Connexion
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.
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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
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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.
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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.
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For our seminar series on cross-disciplinary computing, it was a delight to host Genevieve Smith-Nunes this September. Her research work involving ballet and augmented reality was a perfect fit for our theme.
Genevieve has a background in classical ballet and was also a computing teacher for several years before starting Ready Salted Code, an educational initiative around data-driven dance. She is now coming to the end of her doctoral studies at the University of Cambridge, in which she focuses on raising awareness of data ethics using ballet and brainwave data as narrative tools, working with student Computing teachers.
You may be surprised that there are links between dance, particularly ballet, and computing. Genevieve explained that classical ballet has a strict repetitive routine, using rule-based choreography and algorithms. Her work on data-driven dance had started at the time of the announcement of the new Computing curriculum in England, when she realised the lack of gender balance in her computing classroom. As an expert in both ballet and computing, she was driven by a desire to share the more creative elements of computing with her learners.
Genevieve has been working with a technologist and a choreographer for several years to develop ballets that generate biometric data and include visualisation of such data — hence her term ‘data-driven dance’. This has led to her developing a second focus in her PhD work on how Computing students can discuss questions of ethics based on the kind of biometric and brainwave data that Genevieve is collecting in her research. Students need to learn about the ethical issues surrounding data as part of their Computing studies, and Genevieve has been working with student teachers to explore ways in which her research can be used to give examples of data ethics issues in the Computing curriculum.
Throughout her talk, Genevieve described several examples of dances she had created. One example was [arra]stre, a project that involved a live performance of a dance, plus a series of workshops breaking down the computer science theory behind the performance, including data visualisation, wearable technology, and images triggered by the dancers’ data.
Much of Genevieve’s seminar was focused on the technologies used to capture movement data from the dancers and the challenges this involves. For example, some existing biometric tools don’t capture foot movement — which is crucial in dance — and also can’t capture movements when dancers are in the air. For some of Genevieve’s projects, dancers also wear headsets that allow collection of brainwave data.
Due to interruptions to her research design caused by the COVID-19 pandemic, much of Genevieve’s PhD research took place online via video calls. New tools had to be created to capture dance performances within a digital online setting. Her research uses webcams and mobile phones to record the biometric data of dancers at 60 frames per second. A number of processes are then followed to create a digital representation of the dance: isolating the dancer in the raw video; tracking the skeleton data; using post pose estimation machine learning algorithms; and using additional software to map the joints to the correct place and rotation.
It’s clear from Genevieve’s research that she is collecting a lot of data from her research participants, particularly the dancers. The projects include collecting both biometric data and brainwave data. Ethical issues tied to brainwave data are part of the field of neuroethics, which comprises the ethical questions raised by our increasing understanding of the biology of the human brain.
Teaching learners to be mindful about how to work with personal data is at the core of the work that Genevieve is doing now. She mentioned that there are a number of ethics frameworks that can be used in this area, and highlighted the UK government’s Data Ethics Framework as being particularly straightforward with its three guiding principles of transparency, accountability, and fairness. Frameworks such as this can help to guide a classroom discussion around the security of the data, and whether the data can be used in discriminatory ways.
Data ethics provides lots of material for discussion in Computing classrooms. To exemplify this, Genevieve recorded her own brainwaves during dance, research, and rest activities, and then shared the data during workshops with student computing teachers. In our seminar Genevieve showed two visualisations of her own brainwave data (see the images above) and discussed how the student computing teachers in her workshops had felt that one was more “personal” than the other. The same brainwave data can be presented as a spreadsheet, or a moving graph, or an image. Student computing teachers felt that the graph data (shown above) felt more medical, and more like permanent personal data than the visualisation (shown above), but that the actual raw spreadsheet data felt the most personal and intrusive.
Watch the recording of Genevieve’s seminar to see her full talk:
You can also access her slides and the links she shared in her talk.
There are a variety of online tools you can use to explore augmented reality: for example try out Posenet with the camera of your device.
Genevieve’s seminar used the title ME++, which refers to the data self and the human self: both are important and of equal value. Genevieve’s use of this term is inspired by William J. Mitchell’s book Me++: The Cyborg Self and the Networked City. Within his framing, the I in the digital world is more than the I of the physical world and highlights the posthuman boundary-blurring of the human and non-human.
Genevieve’s work is also inspired by Luciani Floridi’s philosophical work, and his book Ethics of Information might be something you want to investigate further. You can also read ME++ Data Ethics of Biometrics Through Ballet and AR, a paper by Genevieve about her doctoral work
In our final two seminars for this year we are exploring further aspects of cross-disciplinary computing. Just this week, Conrad Wolfram of Wolfram Technologies joined us to present his ideas on maths and a core computational curriculum. We will share a summary and recording of his talk soon.
On 8 November, Tracy Gardner and Rebecca Franks from the Raspberry Pi Foundation team will close out this series by presenting work we have been doing on computing education in non-formal settings. Sign up now to join us for this session:
We will shortly be announcing the theme of a brand-new series of seminars starting in January 2023.
