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.

From AI chatbots to self-driving cars, artificial intelligence (AI) is here and rapidly transforming our world. It holds the potential to solve some of the biggest challenges humanity faces today — but it also has many serious risks and inherent challenges, like reinforcing existing patterns of bias or “hallucinating”, a term that describes AI making up false outputs that do not reflect real events or data.

Teachers want to build young people’s AI literacy

As AI becomes an integral part of our daily lives, it’s essential that younger generations gain the knowledge and skills to navigate and shape this technology. Young people who have a foundational understanding of AI are able to make more informed decisions about using AI applications in their daily lives, helping ensure safe and responsible use of the technology. This has been recognised for example by the UK government’s AI Council, whose AI Roadmap sets out the goal of ensuring that every child in the UK leaves school with a basic sense of how AI works.

But while AI literacy is a key skill in this new era, not every young person currently has access to sufficient AI education and resources. In a recent survey by the EdWeek Research Center in the USA, only one in 10 teachers said they knew enough about AI to teach its basics, and very few reported receiving any professional development related to the topic. Similarly, our work with the Raspberry Pi Computing Education Research Centre has suggested that UK-based teachers are eager to understand more about AI and how to engage their students in the topic.

Bringing AI education into classrooms

Ensuring broad access to AI education is also important to improve diversity in the field of AI to ensure safe and responsible development of the technology. There are currently stark disparities in the field and these start already early on, with school-level barriers contributing to underrepresentation of certain groups of people. By increasing diversity in AI, we bring diverse values, hopes, and concerns into the design and deployment of the technology — something that’s critical for AI to benefit everyone.

By focusing on AI education from a young age, there is an opportunity to break down some of these long-standing barriers. That’s why we partnered with the Raspberry Pi Foundation to co-create Experience AI, a new learning programme with free lesson plans, slide decks, worksheets and videos, to address gaps in AI education and support teachers in engaging and inspiring young people in the subject.

The programme aims to help young people aged 11–14 take their first steps in understanding the technology, making it relevant to diverse learners, and encouraging future careers in the field. All Experience AI resources are freely available to every school across the UK and beyond.

The partnership is built on a shared vision to make AI education more inclusive and accessible. Bringing together the Foundation’s expertise in computing education and our cutting-edge technical knowledge and industry insights has allowed us to create a holistic learning experience that connects theoretical concepts and practical applications.

Experience AI: Informed by AI experts

A group of 15 research scientists and engineers at Google DeepMind contributed to the development of the lessons. From drafting definitions for key concepts, to brainstorming interesting research areas to highlight, and even featuring in the videos included in the lessons, the group played a key role in shaping the programme in close collaboration with the Foundation’s educators and education researchers.

To bring AI concepts to life, the lessons include interactive activities as well as real-life examples, such as a project where Google DeepMind collaborated with ecologists and conservationists to develop machine learning methods to study the behaviour of an entire animal community in the Serengeti National Park and Grumeti Reserve in Tanzania.

Member of the working group, Google DeepMind Research Scientist Petar Veličković, shares: “AI is a technology that is going to impact us all, and therefore educating young people on how to interact with this technology is likely going to be a core part of school education going forward. The project was eye-opening and humbling for me, as I learned of the challenges associated with making such a complex topic accessible — not only to every pupil, but also to every teacher! Observing the thoughtful approach undertaken by the Raspberry Pi Foundation left me deeply impressed, and I’m taking home many useful ideas that I hope to incorporate in my own AI teaching efforts going forward.”

The lessons have been carefully developed to:

• Follow a clear learning journey, underpinned by the SEAME framework which guides learners sequentially through key concepts and acts as a progression framework.
• Build foundational knowledge and provide support for teachers. Focus on teacher training and support is at the core of the programme.
• Embed ethics and responsibility. Crucially, key concepts in AI ethics and responsibility are woven into each lesson and progressively built on. Students are introduced to concepts like data bias, user-focused approaches, model cards, and how AI can be used for social good. 
• Ensure cultural relevance and inclusion. Experience AI was designed with diverse learners in mind and includes a variety of activities to enable young people to pick topics that most interest them. 

What teachers say about the Experience AI lessons

To date, we estimate the resources have reached 200,000+ students in the UK and beyond. We’re thrilled to hear from teachers already using the resources about the impact they are having in the classroom, such as Mrs J Green from Waldegrave School in London, who says: “I thought that the lessons covered a really important topic. Giving the pupils an understanding of what AI is and how it works will become increasingly important as it becomes more ubiquitous in all areas of society. The lessons that we trialled took some of the ‘magic’ out of AI and started to give the students an understanding that AI is only as good as the data that is used to build it. It also started some really interesting discussions with the students around areas such as bias.”

