Connexion
Young people taking part in the European Astro Pi Challenge are about to have their computer programs sent to the International Space Station (ISS). Astro Pi is run annually in collaboration by us and ESA Education, and offers two ways to get involved: Mission Zero and Mission Space Lab.
This year, over 25,000 young people from across Europe and eligible ESA Member States are getting their programs ‘uplinked’ to the Astro Pi computers aboard the ISS, where they will be running over the next few weeks.
Mission Zero is an exciting activity for kids with little or no experience with coding. We invite young people to create a Python program that displays an 8×8 pixel image or animation. This program then gets sent to the ISS, and each pixel art piece is displayed for 30 seconds on the LED matrix display of the Astro Pi computers on the ISS.
We picked the theme ‘fauna and flora’ as the inspiration for young people’s pixel art, as it proved so popular last year, and we weren’t disappointed: this year, 24,378 young people submitted 16,039 Mission Zero creations!
We’ve tested every program and are pleased to announce that 15,942 Mission Zero programs will be sent to run on the ISS from mid May.
Once again, we have been amazed at the wonderful images and animations that young people have created. Seeing all the images that have been submitted is one of the most enjoyable and inspiring things to do as we work on the Astro Pi Challenge. Here is a little selection of some of our favourites submitted this year:
For Mission Space Lab, we invite more experienced young coders to take on a scientific challenge: to calculate the speed that the ISS orbits Earth.
Teams are tasked with writing a program that uses the Astro Pis’ sensors and visible light camera to capture data for their calculations, and we have really enjoyed seeing the different approaches the teams have taken.
Some teams decided to calculate the distance between two points in photos of the Earth’s surface and combine this with how long it took for the ISS to pass over the points to find the speed. This particular method uses feature extraction and needs to account for ground sampling distance — how many square metres are represented in one pixel in an image of the ground taken from above — to get an accurate output.
We’ve also seen teams use data from the gyroscope to calculate the speed using the angle readings and photos to get their outputs. Yet other teams have derived the speed using equations of motion and sampling from the accelerometer.
All teams that took multiple samples from the Astro Pi sensors, or multiple images, had to decide how to output a final estimate for the speed of the ISS. Most teams opted to use the mean average. But a few teams chose to filter their samples to choose only the ‘best’ ones based on prior knowledge (Bayesian filtering), and some used a machine learning model and the Astro Pi’s machine learning dongle to select which images or data samples to use. Some teams even provided a certainty score along with their final estimate.
However the team choses to approach the challenge, before their program can run on the ISS, we need to make sure of a few things. For a start, we check that they’ve followed the challenge rules and meet the ISS security requirements. Next, we check that the program can run without errors on the Astro Pis as the astronauts on board the ISS can’t stop what they’re doing to fix any problems.
So, all programs submitted to us must pass a rigorous testing process before they can be sent into space. We run each program on several replica Astro Pis, then run all the programs sequentially, to ensure there’s no problems. If the program passes testing, it’s awarded ‘flight status’ and can be sent to run in space.
This year, 236 teams have been awarded flight status. These teams represent 889 young people from 22 countries in Europe and ESA member states. The average age of these young people is 15, and 27% of them are girls. The UK has the most teams achieving flight status (61), followed by the Czech Republic (23) and Romania (22). You can see how this compares to last year and explore other breakdowns of participant data in the annual Astro Pi impact report.
Our congratulations to all the Mission Space Lab teams who’ve been awarded flight status: it is a great achievement. All these teams will be invited to join a live online Q&A with an ESA astronaut in June. We can’t wait to see what questions you send us for the astronaut.
Normally, the Astro Pi programs run continuously from the end of April until the end of May. However, this year, there is an interesting event happening in the skies above us that means that programs will pause for a few days. The ISS will be moving its position on the ‘beta angle’ and pivoting its orientation to maximise the sunlight that it can capture with its solar panels.
The ISS normally takes 90 minutes to complete its orbit, 45 minutes of which is in sunlight, and 45 minutes in darkness. When it moves along the beta angle, it will be in continual sunlight, allowing it to capture lots of solar energy and recharge its batteries. While in its new orientation, the ISS is exposed to increased heat from the sun so the window shutters must be closed to help the astronauts stay cool. That means taking photos of the Earth’s surface won’t be possible for a few days.
Once all of the programs have run, we will send the Mission Space Lab teams the data collected during their experiments. All successful Mission Zero and Mission Space Lab teams and mentors will also receive personal certificates to recognise their mission completion.
Congratulations to all of this year’s Astro Pi Challenge participants, and especially to all successful teams.
The post Young people’s Astro Pi code is sent to the International Space Station appeared first on Raspberry Pi Foundation.
We work with mission-aligned educational organisations all over the world to support young people’s computing education. In 2023 we established four partnerships in Kenya and South Africa with organisations Coder:LevelUp, Blue Roof, Oasis Mathare, and Tech Kidz Africa, which support young people in underserved communities. Our shared goal is to support educators to establish and sustain extracurricular Code Clubs and CoderDojos in schools and community organisations. Here we share insights into the impact the partnerships are having.
In the partnerships we used a ‘train the trainer’ model, which focuses on equipping our partners with the knowledge and skills to train and support educators and learners. This meant that we trained a group of educators from each partner, enabling them to then run their own training sessions for other educators so they can set up coding clubs and run coding sessions. These coding sessions aim to increase young people’s skills and confidence in computing and programming.
We also conducted an evaluation of the impact of our work in these partnerships. We shared two surveys with educators (one shortly after they completed their initial training, a second for when they were running coding sessions), and another survey for young people to fill in during their coding sessions. In two of the partnerships, we also conducted interviews and focus groups with educators and young people.
Although we received lots of valuable feedback, only a low proportion of participants responded to our surveys, so the data may not be representative of the experience of all participating educators.
Following our training, our partners themselves trained 332 educators across Kenya and South Africa to work directly in schools and communities running coding sessions. This led to the setup of nearly 250 Code Clubs and CoderDojos and additional coding sessions in schools and communities, reaching more than 11,500 young people.
As a result, access to coding and programming has increased in areas where this provision would otherwise not be available. One educator told us:
“We found it extremely beneficial, because a lot of our children come from areas in the community where they barely know how to read and write, let alone know how to use a computer… [It provides] the foundation, creating a fun way of approaching the computer as opposed to it being daunting.”
We found encouraging signs of the impact of this work on young people.
Nearly 90% of educators reported seeing an increase in young people’s computing skills, with over half of educators reporting that this increase was large. Over three quarters of young people who filled in our survey reported feeling confident in coding and computer programming.
The young people spoke enthusiastically about what they had learned and the programs they had created. They told us they felt inspired to keep learning, linking their interests to what they wanted to do in coding sessions. Interests included making dolls, games, cartoons, robots, cars, and stories.
When we spoke with educators and young people, a key theme that emerged was the enthusiasm and curiosity of the young people to learn more. Educators described how motivated they felt by the excitement of the young people. Young people particularly enjoyed finding out the role of programming in the world around them, from understanding traffic lights to knowing more about the games they play on their phones.
One educator told us:
“…students who knew nothing about technology are getting empowered.”
This confidence is particularly encouraging given that educators reported a low level of computer literacy among young people at the start of the coding sessions. One educator described how coding sessions provided an engaging hook to support teaching basic IT skills, such as mouse skills and computer-related terms, alongside coding.
One educator gave an example of young people using what they are learning in their coding club to solve real-world problems, saying:
“It’s life-changing because some of those kids and the youths that you are teaching… they’re using them to automate things in their houses.”
Many of these young people live in informal settlements where there are frequent fires, and have started using skills they learned in the coding sessions to automate things in their homes, reducing the risk of fires. For example, they are programming a device that controls fans so that they switch on when the temperature gets too high, and ways to switch appliances such as light bulbs on and off by clapping.
From the gathered feedback, we also learned some useful lessons to help improve the quality of our offer and support to our partners. For example, educators faced challenges including lack of devices for young people, and low internet connectivity. As we continue to develop these partnerships, we will work with partners to make use of our unplugged activities that work offline, removing the barriers created by low connectivity.
We are continuing to develop the training we offer and making sure that educators are able to access our other training and resources. We are also using the feedback they have given us to consider where additional training and support may be needed. Future evaluations will further strengthen our evidence and provide us with the insights we need to continue developing our work and support more educators and young people.
Our thanks to our partners at Coder:LevelUp, Blue Roof, Oasis Mathare, and Tech Kidz Africa for sharing our mission to enable young people to realise their full potential through the power of computing and digital technologies. As we continue to build partnerships to support Code Clubs and CoderDojos across South Africa and Kenya, it is heartening to hear first-hand accounts of the positive impact this work has on young people.
If your organisation would like to partner with us to bring computing education to young people you support, please send us a message with the subject ‘Partnerships’.
The post Global impact: Empowering young people in Kenya and South Africa through coding clubs appeared first on Raspberry Pi Foundation.
It’s been almost a year since we launched our first set of Experience AI resources in the UK, and we’re now working with partner organisations to bring AI literacy to teachers and students all over the world.
Developed by the Raspberry Pi Foundation and Google DeepMind, Experience AI provides everything that teachers need to confidently deliver engaging lessons that will inspire and educate young people about AI and the role that it could play in their lives.
Over the past six months we have been working with partners in Canada, Kenya, Malaysia, and Romania to create bespoke localised versions of the Experience AI resources. Here is what we’ve learned in the process.
The Experience AI Lessons address a variety of real-world contexts to support the concepts being taught. Including real-world contexts in teaching is a pedagogical strategy we at the Raspberry Pi Foundation call “making concrete”. This strategy significantly enhances the learning experience for learners because it bridges the gap between theoretical knowledge and practical application.
The initial aim of Experience AI was for the resources to be used in UK schools. While we put particular emphasis on using culturally relevant pedagogy to make the resources relatable to learners from backgrounds that are underrepresented in the tech industry, the contexts we included in them were for UK learners. As many of the resource writers and contributors were also based in the UK, we also unavoidably brought our own lived experiences and unintentional biases to our design thinking.
Therefore, when we began thinking about how to adapt the resources for schools in other countries, we knew we needed to make sure that we didn’t just convert what we had created into different languages. Instead we focused on localisation.
Localisation goes beyond translating resources into a different language. For example in educational resources, the real-world contexts used to make concrete the concepts being taught need to be culturally relevant, accessible, and engaging for students in a specific place. In properly localised resources, these contexts have been adapted to provide educators with a more relatable and effective learning experience that resonates with the students’ everyday lives and cultural background.
Recognising our UK-focused design process, we made sure that we made no assumptions during localisation. We worked with partner organisations in the four countries — Digital Moment, Tech Kidz Africa, Penang Science Cluster, and Asociația Techsoup — drawing on their expertise regarding their educational context and the real-world examples that would resonate with young people in their countries.
We asked our partners to look through each of the Experience AI resources and point out the things that they thought needed to change. We then worked with them to find alternative contexts that would resonate with their students, whilst ensuring the resources’ intended learning objectives would still be met.
Tech Kidz Africa, our partner in Kenya, challenged some of the assumptions we had made when writing the original resources.
Tech Kidz Africa wanted the contexts in the lessons to not just be relatable to their students, but also to demonstrate real-world uses of AI applications that could make a difference in learners’ communities. They highlighted that as agriculture is the largest contributor to the Kenyan economy, there was an opportunity to use this as a key theme for making the Experience AI lessons more culturally relevant.
This conversation with Tech Kidz Africa led us to identify a real-world use case where farmers in Kenya were using an AI application that identifies disease in crops and provides advice on which pesticides to use. This helped the farmers to increase their crop yields.
We included this example when we adapted an activity where students explore the use of AI for “computer vision”. A Google DeepMind research engineer, who is one of the General Chairs of the Deep Learning Indaba, recommended a data set of images of healthy and diseased cassava crops (1). We were therefore able to include an activity where students build their own machine learning models to solve this real-world problem for themselves.
While designing the original set of Experience AI resources, we made the assumption that the vast majority of students in UK classrooms have access to computers connected to the internet. This is not the case in Kenya; neither is it the case in many other countries across the world. Therefore, while we localised the Experience AI resources with our Kenyan partner, we made sure that the resources allow students to achieve the same learning outcomes whether or not they have access to internet-connected computers.
Assuming teachers in Kenya are able to download files in advance of lessons, we added “unplugged” options to activities where needed, as well as videos that can be played offline instead of being streamed on an internet-connected device.
The work with our first four Experience AI partners has given us with lots of localisation learnings, which we will use as we continue to expand the programme with more partners across the globe:
Throughout this process we have also reflected on the design principles for our resources and the choices we can make while we create more Experience AI materials in order to make them more amenable to localisation.
We are very grateful to our partners for collaborating with us to localise the Experience AI resources. Thank you to Digital Moment, Tech Kidz Africa, Penang Science Cluster, and Asociația Techsoup.
We now have the tools to create resources that support a truly global community to access Experience AI in a way that resonates with them. If you’re interested in joining us as a partner, you can register your interest here.
(1) The cassava data set was published open source by Ernest Mwebaze, Timnit Gebru, Andrea Frome, Solomon Nsumba, and Jeremy Tusubira. Read their research paper about it here.
The post Localising AI education: Adapting Experience AI for global impact appeared first on Raspberry Pi Foundation.
As educators, it’s important that we showcase the wide range of career opportunities available in the field of computing, not only to inspire learners, but also to help them feel sure they’re choosing to study a subject that is useful for their future. For example, a survey from the BBC in September 2023 found that more than a quarter of UK teenagers often feel anxious, with “exams and school life” among the main causes. To help young people chart their career paths, we recently hosted two live webinars for National Careers Week in the UK.
Our goal for the webinars was to highlight the breadth of careers within computing and to provide insights from professionals who are pursuing their own diverse and rewarding paths. Each webinar featured engaging discussions and an interactive Q&A session with learners who use our Ada Computer Science platform. The learners could ask their own questions to get firsthand knowledge and perspectives from our guest speakers.
Jess Van Brummelen is a Human–Computer Interaction Research Scientist at Niantic, the video games company behind augmented reality game Pokémon Go. After developing an interest in programming during her undergraduate degree in mechanical engineering, she went on to complete a Master’s degree and PhD in computer science at MIT.
Ashley Edwards is a Senior Research Scientist at Google DeepMind, working on reinforcement learning. She received her PhD in 2019 from Georgia Tech, spent time as an intern at Google Brain, and worked as a research scientist at Uber AI Labs.
You can read extracts from our interviews with Jess and Ashley and watch the full videos below. Teachers have contacted us to say they’ll be using the webinars for careers-focused sessions with their students. We hope you will do the same!
Please note that we have edited the extracts below to add clarity.
Hi Jess. What advice would you give to a student who is thinking about a career in human–computer interaction in the gaming industry?
In terms of HCI and gaming, I’d actually recommend that you keep gaming! It’s a small part of my job but it’s really important to understand what’s fun and enjoyable in games. Not only that; gaming can be great for learning to problem-solve — there’s been all sorts of research on the positive impact of gaming.
A second thing, going back to how I felt in my mechanical engineering classes, I really felt like an ‘other’ and not someone who is the standard computer scientist or engineer. I would encourage students to pursue their dreams anyway because it’s so important to have diversity in these types of careers, especially technology, because it goes out to so many different people and it can really affect society. It’s really important that the people who make it come from many different backgrounds and cultures so we can create technology that is better for everyone.
[From Owen, a student on the livestream] What’s the most impossible idea you’ve come up with while working at Niantic?
I’m currently publishing a paper addressing the question, ‘Can we guide people without using anything visual on their phone?’ That means using audio and haptic (technology that transmits information via touch, e.g. vibrations) prompts instead. We tried out different commands where the phone said ‘turn left’ and ‘turn right’, but we really wanted to test how to guide someone more specifically in a game environment. For example, if there was a hidden object on a wall in a game that a person couldn’t see, could we guide them to that object while they’re walking? So I ran a study where I guided people to scan a statue by moving around it. Scanning is the process of using the camera on your phone to scan an object in real life, which is then reconstructed on your phone. Scanning objects can trigger other augmented reality experiences within a game. For example, you might scan a real-life box in a room and this might trigger an animation of that box opening to reveal a secret within the game. We tested a lot of different things. For example, test subjects listened to music as they were walking and when they were on the right path, the music sounded really good. But when they were off the path, it sounded terrible. So it helped them to look for the right path. Then if you were pointing the phone in the wrong direction for scanning objects, you would get warning vibrations on the phone. So we did the study and we were hoping it would improve safety. It turns out it was neutral on improving safety — I think this is because it was such a novel system. People weren’t used to using it and still bumped into things! But it did make people better at scanning the objects, which was interesting.
Watch Jess’s full interview:
Hi Ashley. Is there something you studied in school that you found to be more useful now than you ever thought it would be?
Maths! I always enjoyed doing maths, but I didn’t realise I would need it as a computer scientist. You see it popping up all the time, especially in machine learning. Having a strong knowledge of calculus and linear algebra is really helpful.
How do you train an AI model using machine learning?
You start by asking the question, ‘What is the problem I’m trying to solve?’ Then typically you need input data and the outputs you want to achieve, so you ask two more questions, ‘What data do I want to come in?’ and ‘What do I want to come out?’ Let’s say you decide to use a supervised learning model (a category of machine learning where labelled data sets are used to train algorithms to detect patterns and predict outcomes) to predict whether a photo contains a cat. You train the model using a giant set of images with labels that say either ‘This is a cat’ or ‘This isn’t a cat’. By training the model with the images, you get to a point where your model can analyse the features of any image and predict whether it contains a cat or not.
In my field of research, I work on something called reinforcement learning, which is where you train your model through trial and error and the use of ‘rewards’. Let’s imagine we are trying to train a robot. We might write a program that tells the robot, ‘I am going to give you a reward if you take the right step forward and it’s going to be a positive reward. If you fall over, I’m going to give you a negative reward.’ So you train the robot to prioritise the right behaviours to optimise the rewards it’s getting.
[From a student] Will I still need to learn to code in the future?
I think it is going to be very different in the future, but we’ll still need to learn how to build different types of algorithms and we’re going to need to understand the concepts behind coding as well. We’ll still need to ask questions like, ‘What is it that I want to build?’ and ‘Is this actually doing the correct thing?’
Watch Ashley’s full interview:
Jess and Ashley are forging successful careers not only through a combination of smart choices, hard work, talent, and a passion for technology; they also had access to opportunities to discover their passion and receive an education in this field. Too many young people around the world still don’t have these opportunities.
That is why we provide free resources and training to help schools broaden access to computing education. For example, our free learning platform, Ada Computer Science, provides students aged 14 to 19 with high-quality computing resources and interactive questions, written by experts from our team. To learn more, visit adacomputerscience.org.
The post Careers in computer science: Two perspectives 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.
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:
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.
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.
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
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
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’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.
We’re really excited to see that Experience AI Challenge mentors are starting to submit AI projects created by young people. There’s still time for you to get involved in the Challenge: the submission deadline is 24 May 2024.
If you want to find out more about the Challenge, join our live webinar on Wednesday 3 April at 15:30 BST on our YouTube channel.
During the webinar, you’ll have the chance to:
Subscribe to our YouTube channel and press the ‘Notify me’ button to receive a notification when we go live.
The Experience AI Challenge, created by the Raspberry Pi Foundation in collaboration with Google DeepMind, guides young people under the age of 18, and their mentors, through the exciting process of creating their own unique artificial intelligence (AI) project. Participation is completely free.
Central to the Challenge is the concept of project-based learning, a hands-on approach that gets learners working together, thinking critically, and engaging deeply with the materials.
In the Challenge, young people are encouraged to seek out real-world problems and create possible AI-based solutions. By taking part, they become problem solvers, thinkers, and innovators.
And to every young person based in the UK who creates a project for the Challenge, we will provide personalised feedback and a certificate of achievement, in recognition of their hard work and creativity. Any projects considered as outstanding by our experts will be selected as favourites and its creators will be invited to a showcase event in the summer.
You don’t need to be an AI expert to bring this Challenge to life in your classroom or coding club. Whether you’re introducing AI for the first time or looking to deepen your young people’s knowledge, the Challenge’s step-by-step resource pack covers all you and your young people need, from the basics of AI, to training a machine learning model, to creating a project in Scratch.
In the resource pack, you will find:
The pack offers a safety net of scaffolding, support, and troubleshooting advice.
By bringing the Experience AI Challenge to young people, you’re inspiring the next generation of innovators, thinkers, and creators. The Challenge encourages young people to look beyond the code, to the impact of their creations, and to the possibilities of the future.
You can find out more about the Experience AI Challenge, and download the resource pack, from the Experience AI website.
The post The Experience AI Challenge: Find out all you need to know 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.
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.
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.
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.
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.
Since November, registration is open for Mission Space Lab, part of the European Astro Pi Challenge 2023/24. The Astro Pi Challenge is an ESA Education project run in collaboration with us here at the Raspberry Pi Foundation that gives young people up to age 19 the amazing opportunity to write computer programs that run on board the International Space Station (ISS). It is free to take part and young people can participate in two missions: Mission Zero, designed for beginners, and Mission Space Lab, designed for more experienced coders.