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The summer months are an exciting time at the Foundation: you can feel the buzz of activity as we prepare for the start of a new school year in many parts of the world. Across our range of fantastic (and free) programmes, everyone works hard to create new and improved resources that help teachers and students worldwide.
We’ve asked some of our programme leads to tell you what’s new in their respective areas. We hope that you’ll come away with a good idea of the breadth and depth of teacher support that’s on offer. Is there something we aren’t doing yet that we should be? Tell us in the comments below.
Sway Grantham has been at the forefront of writing resources for our Teach Computing Curriculum over the last three years. The Curriculum is part of the wider National Centre for Computing Education (NCCE) and provides hundreds of free classroom resources for teachers, from Key Stage 1 to 4. Each resource includes lesson plans, slides, activity sheets, homework, and assessments. Since we published the Curriculum in 2020, all lessons have been reviewed and updated at least once. Managing the process of continuously improving these resources is a key part of Sway’s work.
We make changes to the Teach Computing Curriculum all the time! However, specific things we are excited about ahead of the new school year are updates to how our content is presented on the website so that it’s really easy to see which unit you should be teaching in each half term. We’ve also renamed some of the units to make it clearer what they cover. And to help Key Stage 3 teachers launch Computing in secondary school with skills that are foundational for progress through the requirements of the Key Stage 3 curriculum, we’ve updated the first Year 7 unit, now called Clear messaging in digital media.
We are still in the process of looking through the feedback in detail, but I can share some high-level insights. 96% of teachers who responded to the survey gave a score between 7 and 10 for recommending that other teachers use the Teach Computing Curriculum. Over 80% reported that the Teach Computing Curriculum has improved their confidence, subject knowledge, and the quality of their teaching ‘a little’ or ‘a lot’. Finally, over 90% of respondents said the Curriculum is effective at supporting teachers, developing teachers’ subject knowledge, and saving teachers’ time.
We are grateful to the 907 people who took part in the survey! You have all helped us to ensure the Curriculum has a positive impact on teachers and learners throughout England and beyond.
James Robinson dedicates his work at the Foundation to creating free pedagogical resources that underpin the classroom practice of computing teachers worldwide. He has led the creation of the Pedagogy Quick Reads and the Research Bytes newsletter for the NCCE, and the development of our 12 principles of computing pedagogy, available as a handy poster. He also works on our Hello World magazine, produces the associated Hello World podcast, and curates Hello World’s special issues, such as The Big Book of Computing Pedagogy.
In order to teach any area of the curriculum effectively, educators need to understand both the content they are teaching and the most effective ways to deliver that content. Computing is a broad discipline made up of lots of inter-connected knowledge. Different areas of the subject benefit from different approaches, and this may vary depending on the experience of the learners and the context within which they are learning. Understanding which approaches are best suited to different content helps educators support learners effectively.
Computing education research related to school-aged learners is still in its early stages compared to other subjects, and new approaches and pedagogies are being developed, tested, and evaluated. Staying aware of these developments is important for educators and that’s why it’s something the Foundation is dedicated to supporting.
This year we continue to share best practice and hear from educators applying new ideas in their classroom through Hello World magazine and podcast. Educators should also keep a look out for our second Hello World special edition exploring the breadth and depth of Computing. To get hold of a copy of this later this year, make sure you’re subscribed to Hello World.
Allen Heard and his team have very recently completed a huge project: creating a full curriculum of GCSE topics and associated questions for Isaac Computer Science, our free online learning platform for teachers and students. The new topics cover the entirety of the GCSE exam board specifications for AQA, Edexcel, Eduqas, OCR, and WJEC, and are integrated with our existing A level computer science resources. They are great to pick up and use for classwork, homework, and revision.
I think one of the biggest and most important things that’s been evident to me while working on this project is the care and thought that our content creators have put into each and every piece they worked on. To the end user it will simply be material on a web page, but sitting behind each page are countless discussions involving the whole team around how to present certain facts, concepts, or processes. Sometimes these discussions have even caused us to reevaluate our own thinking around how we deliver computer science content. We have debated the smallest things such as glossary terms, questioning every word to make sure we are as clear and concise as possible. Hopefully the care, expertise, and dedication of the team shines through in what really is a fantastic source of information for teachers and learners.
With 96% of teachers and 88% of students reporting that the content is of high quality and easily accessible, we still need to continue to support them to ultimately enable learners to achieve their potential. Looking ahead, there is still lots of work to do to make sure Isaac offers the best possible user experience. And we plan to add a lot more questions to really bolster the numbers of questions at varying levels of difficulty for learners. This will have the added benefit of being useful for any teachers wanting to up-skill too! A massive strength of the platform is its questions, and we are really keen to give as wide a range of them as possible.
Tamasin Greenough Graham leads the team at Code Club, our global network of free, in-school coding clubs for young people aged 9 to 13. In Code Clubs, participants learn to code while having fun getting creative with their new skills. Clubs can be run by anyone who wants to help young people explore digital technologies — you don’t need coding experience at all. The Code Club team offers everything you need, including coding projects with easy-to-follow, step-by-step instructions, and lots of resources to help you support your club members. They are also on hand to answer your questions.