At North Liverpool Academy, teacher Dave Cross tells us: “AI is such a current and relevant topic in society that [these lessons] will enable Key Stage 3 computing students [ages 11–14] to gain a solid foundation in something that will become more prevalent within the curriculum, and wider subjects too as more sectors adopt AI and machine learning as standard. Our Key Stage 3 computing students now feel immensely more knowledgeable about the importance and place that AI has in their wider lives. These lessons and activities are engaging and accessible to students and educators alike, whatever their specialism may be.”

A stronger global AI community

Our hope is that the Experience AI programme instils confidence in both teachers and students, helping to address some of the critical school-level barriers leading to underrepresentation in AI and playing a role in building a stronger, more inclusive AI community where everyone can participate irrespective of their background. 

Today’s young people are tomorrow’s leaders — and as such, educating and inspiring them about AI is valuable for everybody.

Teachers can visit experience-ai.org to download all Experience AI resources for free.

We are now building a network of educational organisations around the world to tailor and translate the Experience AI resources so that more teachers and students can engage with them and learn key AI literacy skills. Find out more.

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Machine learning: Accuracy and ethics

The landscape for working with machine learning/AI/deep learning has grown considerably over the last couple of years. Students are now able to develop their understanding from the hard-coded end via resources such as Machine Learning for Kids, get their hands dirty using relatively inexpensive hardware such as the Nvidia Jetson Nano, and build a classification machine using the Google-driven Teachable Machine resources. I have used all three of the above with my students, and this article focuses on Teachable Machine.

For this module, I’m more concerned with the fuzzy end of AI, including how credible AI decisions are, and the elephant-in-the-room aspect of bias and potential for harm.

Michael Jones

For the worried, there is absolutely no coding involved in this resource; the ‘machine’ behind the portal does the hard work for you. For my Year 9 classes (students aged 13 to 14) undertaking a short, three-week module, this was ideal. The coding is important, but was not my focus. For this module, I’m more concerned with the fuzzy end of AI, including how credible AI decisions are, and the elephant-in-the-room aspect of bias and potential for harm.

Getting started with Teachable Machine activities

There are three possible routes to use in Teachable Machine, and my focus is the ‘Image Project’, and within this, the ‘Standard image model’. From there, you are presented with a basic training scenario template — see Hello World issue 16 (pages 84–86) for a step-by-step set-up and training guide. For this part of the project, my students trained the machine to recognise different breeds of dog, with border collie, labrador, saluki, and so on as classes. Any AI system devoted to recognition requires a substantial set of training data. Fortunately, there are a number of freely available data sets online (for example, download a folder of dog photos separated by breed by accessing helloworld.cc/dogdata). Be warned, these can be large, consisting of thousands of images. If you have more time, you may want to set students off to collect data to upload using a camera (just be aware that this can present safeguarding considerations). This is a key learning point with your students and an opportunity to discuss the time it takes to gather such data, and variations in the data (for example, images of dogs from the front, side, or top).

Once you have downloaded your folders, upload the images to your Teachable Machine project. It is unlikely that you will be able to upload a whole subfolder at once — my students have found that the optimum number of images seems to be twelve. Remember to build this time for downloading and uploading into your lesson plan. This is a good opportunity to discuss the need for balance in the training data. Ask questions such as, “How likely would the model be to identify a saluki if the training set contained 10 salukis and 30 of the other dogs?” This is a left-field way of dropping the idea of bias into the exploration of AI — more on that later!

Accuracy issues in machine learning models

If you have got this far, the heavy lifting is complete and Google’s training engine will now do the work for you. Once you have set your model on its training, leave the system to complete its work — it takes seconds, even on large sets of data. Once it’s done, you should be ready to test you model. If all has gone well and a webcam is attached to your computer, the Output window will give a prediction of what is being viewed. Again, the article in Hello World issue 16 takes you through the exact steps of this process. Make sure you have several images ready to test. See Figure 1a for the response to an image of a saluki presented to the model. As you might expect, it is showing as a 100 percent prediction.

It will spark an interesting discussion if you now try the same operation with an image with items other than the one you’re testing in it. For example see Figure 1b, in which two people are in the image along with the Samoyed dog. The model is undecided, as the people are affecting the outcome. This raises the question of accuracy. Which features are being used to identify the dogs as border collie and saluki? Why are the humans in the image throwing the model off the scent?

Getting closer to home, training a model on human faces provides an opportunity to explore AI accuracy through the question of what might differentiate a female from a male face. You can find a model at helloworld.cc/maleorfemale that contains 5418 images almost evenly spread across male and female faces (see Figure 2). Note that this model will take a little longer to train.

Once trained, try the model out. Props really help — a top hat, wig, and beard give the model a testing time (pun intended). In this test (see Figure 3), I presented myself to the model face-on and, unsurprisingly, I came out as 100 percent male. However, adding a judge’s wig forces the model into a rethink, and a beard produces a variety of results, but leaves the model unsure. It might be reasonable to assume that our model uses hair length as a strong feature. Adding a top hat to the ensemble brings the model back to a 100 percent prediction that the image is of a male.