This year, Mission Space Lab has a brand-new format. As well as introducing a new activity for teams to work on, we have created new resources to support teams and mentors, and developed a special tool to help teams test their programs.
A big motivator for these changes was to make the activity more accessible and enable more young people to have their code run in space. Listening to feedback from participants and mentors, we are creating the opportunity for even more teams to submit programs that run on the ISS this year, by offering a specific activity and providing more extensive support materials.
For this year’s mission, ESA astronauts have given teams a specific scientific task to solve: to calculate the speed that the ISS is travelling as it orbits the Earth. People working in science often investigate a specific phenomenon or try to solve a particular problem. They have to use their knowledge and skills and the available tools to find ways to answer their research question. For Mission Space Lab, teams will work just like this. They will look at what sensors are available on the Astro Pi computers on board the ISS, develop a solution, and then write a Python program to execute it. To test their program, they will use the new Astro Pi Replay software tool we’ve created, which simulates running their program on board the ISS.
To help teams and mentors take part in Mission Space Lab, we are providing a variety of supporting materials:
We have also run virtual sessions to help mentors and teams familiarise themselves with the new Mission Space Lab activity, and to ask any technical questions they might have. You can watch the recordings of these sessions on YouTube:
Astro Pi Replay is a new simulation tool that we have developed to support Mission Space Lab teams to test their programs. The tool simulates running programs on the Astro Pi computers on board the ISS. It is a Python library available as a plug-in to install in the Thonny IDE where teams write their programs. Thanks to this tool, teams can develop and test their programs on any computer that supports Python, without the need for hardware like the Astro Pi units on board the ISS.
The Astro Pi Replay tool works by replaying a data set captured by a Mission Space Lab team in May 2023. The data set includes readings from the Astro Pi ‘s sensors, and images taken by its visible-light camera like the ones below. Whenever teams run their programs in Thonny with Astro Pi Replay, the tool replays some of this historical data. That means teams can use the historical data to test their programs and calculations.
One of the benefits of using this simulation tool is that it gives teams a taste of what they can expect if their program is run on the ISS. By replaying a sequence of data captured by the Astro Pis in space, teams using sensors will be able to see what kind of data can be collected, and teams using the camera will be able to see some incredible Earth observation images.
If you’re curious about how Astro Pi Replay works, you’ll be pleased to hear we are making it open source soon. That means you’ll be able to look at the source code and find out exactly what the library does and how.
Community members have consistently reported how amazing it is for teams to receive unique Earth observation photos and sensor data from the Astro Pis, and how great the images and data are to inspire young people to participate in their computing classes, clubs, or events. Through the changes we’ve made to Mission Space Lab this year, we want to support as many young people as possible to have the opportunity to engage in space science and capture their own data from the ISS.
If you want a taste of how fantastic Astro Pi is for learners, watch the story of St Joseph’s, a rural Irish school where participating in Astro Pi has inspired the whole community.
Submissions for Mission Space Lab 2023/24 are open until 19 February 2024, so there’s still time to take part! You can find full details and eligibility criteria at astro-pi.org/mission-space-lab.
If you have any questions about the European Astro Pi Challenge, please get in touch at contact@astro-pi.org.
The post Get ready for Mission Space Lab with our new simulation tool appeared first on Raspberry Pi Foundation.
At the heart of our work as a charity is the aim to democratise access to digital skills and technologies. Since 2020, we have partnered with over 100 youth and community organisations in the UK to develop programmes that increase opportunities for young people experiencing educational disadvantage to engage and create with digital technology in underserved communities.
Youth organisations attempting to start a coding club can face a range of practical and logistical challenges, from a lack of space, to funding restrictions, and staff shortages. However, the three issues that we hear about most often are a lack of access to hardware, lack of technical expertise among staff, and low confidence to deliver activities on an ongoing basis.
In 2023, we worked to help youth organisations overcome these barriers by designing and delivering a new hybrid training programme, supported by Amazon Future Engineer. With the programme, we aimed to help youth leaders and educators successfully incorporate coding and digital making activities as part of their provision to young people.
“Really useful, I have never used Scratch so going [through] the project made it clear to understand and how I would facilitate this for the children[.]” – Heather Coulthard, Doncaster Children’s University
We invited 14 organisations from across the UK to participate in the training, based on:
Attendees included a number of previous Learn at Home partners, including Breadline London, Manchester Youth Zone, and Youth Action. They all told us that the additional support they had received from the Foundation and organisations such as The Bloomfield Trust during the coronavirus pandemic had directly inspired them to participate in the training and begin their own coding clubs.
We started with four online training sessions where we introduced the youth leaders to digital making concepts, programming languages, and recommended activities to run with their young people. This included everything from making their own block-based Scratch games, to running Python programs on our Code Editor and trying out physical computing via our new micro:bit project path.
Alongside digital skills and interactive codealongs, the training also focused on how to be an effective CoderDojo mentor, including classroom management best practice, an explanation of the thinking behind our 3…2…1…Make! project paths, and an overview of culturally relevant pedagogy.
This last part explored how youth leaders can adapt and tailor digital making resources designed for a wide, general audience for their specific groups of young people to aid their understanding, boost their learning outcomes, and increase their sense of belonging within a coding club environment — a common blocker for organisations trying to appeal to marginalised youth.
The training culminated in a day-long, in-person session at our head office in Cambridge, so that youth leaders and educators from each organisation could get hands-on experience. They experimented with physical computing components such as the Raspberry Pi Pico, trained their own artificial intelligence (AI) models using our Experience AI resources, and learned more about how their young people can get involved with Coolest Projects and Astro Pi Mission Zero.
The in-person session also gave everyone the chance to get excited about running digital making activities at their centres: the youth leaders got to ask our team questions, and had the invaluable opportunity to meet each other, share their stories, swap advice, and discuss the challenges they face with their peers.
“Having the in-person immensely improved my skills and knowledge. The instructors were all brilliant and very passionate.” – Awale Elmi, RISE Projects
Finally, thanks to the generous support from Amazon Future Engineer, we were able to equip each participating organisation with Raspberry Pi 400 kits so that the youth leaders can practise and share the skills and knowledge they gained on the course at their centres and the organisations can offer computing activities in-house.
Over the next 12 months, we will continue to work with each of these youth and community organisations, supporting them to establish their coding clubs, and helping to ensure that young people in their communities get a fair and equal opportunity to engage and create with technology, no matter their background or challenges they are facing.
“It was really great. The online courses are excellent and being in-person to get answers to questions really helped. The tinkering was really useful and having people on hand to answer questions [was] massively useful.” – Liam Garnett, Leeds Libraries
For more information about how we can support youth and community organisations in the UK to start their own coding clubs, please send us a message with the subject ‘Partnerships’.
The post Working with UK youth and community organisations to tackle the digital divide appeared first on Raspberry Pi Foundation.
Underrepresentation in computing is a widely known issue, in industry and in education. To cite some statistics from the UK: a Black British Voices report from August 2023 noted that 95% of respondents believe the UK curriculum neglects black lives and experiences; fewer students from working class backgrounds study GCSE Computer Science; when they leave formal education, fewer female, BAME, and white working class people are employed in the field of computer science (Kemp 2021); only 21% of GCSE Computer Science students, 15% at A level, and 22% at undergraduate level are female (JCQ 2020, Ofqual 2020, UCAS 2020); students with additional needs are also underrepresented.
Such statistics have been the status quo for too long. Many Computing teachers already endeavour to bring about positive change where they can and engage learners by including their interests in the lessons they deliver, so how can we support them to do this more effectively? Extending the reach of computing so that it is accessible to all also means that we need to consider what formal and informal values predominate in the field of computing. What is the ‘hidden’ curriculum in computing that might be excluding some learners? Who is and who isn’t represented?
In a recent research seminar, Katharine Childs from our team outlined a research project we conducted, which included a professional development workshop to increase primary teachers’ awareness of and confidence in culturally relevant pedagogy. In the workshop, teachers considered how to effectively adapt curriculum materials to make them culturally relevant and engaging for the learners in their classrooms. Katharine described the practical steps teachers took to adapt two graphics-related units, and invited seminar participants to apply their learning to a graphics activity themselves.
Culturally relevant pedagogy is a teaching framework which values students’ identities, backgrounds, knowledge, and ways of learning. By drawing on students’ own interests, experiences and cultural knowledge educators can increase the likelihood that the curriculum they deliver is more relevant, engaging and accessible to all.
The idea of culturally relevant pedagogy was first introduced in the US in the 1990s by African-American academic Gloria Ladson-Billings (Ladson-Billings 1995). Its aim was threefold: to raise students’ academic achievement, to develop students’ cultural competence and to promote students’ critical consciousness. The idea of culturally responsive teaching was later advanced by Geneva Gay (2000) and more recently brought into focus in US computer science education by Kimberly Scott and colleagues (2015). The approach has been localised for England by Hayley Leonard and Sue Sentance (2021) in work they undertook here at the Foundation.
Katharine began her presentation by explaining that the professional development workshop in the Primary culturally adapted resources for computing project built on two of our previous research projects to develop guidelines for culturally relevant and responsive computing and understand how teachers used them in practice. This third project ran as a pilot study funded by Cognizant, starting in Autumn 2022 with a one-day, in-person workshop for 13 primary computing teachers.
Katharine then introduced us to the 10 areas of opportunity (AO) our research at the Raspberry Pi Computing Education Research Centre had identified for culturally relevant pedagogy. These 10 areas were used as practical prompts to frame the workshop discussions:
At first glance it is easy to think that you do most of those things already, or to disregard some items as irrelevant to the computing curriculum. What would your own cultural identity (see AO2) have to do with computing, you might wonder. But taking a less complacent perspective might lead you to consider all the different facets that make up your identity and then to think about the same for the students you teach. You may discover that there are many areas which you have left untapped in your lesson planning.
Katharine explained how this is where the professional development workshop showed itself as beneficial for the participants. It gave teachers the opportunity to reflect on how their cultural identity impacted on their teaching practices — as a starting point to learning more about other aspects of the culturally relevant pedagogy approach.
Our researchers were interested in how they could work alongside teachers to adapt two computing units to make them more culturally relevant for teachers’ specific contexts. They used the Computing Curriculum units on Photo Editing (Year 4) and Vector Graphics (Year 5).
Katharine illustrated some of the adaptations teachers and researchers working together had made to the emoji activity above, and which areas of opportunity (AO) had been addressed; this aspect of the research will be reported in later publications.
Although the number of participants in this pilot study was small, the findings show that the professional development workshop significantly increased teachers’ awareness of culturally relevant pedagogy and their confidence in adapting resources to take account of local contexts:
These quantitative shifts in perspective indicate a positive effect of the professional development pilot.
Katharine described that in our qualitative interviews with the participating teachers, they expressed feeling that their understanding of culturally relevant pedagogy had increased and they recognized the many benefits to learners of the approach. They valued the opportunity to discuss their contexts and to adapt materials they currently used with other teachers, because it made it a more ‘authentic’ and practical professional development experience.
The seminar ended with breakout sessions inviting viewers to consider possible adaptations that could be made to the graphics activities which had been the focus of the workshop.
In the breakout sessions, attendees also discussed specific examples of culturally relevant teaching practices that had been successful in their own classrooms, and they considered how schools and computing educational initiatives could support teachers in their efforts to integrate culturally relevant pedagogy into their practice. Some attendees observed that it was not always possible to change schemes of work without a ‘whole-school’ approach, senior leadership team support, and commitment to a research-based professional development programme.
The seminar reminds us that the education system is not culture neutral and that teachers generally transmit the dominant culture (which may be very different from their students’) in their settings (Vrieler et al, 2022). Culturally relevant pedagogy is an attempt to address the inequities and biases that exist, which result in many students feeling marginalised, disenfranchised, or underachieving. It urges us to incorporate learners’ cultures and experiences in our endeavours to create a more inclusive computing curriculum; to adopt an intersectional lens so that all can thrive.
As a pilot study, the workshop was offered to a small cohort of 13, yet the findings show that the intervention significantly increased participants’ awareness of culturally relevant pedagogy and their confidence in adapting resources to take account of local contexts.
Of course there are many ways in which teachers already adapt resources to make them interesting and accessible to their pupils. Further examples of the sort of adaptations you might make using these areas of opportunity include:
Can you see an opportunity for integrating culturally relevant pedagogy in your classroom? We would love to hear about examples of culturally relevant teaching practices that you have found successful. Let us know your thoughts or questions in the comments below.
You can watch Katharine’s seminar here:
You can download her presentation slides on our ‘previous seminars’ page, and you can read her research paper.
To get a practical overview of culturally relevant pedagogy, read our 2-page Quick Read on the topic and download the guidelines we created with a group of teachers and academic specialists.
Tomorrow we’ll be sharing a blog about how the learners who engaged with the culturally adapted units found the experience, and how it affected their views of computing. Follow us on social media to not miss it!
On 12 December we’ll host the last seminar session in our series on primary (K-5) computing. Anaclara Gerosa will share her work on how to design and structure early computing activities that promote and scaffold students’ conceptual understanding. As always, the seminar is free and takes place online at 17:00–18:30 GMT / 12:00–13:30 ET / 9:00–10:30 PT / 18:00–19:30 CET. Sign up and we’ll send you the link to join on the day.
In 2024, our new seminar series will be about teaching and learning programming, with and without AI tools. If you’re signed up to our seminars, you’ll receive the link to join every monthly seminar.
The post Engaging primary Computing teachers in culturally relevant pedagogy through professional development appeared first on Raspberry Pi Foundation.
Google DeepMind’s Aimee Welch discusses our partnership on the Experience AI learning programme and why equal access to AI education is key. This article also appears in issue 22 of Hello World on teaching and AI.
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.
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.
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.
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:
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.”
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.
The post Experience AI: Making AI relevant and accessible appeared first on Raspberry Pi Foundation.
In our series of community stories, we celebrate some of the amazing young people and educators who are using their passion for technology to create positive change in the world around them.
In our latest story, we’re sharing the inspiring journey of St Joseph’s Secondary School in Rush, Ireland. Over the past few years, the school community has come together to encourage coding and digital skills, harnessing the European Astro Pi Challenge as an opportunity to kindle students’ enthusiasm for tech and teamwork.
We caught up with some of the educators and students at St Joseph’s, fresh off the success of their participation in another round of Astro Pi, to delve a little deeper into the school’s focus on making opportunities to engage with computing technologies accessible to all.
St Joseph’s Secondary School is in the heart of Rush, a rural town steeped in agricultural heritage. The school houses a diverse student population coming from the local multigenerational farming families as well as families who’ve been drawn to Rush more recently by its beautiful countryside and employment opportunities. St Joseph’s leadership team has responded to the changing demographics and increase of its student population by adapting and growing the school’s curriculum to meet the evolving needs of the young people and help them build a strong community.
One of the school’s most popular initiatives has been teaching coding from first year (ages 12–13). This proactive approach has resonated with many students, including Kamaya, a member of the school’s 2022/23 Astro Pi cohort, who first discovered her passion for space science and computing through the movie Interstellar.
I remember the first time I was like, ‘OK, space is cool’ is when I watched a movie. It was called Interstellar. I [realised] I might want to do something like that in my future. So, when I came to [St Joseph’s] secondary school, I saw coding as a subject and I was like, ‘Mum, I’ve got to do coding.’
Kamaya, student at St Joseph’s
A key person encouraging St Joseph’s students to give coding a try has been Mr Murray, or Danny as he is fondly referred to by students and staff alike. Danny was introduced to the importance of engaging with computing technologies while teaching science at a school in England: he attended a Code Club where he saw kids building projects with Raspberry Pis, and he couldn’t wait to get involved. Growing his knowledge from there, Danny changed subject focus when he moved back to Ireland. He took on the challenge of helping St Joseph’s expand their computer science offering, along with leading on all IT-related issues.
When the school introduced mandatory coding taster sessions for all first-year students, Danny was blown away by the students’ eagerness and wanted to provide further opportunities for them to see what they could achieve with digital technologies.
This is where Astro Pi came in. After hearing about this exciting coding challenge through an acquaintance, Danny introduced it to his computer science class, as well as extending an open invitation to all St Joseph’s students. The uptake was vast, especially once he shared that the young people could become the recipients of some very exciting photos.
You get to see photos of Earth that nobody has ever seen. Imagine just talking to somebody and saying, ‘Oh, there’s a picture of the Amazon. I took that picture when I was 14. From space.’
Danny Murray, computing teacher at St Joseph’s
Danny’s mission is to instil in his students the belief that they can achieve anything. Collaborating on Astro Pi projects has enabled young people at St Joseph’s to team up and uncover their strengths, and has helped foster a strong community.
The students’ sense of community has transcended Danny’s classroom, creating a culture of enthusiasm for digital skills at St Joseph’s. Today, a dedicated team of students is in charge of solving tech-related challenges within the school, as Deputy Principal Darren Byrne explains:
Our own students actually go class to class, repairing tech issues. So, every day there are four or five students going around checking PCs in classrooms. They […] give classes to our first-year students on app usage.
Darren Byrne, Deputy Principal at St Joseph’s
It’s invested in the whole school [now], the idea that students can look after this kind of technology themselves. We’re the ones reaching out for help from the students!
To find out how you can get involved in Astro Pi, visit astro-pi.org for further information, deadlines, and more. If you would like to learn more about the other free resources we have available to help you inspire a coding community in your school, head to www.raspberrypi.org/teach.
Help us celebrate St Joseph’s Secondary School by sharing their story on X (formerly Twitter), LinkedIn, and Facebook.
The post Celebrating the community: St Joseph’s Secondary School appeared first on Raspberry Pi Foundation.
I am delighted to announce that the Raspberry Pi Foundation and Google DeepMind are building a global network of educational organisations to bring AI literacy to teachers and students all over the world, starting with Canada, Kenya, and Romania.
We launched Experience AI in September 2022 to help teachers and students learn about AI technologies and how they are changing the world.
Developed by the Raspberry Pi Foundation and Google DeepMind, Experience AI provides everything that teachers need to confidently deliver engaging lessons that will inspire and educate young people about AI and the role that it could play in their lives.
We provide lesson plans, classroom resources, worksheets, hands-on activities, and videos that introduce a wide range of AI applications and the underlying technologies that make them work. The materials are designed to be relatable to young people and can be taught by any teacher, whether or not they have a technical background. Alongside the classroom resources, we provide teacher professional development, including an online course that provides an introduction to machine learning and AI.
The materials are grounded in real-world contexts and emphasise the potential for young people to positively change the world through a mastery of AI technologies.
Since launching the first resources, we have seen significant demand from teachers and students all over the world, with over 200,000 students already learning with Experience AI.
Building on that initial success and in response to huge demand, we are now building a global network of educational organisations to expand the reach and impact of Experience AI by translating and localising the materials, promoting them to schools, and supporting teacher professional development.
Obum Ekeke OBE, Head of Education Partnerships at Google DeepMind, says:
“We have been blown away by the interest we have seen in Experience AI since its launch and are thrilled to be working with the Raspberry Pi Foundation and local partners to expand the reach of the programme. AI literacy is a critical skill in today’s world, but not every young person currently has access to relevant education and resources. By making AI education more inclusive, we can help young people make more informed decisions about using AI applications in their daily lives, and encourage safe and responsible use of the technology.”
Today we are announcing the first three organisations that we are working with, each of which is already doing fantastic work to democratise digital skills in their part of the world. All three are already working in partnership with the Raspberry Pi Foundation and we are excited to be deepening and expanding our collaboration to include AI literacy.
Digital Moment is a Montreal-based nonprofit focused on empowering young changemakers through digital skills. Founded in 2013, Digital Moment has a track record of supporting teachers and students across Canada to learn about computing, coding, and AI literacy, including through supporting one of the world’s largest networks of Code Clubs.
“We’re excited to be working with the Raspberry Pi Foundation and Google DeepMind to bring Experience AI to teachers across Canada. Since 2018, Digital Moment has been introducing rich training experiences and educational resources to make sure that Canadian teachers have the support to navigate the impacts of AI in education for their students. Through this partnership, we will be able to reach more teachers and with more resources, to keep up with the incredible pace and disruption of AI.”
Indra Kubicek, President, Digital Moment
Tech Kidz Africa is a Mombasa-based social enterprise that nurtures creativity in young people across Kenya through digital skills including coding, robotics, app and web development, and creative design thinking.
“With the retooling of teachers as a key objective of Tech Kidz Africa, working with Google DeepMind and the Raspberry Pi Foundation will enable us to build the capacity of educators to empower the 21st century learner, enhancing the teaching and learning experience to encourage innovation and prepare the next generation for the future of work.”
Grace Irungu, CEO, Tech Kidz Africa
Asociația Techsoup works with teachers and students across Romania and Moldova, training Computer Science, ICT, and primary school teachers to build their competencies around coding and technology. A longstanding partner of the Raspberry Pi Foundation, they foster a vibrant community of CoderDojos and support young people to participate in Coolest Projects and the European Astro Pi Challenge.