Running a Code Club really is simple and a lot of fun! We have free training to suit everyone, including webinars that guide you through getting started, a self-study online course you can take to prepare for running your Code Club, and drop-in online Q&A sessions where you can chat about your questions to our friendly team or to other educators who run clubs.
Once you have registered your Code Club, you’ll get access to an online dashboard packed with useful resources: from guidance on preparing and delivering your first session, to certificates to celebrate your club members’ successes, and unplugged activities for learners to do away from the screen.
You don’t need to have any coding experience to run a club, as we provide a giant range of fun coding projects and support materials that can be easily followed by educators and young people alike. You just need to support and encourage your young coders, and you can get in touch with the Code Club team if you need any help!
The project paths we offer provide a framework for young coders to develop their skills, whatever their starting point is. Each path starts with three Explore projects, where coders learn new coding concepts and skills. The next two Design projects in the path help them practise these skills through creating fun games, animations, or websites. The final Invent project of the path gives a design brief, and based on this learners have the space to use their new skills and their creativity to code something based on their own ideas.
Our project paths start with the basics of Scratch, and work through to creating websites in HTML and CSS, and to text-based coding in Python. For more advanced or adventurous coders, we also offer project paths to make physical projects with Raspberry Pi Pico, create 3D models in Blender, or even build 3D worlds in Unity.
Lots of primary-aged children use digital technology every day, whether that be a TV, a phone, playing video games, or a computer at school. But they don’t have to be just consumers of technology. Through learning to code, young people become able to create their own technology, and our projects are designed to help them see how these new skills allow them to express themselves and solve problems that matter to them.
What young people do with their new skills is up to them – that’s the exciting part! Computing skills open paths to a wide range of projects and work where digital skills are helpful. And while learning coding is fun and useful, it also helps learners develop a many other important skills to do with problem solving, teamwork, and creativity.
Martin O’Hanlon heads the team that produces our free online courses programme. If you’re looking for continued professional development in computer science, look no further than to our more than 35 courses. (For teachers in England, a large number of the courses count towards the NCCE’s Primary, Secondary, or GCSE certificates.) Curated in 13 curated learning pathways, all of our courses provide high-quality training that you can take at home, at a time that suits you.
Our online computing courses are free and have something for everyone who is interested in computing. We offer pathways for learning to program in Python or Scratch, teaching computing in the classroom, getting started with physical computing, and many more.
We vary the materials and formats used in our courses, including videos, written articles, quizzes, and discussions to help learners get the most out of the experience. You will find a lot of practical activities and opportunities to practice what you learn. There are loads of opportunities to interact with and learn from others who are doing the course at the same time as you. And educators from the Raspberry Pi Foundation join the courses during facilitation periods to give their advice, support, and encouragement.
We have a large catalogue of online training courses, and the pathways give learners a starting point. They group the courses into useful collections, offering a recommended path for everyone, whether that’s people who are brand-new to computing or who have identified a gap in their existing computing skills or knowledge.
Our aim is that these pathways help people find the right course at the right point in their computing journey.
We’re also very excited to work on new research projects this school year, to help deepen the computing education community’s understanding of how to teach the subject in schools. Are you a primary teacher in England who is interested in making computing culturally relevant for your pupils?
We’re currently looking for teachers to take part in our research project around primary school culturally adapted resources, running from October 2022 to July 2023. Find out more about what taking part involves.
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From experience, being connected to a community of fellow computing educators is really important, especially given that some members of the community may be the only computing educator in their school, district, or country. These professional connections enable educators to share and learn from each other, develop their practice, and importantly reduce any feelings of isolation.
It was great to see the return of the Computer Science Teachers Association (CSTA) Annual Conference to an in-person event this year, and I was really excited to be able to attend.
Our small Raspberry Pi Foundation team headed to Chicago for four and a half days of meetups, professional development, and conversations with educators from all across the US and around the world. Over the week our team ran workshops, delivered a keynote talk, gave away copies of Hello World magazine, and signed up many new subscribers. You too can subscribe to Hello World magazine for free at helloworld.cc/subscribe.
We spoke to so many educators about all parts of the Raspberry Pi Foundation’s work, with a particular focus on the Hello World magazine and podcast, and of course The Big Book of Computing Pedagogy. In collaboration with CSTA, we were really proud to be able to provide all attendees with their own physical copy of this very special edition.
It was genuinely exciting to see how pleased attendees were to receive their copy of The Big Book of Computing Pedagogy. So many came to talk to us about how they’d used the digital copy already and their plans for using the book for training and development initiatives in their schools and districts. We gave away every last spare copy we had to teachers who wanted to share the book with their colleagues who couldn’t attend.
Don’t worry if you couldn’t make it to the conference, The Big Book of Computing Pedagogy is available as a free PDF, which due to its Creative Commons licence you are welcome to print for yourself.
Another goal for us at CSTA was to support and encourage new authors to the magazine in order to ensure that Hello World continues to be the magazine for computing educators, by computing educators. Anyone can propose an article idea for Hello World by completing this form. We’re confident that every computing educator out there has at least one story to tell, lessons or learnings to share, or perhaps a cautionary tale of something that failed.