Machine learning uses a best-fit principle. The outputs, in this case whether I am male or female, have a greater certainty of male (65 percent) versus a lesser certainty of female (35 percent) if I wear a beard (Figure 3, second image from the right). Remove the beard and the likelihood of me being female increases by 2 percent (Figure 3, second image from the left).

Bias in machine learning models

Within a fairly small set of parameters, most human faces are similar. However, when you start digging, the research points to there being bias in AI (whether this is conscious or unconscious is a debate for another day!). You can exemplify this by firstly creating classes with labels such as ‘young smart’, ‘old smart’, ‘young not smart’, and ‘old not smart’. Select images that you think would fit the classes, and train them in Teachable Machine. You can then test the model by asking your students to find images they think fit each category. Run them against the model and ask students to debate whether the AI is acting fairly, and if not, why they think that is. Who is training these models? What images are they receiving? Similarly, you could create classes of images of known past criminals and heroes. Train the model before putting yourself in front of it. How far up the percentage scale are you towards being a criminal? It soon becomes frighteningly worrying that unless you are white and seemingly middle class, AI may prove problematic to you, from decisions on financial products such as mortgages through to mistaken arrest and identification.

It soon becomes frighteningly worrying that unless you are white and seemingly middle class, AI may prove problematic to you, from decisions on financial products such as mortgages through to mistaken arrest and identification.

Michael Jones

Encourage your students to discuss how they could influence this issue of race, class, and gender bias — for example, what rules would they use for identifying suitable images for a data set? There are some interesting articles on this issue that you can share with your students at helloworld.cc/aibias1 and helloworld.cc/aibias2.

Where next with your learners?

In the classroom, you could then follow the route of building models that identify letters for words, for example. One of my students built a model that could identify a range of spoons and forks. You may notice that Teachable Machine can also be run on Arduino boards, which adds an extra dimension. Why not get your students to create their own AI assistant that responds to commands? The possibilities are there to be explored. If you’re using webcams to collect photos yourself, why not create a system that will identify students? If you are lucky enough to have a set of identical twins in your class, that adds just a little more flavour! Teachable Machine offers a hands-on way to demonstrate the issues of AI accuracy and bias, and gives students a healthy opportunity for debate.

Michael Jones is director of Computer Science at Northfleet Technology College in the UK. He is a Specialist Leader of Education and a CS Champion for the National Centre for Computing Education.

More resources for AI and data science education

At the Foundation, AI education is one of our focus areas. Here is how we are supporting you and your learners in this area already:

• Hello World issue 12 focuses on AI and machine learning education, with many practical resources, insightful interviews, and inspiring features from computer science educators. Download your free copy of issue 12 now.
• In Hello World issue 16, the focus is on all things data science and data literacy for your learners. As always, you can download a free copy of the issue.
• On our Hello World podcast, we’ve got episodes where we talk with practicing computing educators about how they bring AI, AI ethics, machine learning, and data science to the young people they teach.
• Since we upgraded the Raspberry Pi–based hardware on board the International Space Station for the European Astro Pi Challenge, young people have better opportunities to use machine learning as part of the scientific experiments they write Python programs for during the Challenge. See what this teams taking part in this round of Astro Pi achieved and how you can get involved in mentoring students at your school to take part in the next round starting in September.
• If you’d like a practical introduction to the basics of machine learning and how to use it, take our free online course.

• Computing education researchers are working to answer the many open questions about what good AI and data science education looks like for young people. To learn more, you can watch the recordings from our research seminar series focused on this. We ourselves are working on research projects in this area and will share the results freely with the computing education community.
• You can find a list of free educational resources about these topics that we’ve collated based on our research seminars, seminar participants’ recommendations, and our own work.

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The ‘I belong’ campaign is part of our work on Isaac Computer Science, our free online learning platform for GCSE and A level students (ages 14 to 18) and their teachers, funded by the Department for Education. The campaign celebrates young computer scientists and how they came to love the subject, what their career journey has been so far, and what their thoughts are about inclusivity and belonging in their chosen field.

These people are role models who demonstrate that everyone belongs in computer science, and that everyone can bring their interests and skills to bear in the field. In this way, we want to show young people that they can do much more with computing than they might think, and to inspire them to consider how computing could be part of their own life and career path.

Meet Salome

Salome is studying Computer Science with Digital Technology Solutions at the University of Leeds and doing a degree apprenticeship with PricewaterhouseCoopers (PwC).

“I was quite lucky, as growing up I saw a lot about women in STEM which inspired me to take this path. I think to improve the online community, we need to keep challenging stereotypes and getting more and more people to join, thereby improving the diversity. This way, a larger number of people can have role models and identify themselves with someone currently there.”

“Another thing is the assumption that computer science is just coding and not a wide and diverse field. I still have to explain to my friends what computer science involves and can become, and then they will say, ‘Wow, that’s really interesting, I didn’t know that.'”