“We are enthusiastic about participating in this global partnership to bring high-quality AI education to all students, regardless of their background. Given the current exponential growth of AI tools and instruments in our daily lives, it is crucial to ensure that students and teachers everywhere comprehend and effectively utilise these tools to enhance their human, civic, and professional potential. Experience AI is the best available method for AI education for middle school students. We couldn’t be more thrilled to work with the Raspberry Pi Foundation and Google DeepMind to make it accessible in Romanian for teachers in Romania and the Republic of Moldova, and to assist teachers in fully integrating it into their classes.”
Elena Coman, Director of Development, Asociația Techsoup
These are the first of what will become a global network of organisations supporting tens of thousands of teachers to equip millions of students with a foundational understanding of AI technologies through Experience AI. If you want to get involved in inspiring the next generation of AI leaders, we would love to hear from you.
The post AI literacy for teachers and students all over the world appeared first on Raspberry Pi Foundation.
Earlier this year, we launched our Code Editor, a free online tool to help make learning text-based programming simple and accessible for kids age 9 and up. We focus on supporting the needs of young people who are learning programming at school, in Code Clubs and CoderDojos, and at home.
Today, we have two exciting updates to share: support for web page projects with HTML/CSS, and an improved mobile and tablet experience.
Learners can use the Code Editor to write and run code in a web browser without installing any additional software. The Editor is currently available as a beta version, and we’ve already received really positive comments:
“The Editor looks really nice! I have tried the Python part, and it is intuitive and concise. My little program worked no problem, and I am sure the Editor will be easy, intuitive, and quick to learn for the young [learners].”
— Volunteer in the CoderDojo community
The Code Editor now supports the HTML and CSS web development languages, giving young people the ability to create and preview their own websites directly in the Editor interface. Learners can have their code and the preview panel side by side, and they can also preview their websites in a separate, larger tab.
We have embedded the Editor in our ‘Introduction to web‘ path on the Projects site. The path contains six HTML and CSS projects for beginners and helps them create fun websites like the ones shown here.
We want the Code Editor to be safe, age-appropriate, and suitable for use in classrooms or coding clubs. With this in mind, we have excluded certain functions, like being able to add links to external websites in the code. Rather than enabling image uploads, we provide a library of images when projects in our free learning paths contain images, in order to support multimedia projects safely.
Whether users are coding in Python or HTML/CSS, the Editor offers accessibility options so you can easily switch settings between light and dark mode, and between small, medium, and large text size. The text size feature is useful for people with visual impairments, as well as for educators who want to demonstrate something to a group of learners.
Our Code Editor now offers a new and improved experience for users of mobile and tablet devices. This improves access for learners in classrooms where tablets are used, and in low- and middle-income countries, where mobile phones are commonly used for digital learning.
The Editor now includes:
We’re continuing to develop the Code Editor and have more improvements planned. If you would like to try it out and provide us with your feedback, we’d love to hear what you think of our latest updates.
Code Editor developments have been made possible with generous support from Endless and the Cisco Foundation.
The post Code Editor update: Support for HTML and mobile devices appeared first on Raspberry Pi Foundation.
Today we’re calling all young people who are excited to explore coding and space science, and the mentors who want to support and inspire them on their journey. Astro Pi Mission Space Lab is officially open again, offering young people all over Europe the amazing chance to have their code for a science experiment run in space on the International Space Station (ISS).
With this year’s Mission Space Lab, astronauts from the European Space Agency are setting young people a task: to write a computer program that runs on the ISS and calculates the speed at which the ISS is orbiting planet Earth. Participation in Mission Space Lab is completely free.
Here’s ESA astronaut candidate Rosemary Coogan to introduce this year’s mission:
Mission Space Lab invites young people up to age 19 to work in teams of 2 to 6 and write a Python program for the Astro Pi computers on board the ISS to collect data and calculate the speed at which the ISS is travelling.
Your role as a mentor is to support teams as they design and create their program — with our free guidance resources to help you and your young creators.
We want as many young people as possible to have the chance to take part in Mission Space Lab, so the way in which teams solve the task set by the ESA astronauts can be different depending on the experience of your team:
The Astro Pis are two Raspberry Pi computers stationed on the ISS, each equipped with a High Quality Camera, a Sense HAT add-on board with a number of sensors, and a Coral machine learning accelerator. Each Astro Pi has a hard casing designed especially for space travel.
There are lots of ways to use sensor data from the Astro Pis to calculate the speed of the ISS, so young people can get creative solving their Mission Space Lab task while learning fascinating facts about physics and the inner workings of the ISS.
All Mission Space Lab participants whose programs run on the ISS will receive a certificate recognising their achievement, and they’ll get the chance to attend a Q&A webinar with an ESA astronaut. Teams also receive back data from the ISS based on their Mission Space Lab programs, for example photos or sensor measurements. That means you’ll have the option to explore and use that data in follow-on activities with your young people.
We are providing lots of supporting materials to help you and your team with Mission Space Lab:
Mission Space Lab is open for submissions from today, 6 November 2023, until 19 February 2024.
Visit the Astro Pi website for full details and eligibility criteria: astro-pi.org/mission-space-lab
The European Astro Pi Challenge is an ESA Education project run in collaboration with us here at the Raspberry Pi Foundation.
You can keep up with all Astro Pi news by following the Astro Pi X account (formerly Twitter) or signing up to the newsletter at astro-pi.org.
The post Explore space science and coding with Astro Pi Mission Space Lab appeared first on Raspberry Pi Foundation.
We are pleased to announce a new AI-themed challenge for young people: the Experience AI Challenge invites and supports young people aged up to 18 to design and make their own AI applications. This is their chance to have a taste of getting creative with the powerful technology of machine learning. And equally exciting: every young creator will get feedback and encouragement from us at the Raspberry Pi Foundation.
As you may have heard, we recently launched a series of classroom lessons called Experience AI in partnership with Google DeepMind. The lesson materials make it easy for teachers of all subjects to teach their learners aged up to 18 about artificial intelligence and machine learning. Now the Experience AI Challenge gives young people the opportunity to develop their skills further and build their own AI applications.
For the Experience AI Challenge, you and the young people you work with will learn how to make a machine learning (ML) classifier that organises data types such as audio, text, or images into different groupings that you specify.
The Challenge resources show young people the basic principles of using the tools and training ML models. Then they will use these new skills to create their own projects, and it’s a chance for their imaginations to run free. Here are some examples of projects your young tech creators could make:
All creators will receive expert feedback on their projects.
To make the Experience AI Challenge as familiar and accessible as possible for young people who may be new to coding, we designed it for beginners. We chose the free, easy-to-use, online tool Machine Learning for Kids for young people to train their machine learning models, and Scratch as the programming environment for creators to code their projects. If you haven’t used these tools before, don’t worry. The Challenge resources will provide all the support you need to get up to speed.
Training an ML model and creating a project with it teaches many skills beyond coding, including computational thinking, ethical programming, data literacy, and developing a broader understanding of the influence of AI on society.
Our resources for creators and mentors walk you through the three stages of the Experience AI Challenge.
The first stage of the Challenge is designed to ignite young people’s curiosity. Through our resources, mentors let participants explore the world of AI and ML and discover how these technologies are revolutionising industries like healthcare and entertainment.
In the second stage, young people choose a data type and embark on a guided example project. They create a training dataset, train an ML model, and develop a Scratch application as the user interface for their model.
In the final stage, mentors support young people to apply what they’ve learned to create their own ML project that addresses a problem they’re passionate about. They submit their projects to us online and receive feedback from our expert panel.
We can’t wait to see how you and your young creators choose to engage with the Experience AI Challenge!
The post The Experience AI Challenge: Make your own AI project appeared first on Raspberry Pi Foundation.
The UK Bebras Challenge is back and ready to accept entries from schools for its annual event, which runs from 6 to 17 November.
More than 3 million students from 59 countries took part in the Bebras Computational Thinking Challenge in 2022. In the UK alone, over 365,000 students participated. Read on to find out how you can get your school involved.
“This is now an annual event for our Year 5 and 6 students, and one of the things I actually love about it is the results are not always what you might predict. There are children who have a clear aptitude for these puzzles who find this is their opportunity to shine!”
– Claire Rawlinson, Primary Teacher, Lancashire
Bebras is a free, annual challenge that helps schools introduce computational thinking to their students. No programming is involved, and it’s completely free for schools to enter. All Bebras questions are self-marking.
We’re making Bebras accessible by offering age-appropriate challenges for different school levels and a challenge tailored for visually impaired students. Schools can enter students from age 6 to 18 and know they’ll get interesting and challenging (but not too challenging) activities.
Students aged 10 to 18 who do particularly well will get invited to the Oxford University Computing Challenge (OUCC).
We want young people to get excited about computing. Through Bebras, they will learn about computational and logical thinking by answering questions and solving problems.
Bebras questions are based on classic computing problems and are presented in a friendly, age-appropriate way. For example, an algorithm-based puzzle for learners aged 6 to 8 is presented in terms of a hungry tortoise finding an efficient eating path across a lawn; for 16- to 18-year-olds, a difficult problem based on graph theory asks students to sort out quiz teams by linking quizzers who know each other.
“This has been a really positive experience. Thank you. Shared results with Head and Head of Key Stage 3. Really useful for me when assessing Key Stage 4 options.”
– Secondary teacher, North Yorkshire
Here’s a Bebras question for the Castors category (ages 8 to 10) from 2021. You will find the answer at the end of this blog.
A robot picks up litter.
Question: Which kind of litter will the robot pick up last?
The Bebras challenge for UK schools takes place from 6 to 17 November. Register at bebras.uk/admin to get free access to the challenge.
By registering, you also get access to the Bebras back catalogue of questions, from which you can build your own quizzes to use in your school at any time during the year. All the quizzes are self-marking, and you can download your students’ results for your mark book. Schools have reported using these questions for end-of-term activities, lesson starters, and schemes of lessons about computational thinking.
The answer to the example puzzle is:
The image below shows the route the robot takes by following the instructions:
The post Take part in the UK Bebras Challenge 2023 for schools appeared first on Raspberry Pi Foundation.
Throughout this year, space agencies have been embarking on new missions to explore our solar system, and young people can get involved too through the European Astro Pi Challenge 2023/24, which we’re launching today.
In the past few months India’s Chandrayaan-3 mission landed near the Moon’s south pole, NASA’s Parker Solar Probe flew by Venus on its way to the sun, and the SpaceX Crew-7 launched to the International Space Station (ISS), led by ESA astronaut Andreas Mogensen. We’re especially excited about Andreas’ mission because he’s the astronaut who will help to run young people’s Astro Pi programs on board the ISS this year.
As you may know, the European Astro Pi Challenge gives young people the amazing opportunity to conduct scientific experiments in space by writing computer programs for the Astro Pis, special Raspberry Pi computers on board the ISS.
The Astro Pi Challenge is free and offers two missions for young people: Mission Zero is an inspiring activity for introducing kids to text-based programming with Python. Mission Space Lab gives teams of young people the chance to take on a more challenging programming task and stretch their coding and science skills.
Participation in Astro Pi is open to young people up to age 19 in ESA Member States (see the Astro Pi website for eligibility details).
In Astro Pi Mission Zero, young people write a simple Python program to take a reading using a sensor on one of the ISS Astro Pi computers and display a personalised pixel art image for the astronauts on board the ISS. They can take part by themselves or as coding teams.
The theme for Mission Zero 2023/24 is ‘fauna and flora’: young people are invited to program pixel art images or animations of animals, plants, or fungi to display on the Astro Pi computers’ LED pixel screen and remind the astronauts aboard the ISS of Earth’s natural wonders.
By following the guide we provide, kids can complete the Mission Zero coding activity in around one hour, for example during a school lesson or coding club session. No coding experience is needed to take part. Kids can write their code in any web browser on any computer connected to the internet, without special equipment or software.
All young people that meet the eligibility criteria and follow the official Mission Zero guidelines will have their program run in space for up to 30 seconds. They will receive a unique and personalised certificate to show their coding achievement. The certificate will display the exact start and end time of their program’s run, and where the ISS was above Earth in this time period.
Mission Zero 2023/24 opens today and is open until Monday 25 March 2024. It’s very easy to support young people to get involved — find out more on the Astro Pi website:
In this year’s Astro Pi Mission Space Lab, ESA astronauts are inviting teams of young people to solve a scientific task by writing a Python program.
The Mission Space Lab task is to gather data with the Astro Pi computers to calculate the speed at which the ISS is travelling. This new format of the mission will allow many more young people to run their programs in space and get a taste of space science.
Mission Space Lab will open on 6 November. We will share more information about how young people and mentors can participate very soon.
The European Astro Pi Challenge is an ESA Education project run in collaboration with us here at the Raspberry Pi Foundation.
You can keep up with all Astro Pi news by following the Astro Pi X account (formerly Twitter) or signing up to the newsletter at astro-pi.org.
The post Launch kids’ code into space with the European Astro Pi Challenge 2023/24 appeared first on Raspberry Pi Foundation.
New artificial intelligence (AI) tools have had a profound impact on many areas of our lives in the past twelve months, including on education. Teachers and schools have been exploring how AI tools can transform their work, and how they can teach their learners about this rapidly developing technology. As enabling all schools and teachers to help their learners understand computing and digital technologies is part of our mission, we’ve been working hard to support educators with high-quality, free teaching resources about AI through Experience AI, our learning programme in partnership with Google DeepMind.
In this article, we take you through the updates we’ve made to the Experience AI Lessons based on teachers’ feedback, reveal two new lessons on large language models (LLMs) and biology, and give you the chance to shape the future of the Experience AI programme.
In April we launched the first Experience AI Lessons as a unit of six lessons for secondary school students (ages 11 to 14, Key Stage 3) that gives you everything you need to teach AI, including lesson plans, slide decks, worksheets, and videos. Since the launch, we’ve worked closely with teachers and learners to make improvements to the lesson materials.
The first big update you’ll see now is an additional project for students to do across Lesson 5 and Lesson 6. Before, students could choose between two projects to create their own machine learning model, either to classify data from the world’s oceans or to identify fake news. The new project we’ve added gives students the chance to use images to train a machine learning model to identify whether or not an item is biodegradable and therefore suitable to be put in a food waste bin.
Our second big update is a new set of teacher-focused videos that summarise each lesson and highlight possible talking points. We hope these videos will help you feel confident and ready to deliver the Experience AI Lessons to your learners.
As well as updating the six existing lessons, we’ve just released a new seventh lesson consisting of a set of activities to help students learn about the capabilities, opportunities, and downsides of LLMs, the models that AI chatbots are based on.
With the LLM lesson’s activities you can help your learners to:
All Experience AI Lessons are designed to be cross-curricular, and for England-based teachers, the LLM lesson is particularly useful for teaching PSHE (Personal, Social, Health and Economic education).
The LLM lesson is designed as a set of five 10-minute activities, so you have the flexibility to teach the material as a single lesson or over a number of sessions. While we recommend that you teach the activities in the order they come, you can easily adapt them for your learners’ interests and needs. Feel free to take longer than our recommended time and have fun with them.
We have also been working on an exciting new lesson to introduce AI to secondary school students (ages 11 to 14, Key Stage 3) in the biology classroom. This stand-alone lesson focuses on how AI can help conservationists with monitoring an ecosystem in the Serengeti.
We worked alongside members of the Biology Education Research Group (BERG) at the UK’s Royal Society of Biology to make sure the lesson is relevant and accessible for Key Stage 3 teachers and their learners.
Register your interest if you would like to be one of the first teachers to try out this thought-provoking lesson.
If you want to use the Experience AI materials but would like more support, our new webinar series will help you. You will get your questions answered by the people who created the lessons. Our first webinar covered the six-lesson unit and you can watch the recording now:
Join us to learn how to use Machine Learning for Kids (ML4K), a child-friendly tool for training AI models that is used for project work throughout the Experience AI Lessons. The September webinar will be with Dale Lane, who has spent his career developing AI technology and is the creator of ML4K.
We need your feedback like a machine learning model needs data. Here are two ways you can share your thoughts:
To find out more about how you can use Experience AI to teach AI and machine learning to your learners this school year, visit the Experience AI website.
The post Experience AI: Teach about AI, chatbots, and biology appeared first on Raspberry Pi Foundation.
Lots of kids are excited about robotics, and we have the free resources you need to help your children start making robots.
Did you know that the concept of robotics dates back to ancient Greece, where a mathematician built a self-propelled flying pigeon to understand bird flight? Today, we have robots assisting people in everything from manufacturing to medicine. But what exactly is a robot? Ask two people, and you might get two different answers. Some may tell you about Star Wars’ C3PO and R2D2, while others may tell you about self-driving cars or even toys.
In my view, a robot is a machine that can carry out a series of physical tasks, programmed via a computer. These tasks could range from picking up an object and placing it elsewhere, to navigating a maze, to even assembling a car without human interaction.
My first encounter with robotics was the Big Trak, a programmable toy vehicle created in 1979. You could program up to 16 commands into Big Trak, which it then executed in sequence. My family and I used the toy to transport items to each other around our house. It was a fun and engaging way to explore the basics of robotics and programming.
Understanding something about robotics is not just for scientists and engineers. It involves learning a range of skills that empower your kids to be creators of our digital world, instead of just consumers.
Robotics combines various aspects of science, technology, engineering, and mathematics (STEM) in a fun and engaging way. It also encourages young people’s problem-solving abilities, creativity, and critical thinking — skills that are key for the innovators of tomorrow.
What happens when we add machine learning to robotics? Machine learning is an area of artificial intelligence where people design computer systems so they “learn” from data. This is not unlike how people learn from experience. Machine learning can enable robots to adapt to new situations and perform tasks that only people used to do.
We’ve already built robots that can play chess with you, or clean your house, or deliver your food. As people develop machine learning for robotics further, the possibilities are vast. By the time our children start their careers, it might be normal to have robots as software-driven “coworkers”. It’s important that we prepare children for the possible future that robotics and machine learning could open up. We need to empower them to contribute to creating robots with capabilities that complement and benefit all people.
To see what free resources we’re offering to help young people understand and create with machine learning and AI, check out this blog post about our Experience AI learning programme.
So, how can kids start diving into the world of robotics? Here are three online resources to kickstart their journey:
‘Physical computing with Scratch and the Raspberry Pi‘ is a fantastic introduction to using electronics with the block-based Scratch programming language for young learners.
Kids will learn to create interactive stories, games, and animations, all while getting a taste of physical computing. They’ll explore how to use sound and light, and even learn how to create improvised buttons.
This project path introduces the Raspberry Pi Pico, a tiny yet powerful digital device that kids can program using the text-based MicroPython language.
It’s a great way to delve deeper into the world of electronics and programming. The path includes a variety of fun and engaging projects that incorporate crafting and allow children to see the tangible results of their coding efforts.
‘Build a robot’ is a project path that allows young people to create a simple programmable buggy. They can then make it remote-controlled and even transform it so it can follow a line by itself.
This hands-on project path not only teaches the basics of robotics but also encourages problem-solving as kids iteratively improve their robot buggy’s design.
Let’s take a moment to celebrate two young tech creators who love building robots.
Selin is a digital maker from Istanbul, Turkey, who is passionate about robotics and AI. Selin’s journey into the world of digital making began with a wish: after her family’s beloved dog Korsan passed away, she wanted to bring him back to life. This led her to design a robotic dog on paper, and to learn coding and digital making to build that robot.
Selin has since built seven different robotics projects. One of them is IC4U, a robotic guide dog designed to help people with impaired sight. Selin’s commitment to making projects that help make the world a better place was recognised when she was awarded the Aspiring Teen Award by Women in Tech.
Jay, a young digital maker from Preston, UK, started experimenting with code at a young age to make his own games. He attended free local coding groups, such as CoderDojo, and was introduced to the block-based programming language Scratch. Soon, Jay was combining his interests in programming with robotics to make his own inventions.
Jay’s dad, Biren, comments: “With robotics and coding, what Jay has learned is to think outside of the box and without any limits. This has helped him achieve amazing things.”
Robotics and machine learning are not just science fiction — they shape our lives today in ways kids might not even realise. Whether your child is just interested in playing with robots, wants to learn more about them, or is considering a career in robotics, our free resources are a great place to start.
If a Greek mathematician was able to build a flying pigeon millennia ago, imagine what children could create today!
The post Make a robot: A fun and educational journey into robotics for kids appeared first on Raspberry Pi Foundation.
Celebrate another year of young people’s computer programs in space with us: today we and our collaborators at the European Space Agency can finally announce the winning and highly commended teams in this year’s Astro Pi Mission Space Lab.
In Mission Space Lab, teams of young people work together to create computer programs for scientific experiments to be carried out on the International Space Station. The programs they design and create run on the two Astro Pi computers: space-adapted Raspberry Pis with cameras and a range of sensors.
Teams’ programs were deployed on the ISS during May and ran for up to 3 hours, collecting data for their experiments. Once we’d sent the teams their data, they started analysing it in order to write their Phase 4 reports. To identify patterns and phenomena they were interested in, many teams chose to compare their data with other sources.
We were especially excited to see the results from the experiments this year, particularly given that the upgraded Astro Pi units with their High Quality Cameras were positioned in a new observation window (WORF) on the ISS. This allowed teams to capture high-resolution images with a much wider field of view.