We’ll review any and all ideas and will support you to craft your idea into a finished article. This is exactly what we began to do at the conference with our workshop for writers led by Gemma Coleman, our fantastic Hello World Editor. We’re really excited to see these ideas flourish into full-blown articles over the coming weeks and months.
Our week culminated in a keynote talk delivered by Sue, Jane, and James, exploring how we developed our 12 pedagogy principles that underpin The Big Book of Computing Pedagogy, as well as much of the content we create at the Raspberry Pi Foundation. These principles are designed to describe a set of approaches that educators can add to their toolkit, giving them a shared language and the agency to select when and how they employ each approach. This was something we explored with teachers in our final breakout session where teachers applied these principles to describe a lesson or activity of their own.
We found the experience extremely valuable and relished the opportunity to talk about teaching and learning with educators and share our work. We are incredibly grateful to the entire CSTA team for organising a fantastic conference and inviting us to participate.
Subscribe now to get each new Hello World straight to your digital inbox, for free! And if you’re based in the UK and do paid or unpaid work in education, you can subscribe for free print issues.
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Connecting face to face with educators around the world is a key part of our mission at the Raspberry Pi Foundation, and it’s something that we’ve sorely missed doing over the last two years. We’re therefore thrilled to be joining over 1000 computing educators in the USA at the Computer Science Teachers Association (CSTA) Annual Conference in Chicago in July.
You will find us at booth 521 in the expo hall throughout the conference, as well as running four sessions. Gemma, Kevin, James, Sue, and Jane are team members representing Hello World magazine, the Raspberry Pi Computing Education Research Centre, and our other free programmes and education initiatives. We thank the team at CSTA for involving us in what we know will be an amazing conference.
Developing and sharing effective computing pedagogy is our theme for CSTA 2022. We’ll be talking to you about our 12 pedagogy principles, laid out in The Big Book of Computing Pedagogy, available to download for free.
An exciting piece of news is that everyone attending CSTA 2022 will find a free print copy of the Big Book in their conference goodie bag!
We’re really looking forward to sharing and discussing the book and all our work with US educators, and to seeing some familiar faces. We’re also hoping to interview lots of old and new friends about your approaches to teaching computing and computer science for future Hello World podcast episodes.
Our team will also be running a number of sessions where you can join us to learn, discuss, and prepare lesson plans.
Thursday 14 July, 9am–12pm: Pre-conference workshop (booking required) with James Robinson and Jane Waite
If you enjoy explaining concepts using unplugged activities, analogy, or storytelling, then this practical pre-conference session is for you. In the session, we’ll introduce the idea of semantic waves, a learning theory that supports learners in building knowledge of new concepts through careful consideration of vocabulary and contexts. Across the world, this approach has been successfully used to teach topics ranging from ballet to chemistry — and now computing.
You’ll learn how this theory can be applied to deliver powerful explanations that connect abstract ideas and concrete experiences. By taking part in the session, you’ll gain a solid understanding of semantic wave theory, see it in practice in some freely available lesson plans, and apply it to your own planning.
Friday 15 July, 1–2pm: Workshop with Gemma Coleman
Do you enjoy sharing your teaching ideas, successes, and challenges with others? Do you want to connect with a global community of over 30,000 computing educators? Have you always wanted to be a published author? Then come along to this workshop session.
Every single computing or CS teacher out there has at least one lesson to share, idea to voice, or story to tell. In the session, you’ll discuss what makes a good article with Gemma Coleman, Hello World’s Editor, and you’ll learn top tips for how to communicate your ideas in writing. Gemma will also guide you through writing a plan for your very own article. Even if you’re not sure whether you want to write an article, doing this is a powerful way to reflect on your teaching practice.
Saturday 16 July, 4–5pm: Keynote talk by Sue Sentance
To teach any subject requires good teaching skills, knowledge about the subject being taught, and specific knowledge that a teacher gains about how to teach a particular topic, to their particular students, in a particular context. Teaching computer science is no different, and it’s a challenge for teachers to develop a go-to set of pedagogical strategies for such a new subject, especially for elements of the subject matter that they are just getting to grips with themselves.
In this keynote talk, our Chief Learning Officer Sue Sentance will focus on some of the 12 pedagogy principles that we developed to support the teaching of computer science. We created this set of principles together with other teachers and researchers to help us and everyone in computing and computer science education reflect on how we teach our learners. Sue will share how we arrived at the principles, and she’ll use classroom examples to illustrate how you can apply them in practice.
Sunday 17 July, 9–10am: Workshop with Sue Sentance
The set of 12 pedagogy principles we’ve developed for teaching computing are presented in our Hello World Big Book of Computing Pedagogy. The book includes summaries, teachers’ perspectives, and lesson plans for each of the 12 principles.
All CSTA attendees will get their own print copy of the Big Book, and in this practical session, we will use the book to explore together how you can use the 12 principles in the planning and delivery of your lessons. The session will be very hands-on, so bring along something you know you want or need to teach.