Meet Devyani

Devyani is a third-year degree apprentice at Cisco. 

“It was at A level where I developed my programming skills, and it was more practical rather than theoretical. I managed to complete a programming project where I utilised PHP, JavaScript, and phpMyAdmin (which is a database). It was after this that I started looking around and applying for degree apprenticeships. I thought that university wasn’t for me, because I wanted a more practical and hands-on approach, as I learn better that way.”

“At the moment, I’m currently doing a product owner role, which is where I hope to graduate into. It’s a mix between both a business role and a technical role. I have to stay up to speed with the current technologies we are using and developing for our clients and customers, but also I have to understand business needs and ensure that the team is able to develop and deliver on time to meet those needs.”

Meet Omar

Omar is a Mexican palaeontologist who uses computer science to study dinosaur bones.

“I try to bring aspects that are very well developed in computer science and apply them in palaeontology. For instance, when digitising the vertebrae, I use a lot of information theory. I also use a lot of data science and integrity to make sure that what we have captured is comparable with what other people have found.”

“What drove me to computers was the fact you are always learning. That’s what keeps me interested in science: that I can keep growing, learn from others, and I can teach people. That’s the other thing that makes me feel like I belong, which is when I am able to communicate the things I know to someone else and I can see the face of the other person when they start to grasp a theory.”

Meet Tasnima

Tasnima is a computer science graduate from Queen Mary University of London, and is currently working as a software engineer at Credit Suisse.

“During the pandemic, one of the good things to come out of it is that I could work from home, and that means working with people all over the world, bringing together every race, religion, gender, etc. Even though we are all very different, the one thing we all have in common is that we’re passionate about technology and computer science. Another thing is being able to work in technology in the real world. It has allowed me to work in an environment that is highly collaborative. I always feel like you’re involved from the get-go.”

“I think we need to also break the image that computer science is all about coding. I’ve had friends that have stayed away from any tech jobs because they think that they don’t want to code, but there’s so many other roles within technology and jobs that actually require no coding whatsoever.”

Meet Aleena

Aleena is a software engineer who works at a health tech startup in London and is also studying for a master’s degree in AI ethics at the University of Cambridge.

“I do quite a lot of different things as an engineer. It’s not just coding, which is part of it but it is a relatively small percentage, compared to a lot of other things. […] There’s a lot of collaborative time and I would say a quarter or third of the week is me by myself writing code. The other time is spent collaborating and working with other people and making sure that we’re all aligned on what we are working on.”

“I think it’s actually a very diverse field of tech to work in, once you actually end up in the industry. When studying STEM subjects at a college or university level it is often not very diverse. The industry is the opposite. A lot of people come from self-taught or bootcamp backgrounds, there’s a lot of ways to get into tech and software engineering, and I really like that aspect of it. Computer science isn’t the only way to go about it.”

Meet Alice

Alice is a final-year undergraduate student of Computer Science with Artificial Intelligence at the University of Brighton. She is also the winner of the Global Challenges COVID-19 Research Scholarship offered by Santander Universities.

“[W]e need to advertise computer science as more than just a room full of computers, and to advertise computer sciences as highly collaborative. It’s very creative. If you’re on a team of developers, there’s a lot of communication involved.”

“There’s something about computer science that I think is so special: the fact that it is a skill anybody can learn, regardless of who they are. With the right idea, anybody can build anything.”

Share these stories to inspire

Help us spread the message that everyone belongs in computer science: share this blog with schools, teachers, STEM clubs, parents, and young people you want to inspire.

You can learn computer science with us

Whether you’re studying or teaching computer science GCSE or A levels in the UK (or thinking about doing so!), or you’re a teacher or student in another part of the world, Isaac Computer Science is here to help you achieve your computer science goals. Our high-quality learning platform is free to use and open to all. As a student, you can register to keep track of your progress. As a teacher, you can sign up to guide your students’ learning.

And for younger learners, we have lots of fun project guides to try out coding and creating with digital technologies.

The post I belong in computer science appeared first on Raspberry Pi Foundation.

Support and engagement for Welsh Code Clubs

Sarah introduced herself to the Welsh education community by running a Code Club training workshop for teachers. Educators from 32 Welsh schools joined her to learn how to start their own Code Club and then tried one of the free coding projects we provide for club sessions for themselves.

The Welsh Code Club network had a chance to meet Sarah at a country-wide online codealong on 11 March, just in time to kick off British Science Week 2022. In this one-hour codealong event, we took beginner coders through the first project of our new ‘Introduction to Scratch’ pathway, Space Talk. Space Talk is a fantastic project for Code Clubs: it provides beginners with a simple introduction to coding in Scratch, and also gives plenty of opportunity for more experienced learners to get creative and make the project their own.

The codealong was fantastically popular, with 90 teachers and 2900 learners from 59 schools participating. Several of the schools shared their excitement with us on Twitter, posting pictures and videos of their Space Talk projects.