We feel very privileged to see the culmination of the team’s experiments in their final reports. So let’s share a few highlights from this year’s experiments:
Team Aretusa from Sicily explored the effects of climate change by cross-referencing the images they captured with the Astro Pis with historical images from Google Earth. They used Near Infrared photography to capture images, and NDVI (Normalised Difference Vegetation Index) image processing in their analysis. Below you can see that they have compared data of Saudi Arabia from 1987 to 2023, showing increasing levels of vegetation grown in attempts to restore degraded land.
Team Barrande from the Czech Republic trained AI models on images they gathered to identify topographical features of Earth. Their Mission Space Lab program used the Astro Pi computer’s machine learning dongle to train one AI model in real time. Later, the team also used the collected images to train another model back on Earth. Comparing the outputs of the two models, the team could tell how well the models had identified different topographical features. The below selection shows an image the team’s experiment captured on the left, the same image after processing by the AI model trained on the Astro Pi computer in the middle, and the image processed by the AI model trained on Earth.
Team DAHspace from Portugal measured the intensity of the Earth’s magnetic field along the orbit path of the ISS. Using the magnetometer on the Astro Pi, their experiment recorded data allowing the team to track changes of intensity. The team mapped this data to the ISS’s coordinates, showing the difference in the Earth’s magnetic field between the North Pole (points 1 and 2 on the chart below) and the South Pole (points 3 and 4).
We and our collaborators at ESA Education have been busy reviewing all of the reports to assess the scientific merit, use of the Astro Pi hardware, experiment design, and data analysis. The ten winning teams come from schools and coding clubs in 11 countries. We are sending each team some cool space swag to recognise their achievement.
| Team | Experiment theme | Based at | Country |
| Magnet47 | Life on Earth | O’Neill CVI | Canada |
| Aretusa | Life on Earth | Liceo Da Vinci Floridia | Italy |
| ASaether | Life on Earth | “Andrei Saguna” National College | Romania |
| Barrande | Life on Earth | Gymnázium Joachima Barranda Beroun | Czech Republic |
| Escapers | Life in space | Code Club | Canada |
| Futura | Life in space | Scuola Svizzera Milano | Italy |
| StMarks | Life on Earth | St Mark’s Church of England School | United Kingdom |
| DAHspace | Life on Earth | EB 2,3 D. Afonso Henriques | Portugal |
| T5Clouds | Life on Earth | Dominican College | Ireland |
| PiNuts | Life in space | TEKNISK GYMNASIUM, Skanderborg | Denmark |
You can click on a team name to read the team’s experiment report.
Along with the winning teams, we would like to commend the following teams for their experiments:
| Team | Experiment theme | Based at | Country |
| Parsec | Life on Earth | Liceo Da Vinci Pascoli Gallarate | Italy |
| Celeste | Life on Earth | International School of Florence | Italy |
| LionTech | Life on Earth | Colegiul Național ”Mihai Eminescu” | Romania |
| OHSpace | Life in Space | Oxford High School | United Kingdom |
| Magneto | Life on Earth | The American School of The Hague | Netherlands |
| GreenEye | Life on Earth | ROBOTONIO | Greece |
| Primus | Life on Earth | Independent coding club | Germany |
You can click on a team name to read the team’s experiment report.
All of the teams whose Mission Space Lab programs ran on the ISS will receive a certificate signed by ESA astronaut Samantha Cristoforetti. The winning and highly commended teams will also be invited to a live video chat with an ESA astronaut in the autumn.
Huge congratulations to every team that participated in Astro Pi Mission Space Lab. We hope you found it fun and inspiring to take part.
A big thank you to everyone who has been involved in the European Astro Pi Challenge this year. An amazing 24,850 young people from 29 countries had their programs run in space this year. We can’t wait to do it all again starting in September.
And it’s not just us saying thanks and well done — here’s a special message from ESA astronaut Matthias Maurer:
On 18 September 2023, we’ll launch the European Astro Pi Challenge for 2023/24. Mission Zero will open in September, and we’ll announce exciting news about Mission Space Lab in September too.
If you know a young person who might be interested in the Astro Pi Challenge, sign up for the newsletter on astro-pi.org and follow the Astro Pi Twitter account for all the latest announcements about how you can support them to take the unique opportunity to write code to run in space.
The post Young people’s amazing experiments in space: Astro Pi Mission Space Lab 2022/23 appeared first on Raspberry Pi Foundation.
A couple of months ago we announced that you can test the online text-based Code Editor we’re building to help young people aged 7 and older learn to write code. Now we’ve made the code for the Editor open source so people can repurpose and contribute to it.
You and your learners can try out the Code Editor in our Python project paths. We’ve included a feedback form for you to let us know what you think about the Editor.
Since the Editor lets learners save their code using their Raspberry Pi Foundation account, it’s easy for them to build on projects they’ve started in the classroom or at home, or bring a project they’ve started at home to their coding club.
Python is the first programming language our Code Editor supports because it’s popular in schools, CoderDojos, and Code Clubs, as well as in industry. We’ll soon be adding support for web development languages (HTML/CSS).
We know that starting out with new programming tools can be tricky and add to the cognitive load of learning new subject matter itself. That’s why our Editor has a simple and accessible user interface and design:
We’ll expand the Editor’s functionalities as we go. For example, at the moment we’re looking at how to improve the Editor’s user interface (UI) for better mobile support.
If there’s a feature you think would help the Editor become more accessible and more suitable for young learners, or make it better for your classroom or club, please let us know via the feedback form.
Our vision is that every young person develops the knowledge, skills, and confidence to use digital technologies effectively, and to be able to critically evaluate these technologies and confidently engage with technological change. We’re part of a global community that shares that vision, so we’ve made the Editor available as an open-source project. That means other projects and organisations focussed on helping people learn about coding and digital technologies can benefit from the work.
To support the widest possible range of learners, we’ve designed the Code Editor application to work well on constrained devices and low-bandwidth connections. Safeguarding, accessibility, and data privacy are also key considerations when we build digital products at the Foundation. That’s why we decided to design the front end of the Editor to work in a standalone capacity, with Python executed through Skulpt, an entirely in-browser implementation of Python, and code changes persisted in local storage by default. Learners have the option of using a Raspberry Pi Foundation account to save their work, with changes then persisted via calls to a back end application programming interface (API).
As safeguarding is always at the core of what we do, we only make features available that comply with our safeguarding policies as well as the ICO’s age-appropriate design code. We considered supporting functionality such as image uploads and code sharing, but at the time of writing have decided to not add these features given that, without proper moderation, they present risks to safeguarding.
There’s an amazing community developing a wealth of open-source libraries. We chose to build our text-editor interface using CodeMirror, which has out-of-the-box mobile and tablet support and includes various useful features such as syntax highlighting and keyboard shortcuts. This has enabled us to focus on building the best experience for learners, rather than reinventing the wheel.
Diving a bit more into the technical details:
You can find out more about our Editor’s code for both the UI front end and API back end in our GitHub readme and contributions documentation. These kick-starter docs will help you get up and running faster:
The Editor’s front end is licensed as permissively as possible under the Apache Licence 2.0, and we’ve chosen to license the back end under the copyleft AGPL V3 licence. Copyleft licences mean derived works must be licensed under the same terms, including making any derived projects also available to the community.
We’d greatly appreciate your support with developing the Editor further, which you can give by:
Our work to develop and publish the Code Editor as an open-source project has been funded by Endless. We thank them for their generous support.
If you are interested in partnering with us to fund this key work, or you are part of an organisation that would like to make use of the Code Editor, please reach out to us via email.
The post Our Code Editor is open source appeared first on Raspberry Pi Foundation.
From 27 to 29 September 2023, we and the University of Cambridge are hosting the WiPSCE International Workshop on Primary and Secondary Computing Education Research for educators and researchers. This year, this annual conference will take place at Robinson College in Cambridge. We’re inviting all UK-based teachers of computing subjects to apply for one of five ‘all expenses paid’ places at this well-regarded annual event.
WiPSCE is where teachers and researchers discuss research that’s relevant to teaching and learning in primary and secondary computing education, to teacher training, and to related topics. You can find more information about the conference, including the preliminary programme, at wipsce.org.
As a teacher at the conference, you will:
We are delighted to welcome Google as a sponsor of WiPSCE. Google believes that every student deserves the opportunity to access the benefits of a computing education to help shape their future. However, many students aren’t getting the education they need, and teachers don’t have sufficient resources to provide it. Google recognises the responsibility they have to support organisations, universities, and schools with deep expertise and a commitment to computing education, especially within communities that have been historically underserved.
With support from Google, we will offer free places to five UK computing teachers, covering:
To apply, you just need to fill in a short form. The application deadline is Wednesday 19 July.
To be eligible to apply:
The application form will ask your for:
After the 19 July deadline, we’re aiming to inform you of the outcome of your application on Friday 21 July.
Your application will be reviewed by the 2023 WiPSCE Chairs:
Sue and Mareen will:
We’d be delighted to receive your application. Being able to facilitate teachers’ attendance at the conference is very much aligned with our approach to research. Both at the Foundation and the Raspberry Pi Computing Education Research Centre, we’re committed to conducting research that’s directly relevant to schools and teachers, and to working in close collaboration with teachers.
We hope you are interested in attending WiPSCE and becoming an advocate for research-informed computing education practice. If your application is unsuccessful, we hope you consider coming along anyway. We’re looking forward to meeting you there. In the meantime, you can keep up with WiPSCE news on Twitter.
The post Apply for a free UK teacher’s place at the WiPSCE conference appeared first on Raspberry Pi Foundation.
Our ‘Intro to Unity’ educational project path is a big success, sparking lots of young people’s passion for 3D game design and programming. Today we introduce the ‘More Unity‘ project path — the perfect next step for young people who have completed our ‘Intro to Unity‘ path. This new free path is designed to bridge the gap for young people before they start on the tutorials on the Unity learning platform.
Our work to create this path builds on our partnership with Unity, through which we aim to offer any young person, anywhere, the opportunity to take their first steps in creating virtual worlds using real-time 3D.
After young people have tried out the Unity Engine and C# programming through the ‘Intro to Unity’ path, they’re ready for a deeper exploration of 3D game design. ‘More Unity’ helps them build on the foundational skills they learned in the ‘Intro to Unity’ path. After completing this new path, they’ll be able to add complexity, new challenges, and heaps of fun to all their 3D creations.
We’ve prepared a comprehensive Unity Guide to assist with getting ready to start either the ‘Intro to Unity’ or ‘More Unity’ path. To create with Unity, learners need access to a computer with a graphics card, the latest version of the free Unity Games Engine, and a code editor. For the extra Blender-based projects (see below), they need the latest version of the free Blender software.
The project path consists of six projects. Like in ‘Intro to Unity’, each project introduces new skills bit by bit, enabling young people to independently code their own, next-level Unity creation in the final project.
This first project shows how to build an exciting 3D simulation. With ‘Rainbow run’, learners create colourful tracks and guide a marble to race along them. We also offer them an extra project guide where they can customise the look of their marble using Blender.
Next, with ‘Disco dance floor’, learners code an interactive, tilting dance floor that responds to a rolling ball with sound and colour. They can add their own style to the dance floor by following our extra Blender project.
‘Don’t fall through’ is the third project in the path. Here, learners code a two-player game that requires strategy and timing as marbles traverse a vanishing tiled floor.
‘Pixel art reveal’ comes next in the path. It helps learners design unique pixel art on a tiled floor and reveal their awesome artwork by rolling a ball across the surface.
In ‘Track designer’, we invite learners to truly think like game designers. This project empowers learners to design unique tilting tracks filled with obstacles, personalised effects, sounds, and more.
Finally ‘Marble mayhem’ lets young people bring to life all the principles of physics and materials in the Unity Game Engine they’ve learned about while following the ‘More Unity’ path. This is their place to create a one-of-a-kind game or digital toy that truly reflects their creativity.
‘More Unity’ promotes young people’s creativity, problem-solving, and independence. Each project presents them with the chance to create a virtual world of physics, materials, and mechanics. With each project they’ll learn lots of new skills in 3D modeling, gameplay design, and programming.
The path includes a community gallery where young people can share their new 3D creations and see what their peers all over the world have made.
The skills young people gain through the ‘Intro to Unity’ and ‘More Unity’ path provide them with a solid foundation to continue to learn and create with Unity. To follow their passion for 3D worlds, game design, and programming further, they can move on to the hundreds of tutorials available on Unity’s learning platform.
Our detailed Unity guide will help you get everything set up for your young people to start with Unity, and the ‘Intro to Unity‘ path is the place for them to begin before they move on to ‘More Unity‘.
If you or your young people want to get a taste of the fun ‘More Unity’ has in store, there’s the Collision and colours Discover project to try out. This short learning experience showcases the new components the ‘More Unity’ path introduces.
To help our community of CoderDojo and Code Club volunteers bring Unity to their learners, we will host a free Unity-focused webinar on 13 July. Sign up to get a walkthrough of the path from our Learning Manager Mac Bowley, and to ask him any questions you might have.
The post More Unity: Dive deeper into 3D worlds, game design and programming appeared first on Raspberry Pi Foundation.
After seven successful years on the International Space Station, 250 vertical miles above our planet, the original two Astro Pi computers that we sent to the ISS to help young people run their code in space have been returned to Earth.
From today, one of these Astro Pi computers will be displayed in the Science Museum, London. You can visit it in the new Engineers Gallery, which is dedicated to world-changing engineering innovations and the diverse and fascinating range of people behind them.
The original Astro Pis, nicknamed Izzy and Ed, have played a major part in feeding tens of thousands of young people’s understanding and passion for science, mathematics, engineering, computing, and coding. In their seven years on the International Space Station (ISS), Izzy and Ed had the job of running over 70,000 programs created by young people as part of the annual Astro Pi Challenge.
Nicki Ashworth, 21, took part in the first-ever Astro Pi challenge after hearing about the opportunity at a science fair: “I thought it sounded like an interesting project, and good practice for my programming skills. I was young and had no idea of the extent of the project and how much it would influence my future.”
Like many young people who have participated in the Astro Pi Challenge, Nicki credits the Astro Pi Challenge as an inspiration to learn more about space and programming, and to decide on a career path: “My experience with Astro Pi definitely helped to shape my future choices. I’m currently in my third year of a Mechanical Engineering degree at University of Southampton, specialising in Computational Engineering and Design. I’ve always loved programming, which is why I took part in the Astro Pi competition, but it led to a fascination with space. This encouraged me to look at engineering as a future, and led me to where I am today!”
It all started in 2014, when we started collaborating with organisations including the UK Space Agency and European Space Agency (ESA) to fly two Astro Pi computers to the ISS for educational activities during the six-month Principia mission of British ESA astronaut Tim Peake.
The Astro Pi computers each consist of a Raspberry Pi computer integrated with a digital camera and an add-on board filled with environmental sensors, all enclosed in a protective aluminium flight case.
Commander Tim Peake, Britain’s first visitor to the ISS, accompanied the two first Astro Pi computers on the ISS. He used them to run experiments imagined, designed, and coded by school-age young people across the UK.
We held a competition in UK schools and coding clubs to invite young people to create experiments that could be run on the Astro Pis. Students conceived experiments and coded them in Python; we tested their Python programs and eventually picked seven to run on Izzy and Ed on the ISS.
The students’ experiments ranged from a simple but beautiful program to display the flag of the country over which the ISS was flying at a given time, to a reaction-time test for Tim Peake to measure his changing abilities across the six-month mission. The measurements from all the experiments were downloaded to Earth and analysed by the students.
“I still feel incredibly honoured to have competed in the very first [Astro Pi Challenge],” says Aaron Chamberlain, 18, who was 11 years old when he took part in the first-ever Astro Pi Challenge in 2015. “The experience was incredible and really cemented my enthusiasm for all things computing and coding. Finally looking at the photos the Raspberry Pi had taken of the astronauts floating 400 km above us was a feeling of awe that I will never forget.”
The next year, 2016, we expanded our partnership with ESA Education to be able to open up Astro Pi to young people across ESA Member states. The European Astro Pi Challenge has been going from strength to strength each year since, inspiring young people and adult mentors alike.
In 2021 we decided it was time to retire Izzy and Ed and replace them with upgraded Astro Pi computers with plenty of new and improved hardware, including a Raspberry Pi 4 Model B with 8 GB RAM.
Dave Honess, STEM Didactics Expert at the European Space Agency, was engineering lead at the Foundation for the first Astro Pi Challenge, and the return of the original hardware is a special event and moment of reflection for him: “It was a strange experience to open the box and hold the original Astro Pis again after all that time and distance they have travelled — literally billions of miles. Even though their mission is over, we will continue to learn from them with a tear-down analysis to find out if they have been affected by their time in space. Since Principia, I have watched the European Astro Pi Challenge grow with pride year on year, but I still feel very fortunate to have been there at the beginning.”
Thanks to the upgraded hardware, we are able to continue to grow the Astro Pi Challenge in collaboration with ESA Education. And each year it’s so exciting to see the creative and ingenious programs tens of thousands of young people from across Europe send us; 24,850 young people took part in the Challenge in the 2022/2023 cycle.
But how have Astro Pis Izzy and Ed fared in space over these seven years? Jonathan Bell, Principal Software Engineer at Raspberry Pi Limited, had a chance to find out first-hand: “I was lucky enough to have a look inside the returned Astro Pis. I was looking for the cosmetic effects of the unit being on the ISS for so long. On the inside they still look as pristine as when I assembled them! Barely a speck of dust on the internal boards, nor any signs that the external interface ports were worn from their years of use. A few dings and scrapes on the anodised exterior were all that I could see — and a missing joystick cap (as it turns out, hot-melt glue isn’t a permanent adhesive…). It was great to see that they still worked! It made me feel proud for what the team and the Astro Pi programme has achieved over the years. It’s good to have Izzy and Ed back!”
The new Engineers Gallery in the Science Museum opens today and is free to visit. Astro Pi computer Izzy is among the amazing exhibits. Learn more at sciencemuseum.org.uk/engineers.
To find out more about the Astro Pi Challenge and how to get involved with your kids at home, your school, or your STEM or coding club, visit astro-pi.org.
The next round of the Challenge starts in September — sign up for news to be the first to hear when we launch it.
The post Welcome home! An original Astro Pi computer back from space is now on display at the Science Museum appeared first on Raspberry Pi Foundation.
In the Columbus module of the International Space Station (ISS), there are two Astro Pi computers called Marie Curie and Nikola Tesla. These computers run the programs young people create as part of the annual European Astro Pi Challenge.
For this year’s Astro Pi Mission Zero, young people sent us over 15000 programs to show the ISS astronauts colourful images and animations of animals and plants on the Astro Pi displays and remind them of home.
Mission Zero is a free beginners’ coding activity. It gives young people the unique opportunity to follow our step-by-step guide to write a simple program in Python that can run in space on the ISS orbiting planet Earth.
The Mission Zero activity this year was to write code to use the Astro Pi’s colour sensor to measure the lighting conditions in the Columbus module, and to then use that measurement to set a colour in an image or animation on the Astro Pi’s 8×8 LED display. We invited young people to design images of fauna and flora to give the astronauts on board the ISS a reminder of the beautiful creatures, plantlife, and landscapes found on planet Earth.
The Mission Zero activity is ideal for learners trying text-based programming for the first time. It covers some key programming concepts, including variables, sequence, and iteration.
This year we received 15551 Mission Zero programs, and after carefully checking them against the entry and safety criteria, we were able to run 15475 programs. They were sent to us by 23605 learners working in teams or independently, and 10207 of this year’s participants were girls.
This year the most Mission Zero programs came from young people in the UK, followed by Spain, France, Italy, and Greece. Lots of different organisations supported young people to take part, including publicly funded primary and secondary schools, as well as educator- and volunteer-led Code Clubs and CoderDojos we support.
We’re celebrating the many different people involved in this year’s mission with a mosaic of the Mission Zero logo made up of lots of the inspiring designs participants sent us. You can explore an interactive version of the image too!
All of the participants whose programs ran on the ISS will be receiving a certificate to recognise their efforts, which will include the time and coordinates of the ISS when their program ran. Programs created by young people from across Europe ran on board the ISS in the final week of May.
If you enjoyed Astro Pi Mission Zero this year, we would be delighted to see you again in the next annual round. If you’re feeling inspired by the images young people have created, we invite you to get involved too. We provide guides and help for all adult mentors who want to support young people to take part, and the step-by-step guide for coding a Mission Zero program in 19 European languages.
The activity of designing an image has been really popular, and we have been super impressed with the creativity of young people’s designs. That’s why we’ll be running Mission Zero in the same format again starting in September.
If you’d like to hear news of the Astro Pi Challenge, please sign up to the newsletter on astro-pi.org:
We are always interested to hear your feedback about Mission Zero, as a mentor or participant. If you would like to share your thoughts with us, please email enquiries@astro-pi.org.
PS Look out for some cool news about the Astro Pi computers, which we’ll announce soon on this blog!
The post Young people designed 15000 images for astronauts in Astro Pi Mission Zero 2022/23 appeared first on Raspberry Pi Foundation.
What do we talk about when we talk about artificial intelligence (AI)? It’s becoming a cliche to point out that, because the term “AI” is used to describe so many different things nowadays, it’s difficult to know straight away what anyone means when they say “AI”. However, it’s true that without a shared understanding of what AI and related terms mean, we can’t talk about them, or educate young people about the field.