CSTA is now just a month away, and we can’t wait to meet old friends, make new connections, and learn from each other! Come find us at booth 521 or at our sessions to meet the team, discover Hello World magazine and the Hello World podcast, and find out more about our educational work. We hope to see you soon.
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At the Raspberry Pi Foundation, we believe everyone belongs in computer science, and that it is a much more varied field than is commonly assumed. One of the ways we want to promote inclusivity and highlight the variety of skills and interests needed in computer science is through our ‘I belong’ campaign. We do this because the tech sector lacks diversity. Similarly, in schools, there is underrepresentation of students in computing along the axes of gender, ethnicity, and economic situation. (See how researchers describe data from England, and data from the USA.)
The ‘I belong’ campaign is part of our work on Isaac Computer Science, our free online learning platform for GCSE and A level students (ages 14 to 18) and their teachers, funded by the Department for Education. The campaign celebrates young computer scientists and how they came to love the subject, what their career journey has been so far, and what their thoughts are about inclusivity and belonging in their chosen field.
These people are role models who demonstrate that everyone belongs in computer science, and that everyone can bring their interests and skills to bear in the field. In this way, we want to show young people that they can do much more with computing than they might think, and to inspire them to consider how computing could be part of their own life and career path.
Salome is studying Computer Science with Digital Technology Solutions at the University of Leeds and doing a degree apprenticeship with PricewaterhouseCoopers (PwC).
“I was quite lucky, as growing up I saw a lot about women in STEM which inspired me to take this path. I think to improve the online community, we need to keep challenging stereotypes and getting more and more people to join, thereby improving the diversity. This way, a larger number of people can have role models and identify themselves with someone currently there.”
“Another thing is the assumption that computer science is just coding and not a wide and diverse field. I still have to explain to my friends what computer science involves and can become, and then they will say, ‘Wow, that’s really interesting, I didn’t know that.'”
Devyani is a third-year degree apprentice at Cisco.
“It was at A level where I developed my programming skills, and it was more practical rather than theoretical. I managed to complete a programming project where I utilised PHP, JavaScript, and phpMyAdmin (which is a database). It was after this that I started looking around and applying for degree apprenticeships. I thought that university wasn’t for me, because I wanted a more practical and hands-on approach, as I learn better that way.”
“At the moment, I’m currently doing a product owner role, which is where I hope to graduate into. It’s a mix between both a business role and a technical role. I have to stay up to speed with the current technologies we are using and developing for our clients and customers, but also I have to understand business needs and ensure that the team is able to develop and deliver on time to meet those needs.”
Omar is a Mexican palaeontologist who uses computer science to study dinosaur bones.
“I try to bring aspects that are very well developed in computer science and apply them in palaeontology. For instance, when digitising the vertebrae, I use a lot of information theory. I also use a lot of data science and integrity to make sure that what we have captured is comparable with what other people have found.”
“What drove me to computers was the fact you are always learning. That’s what keeps me interested in science: that I can keep growing, learn from others, and I can teach people. That’s the other thing that makes me feel like I belong, which is when I am able to communicate the things I know to someone else and I can see the face of the other person when they start to grasp a theory.”
Tasnima is a computer science graduate from Queen Mary University of London, and is currently working as a software engineer at Credit Suisse.
“During the pandemic, one of the good things to come out of it is that I could work from home, and that means working with people all over the world, bringing together every race, religion, gender, etc. Even though we are all very different, the one thing we all have in common is that we’re passionate about technology and computer science. Another thing is being able to work in technology in the real world. It has allowed me to work in an environment that is highly collaborative. I always feel like you’re involved from the get-go.”
“I think we need to also break the image that computer science is all about coding. I’ve had friends that have stayed away from any tech jobs because they think that they don’t want to code, but there’s so many other roles within technology and jobs that actually require no coding whatsoever.”
Aleena is a software engineer who works at a health tech startup in London and is also studying for a master’s degree in AI ethics at the University of Cambridge.
“I do quite a lot of different things as an engineer. It’s not just coding, which is part of it but it is a relatively small percentage, compared to a lot of other things. […] There’s a lot of collaborative time and I would say a quarter or third of the week is me by myself writing code. The other time is spent collaborating and working with other people and making sure that we’re all aligned on what we are working on.”
“I think it’s actually a very diverse field of tech to work in, once you actually end up in the industry. When studying STEM subjects at a college or university level it is often not very diverse. The industry is the opposite. A lot of people come from self-taught or bootcamp backgrounds, there’s a lot of ways to get into tech and software engineering, and I really like that aspect of it. Computer science isn’t the only way to go about it.”
Alice is a final-year undergraduate student of Computer Science with Artificial Intelligence at the University of Brighton. She is also the winner of the Global Challenges COVID-19 Research Scholarship offered by Santander Universities.
“[W]e need to advertise computer science as more than just a room full of computers, and to advertise computer sciences as highly collaborative. It’s very creative. If you’re on a team of developers, there’s a lot of communication involved.”
“There’s something about computer science that I think is so special: the fact that it is a skill anybody can learn, regardless of who they are. With the right idea, anybody can build anything.”