Tamasin Greenough Graham, Head of Code Club, says: “It was wonderful to see so many children and teachers from Wales coding with us. I really loved the creativity they showed in all their projects!”

Welsh translations of Code Club learning materials

Although the codealong took place in English, Space Talk and the whole ‘Introduction to Scratch’ pathway are available in the Welsh language. The pathway includes a total of six projects, bringing the total number of Welsh-language coding projects we offer to 37. It’s really important to us to offer our learning materials in Welsh, especially because we know it helps young people engage with our free coding activities.

The translation of learning materials is a collaborative effort at the Raspberry Pi Foundation: we work with a team of 1465 volunteer translators, who translate our materials into  33 languages, making them accessible for more children and educators around the world.

Two of these translators, Marcus and Julia Davage, are based in Wales. They help to make our projects accessible to Welsh-speaking learners. Marcus and Julia have been part of the community for 6 years, volunteering at Code Club and running their own club:

“I started volunteering for Code Club in 2016 when my daughter was in a Welsh-medium primary school and her teacher had started a Code Club. This lasted until 2019. Last year I started my own Code Club at the Welsh-medium primary school at which my wife Julia teaches. Since helping out, she has taught Scratch in her own lessons!”

– Marcus Davage, Code Club volunteer & Welsh translation volunteer

Marcus and Julia have translated numerous learning resources and communications for our Welsh community. Marcus describes the experience of translating:

“I noticed that several of the projects hadn’t been completely translated into Welsh, so when my company, BMC Software, promoted a Volunteering Day for all of its staff, I jumped at the opportunity to spend the whole day finishing off many of the missing translations! I must admit, I did laugh at a few terms, like ’emoji’ (which has no official translation), ’emoticon’ (‘gwenoglun’ or ‘smiley face’), and ‘wearable tech’ (‘technoleg gwisgadwy’).”

– Marcus Davage, Code Club volunteer & Welsh translation volunteer

We’re thankful to Marcus and Julia and to all the teachers and volunteers in Wales who bring coding skills to the young people in their schools.

Get involved in Code Club, in Wales or elsewhere

Keen readers may have noticed that this year marks the tenth anniversary of Code Club! We have lots of celebrations planned for the worldwide community of volunteers and learners, in long-running clubs as well as in brand-new ones.

So now is an especially great time to get involved by starting a Code Club at your school, or by signing up to volunteer at an up-and-running club. Find out more at codeclub.org.

And if you’re interested in learning more about Code Club in Wales, email us at support@codeclub.org so Sarah can get in touch.

The post Code Club in Wales with translations, teacher training and a country-wide codealong appeared first on Raspberry Pi Foundation.

If you know young coders who love gaming, digital art, or storytelling and need a new programming challenge, this is the event for them. So mark your calendars!

Our free Unity project path, in partnership with Unity Technologies

In January, we launched an all-new online learning path of Unity projects, in partnership with Unity. With this path, young people who enjoy writing code will learn how to start using the free Unity Real-Time Development Platform to build their own digital 3D games and worlds.

Professional developers are using Unity to create well-known games such as Mario Kart Tour and Pokémon Mystery Dungeon: Rescue Team DX. We’ve partnered with Unity to offer any young person, anywhere, the opportunity to take their first steps in creating virtual worlds using real-time 3D. The five-part Unity path we offer is educational and shows young people that if they can imagine something, then they can create it digitally with Unity. 

Who is the Unity livestream for? Why should young people join?

For young people, coding in Unity can be a fun experience of creating their own 3D worlds. And it also helps them learn skills that can be useful and desirable in the tech sector.

Unity is a step up for young people who have coded in a text-based language before and are interested in creating interactive 3D games and stories. In Unity, they’ll write code in the programming language C# — pronounced ‘cee sharp’. It’s a great opportunity to build on their existing coding and problem-solving skills.

Introducing young people to Unity means that they will begin to use the same tools as professional 3D developers. Maybe attending the Unity livestream is going to be your coders’ first step towards creating the next videogame sensation.

What will happen on the livestream? 

The livestream will run for around 45 minutes. It will be the perfect introduction to Unity and our project path for you and your experienced coders.  

The livestream will include: 

1. A ‘question and answer’ section with Unity expert Thomas Winkley. Thomas is a Unity Certified Programmer and product evangelist. He’s passionate about helping others learn new skills and follow their interests. Thomas will be answering your questions about Unity and what you can do with it, as well as talking about some of the cool creations he’s made. 
2. An introduction to the Unity project path with Liz from our team: You’ll get to ask your questions about our Unity project path, and you’ll learn what you can make with each project and see an example of a final project — like what you’ll create by completing the project path. 
3. A live coding section with Rebecca and Mr C: Your young people get to join in coding their first characters and objects in the 3D environment of Unity.  