So when we started designing materials for the Experience AI learning programme in partnership with leading AI unit Google DeepMind, we decided to create short explanations of key AI and machine learning (ML) terms. The explanations are doubly useful:
As an example, here is our explanation of the term “artificial intelligence” for learners aged 11–14:
Artificial intelligence (AI) is the design and study of systems that appear to mimic intelligent behaviour. Some AI applications are based on rules. More often now, AI applications are built using machine learning that is said to ‘learn’ from examples in the form of data. For example, some AI applications are built to answer questions or help diagnose illnesses. Other AI applications could be built for harmful purposes, such as spreading fake news. AI applications do not think. AI applications are built to carry out tasks in a way that appears to be intelligent.
You can find 32 explanations in the glossary that is part of the Experience AI Lessons. Here’s an insight into how we arrived at the explanations.
In order to ensure the explanations are as precise as possible, we first identified reliable sources. These included among many others:
Vocabulary is an important part of teaching and learning. When we use vocabulary correctly, we can support learners to develop their understanding. If we use it inconsistently, this can lead to alternate conceptions (misconceptions) that can interfere with learners’ understanding. You can read more about this in our Pedagogy Quick Read on alternate conceptions.
Some of our principles for writing explanations of AI terms were that the explanations need to:
We engaged in an iterative process of writing explanations, gathering feedback from our team and our Experience AI project partners at Google DeepMind, and adapting the explanations. Then we went through the feedback and adaptation cycle until we all agreed that the explanations met our principles.
An important part of what emerged as a result, aside from the explanations of AI terms themselves, was a blueprint for how not to talk about AI. One aspect of this is avoiding anthropomorphism, detailed by Ben Garside from our team here.
As part of designing the the Experience AI Lessons, creating the explanations helped us to:
One of the ways education research informed the explanations was that we used semantic waves to structure each term’s explanation in three parts:
Most explanations also contain ‘middle of the wave’ sentences, which add additional abstract content, bridging the ‘bottom of the wave’ concrete example to the ‘top of the wave’ abstract content.
Here’s the “artificial intelligence” explanation broken up into the parts of the semantic wave:
Some of the explanations went through 10 or more iterations before we agreed they were suitable for publication. After months of thinking about, writing, correcting, discussing, and justifying the explanations, it’s tempting to wonder whether I should have just prompted an AI chatbot to generate the explanations for me.
I tested this idea by getting a chatbot to generate an explanation of “artificial intelligence” using the prompt “Explain what artificial intelligence is, using vocabulary suitable for KS3 students, avoiding anthropomorphism”. The result included quite a few inconsistencies with our principles, as well as a couple of technical inaccuracies. Perhaps I could have tweaked the prompt for the chatbot in order to get a better result. However, relying on a chatbot’s output would mean missing out on some of the value of doing the work of writing the explanations in collaboration with my team and our partners.
The visible result of that work is the explanations themselves. The invisible result is the knowledge we all gained, and the coherence we reached as a team, both of which enabled us to create high-quality resources for Experience AI. We wouldn’t have gotten to know what resources we wanted to write without writing the explanations ourselves and improving them over and over. So yes, it was worth our time.
The process of creating and iterating the AI explanations highlights how opaque the field of AI still is, and how little we yet know about how best to teach and learn about it. At the Raspberry Pi Foundation, we now know just a bit more about that and are excited to share the results with teachers and young people.
You can access the Experience AI Lessons and the glossary with all our explanations at experience-ai.org. The glossary of AI explanations is just in its first published version: we will continue to improve it as we find out more about how to best support young people to learn about this field.
Let us know what you think about the explanations and whether they’re useful in your teaching. Onwards with the exciting work of establishing how to successfully engage young people in learning about and creating with AI technologies.
The post How we’re learning to explain AI terms for young people and educators appeared first on Raspberry Pi Foundation.
Over 15,000 teams of young people from across Europe had their computer programs run on board the International Space Station (ISS) this month as part of this year’s European Astro Pi Challenge.
Astro Pi is run in collaboration by us and ESA Education, and offers two ways to get involved: Mission Zero and Mission Space Lab.
Mission Zero is the Astro Pi beginners’ activity. To take part, young people spend an hour writing a short Python program for the Astro Pi computers on the International Space Station (ISS). This year we invited them to create an 8×8 pixel image or animation on the theme of fauna and flora, which their program showed on an Astro Pi LED matrix display for 30 seconds.
This year, 23605 young people’s Mission Zero programs ran on the ISS. We need to check all the programs before we can send them to space and that means we got to see all the images and animations that the young people created. Their creativity was absolutely incredible! Here are some inspiring examples:
Mission Space Lab runs over eight months and empowers teams of young people to design real science experiments on the ISS, executed by Python programs they write themselves. Teams choose between two themes: ‘Life in space’ and ‘Life on Earth’.
This year, the Mission Space Lab programs of 1245 young people in 294 teams from 21 countries passed our rigorous judging and testing process. These programs were awarded flight status and sent to the Astro Pis on board the ISS, where they captured data for the teams to analyse back down on Earth.
Mission Space Lab teams this year decided to design experiments such as analysing cloud formations to identify where storms commonly occur, looking at ocean colour as a measure of depth, and analysing freshwater systems and the surrounding areas they supply water to.
Teams will be receiving their experiment data later this week, and will be analysing and interpreting it over the next few weeks. For example, the team analysing freshwater systems want to investigate how these systems may be affected by climate change. What their Mission Space Lab program has recorded while running on the Astro Pis is a unique data set that the team can compare against other scientific data.
For the ‘Life on Earth’ category of Mission Space Lab experiments this year, the Astro Pis were positioned in a different place to previous years: in the Window Observational Research Facility (WORF). Therefore the Astro Pis could take photos with a wider view. Combined with the High Quality Camera of the upgraded Astro Pi computers we sent to the ISS in 2021, this means that the teams got amazing-quality photos of the Earth’s surface.
Once the experiments for ‘Life on Earth’ were complete, the astronauts moved the Astro Pis back to the Columbus module and replaced their SD cards, ready for capturing the data for the ‘Life in Space’ experiments.
Running programs in an environment as unique as the ISS, where all hardware and software is put to the test, brings many complexities and challenges. Everything that happens on the ISS has to be scheduled well in advance, and astronauts have a strict itinerary to follow to keep the ISS running smoothly.
As usual, this year’s experiments met with their fair share of challenges. One initial challenge the Astro Pis had this year was that the Canadarm, a robotic arm on the outside of the ISS, was in operation during some of the ‘Life on Earth’ experiments. Although it’s fascinating to see part of the ISS in-shot, it also slightly obscured some of the photos.
Another challenge was that window shutters were scheduled to close during some of the experiments, which meant we had to switch around the schedule for Mission Space Lab programs to run so that all of the experiments aiming to capture photos could do so.
Well done to all the young people who’ve taken part in the European Astro Pi Challenge this year.
If you’d like to hear about upcoming Astro Pi Challenges, sign up to the newsletter at astro-pi.org.
The post 24850 young people’s programs ran in space for Astro Pi 2022/23 appeared first on Raspberry Pi Foundation.
Every day, most of us both consume and create data. For example, we interpret data from weather forecasts to predict our chances of a good weather for a special occasion, and we create data as our carbon footprint leaves a trail of energy consumption information behind us. Data is important in our lives, and countries around the world are expanding their school curricula to teach the knowledge and skills required to work with data, including at primary (K–5) level.
In our most recent research seminar, attendees heard about a research-based initiative called Data Education in Schools. The speakers, Kate Farrell and Professor Judy Robertson from the University of Edinburgh, Scotland, shared how this project aims to empower learners to develop data literacy skills and succeed in a data-driven world.
“Data literacy is the ability to ask questions, collect, analyse, interpret and communicate stories about data.”
– Kate Farrell & Prof. Judy Robertson
Scotland’s national curriculum does not explicitly mention data literacy, but the topic is embedded in many subjects such as Maths, English, Technologies, and Social Studies. Teachers in Scotland, particularly in primary schools, have the flexibility to deliver learning in an interdisciplinary way through project-based learning. Therefore, the team behind Data Education in Schools developed a set of cross-curricular data literacy projects. Educators and education policy makers in other countries who are looking to integrate computing topics with other subjects may also be interested in this approach.
The Data Education in Schools projects are aimed not just at giving learners skills they may need for future jobs, but also at equipping them as data citizens in today’s world. A data citizen can think critically, interpret data, and share insights with others to effect change.
Kate and Judy shared an example of data citizenship from a project they had worked on with a primary school. The learners gathered data about how much plastic waste was being generated in their canteen. They created a data visualisation in the form of a giant graph of types of rubbish on the canteen floor and presented this to their local council.
As a result, the council made changes that reduced the amount of plastic used in the canteen. This shows how data citizens are able to communicate insights from data to influence decisions.
Across its projects, the Data Education in Schools initiative uses a problem-solving cycle called the PPDAC cycle. This cycle is a useful tool for creating educational resources and for teaching, as you can use it to structure resources, and to concentrate on areas to develop learner skills.
The five stages of the cycle are:
Smaller data literacy projects may focus on one or two stages within the cycle so learners can develop specific skills or build on previous learning. A large project usually includes all five stages, and sometimes involves moving backwards — for example, to refine the problem — as well as forwards.
At primary school, the aim of data literacy projects is to give learners an intuitive grasp of what data looks like and how to make sense of graphs and tables. Our speakers gave some great examples of playful approaches to data. This can be helpful because younger learners may benefit from working with tangible objects, e.g. LEGO bricks, which can be sorted by their characteristics. Kate and Judy told us about one learner who collected data about their clothes and drew the results in the form of clothes on a washing line — a great example of how tangible objects also inspire young people’s creativity.
As learners get older, they can begin to work with digital data, including data they collect themselves using physical computing devices such as micro:bit microcontrollers or Raspberry Pi computers.
You can access the seminar slides here.
For many attendees, one of the highlights of the seminar was seeing the range of high-quality teaching resources for learners aged 3–18 that are part of the Data Education in Schools project. These include:
More resources are due to be published later in 2023, including a set of prompt cards to guide learners through the PPDAC cycle, a handbook for teachers to support the teaching of data literacy, and a set of virtual data-themed escape rooms.
You may also be interested in the units of work on data literacy skills that are part of The Computing Curriculum, our complete set of classroom resources to teach computing to 5- to 16-year-olds.
At our next seminar we welcome Aim Unahalekhaka from Tufts University, USA, who will share research about a rubric to evaluate young learners’ ScratchJr projects. If you have a tablet with ScratchJr installed, make sure to have it available to try out some activities. The seminar will take place online on Tuesday 6 June at 17.00 UK time, sign up now to not miss out.
To find out more about connecting research to practice for primary computing education, you can see a list of our upcoming monthly seminars on primary (K–5) teaching and learning and watch the recordings of previous seminars in this series.
The post Introducing data science concepts and skills to primary school learners appeared first on Raspberry Pi Foundation.
Today we share a guest blog from Chris Roffey, who manages the UK Bebras Challenge, a computational thinking challenge we run every year in partnership with the University of Oxford.
Bebras is a free annual challenge that helps schools introduce computational thinking to their learners through online, self-marking tasks. Taking part in Bebras, students solve accessible, interesting problems using their developing computational thinking skills. No programming is involved in taking part. The UK challenge is for school students aged 6 to 18 years old, with a special category for students with severe visual impairments.
While UK schools take part in Bebras throughout two weeks in November, for me the annual cycle starts much earlier. May is the time of the annual Bebras international workshop where the year’s new tasks get decided. In 2022, 60 countries were represented — some online, some in person. For nearly a week, computer scientists and computing teachers met to discuss and work on the new cycle’s task proposals submitted by participating countries a little earlier.
After the workshop, in collaboration with teams from other European countries, the UK Bebras team chose its task sets and then worked to localise, copy-edit, and test them to get them ready for schools participating in Bebras during November. From September, schools across the UK create accounts for their students, with over 360,000 students ultimately taking part in 2022. All in all, more than 3 million students from 59 countries took part in the 2022/2023 Bebras challenge cycle.
In this cycle, the UK Bebras partnership between the Raspberry Pi Foundation and the University of Oxford has been extended to include the Oxford University Computing Challenge (OUCC). This is an invitation-based, online coding challenge for students aged 10 to 18, offered in the UK as well as Australia, Jamaica, and China. We invited the students with the top 10% best results in the UK Bebras challenge to take part in the OUCC — an exciting opportunity for them.
In contrast to Bebras, which doesn’t require participants to do any coding, the OUCC asks students to create code to solve computational thinking problems. This requires students to prepare and challenges them to develop their computational thinking skills further. The two younger age groups, 10- to 14-year-olds, solve problems using the Blockly programming language. The older two age groups can use one of the 11 programming languages that Bebras supports, including all the most common ones taught in UK schools.
Over 20,000 Bebras participants took up the invitation to the first round of the OUCC in the third week of January. Then in March, the top 20 participants from each of the four OUCC age groups took part in the final round. The finalists all did amazingly well. In the first round, many of them had solved all the available tasks correctly, even though the expectation is that participants only try to solve as many as they can within the round’s time limit. In the final round, a few of the finalists managed to repeat this feat with the even more advanced tasks — which is, in modern parlance, literally impossible!
Many of the participants are about to take school exams, so the last stage of the annual cycle — the prize winners’ celebration day— takes place when the exam period has ended. This year we are holding this celebration on Friday 30 June at the Raspberry Pi Foundation’s headquarters in Cambridge. It will be a lovely way to finish the annual Bebras cycle and I am looking forward to it immensely.
The post UK Bebras participants in the Oxford University Computing Challenge appeared first on Raspberry Pi Foundation.
Programming is becoming an increasingly useful skill in today’s society. As we continue to rely more and more on software and digital technology, knowing how to code is also more and more valuable. That’s why many parents are looking for ways to introduce their children to programming. You might find it difficult to know where to begin, with so many different kids’ coding languages and platforms available. In this blog post, we explore how children can progress through different programming languages to realise their potential as proficient coders and creators of digital technology.
Everyone needs to start somewhere, and one great option for children aged 5–7 is ScratchJr (Scratch Junior), a visual programming language with drag-and-drop blocks for creating simple programs. ScratchJr is available for free on Android and iOS mobile devices. It’s great for introducing young children to the basics of programming, and they can use it to create interactive stories and games.
Moving on from ScratchJr, there’s its web-based sibling Scratch. Scratch offers drag-and-drop blocks for creating programs and comes with an assortment of graphics, sounds, and music for your child to bring their programs to life. This visual programming language is designed specifically for children to learn programming fundamentals. Scratch is available in multiple spoken languages and is perfect for beginners. It allows kids to create interactive stories, animations, and games with ease.
The Raspberry Pi Foundation has a wealth of free Scratch resources we have created specifically for young people who are beginners, such as the ‘Introduction to Scratch’ project path. And if your child is interested in physical computing to interact with the real world using code, they can also learn how to use electronic components, such as buzzers and LEDs, with Scratch and a Raspberry Pi computer.
Another fun option for children who want to explore coding and physical computing is the micro:bit. This is a small programmable device with an LED display, buttons, and sensors, and it can be used to create games, animations, interactive projects, and lots more. To control a micro:bit, a visual programming language called MakeCode can be used. The micro:bit can also be programmed using Scratch or text-based languages such as Python, offering an easy transition for children as their coding skills progress. Have a look at our free collection of micro:bit resources to learn more.
Everyone is familiar with websites, but fewer people know how they are coded. HTML is a markup language that is used to create the webpages we use every day. It’s a great language for children to learn because they can see the results of their code in real time, in their web browser. They can use HTML and CSS to create simple webpages that include links, videos, pictures, and interactive elements, all the while learning how websites are structured and designed. We have many free web design resources for your child, including a basic ‘Introduction to web development’ project path.
If your child is becoming confident with Scratch and HTML, then using Python is the recommended next stage in their learning. Python is a high-level text-based programming language that is easy to read and learn. It is a popular choice for beginners as it has a simple syntax that often reads like plain English. Many free Python projects for young people are available on our website, including the ‘Introduction to Python’ path.
The Python community is also really welcoming and has produced a myriad of online tutorials and videos to help learners explore this language. Python can be used to do some very powerful things with ease, which is why it is so popular. For example, it is relatively simple to create Python programs to engage in machine learning and data analysis. If you wanted to explore large language models such as GPT, on which the ChatGPT chatbot is based, then Python would be the language of choice.
JavaScript is the language of the web, and if your child has become proficient in HTML, then this is the next language for them. JavaScript is used to create interactive websites and web applications. As young people become more comfortable with programming, JavaScript is a useful language to progress to, given how ubiquitous the web is today. It can be tricky to learn, but like Python, it has a vast number of libraries of functions that people have already created for it to achieve things more quickly. These libraries make JavaScript a very powerful language to use.
There are many different programming languages, and each one has its own strengths and weaknesses. Some are easy to learn and use, some are really fast, and some are very secure.
Starting with visual languages such as Scratch or MakeCode allows your child to begin to understand the basic concepts of programming without needing any developed reading and keyboard skills. Once their understanding and skills have improved, they can try out text-based languages, find the one that they are comfortable with, and then continue to learn. It’s fairly common for people who are proficient in one programming language to learn other languages quite quickly, so don’t worry about which programming language your child starts with.
Whether your child is interested in working in software development or just wants to learn a valuable — and creative — skill, helping them learn to code and try out different kids’ coding languages is a great way for you to open up new opportunities for them.
The post Kids’ coding languages appeared first on Raspberry Pi Foundation.
We are excited to share that 294 teams of young people participating in this year’s Astro Pi Mission Space Lab achieved Flight Status: their programs will run on the Astro Pis installed on the International Space Station (ISS) in April.
Mission Space Lab is part of the European Astro Pi Challenge, an ESA Education project run in collaboration with the Raspberry Pi Foundation. It offers young people the amazing opportunity to conduct scientific investigations in space, by writing computer programs that run on Raspberry Pi computers on board the International Space Station.
To take part in Mission Space Lab, young people form teams and choose between two themes for their experiments, investigating either ‘Life in space’ or ‘Life on Earth’. They send us their experiment ideas in Phase 1, and in Phase 2 they write Python programs to execute their experiments on the Astro Pis onboard the ISS. As we sent upgraded Astro Pis to space at the end of 2021, Mission Space Lab teams can now also choose to use a machine learning accelerator during their experiment time.
In total, 771 teams sent us ideas during Phase 1 in September 2022, so achieving Flight Status is a huge accomplishment for the successful teams. We are delighted that 391 teams submitted programs for their experiments. Teams who submitted had their programs checked for errors and their experiments tested, resulting in 294 teams being granted Flight Status. 134 of these teams included some aspects of machine learning in their experiments using the upgraded Astro Pis’ machine learning accelerator.
The 294 teams to whom we were able to award Flight Status this year represent 1245 young people. 34% of team members are female, and the average participant age is 15. The 294 successful teams hail from 21 countries; Italy has the most teams progressing to the next phase (48), closely followed by Spain (37), the UK (34), Greece (25), and the Czech Republic (25).
Teams can use the Astro Pis to investigate life inside ESA’s Columbus module of the ISS, by writing a program to detect things with at least one of the Astro Pi’s sensors. This can include for example the colour and intensity of light in the module, or the temperature and humidity.
81 teams that created ‘Life in space’ experiments have achieved Flight Status this year. Examples of experiments from this year are investigating how the Earth’s magnetic field is felt on the ISS, what environmental conditions the astronauts experience compared to those on Earth directly beneath the ISS as it orbits, or whether the cabin might be suitable for other lifeforms, such as plants or bacteria.
In the ‘Life on Earth’ theme, teams investigate features on the Earth’s surface using the cameras on the Astro Pis, which are positioned to view Earth from a window on the ISS.
This year the Astro Pis will be located in the Window Observational Research Facility (WORF), which is larger than the window the computers were positioned in in previous years. This means that teams running ‘Life on Earth’ experiments can capture better images. 206 teams that created experiments in the ‘Life on Earth’ theme have achieved Flight Status.
Thanks to the upgraded Astro Pi hardware, this is the second year that teams could decide whether to use visible-light or infrared (IR) photography. Teams running experiments using IR photography have chosen to examine topics such as plant health in different regions, the effects of deforestation, and desertification. Teams collecting visible light photography have chosen to design experiments analysing clouds in different regions, changes in ocean colour, the velocity of the ISS, and classification of biomes (e.g. desert, forest, grassland, wetland).
Each of this year’s 391 submissions has been through a number of tests to ensure they follow the challenge rules, meet the ISS security requirements, and can run without errors on the Astro Pis. Once the experiments have started, we can’t rely on astronaut intervention to resolve any issues, so we have to make sure that all of the programs will run without any problems.
This means that the start of the year is a very busy time for us. We run tests on Mission Space Lab teams’ programs on a number of exact replicas of the Astro Pis, including a final test to run every experiment that has passed all tests for the full three-hour experiment duration. The 294 experiments that received Flight Status will take over 5 weeks to run.
