. This theory suggests that children don’t merely passively absorb knowledge; they physically interact with it, quite literally ‘grasping’ concepts in the process.

Grounded cognition, also known in variations as embodied and situated cognition, offers a new perspective on how we absorb and process information. At its core, this theory suggests that all cognitive processes, including language and thought, are rooted in the body’s dynamic interactions with the environment. This notion challenges the conventional view of learning as a purely cognitive activity and highlights the impact of action and simulation.

There is evidence from many studies in psychology and pedagogy that using hands-on activities can enhance comprehension and abstraction. For instance, finger counting has been found to be essential in understanding numerical systems and mathematical concepts. A recent study in this field has shown that children who are taught basic computing concepts with unplugged methods can grasp abstract ideas from as young as 3. There is therefore an urgent need to understand exactly how we could use grounded cognition methods to teach children computing — which is arguably one of the most abstract subjects in formal education.

A recent study in this field has shown that children who are taught basic computing concepts with unplugged methods can grasp abstract ideas from as young as 3.

A new framework for teaching computing

Anaclara is part of a group of researchers at the University of Glasgow who are currently developing a new approach to structuring computing education. Their EIFFEL (Enacted Instrumented Formal Framework for Early Learning in Computing) model suggests a progression from enacted to formal activities.

Following this model, in the early years of computing education, learners would primarily engage with activities that allow them to work with tangible 3D objects or manipulate intangible objects, for instance in Scratch. Increasingly, students will be able to perform actions in an instrumented or virtual environment which will require the knowledge of abstract symbols but will not yet require the knowledge of programming languages. Eventually, students will have developed the knowledge and skills to engage in fully formal environments, such as writing advanced code.

In a recent literature review, Anaclara and her colleagues looked at existing research into using grounded cognition theory in computing education. Although several studies report the use of grounded approaches, for instance by using block-based programming, robots, toys, or construction kits, the focus is generally on looking at how concrete objects can be used in unplugged activities due to specific contexts, such as a limited availability of computing devices.

The next steps in this area are looking at how activities that specifically follow the EIFFEL framework can enhance children’s learning. 

You can watch Anaclara’s seminar here: 

You can also access the presentation slides here.

Try grounded activities in your classroom

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:

• Try out one of our hands-on lessons and learning activities for young learners from The Computing Curriculum. For example, in this Programming unit for Year 1 learners (aged 5–6), students learn how to program with the help of reenactments and classroom robots.
• Explore the ‘Teach Data Literacy’ guide, developed by the Data Education in Schools team, which offers some practical activities to support young learners to develop their data literacy skills. You can find out more about the Data Education in Schools initiative in Kate Farrell and Judy Robertson’s seminar on teaching primary learners how to be data citizens from May 2023.
• Check out Barefoot Computing, which offers a range of resources for early years education that involve physical manipulation and simulation.

Join us at our next seminar

In 2024, we are exploring different ways to teach and learn programming, with and without AI tools. In our next seminar, on 13 February at 17:00 GMT, Majeed Kazemi from the University of Toronto will be joining us to discuss whether AI-powered code generators can help K–12 students learn to program in Python. All of our online seminars are free and open to everyone. Sign up and we’ll send you the link to join on the day.

The post Grounded cognition: physical activities and learning computing appeared first on Raspberry Pi Foundation.

“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:

1. Debugging
2. Abstraction
3. Decomposition
4. 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. 

Watch the recording of Aman’s presentation:

You can access Aman’s seminar slides as well.

You can find out more about connecting research to practice for primary computing education by watching the recordings of the other seminars in our series on primary (K–5) teaching and learning. In particular, Bobby Whyte discusses similar concepts to Aman in his talk on integrating primary computing and literacy through multimodal storytelling. 

Sign up for our seminars

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.

On 13 February, we’ll hear from Majeed Kazemi (University of Toronto) about his work investigating whether AI code generator tools can support K-12 students to learn Python programming.

Sign up now to join the seminar:

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