By joining the livestream, your young people will: 

• Learn more about Unity and get inspired to start creating
• See what our free online Unity learning path is all about and understand what they’ll get from completing it
• Have the chance to see what it’s like to make their own creations with Unity, and code along if they want to      

Do you need to do anything before the livestream? 

The livestream takes place on Thursday 24 March at 18:30 GMT on our YouTube channel. Everyone can tune in without signing up, wherever you are in the world. If you have a Google account, you can click the ‘Set a reminder’ button to make sure you and your keen coders don’t miss a thing.

Unity is free for anyone to use. If your young people want to code along during the livestream, they need to prepare by downloading and installing all the free software beforehand. Young people will need to:

• Have access to a computer with enough processing power (learn more from Unity directly)
• Have downloaded and installed Unity Hub, from where they need to install Unity Editor and Visual Studio Community Edition. The first project in our Unity learning path links to instructions for how to do all this step by step.

We cannot wait for you to join us and our special guests on our Unity livestream!

Share Unity creations at Coolest Projects Global

Whatever your young people create with Unity — or other digital tech —, they can register to share it for the world to see in the online gallery of Coolest Projects Global. This is our free and completely online tech showcase, for young people up to age 18 all over the world.

Registering to showcase their tech creation means young people will get cool swag, feedback on what they’ve made, and a chance to win recognition from our special judges. And above all, they’ll become part of a worldwide community of young tech creators who celebrate and inspire each other.

Find out more at coolestprojects.org.

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In this post, we want to highlight two particular pieces of work that have influenced our work over the last year, shared by Dr Tia C. Madkins (University of Texas at Austin), Dr Nicol R. Howard (University of Redlands), and Dr Jakita O. Thomas (Auburn University, blackcomputeHER.org) at our research seminars.

Moving beyond access and achievement, towards equity and justice

Tia C. Madkins and Nicol R. Howard described that educators in schools (and associated professionals) need to build an awareness of how the learning in their classrooms might be affected by:

• Personal beliefs, ways of knowing or thinking, stereotypes, and the cultural lens of the educator and the learners
• Power dynamics and intersectional identities

They say: “Instead of viewing learners as deficient individuals who we need to ‘fix’ in our classrooms, we use strengths-based approaches where we as educators learn to recognise, draw on, and build upon learners’ strengths and lived experiences.”

The researchers encourage educators to connect with learners’ cultural practices and lived experiences, and to foster and maintain relationships with learners’ families and communities, in order to work together to facilitate equitable, social justice–oriented CS learning

To hear from Tia, Nicol, and their collaborator Shomari Jones, watch their seminar. You can also read Tia and Nicol’s article in our seminar proceedings, where you’ll find a list of their recommended resources to explore this thinking further.

Valuing existing knowledge and lived experience as expertise

Jakita O. Thomas described findings from her research project based on a free enrichment programme exploring how Black middle-school girls develop computational algorithmic thinking skills in the context of game design.

The programme was intentionally designed to position Black girls as knowledge holders with valuable experiences, and to offer them opportunities to shape their identities as producers, innovators, and people who challenge deficit perspectives. These are perspectives that include implicit assumptions that privilege the values, beliefs, and practices of one group over another, especially where the groups are racially, ethnically, or culturally different.

Jakita emphasised that it’s very important for educators to ask the questions “STEM learning for what?”, “For whom?”, “How?”, and “To what ends?” when they consider how to bring STEM learning experiences to Black girls (or other young people with multiple marginal identities). Educators need an awareness that the economic reasons of STEM learning, which are commonly spotlighted, may not be sufficient to convince young people who are marginalised to engage in these subjects.

To hear more about this from Jakita directly, watch her seminar:

Empowering learners to be agents of change

One thing these researchers’ work makes clear is that the reasons for why learners choose to engage in CS education are many, and that gaining CS skills to prepare for the job market is only one of them.

In both seminars, the speakers emphasised how important it is for educators to contribute to their learners’ self-view as agents of change, not only by demonstrating how CS can be used to solve problems, but also by being open and direct about existing technological inequities. This teaches learners to use CS as a tool, and to also examine the social context in which CS is being applied, and the positive and negative consequences of these applications. Learning CS can empower young people to address challenges their communities face, and educators, learners, and families can work together through CS on social justice issues.

Putting the power of computing into the hands of young people is the core of our mission, and we have a research project underway right now that looks at equitable computing education in UK schools. Find out more about it here, and download our practical guide for teachers.

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Season 3 of the Hello World podcast starts on Monday

The Hello World podcast helps connect the global community of computing educators and Hello World readers, and lets them share their experiences. After two seasons and a short pause during the autumn, we are finally back with a brand-new Hello World podcast season. Regular listeners will also notice a new theme music!

Each episode, we explore computing, coding, and digital making education by delving into an exciting topic together with our guests: experts, practitioners, and other members of the Hello World community.