97 programs submitted by teams during Phase 2 of Mission Space Lab this year did not pass testing and so could not be awarded Flight Status. We wish we could run every experiment that is submitted, but there is only limited time available for the Astro Pis to be positioned in the ISS window. Therefore, we have to be extremely rigorous in our selection, and many of the 97 teams were not successful because of only small issues in their programs. We recognise how much work every Mission Space Lab team does, and all teams can be very proud of designing and creating an experiment.
Even if you weren’t successful this year, we hope you enjoyed participating and will take part again in next year’s challenge.
Once all of the experiments have run, we will send the teams the data collected during their experiments. Teams will then have time to analyse their data and write a short report to share their findings. Based on these reports, we will select winners of this year’s Mission Space Lab. The winning and highly commended teams will receive a special surprise.
Congratulations to all successful teams! We are really looking forward to seeing your results.
The post Astro Pi Mission Space Lab 2022/23: 294 teams achieved Flight Status appeared first on Raspberry Pi Foundation.
We are delighted to announce that we’ve launched Experience AI, our new learning programme to help educators to teach, inspire, and engage young people in the subject of artificial intelligence (AI) and machine learning (ML).
Experience AI is a new educational programme that offers cutting-edge secondary school resources on AI and machine learning for teachers and their students. Developed in partnership by the Raspberry Pi Foundation and DeepMind, the programme aims to support teachers in the exciting and fast-moving area of AI, and get young people passionate about the subject.
Artificial intelligence and machine learning applications are already changing many aspects of our lives. From search engines, social media content recommenders, self-driving cars, and facial recognition software, to AI chatbots and image generation, these technologies are increasingly common in our everyday world.
Young people who understand how AI works will be better equipped to engage with the changes AI applications bring to the world, to make informed decisions about using and creating AI applications, and to choose what role AI should play in their futures. They will also gain critical thinking skills and awareness of how they might use AI to come up with new, creative solutions to problems they care about.
The AI applications people are building today are predicted to affect many career paths. In 2020, the World Economic Forum estimated that AI would replace some 85 million jobs by 2025 and create 97 million new ones. Many of these future jobs will require some knowledge of AI and ML, so it’s important that young people develop a strong understanding from an early age.
Something we get asked a lot is: “How do I teach AI and machine learning with my class?”. To answer this question, we have developed a set of free lessons for secondary school students (age 11 to 14) that give you everything you need including lesson plans, slide decks, worksheets, and videos.
The lessons focus on relatable applications of AI and are carefully designed so that teachers in a wide range of subjects can use them. You can find out more about how we used research to shape the lessons and how we aim to avoid misconceptions about AI.
The lessons are also for you if you’re an educator or volunteer outside of a school setting, such as in a coding club.
As part of this exciting first phase, we’re inviting teachers to participate in research to help us further develop the resources. All you need to do is sign up through our website, download the lessons, use them in your classroom, and give us your valuable feedback.
We’ve designed the Experience AI lessons with teacher support in mind, and so that you can deliver them to your learners aged 11 to 14 no matter what your subject area is. Each of the lesson plans includes a section that explains new concepts, and the slide decks feature embedded videos in which DeepMind’s AI researchers describe and bring these concepts to life for your learners.
We will also be offering you a range of new teacher training opportunities later this year, including a free online CPD course — Introduction to AI and Machine Learning — and a series of AI-themed webinars.
We will be inviting schools across the UK to test and improve the Experience AI lessons through feedback. We are really looking forward to working with you to shape the future of AI and machine learning education.
Visit the Experience AI website today to get started.
The post Experience AI: The excitement of AI in your classroom appeared first on Raspberry Pi Foundation.
In the 1950s, Alan Turing explored the central question of artificial intelligence (AI). He thought that the original question, “Can machines think?”, would not provide useful answers because the terms “machine” and “think” are hard to define. Instead, he proposed changing the question to something more provable: “Can a computer imitate intelligent behaviour well enough to convince someone they are talking to a human?” This is commonly referred to as the Turing test.
It’s been hard to miss the newest generation of AI chatbots that companies have released over the last year. News articles and stories about them seem to be everywhere at the moment. So you may have heard of machine learning (ML) chatbots such as ChatGPT and LaMDA. These chatbots are advanced enough to have caused renewed discussions about the Turing Test and whether the chatbots are sentient.
Without any knowledge of how people create such chatbots, it’s easy to imagine how someone might develop an incorrect mental model around these chatbots being living entities. With some awareness of Sci-Fi stories, you might even start to imagine what they could look like or associate a gender with them.
The reality is that these new chatbots are applications based on a large language model (LLM) — a type of machine learning model that has been trained with huge quantities of text, written by people and taken from places such as books and the internet, e.g. social media posts. An LLM predicts the probable order of combinations of words, a bit like the autocomplete function on a smartphone. Based on these probabilities, it can produce text outputs. LLM chatbots run on servers with huge amounts of computing power that people have built in data centres around the world.
AI applications are often described as “black boxes” or “closed boxes”: they may be relatively easy to use, but it’s not as easy to understand how they work. We believe that it’s fundamentally important to help everyone, especially young people, to understand the potential of AI technologies and to open these closed boxes to understand how they actually work.
As always, we want to demystify digital technology for young people, to empower them to be thoughtful creators of technology and to make informed choices about how they engage with technology — rather than just being passive consumers.
That’s the goal we have in mind as we’re working on lesson resources to help teachers and other educators introduce KS3 students (ages 11 to 14) to AI and ML. We will release these Experience AI lessons very soon.
Our researchers at the Raspberry Pi Computing Education Research Centre have started investigating the topic of AI and ML, including thinking deeply about how AI and ML applications are described to educators and learners.
To support learners to form accurate mental models of AI and ML, we believe it is important to avoid using words that can lead to learners developing misconceptions around machines being human-like in their abilities. That’s why ‘anthropomorphism’ is a term that comes up regularly in our conversations about the Experience AI lessons we are developing.
To anthropomorphise: “to show or treat an animal, god, or object as if it is human in appearance, character, or behaviour”
https://dictionary.cambridge.org/dictionary/english/anthropomorphize
Anthropomorphising AI in teaching materials might lead to learners believing that there is sentience or intention within AI applications. That misconception would distract learners from the fact that it is people who design AI applications and decide how they are used. It also risks reducing learners’ desire to take an active role in understanding AI applications, and in the design of future applications.
Avoiding anthropomorphism helps young people to open the closed box of AI applications. Take the example of a smart speaker. It’s easy to describe a smart speaker’s functionality in anthropomorphic terms such as “it listens” or “it understands”. However, we think it’s more accurate and empowering to explain smart speakers as systems developed by people to process sound and carry out specific tasks. Rather than telling young people that a smart speaker “listens” and “understands”, it’s more accurate to say that the speaker receives input, processes the data, and produces an output. This language helps to distinguish how the device actually works from the illusion of a persona the speaker’s voice might conjure for learners.
Another example is the use of AI in computer vision. ML models can, for example, be trained to identify when there is a dog or a cat in an image. An accurate ML model, on the surface, displays human-like behaviour. However, the model operates very differently to how a human might identify animals in images. Where humans would point to features such as whiskers and ear shapes, ML models process pixels in images to make predictions based on probabilities.
The Experience AI lesson resources we are developing introduce students to AI applications and teach them about the ML models that are used to power them. We have put a lot of work into thinking about the language we use in the lessons and the impact it might have on the emerging mental models of the young people (and their teachers) who will be engaging with our resources.
It’s not easy to avoid anthropomorphism while talking about AI, especially considering the industry standard language in the area: artificial intelligence, machine learning, computer vision, to name but a few examples. At the Foundation, we are still training ourselves not to anthropomorphise AI, and we take a little bit of pleasure in picking each other up on the odd slip-up.
Here are some suggestions to help you describe AI better:
| Avoid using | Instead use |
| Avoid using phrases such as “AI learns” or “AI/ML does” | Use phrases such as “AI applications are designed to…” or “AI developers build applications that…” |
| Avoid words that describe the behaviour of people (e.g. see, look, recognise, create, make) | Use system type words (e.g. detect, input, pattern match, generate, produce) |
| Avoid using AI/ML as a countable noun, e.g. “new artificial intelligences emerged in 2022” | Refer to ‘AI/ML’ as a scientific discipline, similarly to how you use the term “biology” |
If we are correct in our approach, then whether or not the young people who engage in Experience AI grow up to become AI developers, we will have helped them to become discerning users of AI technologies and to be more likely to see such products for what they are: data-driven applications and not sentient machines.
If you’d like to get involved with Experience AI and use our lessons with your class, you can start by visiting us at experience-ai.org.
The post How anthropomorphism hinders AI education appeared first on Raspberry Pi Foundation.
On 24 and 25 March, more than 140 members of the Code Club and CoderDojo communities joined us in Cambridge for our first-ever Clubs Conference.
At the Clubs Conference, volunteers and educators came together to celebrate their achievements and explore new ways to support young people to create with technology. The event included community display tables, interactive workshops, discussions, and talks.
For everyone who couldn’t join us in person, we recorded all of the talks that community members gave on the main stage. Here’s what you can learn from the speakers.
Thank you to everyone who gave talks, ran workshops, presented posters, and had conversations to share their questions and insights. It was wonderful to meet all of you, and we came away from the Clubs Conference feeling super inspired by the amazing work Code Club and CoderDojo volunteers all over the world do to help young people learn to create with digital technologies.
We learned so much from listening to you, and we will take the lessons into our work to support you and your clubs in the best way we can.
The post Clubs Conference 2023: Ideas and tools for CoderDojos and Code Clubs appeared first on Raspberry Pi Foundation.
People have many different reasons to think that children and teenagers need to learn about artificial intelligence (AI) technologies. Whether it’s that AI impacts young people’s lives today, or that understanding these technologies may open up careers in their future — there is broad agreement that school-level education about AI is important.
But how do you actually design lessons about AI, a technical area that is entirely new to young people? That was the question we needed to answer as we started Experience AI, our exciting collaboration with DeepMind, a leading AI company.
As part of Experience AI, we are creating a free set of lesson resources to help teachers introduce AI and machine learning (ML) to KS3 students (ages 11 to 14). In England this area is not currently part of the national curriculum, but it’s starting to appear in all sorts of learning materials for young people.
While developing the six Experience AI lessons, we took a research-informed approach. We built on insights from the series of research seminars on AI and data science education we had hosted in 2021 and 2022, and on research we ourselves have been conducting at the Raspberry Pi Computing Education Research Centre.
We reviewed over 500 existing resources that are used to teach AI and ML.
As part of this research, we reviewed over 500 existing resources that are used to teach AI and ML. We found that the vast majority of them were one-off activities, and many claimed to be appropriate for learners of any age. There were very few sets of lessons, or units of work, that were tailored to a specific age group. Activities often had vague learning objectives, or none at all. We rarely found associated assessment activities. These were all shortcomings we wanted to avoid in our set of lessons.
To analyse the content of AI education resources, we use a simple framework called SEAME. This framework is based on work I did in 2018 with Professor Paul Curzon at Queen Mary University of London, running professional development for educators on teaching machine learning.
The SEAME framework gives you a simple way to group learning objectives and resources related to teaching AI and ML, based on whether they focus on social and ethical aspects (SE), applications (A), models (M), or engines (E, i.e. how AI works). We hope that it will be a useful tool for anyone who is interested in looking at resources to teach AI.
The four levels of the SEAME framework do not indicate a hierarchy or sequence. Instead, they offer a way for teachers, resource developers, and researchers to talk about the focus of AI learning activities.
The SE level covers activities that relate to the impact of AI on everyday life, and to its implications for society. Learning objectives and their related resources categorised at this level introduce students to issues such as privacy or bias concerns, the impact of AI on employment, misinformation, and the potential benefits of AI applications.
The A level refers to activities related to applications and systems that use AI or ML models. At this level, learners do not learn how to train models themselves, or how such models work. Learning objectives at this level include knowing a range of AI applications and starting to understand the difference between rule-based and data-driven approaches to developing applications.
The M level concerns the models underlying AI and ML applications. Learning objectives at this level include learners understanding the processes used to train and test models. For example, through resources focused on the M level, students could learn about the different learning paradigms of ML (i.e., supervised, unsupervised, or reinforcement learning).
The E level is related to the engines that make AI models work. This is the most hidden and complex level, and for school-aged learners may need to be taught using unplugged activities and visualisations. Learning objectives could include understanding the basic workings of systems such as data-driven decision trees and artificial neural networks.
Some learning activities may focus on a single level, but activities can also span more than one level. For example, an activity may start with learners trying out an existing ‘rock-paper-scissors’ application that uses an ML model to recognise hand shapes. This would cover the applications level. If learners then move on to train the model to improve its accuracy by adding more image data, they work at the models level.
Other activities cover several SEAME levels to address a specific concept. For example, an activity focussed on bias might start with an example of the societal impact of bias (SE level). Learners could then discuss the AI applications they use and reflect on how bias impacts them personally (A level). The activity could finish with learners exploring related data in a simple ML model and thinking about how representative the data is of all potential application users (M level).
The set of lessons on AI we are developing in collaboration with DeepMind covers all four levels of SEAME.
The set of Experience AI lessons we are developing in collaboration with DeepMind covers all four levels of SEAME. The lessons are based on carefully designed learning objectives and specifically targeted to KS3 students. Lesson materials include presentations, videos, student activities, and assessment questions.
For researchers, we think the SEAME framework will, for example, be useful to analyse school curriculum material to see whether some age groups have more learning activities available at one level than another, and whether this changes over time. We may find that primary school learners work mostly at the SE and A levels, and secondary school learners move between the levels with increasing clarity as they develop their knowledge. It may also be the case that some learners or teachers prefer activities focused on one level rather than another. However, we can’t be sure: research is needed to investigate the teaching and learning of AI and ML across all year groups.
That’s why we’re excited to welcome Salomey Afua Addo to the Raspberry Pi Computing Education Research Centre. Salomey joined the Centre as a PhD student in January, and her research will focus on approaches to the teaching and learning of AI. We’re looking forward to seeing the results of her work.
If you’d like to get involved with Experience AI as an educator and use our lessons with your class, you can start by visiting us at experience-ai.org.
The post AI education resources: What do we teach young people? appeared first on Raspberry Pi Foundation.
We are excited to launch Ada Computer Science, the new online learning platform for teachers, students, and anyone interested in learning about computer science.
With the rapid advances being made in AI systems and chatbots built on large language models, such as ChatGPT, it’s more important than ever that all young people understand the fundamentals of computer science.
Our aim is to enable young people all over the world to learn about computer science through providing access to free, high-quality and engaging resources that can be used by both students and teachers.
A partnership between the Raspberry Pi Foundation and the University of Cambridge, Ada Computer Science offers comprehensive resources covering everything from algorithms and data structures to computational thinking and cybersecurity. It also has nearly 1000 rigorously researched and automatically marked interactive questions to test your understanding. Ada Computer Science is improving all the time, with new content developed in response to user feedback and the latest research. Whatever your interest in computer science, Ada is the place for you.
If you’re teaching or studying a computer science qualification at school, you can use Ada Computer Science for classwork, homework, and revision. Computer science teachers can select questions to set as assignments for their students and have the assignments marked directly. The assignment results help you and your students understand how well they have grasped the key concepts and identify areas where they would benefit from further tuition. Students can learn with the help of written materials, concept illustrations, and videos, and they can test their knowledge and prepare for exams.
Ada Computer Science builds on work we’ve done to support the English school system as part of the National Centre for Computing Education, funded by the Department for Education.
The topics on the website map to exam board specifications for England’s Computer Science GCSE and A level, and will map to other curricula in the future.
In addition, we want to make it easy for educators and learners across the globe to use Ada Computer Science. That’s why each topic is aligned to our own comprehensive taxonomy of computing content for education, which is independent of the English curriculum, and organises the content into 11 strands, including programming, computing systems, data and information, artificial intelligence, creating media, and societal impacts of digital technology.
If you are interested in how we can specifically adapt Ada Computer Science for your region, exam specification, or specialist area, please contact us.
Ada Computer Science enables teachers to:
Students get:
In addition:
Get started with Ada Computer Science today by visiting adacomputerscience.org.
The post Launching Ada Computer Science, the new platform for learning about computer science appeared first on Raspberry Pi Foundation.
We are working in partnership with Amala Education to pilot a vocational skills course for displaced learners aged 16 to 25 in Kakuma refugee camp, Kenya.
Kakuma camp was set up in Kenya in 1992, following a civil war in neighbouring South Sudan in East Africa. The UNHCR estimates that 200,000 people live in the camp today, although other data sources may record larger numbers of residents. 6 out of 10 people living in Kakuma camp are age 18 or younger.
We’ve designed a 100-hour, 10-week course called Using online digital technologies to create change for the Amala learners in Kakuma camp. The course focused on digital skills including making media and websites, with its content we adapted from our Computing Curriculum. The course pilot was delivered alongside Amala’s High School Diploma programme, which is the first internationally accredited course programme enabling refugee and host community youth to complete their education through flexible study.
Our thanks go to the Ezrah Charitable Trust for generously funding our work in this partnership.
We are learning a lot during this pilot, so we are writing a set of three blogs to share these lessons with you.
Today’s blog is Amala Education‘s perspective on their learners in Kakuma Camp, the purpose of digital skills education, and the course design and facilitation. We will also share our approach to adapting learning resources for the context of the Amala learners and using data to assess the course, and what other support we’ve put in place to ensure this educational project is self-sustaining.
By Polly Akhurst (Co-founder and Co-Executive Director, Amala Education), Louie Barnett (Education Lead, Amala Education) & Ajak Mayen Jok (Programme Coordinator, Amala Education)
Our learners wanted a course that develops not just their digital literacy, but one that aligns with Amala’s agency-based learning model, which gives young people the skills to improve their communities. Many of our learners have limited experience of using digital tools but a huge desire to develop these skills, which they see as essential to improving their lives and the lives of their community members.
So we knew we needed a course that not just builds learners’ technical knowledge and skills but can also enrich their lived experience.
How would we do it?
Enter the Raspberry Pi Foundation team. We combined Amala’s agency-based educational approach with the Raspberry Pi Foundation’s experience in pedagogy and teaching about technology and digital literacy to design a course that truly resonates with our learners.
Before developing the course, the Raspberry Pi Foundation team held focus groups with facilitators and learners in Kakuma camp to understand their needs. This helped them to pitch the 100 hours of course materials at the right level for the learners.
We called the course Using online technologies to create change. It takes the learners on a journey, building their foundation elements of computing and digital literacy. Learners start by finding out how digital devices work using input, process, and output. Then they move on to understanding computer networks. The course includes hands-on activities related to creating media, like filming and reviewing content and creating and choosing sounds to use in a podcast. There is also some light-touch web development with HTML and JavaScript. At the end of the course, learners design and deliver a presentation that reflects the work they’ve completed.
“Before I joined the course, I really didn’t know much about how to operate technology, but through the learning and the process, now I am able to learn something that will be beneficial for me and the people in my community.” — Learner in Kakuma refugee camp
Throughout the course, learners use their newly gained skills and knowledge to make their own project aimed at creating positive change. One example project is this website developed by Shyaka Cedric and other learners, which shares how podcasts and remote learning helped their community stay safe and healthy during the pandemic. Another group of learners used their photography and design skills to develop ID cards to keep Amala students safe within the camp. Having an Amala student ID card protects learners because they can prove their identity to their community and the police.
One of the great things about this course is that the Amala facilitators who taught the learners look, speak, and sound like them. Amala facilitators are from within the camp, and that they are relatable is great for learners’ self-confidence.
Having the course facilitated by fellow refugees removes the stigmatisation that the learners are vulnerable and sets the precedent that they can do anything if they put their mind to it.
“It gave me power of… getting involved with new things…Any challenge that comes my way I am willing to take after the Raspberry Pi class now…” — Learner in Kakuma refugee camp
While the Raspberry Pi Foundation team worked to make the course content relevant for the learners, our facilitators further localised the content to ensure its relatability for learners. Local contextualisation helps students to understand what they are learning, and to identify with the content — it’s not something out of the blue for them. Localisation is also important because it helps implement one of Amala’s cornerstones: decolonising the African curriculum.
Because the learners in Kakuma camp lead complex social lives and face high levels of precarity, we decided to make the pilot course optional through our existing Diploma programme. We anticipated a modest enrollment rate, but instead over 100 people within the Amala learner community expressed an interest in this 75-person course. This showed us that the value and urgency of digital literacy in refugee communities is more pertinent than ever.
In a world where a lack of access to technology and digital skills exacerbates existing inequalities, it is critically important for young people who are disadvantaged to access meaningful learning opportunities. As one learner put it:
“I want to study this course because the current world is a digital world and I would like to acquire the skills to boost my computer skills and be able to help myself by getting a job and transforming the community through the digital world.” — Learner in Kakuma refugee camp
We have a blueprint of what works in Kakuma refugee camp, and we are also learning what doesn’t. Bringing these lessons together will help us offer the course to more learners in Kakuma, and adapt the content in other locations, like our site in Amman, Jordan.
Look out for our follow-up blogs about the support we put in place to enable learners in Kakuma camp to participate in the course, and how we worked to create course content that is suitable for them.