Help us spread the message that everyone belongs in computer science: share this blog with schools, teachers, STEM clubs, parents, and young people you want to inspire.
Whether you’re studying or teaching computer science GCSE or A levels in the UK (or thinking about doing so!), or you’re a teacher or student in another part of the world, Isaac Computer Science is here to help you achieve your computer science goals. Our high-quality learning platform is free to use and open to all. As a student, you can register to keep track of your progress. As a teacher, you can sign up to guide your students’ learning.
And for younger learners, we have lots of fun project guides to try out coding and creating with digital technologies.
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Last summer, the Raspberry Pi Foundation and the University of Cambridge Department of Computer Science and Technology created a new research centre focusing on computing education research for young people in both formal and non-formal education. The Raspberry Pi Computing Education Research Centre is an exciting venture through which we aim to deliver a step-change for the field.
Computing education research that focuses specifically on young people is relatively new, particularly in contrast to established research disciplines such as those focused on mathematics or science education. However, computing is now a mandatory part of the curriculum in several countries, and being taken up in education globally, so we need to rigorously investigate the learning and teaching of this subject, and do so in conjunction with schools and teachers.
To celebrate the official launch of the Raspberry Pi Computing Education Research Centre, we will be holding an in-person event in Cambridge, UK on Weds 20 July from 15.00. This event is free and open to all: if you are interested in computing education research, we invite you to register for a ticket to attend. By coming together in person, we want to help strengthen a collaborative community of researchers, teachers, and other education practitioners.
The launch event is your opportunity to meet and mingle with members of the Centre’s research team and listen to a series of short talks. We are delighted that Prof. Mark Guzdial (University of Michigan), who many readers will be familiar with, will be travelling from the US to join us in cutting the ribbon. Mark has worked in computer science education for decades and won many awards for his research, so I can’t think of anybody better to be our guest speaker. Our other speakers are Prof. Alastair Beresford from the Department of Computer Science and Technology, and Carrie Anne Philbin MBE, our Director of Educator Support at the Foundation.
The event will take place at the Department of Computer Science and Technology in Cambridge. It will start at 15.00 with a reception where you’ll have the chance to talk to researchers and see the work we’ve been doing. We will then hear from our speakers, before wrapping up at 17.30. You can find more details about the event location on the ticket registration page.
The aim of the Raspberry Pi Computing Education Research Centre is to increase our understanding of teaching and learning computing, computer science, and associated subjects, with a particular focus on young people who are from backgrounds that are traditionally under-represented in the field of computing or who experience educational disadvantage.
We have been establishing the Centre over the last nine months. In October, I was appointed Director, and in December, we were awarded funding by Google for a one-year research project on culturally relevant computing teaching, following on from a project at the Raspberry Pi Foundation. The Centre’s research team is uniquely positioned, straddling both the University and the Foundation. Our two organisations complement each other very well: the University is one of the highest-ranking universities in the world and renowned for its leading-edge academic research, and the Raspberry Pi Foundation works with schools, educators, and learners globally to pursue its mission to put the power of computing into the hands of young people.
In our research at the Centre, we will make sure that:
We are excited to work with a large community of teachers and researchers, and we look forward to meeting you at the launch event.
At the end of June, we’ll be launching a new website for the Centre at computingeducationresearch.org. This will be the place for you to find out more about our projects and events, and to sign up to our newsletter. For announcements on social media, follow the Raspberry Pi Foundation on Twitter or Linkedin.
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We are excited to announce our second new Python learning path, ‘More Python’, which shows young coders how to add real data to their programs while creating projects from a chart of Olympic medals to an interactive world map. The six guided Python projects in this free learning path are designed to enable young people to independently create their own Python projects about the topics that matter to them.
In this post, we’ll show you how kids use the projects in the ‘More Python’ path, what they can make by following the path, and how the path structure helps them become confident and independent digital makers.
Our ‘Introduction to Python’ learning path is the perfect place to start learning how to use Python, a text-based programming language. When we launched the Intro path in February, we explained why Python is such a popular, useful, and accessible programming language for young people.
Because Python has so much to offer, we have created a second Python path for young people who have learned the basics in the first path. In this new set of six projects, learners will discover new concepts and see how to add different types of real data to their programs.
We have written the projects in this path with young people around the age of 10 to 13 in mind. To code in a text-based language, a young person needs to be familiar with using a keyboard, due to the typing involved. Learners should have already completed the ‘Introduction to Python’ project path, as they will build on the learning from that path.
Young people need access to a web browser to complete our project paths. Each project contains step-by-step instructions for learners to follow, and tick boxes to mark when they complete each step. On top of that, the projects have steps for learners to:
Young people also have the option to sign up for an account with us so they can save their progress at any time and collect badges.
While learners follow the project instructions in this project path, they write their code into Trinket, a free web-based coding platform accessible in a browser. Each project contains a link to a starter Trinket, which includes everything to get started writing Python code — no need to install any additional software.
If they prefer, however, young people also have the option of instead writing their code in a desktop-based programming environment, such as Thonny, as they work through the projects.