 In season 3, we’re exploring:

• The role of makerspaces, both within schools and the wider community 
• The relevance of imagination and storytelling to computing 
• Computing in the context of science and ecology
• How learners can promote and support computing as digital leaders
• And much more…

Meet our guests for episode 1 of the new season

In our first episode, which will be available from 7 February, your hosts Carrie Anne and James ask the question “What role do makerspaces play in the classroom?”. We talk to two fantastic guests, each with a wealth of experience in designing and developing makerspaces:

Nick Provenzano, who is a Teacher and Makerspace Director at University Liggett School in Michigan. He is also an author, makerspace builder, international keynote speaker and Raspberry Pi Certified Educator.

Chris Hillidge, who established FabLab Warrington in 2016 and manages the STEM strategy for students aged 4 to 19 across The Challenge Academy Trust. Chris is a Specialist Leader of Education, consultant, and Raspberry Pi Certified Educator.

Dive in with our three most popular episodes so far

If you’ve not tried out the Hello World podcast yet, why not get started by diving into one of our most popular episodes?

• How moral is your machine? Ethics in computing education
• Accessible and inclusive computing education: Where to start?
• How can we get everyone excited about code?

You’ll find the upcoming season and past episodes on your favourite podcast platform, where you can also subscribe to never miss an episode. Alternatively, you can listen via your browser at helloworld.cc/podcast.

The post It’s back: The Hello World podcast for the computing education community appeared first on Raspberry Pi Foundation.

The Unity Charitable Fund, a fund of the Tides Foundation, has awarded us a generous grant for $50,000 to help underrepresented youth learn to use Unity, upleveling their skills for future career success.

Create a world, don’t just explore it

Our new path of six projects for Unity is a learning journey for young people who have some experience of text-based programming and now want to try out building digital 3D creations.

Unity is the world’s leading platform for creating and operating real-time 3D and is hugely popular for creating 3D video games and virtual, interactive worlds and stories. The best thing about it for young people? While professional developers use Unity to create well-known games such as Pokémon Brilliant Diamond and Shining Pearl and Among Us, it is also free for anyone to use.

Young people who learn to use Unity can do more and more complex things with it as they gain experience. Many successful indie games have been made in Unity — maybe a young person you know will create the next indie game sensation!

For young people, our new project path is the ideal introduction to Unity. The new project path:

• Is for learners who have already coded some projects in Python or another text-based language.
• Introduces the Unity software and how to write code for it in the programming language C# (pronounced ‘cee sharp’).
• Guides learners to create a 3D role playing game or interactive story that they can tailor to suit their imaginations. Learners gain more and more independence with each project in the path.
• Covers common elements such as non-playable characters, mini games, and bonuses.

After young people have completed the path, they’ll have:

• Created their very own 3D video game or interactive story they can share with their friends and family.
• Gained familiarity with key functions of Unity.
• Built the independence and confidence to explore Unity further and create more advanced games and 3D worlds.

Young people gain real-world skills while creating worlds in Unity

Since Unity is a platform used by professional digital creators, young people who follow our new Unity path gain real-world skills that are sought after in the tech sector. While they learn to express their creativity with Unity, young people improve their coding and problem-solving skills and feel empowered because they get to use their imagination to bring their ideas to life.

“Providing opportunities for underrepresented youth to learn critical tech skills is essential to Unity Social Impact’s mission,” said Jessica Lindl, Vice President, Social Impact at Unity. “We’re thrilled that the Raspberry Pi Foundation’s Unity path will allow thousands of student learners to take part in game design in an accessible way, setting them up for future career success.”

What you need to support young people with Unity Real-Time 3D

The project path includes instructions for how to download and install all the necessary software to start creating with Unity.

Before they can start, young people will need to:

• Have access to a computer with enough processing power (find out more from Unity directly)
• Have downloaded and installed Unity Hub, from where they need to install Unity Editor and Visual Studio Community Edition

For club volunteers who support young people attending Code Clubs and CoderDojos with the new path, we are going to run two free online workshops in February. During the workshops, volunteers will be introduced to the path and the software setup, and we’ll try out Unity together. Keep your eyes on the CoderDojo and Code Club blogs for details!

Club volunteers, if your participants are creating Blender projects, they can import these into Unity too.

Young people can share their Unity creations with the world through Coolest Projects

It’s really exciting for us that we can bring this new project path to young people who dream about creating interactive 3D worlds. We hope to see many of their creations in this year’s Coolest Projects Global, our free online tech showcase for young creators all over the world!

The post New free resources for young people to create 3D worlds with code in Unity appeared first on Raspberry Pi Foundation.

The story of artificial intelligence (AI) is a story about humans trying to understand what makes them human. Some of the episodes in this story are fascinating. These could help your learners catch a glimpse of what this field is about and, with luck, compel them to investigate further.                   