The post A vocational digital skills course in Kakuma refugee camp: Connecting to learners’ lives appeared first on Raspberry Pi Foundation.
This year’s International Women’s Day (IWD) focuses on innovation and technology for gender equality. This cause aligns closely with our mission as a charity: to enable young people to realise their full potential through the power of computing and digital technologies. An important part of our mission is to shift the gender balance in computing education.
As the UN Women’s announcement for IWD 2023 says: “Growing inequalities are becoming increasingly evident in the context of digital skills and access to technologies, with women being left behind as the result of this digital gender divide. The need for inclusive and transformative technology and digital education is therefore crucial for a sustainable future.”
According to the UN, women currently hold only 2 in every 10 science, engineering, and information and communication technology jobs globally. Women are a minority of university-level students in science, technology, engineering, and mathematics (STEM) courses, at only 35%, and in information and communication technology courses, at just 3%. This is especially concerning since the WEF predicts that by 2050, 75% of jobs will relate to STEM.
We see this situation reflected in England: computer science is the secondary school subject with the largest gender gap at A level, with girls accounting for only 15% of students. That’s why over the past three years, we have run a research programme to trial ways to encourage more young women to study Computer Science. The programme, Gender Balance in Computing, has produced useful insights for designing equitable computing education around the world.
The UN says that “across countries, girls are systematically steered away from science and math careers. Teachers and parents, intentionally or otherwise, perpetuate biases around areas of education and work best ‘suited’ for women and men.” There is strong evidence to suggest that the representation of women and girls in computing can be improved by introducing them to computing role models such as female computing students or women in tech careers.
Presenting role models was central to the Belonging trial in our Gender Balance in Computing programme. One arm of this trial used resources developed by WISE called My Skills My Life to explore the effect of introducing role models into computing lessons for primary school learners. The trial provided opportunities for learners to speak to women who work in technology. It also offered a quiz to help learners identify their strengths and characteristics and to match them with role models who were similar to them, which research shows is more effective for increasing learners’ confidence.
Teachers who used the resources reported learners’ increased understanding of the types and range of technology jobs, and a widening of learners’ career aspirations.
“Learning about computing makes me feel good because it helps me think more about what I want to be.” — Primary school learner in the Belonging trial
“When [the resources were] showing all of the females in the jobs, nobody went ‘Oh, I didn’t know that a female could do that’, but I think they were amazed by the role of jobs and the fact it was all females doing it.“ — Primary school teacher in the Belonging trial
When teachers and students enter a computing classroom, they bring with them diverse social identities that affect the dynamics of the classroom. Although these dynamics are often unspoken, they can become apparent in which students answer questions or succeed visibly in activities. Without intervention, a dominant group of confident speakers can emerge, and students who are not in this dominant group may lose confidence in their abilities. When teachers set collaborative learning activities that use defined roles or structured discussions, this gives a wider range of students the opportunity to speak up and participate.
Pair programming is one such activity that has been used in research studies to improve learner attitudes and confidence towards computing. In pair programming, one learner is the ‘driver’. They control the keyboard and mouse to write the code. The other learner is the ‘navigator’. They read out the instructions and monitor the code for errors. Learners swap roles regularly, so that both can participate equitably. The Pair Programming trial we conducted as part of Gender Balance in Computing explored the use of this teaching approach with students aged 8 to 11. Feedback from the teachers showed that learners found working in structured pairs engaging.
“Even those who are maybe a little bit more reluctant… those who put their hands up today and said they still prefer to work independently, they are still all engaging quite clearly in that with their pair and doing it really, really well. However much they say they prefer working independently, I think they clearly showed how much they enjoy it, engage with it. And you know they’re achieving with it — so we should be doing this.” – Primary school teacher in the Pair Programming trial
Another collaborative teaching approach is peer instruction. In lessons that use peer instruction, students work in small groups to discuss the answer to carefully constructed multiple choice questions. A whole-class discussion then follows. In the Peer Instruction trial with learners aged 12 to 13 in our Gender Balance in Computing programme, we found that this approach was welcomed by the learners, and that it changed which learners offered answers and ideas.
“I prefer talking in a group because then you get the other side of other people’s thoughts.” – Secondary school learner (female) in the Peer Instruction trial
“[…] you can have a bit of time to think for yourself then you can bounce ideas off other people.” – Secondary school learner (male) in the Peer Instruction trial
“I was very pleased that a lot of the girls were doing a lot of the talking.” – Secondary school teacher in the Peer Instruction trial
Our Gender Balance in Computing research programme showed that no single intervention we trialled significantly increased girls’ engagement in computing or their intention to study it further. Combining several of the approaches we tested may be more impactful. If you’re part of an educational setting where you’d like to adopt multiple approaches at the same time, you can freely access the materials associated with the research programme (see our blog posts about the trials for links).
The research programme also showed that age matters: across Gender Balance in Computing, we observed a big difference in intent to study Computing between primary school and secondary school learners (data from ages 8–11 and 12–13). Fewer secondary school learners reported intent to study the subject further, and while this difference was apparent for both girls and boys, it was more marked for girls.
This finding from England is mirrored by a study the UN Women’s Gender Snapshot 2022 refers to: “A 2020 study of Filipina girls demonstrated that loss of interest in STEM subjects started as early as age 10, when girls began perceiving STEM careers as male-dominated and believing that girls are naturally less adept in STEM subjects. The relative lack of female STEM role models reinforced such perceptions.” That’s why it’s necessary that all primary school learners — no matter what their gender is — have a successful start in the computing classroom, that they encounter role models they can relate to, and that they are supported to engage in computing and creating with technology by their parents, teachers, and communities.
The Foundation’s vision is that every young person develops the knowledge, skills, and confidence to use digital technologies effectively, and to be able to critically evaluate these technologies and confidently engage with technological change. While making changes inside the computing classroom will be beneficial for gender equality, this is just one aspect of building an equitable digital future. We all need to contribute to creating a world where innovation and technology support gender equity.
In all our work, we make sure gender equity is at the forefront, whether that’s in programmes we run for young people, in resources we create for schools, or in partnerships we have, such as with Pratham Education Foundation in India or Team4Tech and Kenya Connect in Wamunyu, Kenya. Computing education is a global challenge, and we are proud to be part of a community that is committed to making it equitable.
This IWD, we invite you to share your thoughts on what equitable computing education means to you, and what you think is needed to achieve it, whether that’s in your school or club, in your local community, or in your country.
The post How can computing education promote an equitable digital future? Ideas from research appeared first on Raspberry Pi Foundation.
The European Astro Pi Challenge offers young people the opportunity to write computer programs that run on Raspberry Pi computers on board the International Space Station (ISS). There are two free, annual missions to participate in: Mission Zero and Mission Space Lab.
Sending your computer program to space is amazing already, and to inspire even more young people about this opportunity, we’re sharing some of the fascinating stories European Space Agency astronaut Matthias Maurer told last round’s Mission Space Lab team winners about his experiences on the ISS.
Last round’s winning Mission Space Lab teams were invited to a very special online session with Matthias, and he shared lots of thoughtful and surprising insights from his mission on the International Space Station. Here are three of the questions from the teams and what Matthias had to say:
Lots of the teams wanted to know about the practicalities of life on the ISS. Team Ad Astra from the UK asked “How did you and your crewmates ensure that you got on well together?” Matthias talked about how supporting each member of the team helps everyone work well together:
It was surprising to hear that the astronauts on the ISS have lots of opportunities to communicate with people on Earth. Matthias explained how the astronauts can keep in regular contact with their family while answering the question from Team Atlantes from Spain:
Team NanoKids asked Matthias about the technologies astronauts use on the ISS, and Matthias shared some fascinating glimpses into what tools help the astronauts in their surroundings:
Thank you to all the teams for these great questions. And thank you to Matthias for offering young people a peek into what life is like in space!
We hope Matthias’ stories inspire lots of young people to take part in the European Astro Pi Challenge. Registration for this round of Mission Space Lab is closed, so why not sign up for news about the next round?
But it’s not too late for young people to get involved today and become part of space history. Astro Pi Mission Zero is still open for participation a little while longer — until 17 March.
Mission Zero is a beginner’s coding activity, so it’s really easy to get involved: young people just need a grown-up to register for them, and a computer with a web browser to participate. In Mission Zero, young people up to age 19 in eligible countries have the chance to send their own simple computer program into space to display a colourful image for the astronauts to see on the ISS.
The one-hour Mission Zero activity comes with step-by-step instructions for young people to follow. No special equipment or coding skills are needed, and all eligible young people who follow the guidelines will have their program run in space. Every Mission Zero participants receives a certificate to show the exact time and the location of the ISS during their programs run, so they’ll have something to remember their stellar achievement.
The European Astro Pi Challenge is an ESA Education project run in collaboration with us here at the Raspberry Pi Foundation.
The post Inspiring young people to code with the Astro Pi Challenge and astronaut Matthias Maurer appeared first on Raspberry Pi Foundation.
Today we’re sharing an Astro Pi Mission Zero codealong video to help even more young people send their code into space.
In Mission Zero, young people write a simple program and display a colourful image on an Astro Pi computer on board the International Space Station (ISS). When the astronauts on mission on the ISS are working nearby, they can see the images young people have designed.
No coding experience is needed for Mission Zero. It’s a free and inspiring beginners’ coding activity. All young people need is an hour to write the program, a web browser on any computer with internet access, and an adult mentor who can register online to access the Mission Hub (see below).
In the codealong video, Rebecca from our team shows young people how to write their Mission Zero program step by step. We hope that it will open up this amazing coding activity to even more young people. (There’s also the written step-by-step guide to creating your program, available in 20 languages.)
Young people up to age 19 in ESA Member States are invited to take part, individually or as teams (see the eligibility details).
Every participant will receive a piece of space science history to keep: a personalised, printable certificate that shows their Mission Zero program’s exact start and end time, and the position of the ISS while their program ran.
The theme to inspire images for Mission Zero this year is ‘flora and fauna’, to remind the ISS astronauts of their home. The images can show anything from flowers and trees to birds, insects, and other animals. Young people could even create a series of images to show as an animation during the 30 seconds their program will run.
Mission Zero 2022/23 is open until 17 March 2023.
If you’re an adult mentor supporting young people to take part, read the mission guidelines to find out all you need to know. You can also watch this short video showing you exactly how to register to access the Mission Hub and get the code to identify your young people’s programs.
The post Code along with our Astro Pi Mission Zero video appeared first on Raspberry Pi Foundation.
Welcome to 2023. I hope that you had a fantastic 2022 and that you’re looking forward to an even better year ahead. To help get the year off to a great start, I thought it might be fun to share a few of the things that we’ve got planned for 2023.
Whether you’re a teacher, a mentor, or a young person, if it’s computer science, coding, or digital skills that you’re looking for, we’ve got you covered.
Through our collaboration with the European Space Agency, Astro Pi, young people can write computer programs that are guaranteed to run on the Raspberry Pi computers on the International Space Station (terms and conditions apply).
Astro Pi Mission Zero is open to participants until 17 March 2023 and is a perfect introduction to programming in Python for beginners. It takes about an hour to complete and we provide step-by-step guides for teachers, mentors, and young people.
Kids all over the world are already working on their entries to Coolest Projects Global 2023, our international online showcase that will see thousands of young people share their brilliant tech creations with the world. Registration opens on 6 February and it’s super simple to get involved. If you’re looking for inspiration, why not explore the judges’ favourite projects from 2022?
While we all love the Coolest Projects online showcase, I’m also looking forward to attending more in-person Coolest Projects events in 2023. The word on the street is that members of the Raspberry Pi team have been spotted scouting venues in Ireland… Watch this space.
I am sure I wasn’t alone in disappearing down a ChatGPT rabbit hole at the end of last year after OpenAI made their latest AI chatbot available for free. The internet exploded with both incredible examples of what the chatbot can do and furious debates about the limitations and ethics of AI systems.
With the rapid advances being made in AI technology, it’s increasingly important that young people are able to understand how AI is affecting their lives now and the role that it can play in their future. This year we’ll be building on our research into the future of AI and data science education and launching Experience AI in partnership with leading AI company DeepMind. The first wave of resources and learning experiences will be available in March.
With pandemic restrictions now almost completely unwound, we’ve seen a huge resurgence in Code Clubs and CoderDojos meeting all over the world. To build on this momentum, we are delighted to be welcoming Code Club and CoderDojo mentors and educators to a big Clubs Conference in Churchill College in Cambridge on 24 and 25 March.
This will be the first time we’re holding a community get-together since 2019 and a great opportunity to share learning and make new connections.
As part of our global mission to ensure that every young person is able to learn how to create with digital technologies, we have been focused on building partnerships in India, Kenya, and South Africa, and that work will be expanding in 2023.
In India we will significantly scale up our work with established partners Mo School and Pratham Education Foundation, training 2000 more teachers in government schools in Odisha, and running 2200 Code Clubs across four states. We will also be launching new partnerships with community-based organisations in Kenya and South Africa, helping them set up networks of Code Clubs and co-designing learning experiences that help them bring computing education to their communities of young people.
Over the past few years, our research seminar series has covered computing education topics from diversity and inclusion, to AI and data science. This year, we’re focusing on current questions and research in primary computing education for 5- to 11-year-olds.
As ever, we’re providing a platform for some of the world’s leading researchers to share their insights, and convening a community of educators, researchers, and policy makers to engage in the discussion. The first seminar takes place today (Tuesday 10 January) and it’s not too late to sign up.
That’s just a few of the super cool things that we’ve got planned for 2023. I haven’t even mentioned the new online projects we’re developing with our friends at Unity, the fun we’ve got planned with our very own online text editor, or what’s next for our curriculum and professional development offer for computing teachers.
You can sign up to our monthly newsletter to always stay up to date with what we’re working on.
The post What to expect from the Raspberry Pi Foundation in 2023 appeared first on Raspberry Pi.
Improving gender balance in computing is part of our work to ensure equitable learning opportunities for all young people. Our Gender Balance in Computing (GBIC) research programme has been the largest effort to date to explore ways to encourage more girls and young women to engage with Computing.
Commissioned by the Department for Education in England and led by the Raspberry Pi Foundation as part of our National Centre for Computing Education work, the GBIC programme was a collaborative effort involving the Behavioural Insights Team, Apps for Good, and the WISE Campaign.
Gender Balance in Computing ran from 2019 to 2022 and comprised seven studies relating to five different research areas:
In December we published the last of seven reports describing the results of the programme. In this blog post I summarise our overall findings and reflect on what we’ve learned through doing this research.
I was fascinated to read a paper by Deborah Butler from 2000 which starts by summarising themes from research into gender balance in computing from the 1980s and 1990s, for example that boys may have access to more role models in computing and may receive more encouragement to pursue the subject, and that software may be developed with a bias towards interests traditionally considered to be male. Butler’s paper summarises research from at least two decades ago — have we really made progress?
In England, it’s true that making Computing a mandatory subject from age 5 means we have taken great strides forward; the need for young people to make a choice about studying the subject only arises at age 14. However, statistics for England’s externally assessed high-stakes Computer Science courses taken at ages 14–16 (GCSE) and 16–18 (A level) clearly show that, although there is a small upwards trend in the proportion of female students, particularly for A level, gender balance among the students achieving GCSE/A level qualifications remains an issue:
| Computer Science qualification (England): | In 2018: | In 2021: | In 2022: |
| GCSE (age 16) | 20.41% | 20.77% | 21.37% |
| A level (age 18) | 11.74% | 14.71% | 15.17% |
In GBIC, we carried out a range of research studies involving more than 14,500 pupils and 725 teachers in England. Implementation teams came from the Foundation, Apps For Good, the WISE Campaign, and the Behavioural Insights Team (BIT). A separate team at BIT acted as the independent evaluators of all the studies.
In total we conducted the following studies:
Each study (apart from the exploratory research study) involved a 12-week intervention in schools. Bespoke materials were developed for all the studies, and teachers received training on how to deliver the intervention they were a part of. For the RCTs, randomisation was done at school level: schools were randomly divided into treatment and control groups. The independent evaluators collected both quantitative and qualitative data to ensure that we gained comprehensive insights from the schools’ experiences of the interventions. The evaluators’ reports and our associated blog posts give full details of each study.
The research programme ran from 2019 to 2022, and as it was based in schools, we faced a lot of challenges due to the coronavirus pandemic. Many research programmes meant to take place in school were cancelled as soon as schools shut during the pandemic.
Although we were fortunate that GBIC was allowed to continue, we were not allowed to extend the end date of the programme. Thus our studies were compressed into the period after schools reopened and primarily delivered in the academic year 2021/2022. When schools were open again, the implementation of the studies was affected by teacher and pupil absences, and by schools necessarily focusing on making up some of the lost time for learning.
Quantitatively, none of the RCTs showed a statistically significant impact on the primary outcome measured, which was different in different trials but related to either learners’ attitudes to computer science or their intention to study computer science. Most of the RCTs showed a positive impact that fell just short of statistical significance. The evaluators went to great lengths to control for pandemic-related attrition, and the implementation teams worked hard to support teachers in still delivering the interventions as designed, but attrition and disruptions due to the pandemic may have played a part in the results.
The qualitative research results were more encouraging. Teachers were enthusiastic about the approaches we had chosen in order to address known barriers to gender balance, and the qualitative data indicated that pupils reacted positively to the interventions. One key theme across the Teaching Approach (and other) studies was that girls valued collaboration and teamwork. The data also offered insights that enable us to improve on the interventions.
We designed the studies so they could act as pilots that may be rolled out at a national scale. While we have gained sufficient understanding of what works to be able to run the interventions at a larger scale, two particular learnings shape our view of what a large-scale study should look like:
The GBIC results highlight that there is no quick fix and suggest that we should combine some of the approaches we’ve been trialling to provide a more holistic approach to teaching Computing in an equitable way. We would recommend that schools adopt several of the approaches we’ve tested; the materials associated with each intervention are freely available (see our blog posts for links).
One of the very interesting overall findings from this research programme was the difference in intent to study Computing between primary school and secondary school learners; fewer secondary school learners reported intent to study the subject further. This difference was observed for both girls and boys, but was more marked for girls, as shown in the graph below. This suggests that we need to double down on supporting children, especially girls, to maintain their interest in Computing as they enter secondary school at age 11. It also points to a need for more longitudinal research to understand more about the transition period from primary to secondary school and how it impacts children’s engagement with computer science and technology in general.
We think that more time (in excess of 12 weeks) is needed to both deliver the interventions and measure their outcome, as the change in learners’ attitudes may be slow to appear, and we’re hoping to engage in more longitudinal research moving forward.
We know that an understanding of computer science can improve young people’s access to highly skilled jobs involving technology and their understanding of societal issues, and we need that to be available to all. However, gender balance relating to computing and technology is a deeply structural issue that has existed for decades throughout the computing education and workplace ecosystem. That’s why we intend to pursue more work around a holistic approach to improving gender balance, aligning with our ongoing research into making computing education culturally relevant.
We are very keen to continue to build on our research on gender balance in computing. If you’d like to support us in any way, we’d love to hear from you. To explore the research projects we’re currently involved in, check out our research pages and visit the website of the Raspberry Pi Computing Education Research Centre at the University of Cambridge.
The post Gender Balance in Computing — the big picture appeared first on Raspberry Pi.
Over the past months, we’ve been working with two partner organisations, Team4Tech and Kenya Connect, to support computing education across the rural county of Machakos, Kenya.
In line with our 2025 strategy, we have started work to improve computing education for young people in Kenya and South Africa. We are especially eager to support communities that experience educational disadvantage. One of our projects in this area is in partnership with Team4Tech and Kenya Connect. Together we have set up the Dr Isaac Minae EdTech Hub in the community Kenya Connect supports in the rural county of Machakos, and we are training teachers so they can equip their learners with coding and physical computing skills.
“Watching teachers and students find joy and excitement in learning has been tremendous! The Raspberry Pi Foundation’s hands-on approach is helping learners make connections through seeing how technology can be used for innovation to solve problems. We are excited to be partnering with Raspberry Pi Foundation and Team4Tech in bringing technology to our rural community.”
– Sharon Runge, Executive Director, Kenya Connect
We are providing the Wamunyu community with the hardware and the skills and knowledge training they need to use digital technology to create solutions to problems they see. The training will make sure that teachers across Machakos can sustain the EdTech Hub and computing education activities independently. This is important because we want the community to be empowered to solve problems that matter to them and for all the local young people to have opportunities that are open to their peers in Nairobi, Kisumu, Mombasa, and other cities in Kenya.
In October this year, we travelled to Wamunyu to help Kenya Connect set up and launch the Dr Isaac Minae EdTech Hub, for which we provided hardware including Raspberry Pi 400 computers and physical computing kits with Raspberry Pi Pico microcontrollers, LEDs, buzzers, buttons, motors and more. We also held a teacher training session to start setting up the local educators with the skills and knowledge they need to teach coding and physical computing. In the training, educators brought a range of experiences with using computers. Some were unfamiliar with computer hardware, but at the end of the training session, they all had designed and created physical computing projects using electronic circuits and code. It was hugely inspiring to work with these teachers and see their enthusiasm and commitment to learning.