To use data in their Python programs, the project instructions show learners how to:
The projects support learners as they explore new concepts of digital visual media and:
In each project, learners reflect and answer questions about their work, which is important for connecting the project’s content to their pre-existing knowledge.
As they work through the projects, learners see different ways to present data and then decide how they want to present their data in the final project in the path. You’ll find out what the projects are on the path page, or at the bottom of this blog post.
The project path helps learners become independent coders and digital makers, as each project contains slightly less support than the one before. You can read about how our project paths are designed to increase young people’s independence, and explore our other free learning paths for young coders
We’ve designed the path to be completed in around six one-hour sessions, with one hour per project, at home, in school, or at a coding club. The project instructions encourage learners to add code to upgrade their projects and go further if they wish. This means that young people might want to spend a little more time getting their projects exactly as they imagine them.
Unity is a free development environment for creating 3D virtual environments, including games, visual novels, and animations, all with the text-based programming language C#. Our ‘Introduction to Unity’ project path for keen coders shows how to make 3D worlds and games with collectibles, timers, and non-player characters.
At the end of the ‘More Python’ path, learners are encouraged to register a project they’ve made using their new coding skills for Coolest Projects Global, our free and world-leading online technology showcase for young tech creators. The project they register will become part of the online gallery, where members of the Coolest Projects community can celebrate each other’s creations.
We welcome projects from all young people, whether they are beginners or experienced coders and digital makers. Coolest Projects Global is a unique opportunity for young people to share their ingenuity with the world and with other young people who love coding and creating with digital technology.
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At the Raspberry Pi Foundation, we’ve been thinking about questions relating to artificial intelligence (AI) education and data science education for several months now, inviting experts to share their perspectives in a series of very well-attended seminars. At the same time, we’ve been running a programme of research trials to find out what interventions in school might successfully improve gender balance in computing. We’re learning a lot, and one primary lesson is that these topics are not discrete: there are relationships between them.
We can’t talk about AI education — or computer science education more generally — without considering the context in which we deliver it, and the societal issues surrounding computing, AI, and data. For this International Women’s Day, I’m writing about the intersection of AI and gender, particularly with respect to gender bias in machine learning.
Gender inequality is everywhere, and researchers, activists, and initiatives, and governments themselves, have struggled since the 1960s to tackle it. As women and girls around the world continue to suffer from discrimination, the United Nations has pledged, in its Sustainable Development Goals, to achieve gender equality and to empower all women and girls.
While progress has been made, new developments in technology may be threatening to undo this. As Susan Leavy, a machine learning researcher from the Insight Centre for Data Analytics, puts it:
Artificial intelligence is increasingly influencing the opinions and behaviour of people in everyday life. However, the over-representation of men in the design of these technologies could quietly undo decades of advances in gender equality.
Susan Leavy, 2018 [1]
In her 2019 award-winning book Invisible Women: Exploring Data Bias in a World Designed for Men [2], Caroline Criado Perez discusses the effects of gender-biased data. She describes, for example, how the designs of cities, workplaces, smartphones, and even crash test dummies are all based on data gathered from men. She also discusses that medical research has historically been conducted by men, on male bodies.
Looking at this problem from a different angle, researcher Mayra Buvinic and her colleagues highlight that in most countries of the world, there are no sources of data that capture the differences between male and female participation in civil society organisations, or in local advisory or decision making bodies [3]. A lack of data about girls and women will surely impact decision making negatively.
Machine learning (ML) is a type of artificial intelligence technology that relies on vast datasets for training. ML is currently being use in various systems for automated decision making. Bias in datasets for training ML models can be caused in several ways. For example, datasets can be biased because they are incomplete or skewed (as is the case in datasets which lack data about women). Another example is that datasets can be biased because of the use of incorrect labels by people who annotate the data. Annotating data is necessary for supervised learning, where machine learning models are trained to categorise data into categories decided upon by people (e.g. pineapples and mangoes).
In order for a machine learning model to categorise new data appropriately, it needs to be trained with data that is gathered from everyone, and is, in the case of supervised learning, annotated without bias. Failing to do this creates a biased ML model. Bias has been demonstrated in different types of AI systems that have been released as products. For example:
Facial recognition: AI researcher Joy Buolamwini discovered that existing AI facial recognition systems do not identify dark-skinned and female faces accurately. Her discovery, and her work to push for the first-ever piece of legislation in the USA to govern against bias in the algorithms that impact our lives, is narrated in the 2020 documentary Coded Bias.
Natural language processing: Imagine an AI system that is tasked with filling in the missing word in “Man is to king as woman is to X” comes up with “queen”. But what if the system completes “Man is to software developer as woman is to X” with “secretary” or some other word that reflects stereotypical views of gender and careers? AI models called word embeddings learn by identifying patterns in huge collections of texts. In addition to the structural patterns of the text language, word embeddings learn human biases expressed in the texts. You can read more about this issue in this Brookings Institute report.
There is much debate about the level of bias in systems using artificial intelligence, and some AI researchers worry that this will cause distrust in machine learning systems. Thus, some scientists are keen to emphasise the breadth of their training data across the genders. However, other researchers point out that despite all good intentions, gender disparities are so entrenched in society that we literally are not aware of all of them. White and male dominance in our society may be so unconsciously prevalent that we don’t notice all its effects.