The imitation game

In 1950, Alan Turing published a philosophical essay titled Computing Machinery and Intelligence, which started with the words: “I propose to consider the question: Can machines think?” Yet Turing did not attempt to define what it means to think. Instead, he suggested a game as a proxy for answering the question: the imitation game. In modern terms, you can imagine a human interrogator chatting online with another human and a machine. If the interrogator does not successfully determine which of the other two is the human and which is the machine, then the question has been answered: this is a machine that can think.

This imitation game is now a fiercely debated benchmark of artificial intelligence called the Turing test. Notice the shift in focus that Turing suggests: thinking is to be identified in terms of external behaviour, not in terms of any internal processes. Humans are still the yardstick for intelligence, but there is no requirement that a machine should think the way humans do, as long as it behaves in a way that suggests some sort of thinking to humans.

In his essay, Turing also discusses learning machines. Instead of building highly complex programs that would prescribe every aspect of a machine’s behaviour, we could build simpler programs that would prescribe mechanisms for learning, and then train the machine to learn the desired behaviour. Turing’s text provides an excellent metaphor that could be used in class to describe the essence of machine learning: “Instead of trying to produce a programme to simulate the adult mind, why not rather try to produce one which simulates the child’s? If this were then subjected to an appropriate course of education one would obtain the adult brain. We have thus divided our problem into two parts: the child-programme and the education process.”

It is remarkable how Turing even describes approaches that have since been evolved into established machine learning methods: evolution (genetic algorithms), punishments and rewards (reinforcement learning), randomness (Monte Carlo tree search). He even forecasts the main issue with some forms of machine learning: opacity. “An important feature of a learning machine is that its teacher will often be very largely ignorant of quite what is going on inside, although he may still be able to some extent to predict his pupil’s behaviour.”

The evolution of a definition

The term ‘artificial intelligence’ was coined in 1956, at an event called the Dartmouth workshop. It was a gathering of the field’s founders, researchers who would later have a huge impact, including John McCarthy, Claude Shannon, Marvin Minsky, Herbert Simon, Allen Newell, Arthur Samuel, Ray Solomonoff, and W.S. McCulloch.   

The simple and ambitious definition for artificial intelligence, included in the proposal for the workshop, is illuminating: ‘making a machine behave in ways that would be called intelligent if a human were so behaving’. These pioneers were making the assumption that ‘every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it’. This assumption turned out to be patently false and led to unrealistic expectations and forecasts. Fifty years later, McCarthy himself stated that ‘it was harder than we thought’.

Modern definitions of intelligence are of distinctly different flavour than the original one: ‘Intelligence is the quality that enables an entity to function appropriately and with foresight in its environment’ (Nilsson). Some even speak of rationality, rather than intelligence: ‘doing the right thing, given what it knows’ (Russell and Norvig).

Read the whole of this brief history of AI in Hello World #12

In the full article, which you can read in the free PDF copy of the issue, George looks at:

• Early advances researchers made from the 1950s onwards while developing games algorithms, e.g. for chess.
• The 1997 moment when Deep Blue, a purpose-built IBM computer, beating chess world champion Garry Kasparov using a search approach.
• The 2011 moment when Watson, another IBM computer system, beating two human Jeopardy! champions using multiple techniques to answer questions posed in natural language.
• The principles behind artificial neural networks, which have been around for decades and are now underlying many AI/machine learning breakthroughs because of the growth in computing power and availability of vast datasets for training.
• The 2017 moment when AlphaGo, an artificial neural network–based computer program by Alphabet’s DeepMind, beating Ke Jie, the world’s top-ranked Go player at the time.

More on machine learning and AI education in Hello World #12

In your free PDF of Hello World issue 12, you’ll also find:

• An interview with University of Cambridge statistician David Spiegelhalter, whose work shaped some of the foundations of AI, and who shares his thoughts on data science in schools and the limits of AI 
• An introduction to Popbots, an innovative project by MIT to open AI to the youngest learners
• An article by Ken Kahn, researcher in the Department of Education at the University of Oxford, on using the block-based Snap! language to introduce your learners to natural language processing
• Unplugged and online machine learning activities for learners age 7 to 16 in the regular ‘Lesson plans’ section
• And lots of other relevant articles

You can also read many of these articles online on the Hello World website.

Find more resources for AI and data science education

In Hello World issue 16, the focus is on all things data science and data literacy for your learners. As always, you can download a free copy of the issue. And on our Hello World podcast, we chat with practicing computing educators about how they bring AI, AI ethics, machine learning, and data science to the young people they teach.

If you want a practical introduction to the basics of machine learning and how to use it, take our free online course.

There are still many open questions about what good AI and data science education looks like for young people. To learn more, you can watch our panel discussion about the topic, and join our monthly seminar series to hear insights from computing education researchers around the world.

We are also collating a growing list of educational resources about these topics based on our research seminars, seminar participants’ recommendations, and our own work. Find the resource list here.

The post Snapshots from the history of AI, plus AI education resources appeared first on Raspberry Pi Foundation.