Through our two-year partnership with Kenya Connect, we aim to reach at least 1000 learners between the ages of 9 to 14 from 62 schools in Machakos county. We will work with at least 150 teachers to build their knowledge, skills, and confidence to teach coding, digital making, and robotics, and to run after-school Code Clubs. We’ll help teachers offer learning experiences based on our established learning paths to their students, and these experiences will include basic coding skills aligned to Kenya’s Competency Based Curriculum (CBC). We are putting particular focus on adapting our learning content so that teachers in Machakos can offer culturally relevant educational activities in their community.
“Our partnership with the Raspberry Pi Foundation will open up new avenues for teachers to learn coding and physical computing. This is in line with the current Competency Based Curriculum that requires students to start learning coding at an early age. Though coding is entrenched in the curriculum, teachers are ill-prepared and schools lack devices. We are so grateful to the Raspberry Pi Foundation for providing teachers and students access to devices and the Raspberry Pi learning paths.”
– Patrick Munguti, Director of Education and Technology, Kenya Connect
Next up for our work on this project is to continue supporting Kenya Connect to scale the program in the county.
In all our work in Sub-Saharan Africa, we are committed to strengthening and growing our partnerships with locally led youth and community organisations, the private sector, and the public sector, in line with our mission to open up more opportunities for young people to realise their full potential through the power of computing and digital technologies.
Our work in Sub-Saharan Africa is generously funded by the Ezrah Charitable Trust.
The post Training teachers and empowering students in Machakos, Kenya appeared first on Raspberry Pi.
This November, teachers across the UK helped 367,023 learners participate in the annual free UK Bebras Challenge of computational thinking.
We support this challenge in the UK, together with Oxford University, and Bebras Challenges run across the world, with more than 3 million learners from schools in 54 countries taking part in 2021. Bebras encourages a love of computational thinking, computer science, and problem solving, especially among learners who haven’t yet realised they have these skills.
Nearly every year since 2013, more UK schools have been participating in Bebras. We think this is because for teachers, registering and entering learners is easy, the online system does all the marking automatically, and teachers receive comprehensive results that can be helpful for assessment.
The computational thinking problems within Bebras are tailored for different age groups, use clear language, and are accessible to colour-blind learners. There is also a challenge for learners with visual impairments. Teachers who run Bebras in their schools seem to love it and regularly tell colleagues about it.
“Our pupils really enjoy [Bebras] and I find it so helpful to teach computational thinking with real-life strategies. We also find the data and information about our pupils’ performance extremely helpful.” — Teacher in London
In the UK Bebras Challenge, the younger learners aged 6 to 10 usually take part in teams and have plenty of time to discuss how to solve the computational thinking problems they are presented with.
Older learners, aged 10 to 18, try to solve as many problems as they can in 40 minutes. The problems they are presented with start off easy and get increasingly difficult. The 10% of participants who solve the most problems are then invited to take part in the Oxford University Computing Challenge (OUCC), an annual programming challenge.
Although the OUCC is only open to some Bebras participants, all of the OUCC problems are archived and teachers registered with Bebras can use them to make auto-marking quizzes for all of their learners at any time of the year. Part of the goal of UK Bebras is to support teachers with free resources, and the UK Bebras online quizzes facility now has computational thinking tasks from the Bebras archive, plus auto-marking Blockly programming problems and text-based programming problems, which can be solved using commonly taught programming languages.
If you want to get a taste of Bebras, check out some of the interactive challenges that require no registration. And if you’d like to register to make quizzes for your learners and find out about next year’s challenge, you can do so at bebras.uk/admin.
The post Computational thinking all year round with UK Bebras appeared first on Raspberry Pi.
Reflecting is important within any line of work, and computing education is no different. Reflective practice is always valuable, whether you support learners in a non-formal setting, such as a Code Club or CoderDojo, or in a more formal environment, such as a school or college. When you reflect, you might for example evaluate a session or lesson and make changes for next time, or consider whether to reorder activities and learning across a longer time period, or even think broadly about what you teach and how you teach it.
This is where our two special editions of Hello World come in: The Big Book of Computing Content and The Big Book of Computing Pedagogy. Both available as free downloads, they help you reflect on what you teach within Computing and how you teach it.
Computing is a broad and interdisciplinary subject, and different curricula and courses around the world focus on different aspects of it. For all of us, therefore, computing is framed by the curricula with which we are working and the terms which we’re using to talk about the subject. Over the past years at the Foundation, we have been developing a Computing taxonomy to help describe the different aspects of the subject. The Big Book of Computing Content is based on this taxonomy. The aim of this special edition of Hello World is to illustrate the breadth of Computing, and to model language that describes the different concepts, knowledge, and skills that comprise it.
We have organised this Big Book according to our taxonomy’s 11 content strands and also included progressive learning outcomes for each strand at different stages of learning. These outcomes are not prescriptive; instead they illustrate the wide applications of the subject by highlighting the kinds of knowledge and understanding that learners could develop in each area of Computing.
We hope that The Big Book of Computing Content encourages educators to reflect on all aspects of Computing and how they interconnect, as well as on the language we use to describe Computing. Whether the Big Book helps you to discover new aspects to Computing, to think about the subject differently, or simply to see the differences in how we as educators talk about our subject, the time you spend reflecting is important and valuable.
One part of our work as educators is understanding the breadth of Computing and the specific ideas within it. The other part is reflecting on how we teach the subject: the specific methods, strategies, and practices we can use with our learners. The Big Book of Computing Pedagogy describes a range of teaching approaches framed around our 12 pedagogical principles for teaching Computing. Each research-informed principle either reflects how general-purpose pedagogy applies within Computing or explores pedagogies specific to Computing itself. This Big Book consists of research summaries as well as practical articles from educators which illustrate how you can apply the different pedagogies.
Rather than prescribing a set of principles that educators must follow, the aim of The Big Book of Computing Pedagogy is to help you develop your understanding of a range of pedagogical approaches which you can select, apply, and adapt to suit your context.
Ultimately we want to support all Computing and Computer Science educators to build their understanding of subject matter (that is, content) and pedagogy, or what is called pedagogical content knowledge (PCK, a term popularised by Lee Shulman). Combining your PCK with your grasp of the context of your learners, curricula, and setting will enable you to choose suitable practices for your content and context.
We hope that you find the two Big Books to be valuable reference tools to help you and your peers reflect on what it is you mean when you talk about Computing, and on how you teach the concepts, knowledge, and skills within it. Both books are available as free PDF downloads.
We would love to hear examples of how you have used The Big Book of Computing Pedagogy or The Big Book of Computing Content to inform your own teaching practice or to discuss practice with colleagues. Tell us in the comments.
The post Reflecting on what we teach in computing education and how we teach it appeared first on Raspberry Pi.
Today we are sharing an evaluation report on another study that’s part of our Gender Balance in Computing research programme. In this study, we investigated the impact of using relevant contexts in classroom programming activities for 12- to 13-year-olds on girls’ and boys’ attitudes towards Computing.
We have been working on Gender Balance in Computing since 2018, together with partner organisations Behavioural Insights Team, Apps for Good, and WISE, to conduct research studies exploring ways to encourage more girls and young women to engage with Computing in school. The research programme has been funded by the Department for Education, and we deliver it as part of the National Centre for Computing Education. The report we share today is about the penultimate study in the programme.
A typical Computing curriculum is built around content: a list of concepts, knowledge, and skills that will be covered during the course. For some learners, that list will be enough to motivate and engage them in Computing. But other learners require more to engage with the subject, such as context about how they can use the computing skills they learn in the real world. Crucially, this difference between learners is often gendered. Research has shown that many boys become absorbed by the content in Computing courses, whereas for many girls the context for using computing skills is more important, and this context needs to relate to a variety of relevant scenarios where computing can solve problems.
In the Relevance study, we worked together with colleagues from Apps for Good to create teaching materials that present Computing in contexts that were relevant to pupils’ own interests. To do this, we drew on a research concept called identification. This states that when learners become interested in a topic because it relates to part of their own identity, that makes the subject more personally meaningful to them, which in turn means that they are more likely to continue studying it. In the materials we created, we drew on learners’ identities based on the communities that they belonged to (see image below). The materials asked them to identify the connections they had to their own communities, and to then use this as the context to design and create a mobile phone app.
“I feel a sense of achievement in Computing when making your ideas a reality makes you proud of your creation, which is rewarding.” (Female learner, Relevance study evaluation report p. 57)
Between January 2022 and April 2022, more than 95 secondary schools were part of our study investigating the effect that learning with these resources might have on the attitudes of Year 8 pupils (aged 12–13) towards Computing. We are very grateful to all the schools, pupils, and teachers who took part in this study.
To enable evaluation of the study as a randomised controlled trial, the schools were randomly divided into two groups: a ‘control’ group that taught standard Computing lessons, and a ‘treatment’ group that delivered the intervention materials we had developed. The impact of the intervention was independently evaluated by the Behavioural Insights Team using data collected from pupils via surveys at the start and end of the intervention. The evaluators also collected data while conducting lesson observations, pupil group discussions, teacher interviews, and teacher surveys to understand how the intervention was delivered.
The girls who took part in the intervention chose an interesting range of contexts for their apps, including:
“I feel like it’s an important subject, and I feel like sea life is at risk right now, and I want to help people realise that.” (Female learner, Relevance study evaluation report p. 60)
“I feel like computing can create apps to do with solving mental health problems, which I think are very important and personally need a lot of improvement on the way we can cope with mental health.” (Female learner, Relevance study evaluation report p. 60)
The start of this blog refers to the core components of a Computing curriculum: concepts, knowledge, and skills. One way of building a curriculum is to list these components and develop a scheme of work which covers them all. However, in a recent computing education paper, researchers present an alternative way: developing curricula around the possible endpoints of learners. For computing, one endpoint could be the economic opportunities of a programming career, but equally, another could be using digital technologies for creative expression. The researchers argue that when learners have the opportunity to use computing as a tool related to personally meaningful contexts, a more diverse group of learners can become engaged in the subject.
Girls who took part in our Relevance study expressed the importance of creativity. “I think last term we had instructions and you follow them, whereas now it’s like your own ideas and your own creativity and whatever you make,” said one female learner (report, p. 56). The series of lessons where learners designed a prototype of their app was particularly popular among girls because this activity included creative expression. Girls who see themselves as creative align their interests with subjects that allow them to express this part of their identity.
Based on learner responses to a ‘yes/no’ question about whether they were likely to choose GCSE Computer Science, the evaluators of the study found no statistically significant differences between the students who were part of the treatment and control groups. However, when learners were asked instead to select from a list which subjects they were likely to choose at GCSE, there was a statistically significant difference in the results: girls from schools in the treatment group were more likely to choose GCSE Computer Science as one of their options than girls in the control group. This finding suggests that it would be beneficial to gender balance in Computing if educators who design Computing curricula consider multiple endpoints for learners and include personally meaningful contexts to create learning experiences that are relevant to diverse groups of learners.
This is the penultimate report to be published about the studies that are part of the Gender Balance in Computing programme. If you would like to stay up-to-date with the programme, you can sign up to our newsletter. Our final report is about a study that explored the role that options booklets and evenings play in students’ subject choice.
The post Using relevant contexts to engage girls in the Computing classroom: Study results appeared first on Raspberry Pi.
Hello World, our free magazine for computing and digital making educators, has just published its second special edition: The Big Book of Computing Content.
While Hello World‘s first special edition, The Big Book of Computing Pedagogy, focused on how we can teach Computing, this new book is about what we mean by Computing. It aims to demonstrate the breadth of knowledge and skills contained within this constantly evolving subject.
We have structured the new special edition around our taxonomy for formal Computing education, to which we map all our formal education resources. Originally we developed the taxonomy when we started work in the consortium setting up and delivering England’s National Centre for Computing Education, and specifically when we designed the 500 hours of classroom materials in the Teach Computing Curriculum.
Our Computing taxonomy comprises eleven strands and aims to categorise Computing conceptual knowledge and skills to both demonstrate the breadth of Computing as a discipline, and to provide a common language to describe the different areas of study and competencies.
The Big Book of Computing Content complements our first Hello World special edition and follows the same principle of introducing readers to up-to-date research, followed by our favourite stories from past Hello World issues by educators who put that content into practice. For each of the eleven strands in our taxonomy, we also present a table of learning outcomes, which provides examples of knowledge and skills that learners from ages 5 to 19 could develop at each stage of their formal computing education.
Hello World’s first special edition was very popular around the world, with educators setting up Big Book of Computing Pedagogy reading groups, leaders using the book to support pre-service teachers, and even of an upcoming translation into Thai.
We’ve already started to hear similar stories about The Big Book of Computing Content from Hello World readers, including CSEdResearch dedicating their Computer Science Education Discussion Group to all things Big Book of Computing Content in its first week of publication.
We’d love to hear from more educators about how you are using this new special edition, and how it complements your reading of the first Big Book.
You can also subscribe now to get each new Hello World — whether regular issue or special edition — straight to your digital inbox, for free! And if you’re based in the UK and do paid or voluntary work in education, you can subscribe for free print issues.
PS Have you listened to our Hello World podcast yet? Listen and subscribe wherever you get your podcasts.
The post Out now: Hello World’s special edition on Computing content appeared first on Raspberry Pi.
Coding, or computer programming, is a way of writing instructions so that computers can complete tasks. Those instructions can be as simple as ‘move a toy robot forwards for three seconds and then make a beep’, or more complicated instructions, such as ‘check the weather in my local area and then adjust the heating in my house accordingly’.
Even if your child never writes computer programs, it is likely they already use software that coders have created, and in the future they may work with, manage, or hire people who write code. This is why it is important that everyone has an understanding of what coding is all about, and why we at the Raspberry Pi Foundation are passionate about inspiring and supporting children to learn to code for free.
When young people are given opportunities to create with code, they can do incredible things — from expressing themselves, to addressing real-world issues, to trying out the newest technologies. Learning to code also helps them develop resilience and problem-solving skills.
But at what age should you start your child on their journey to learn about coding? Can they be too young? Will they miss out on opportunities if they start too late?
No matter at what age you introduce children to coding, one key element is empowering them to create things that are relevant to them. Above all else, coding should be a fun activity for kids.
You might be surprised how young you can start children on their coding adventure. My own child started to learn when they were about six years old. And you can never be too old to learn to code. I didn’t start learning to program until I was in my late thirties, and I know many learners who decided to take up coding after their retirement.
Acquiring new skills and knowledge is often best accomplished when you are young. Learning a programming language is a little like learning a new spoken or written language. There are strict rules, special words to be used in specific orders and in different contexts, and even different ways of thinking depending on the languages you already know.
When people first introduced computer programming into the world, there were big barriers to entry. People had to pay thousands of dollars for a computer and program it using punch cards. It was very unlikely that any child had access to the money or the skills required to create computer programs. Today’s world is very different, with computers costing as little as $35, companies creating tools and toys aimed at coding for children, and organisations such as ours, the Raspberry Pi Foundation and our children’s coding club networks Code Club and CoderDojo, that have the mission to introduce children to the world of coding for free.
By the age of about four, a child is likely to have the motor skills and understanding to begin to interact with simple toys that introduce the very basics of coding. Bee-Bot and Cubelets are both excellent examples of child-friendly toy robots that can be programmed.
Bee-Bot is a small floor robot that children program by pressing simple combinations of direction buttons so that it moves following the instructions provided. This is a great way of introducing children to the concept of sequencing. Sequencing is the way computers follow instructions one after the other, executing each command in turn.
Cubelets can be used to introduce physical computing to children. With Cubelets, children can snap together physical blocks to create their own unique robots. These robots will perform actions such as moving or lighting up, depending on their surroundings, such as the distance your hand is from the robot or the brightness of light in the room. These are a good example of teaching how inputs to a program can affect the outputs — another key concept in coding.
As your child gets older and becomes more used to using technology, and their eye-hand coordination improves, they might want to try out tools for visual programming. They can use free online programming platforms, such as ScratchJr on a tablet or phone or Scratch or Code Club World in a computer’s web browser. To learn more about these visual programming tools and what your child can create with them, read our blog post How do I start my child coding.
Children can begin to explore Scratch or Code Club World from about the age of six, although it is important to understand that all young people develop at different speeds. We offer many free resources to help learners get started with visual, block-based programming languages, and the easiest places to start are our Introduction to Scratch path and the home island on Code Club World. Children and adults of all ages can learn a lot from Scratch, develop their own engaging activities, and most importantly, have fun doing so.
At around the ages of nine or ten, children’s typing skills are often sufficient for them to start using text-based languages. Again, it is important that they are allowed to have fun and express themselves, especially if they are moving on from Scratch. Our Introduction to Python path allows children to continue creating graphics while they program, as they are used to doing in Scratch; our Introduction to Web path will let them build their own simple websites to allow them to express their creative selves.
In my time at the Raspberry Pi Foundation, I have taught children as young as five and adults as old as seventy. There is no correct age at which a child can begin coding, and there are opportunities to begin at almost any age. The key to introducing coding to anyone is to make it engaging, relevant, and most of all fun!
The post At what age can a child start coding? appeared first on Raspberry Pi.
With our new free ‘Introduction to web development’ path, young people are able to learn HTML and create their own webpages on topics that matter to them. The path is made up of six projects that show children and teenagers how to structure pages using HTML, and style them using CSS.
Webpage creation has come a long way since the 1990s, but HTML is still the markup language that is used to display almost every page on the World Wide Web. By knowing how it works, you can deepen your understanding of the technology you use every day.
If you want to build your own website today, there are many tools to get you quickly up and running. These tools often involve dragging and dropping predefined elements and choosing from a wide collection of themed looks. Learning HTML and CSS skills is important for web designers, developers, and content creators who want to build unique webpage designs that make their content stand out.
With our new ‘Introduction to web development’ path, we want creators (the young people who use our projects) to be able to quickly make fantastic-looking websites that follow modern best practices, while they also learn how HTML and CSS work together to create a webpage. Creators write their own HTML to develop the content and structure of their webpages. And they customise our pre-built CSS style sheets to get their webpages to look like they imagine.
This really is a fun and unique approach to learning HTML and building a webpage, and we think young people will quickly engage with it. They start by finding out how to structure pages using HTML before applying CSS styles that bring their pages to life. Through the six projects, they build all the skills and independence they need to make webpages that matter to them.
We believe that young people should find out about website accessibility right from the start of their learning journey. That’s why the path for learning HTML shows creators how they can make their websites accessible to all their users regardless of the users’ needs or digital devices.
That’s why our new path uses semantic HTML. Older HTML tutorials might show you how to structure a webpage using tags like <div> and <span>. In contrast, the meaning and purpose of tags in semantic HTML is very clear. For example:
Lateo.net - Flux RSS en pagaille (pour en ajouter : @ moi) 
<main> is used to tag the main content for the webpage<footer> is used for content to be displayed in the footer<blockquote> contains a quote and typically the author of the quote<section> contains a portion of content that usually sits within the main part of the webpageSemantic HTML supports accessibility because it allows people who use a screen reader to more easily navigate a webpage and read it in a logical way.
Another element of accessible design that the path introduces is the colour combinations used on webpages. It is really important that contrasting colours are used for the background and the text. High contrast makes the text more readable, which means the webpage is more suitable for visually impaired users.
The path also shows creators the importance of adding meaningful alternative text for images. Good alternative text helps visually impaired users, and users who have a very low bandwidth and therefore turn images off in their web browser.
Finally, our path for learning HTML introduces creators to the concept of responsive web design. Responsive design is helpful because websites can be viewed on thousands of different devices. Some people view pages on large, high-resolution monitors, and others view them on a mobile phone screen. We show learners how they can use HTML and CSS to make their pages responsive so they display in the way that works best for the specific screen on which a user is viewing them.
We have written the projects in this path with young people of around the age from 9 to 17 in mind.
HTML and CSS are text-based markup languages. This means a young person who wants to start learning HTML needs to be familiar with typing on a keyboard. It would also be helpful to have experience of using the copy and paste function, which is useful when changing the layout of a page or copying similar pieces of code.
If a young person is unsure whether they have the right skills to get started with the path, they can first try out a short ‘Discover’ project. With this Discover project, young people can choose between the themes ‘space’, ‘sunsets’, ‘forests’, or ‘animals’ to see how they can create their first webpage in just five steps. (We’re still working on the ‘Discover’ project type, so if you have any feedback about it, let us know.)
Creators will learn how to use HTML and CSS to build webpages that have:
They will also learn about how to make their webpages accessible to all through use of:
We’ve designed the path so young people can complete it in six one-hour sessions, with one hour for each project. Since the project instructions encourage creators to upgrade their projects, they may wish to go further and spend a little more time getting their projects exactly as they imagine them.
Young people only need a standard web browser to follow the project instructions and use an online code editor to create their webpages.
There are 28 other step-by-step projects for creators to choose from on our website. They can browse through these to see what cool things they’d like to make and what new skills they want to learn.
If your kid is proud of the webpage they create with the final ‘Invent’ project in the path, they can share it with a worldwide community of young creators in our free Coolest Projects tech showcase. Project registration will open again in spring 2023. You can sign up to hear news about the showcase on the Coolest Projects homepage.
The post Get kids creating webpages with HTML and CSS appeared first on Raspberry Pi.