As sociologist Pierre Bourdieu famously asserted in 1977: “What is essential goes without saying because it comes without saying: the tradition is silent, not least about itself as a tradition.” [4]. This view holds that people’s experiences are deeply, or completely, shaped by social conventions, even those conventions that are biased. That means we cannot be sure we have accounted for all disparities when collecting data.
Developers and researchers of AI systems have been trying to establish rules for how to avoid bias in AI models. An example rule set is given in an article in the Harvard Business Review, which describes the fact that speech recognition systems originally performed poorly for female speakers as opposed to male ones, because systems analysed and modelled speech for taller speakers with longer vocal cords and lower-pitched voices (typically men).
The article recommends four ways for people who work in machine learning to try to avoid gender bias:
The above points can help people in the AI industry, which is of course important — but what about the rest of us? It’s important to raise awareness of the issues around gender data bias and AI lest we find out too late that we are reintroducing gender inequalities we have fought so hard to remove. Awareness is a good start, and some other suggestions, drawn out from others’ work in this area are:
Having more women in AI and data science, particularly in both technical and leadership roles, will help to reduce gender bias. A 2020 report by the World Economic Forum (WEF) on gender parity found that women account for only 26% of data and AI positions in the workforce. The WEF suggests five ways in which the AI workforce gender balance could be addressed:
We need high-quality dataset on women and girls, with good coverage, including country coverage. Data needs to be comparable across countries in terms of concepts, definitions, and measures. Data should have both complexity and granularity, so it can be cross-tabulated and disaggregated, following the recommendations from the Data2x project on mapping gender data gaps.
At the Raspberry Pi Foundation we believe that introducing some of the potential (positive and negative) impacts of AI systems to young people through their school education may help to build awareness and understanding at a young age. The jury is out on what exactly to teach in AI education, and how to teach it. But we think educating young people about new and future technologies can help them to see AI-related work opportunities as being open to all, and to develop critical and ethical thinking.
In our AI education seminars we heard a number of perspectives on this topic, and you can revisit the videos, presentation slides, and blog posts. We’ve also been curating a list of resources that can help to further AI education — although there is a long way to go until we understand this area fully.
We’d love to hear your thoughts on this topic.
References
[1] Leavy, S. (2018). Gender bias in artificial intelligence: The need for diversity and gender theory in machine learning. Proceedings of the 1st International Workshop on Gender Equality in Software Engineering, 14–16.
[2] Perez, C. C. (2019). Invisible Women: Exploring Data Bias in a World Designed for Men. Random House.
[3] Buvinic M., Levine R. (2016). Closing the gender data gap. Significance 13(2):34–37
[4] Bourdieu, P. (1977). Outline of a Theory of Practice (No. 16). Cambridge University Press. (p.167)
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In Hello World issue 12, our free magazine for computing educators, George Boukeas, DevOps Engineer for the Astro Pi Challenge here at the Foundation, introduces big moments in the history of artificial intelligence (AI) to share with your learners:
The story of artificial intelligence (AI) is a story about humans trying to understand what makes them human. Some of the episodes in this story are fascinating. These could help your learners catch a glimpse of what this field is about and, with luck, compel them to investigate further.
In 1950, Alan Turing published a philosophical essay titled Computing Machinery and Intelligence, which started with the words: “I propose to consider the question: Can machines think?” Yet Turing did not attempt to define what it means to think. Instead, he suggested a game as a proxy for answering the question: the imitation game. In modern terms, you can imagine a human interrogator chatting online with another human and a machine. If the interrogator does not successfully determine which of the other two is the human and which is the machine, then the question has been answered: this is a machine that can think.
This imitation game is now a fiercely debated benchmark of artificial intelligence called the Turing test. Notice the shift in focus that Turing suggests: thinking is to be identified in terms of external behaviour, not in terms of any internal processes. Humans are still the yardstick for intelligence, but there is no requirement that a machine should think the way humans do, as long as it behaves in a way that suggests some sort of thinking to humans.
In his essay, Turing also discusses learning machines. Instead of building highly complex programs that would prescribe every aspect of a machine’s behaviour, we could build simpler programs that would prescribe mechanisms for learning, and then train the machine to learn the desired behaviour. Turing’s text provides an excellent metaphor that could be used in class to describe the essence of machine learning: “Instead of trying to produce a programme to simulate the adult mind, why not rather try to produce one which simulates the child’s? If this were then subjected to an appropriate course of education one would obtain the adult brain. We have thus divided our problem into two parts: the child-programme and the education process.”
It is remarkable how Turing even describes approaches that have since been evolved into established machine learning methods: evolution (genetic algorithms), punishments and rewards (reinforcement learning), randomness (Monte Carlo tree search). He even forecasts the main issue with some forms of machine learning: opacity. “An important feature of a learning machine is that its teacher will often be very largely ignorant of quite what is going on inside, although he may still be able to some extent to predict his pupil’s behaviour.”
The term ‘artificial intelligence’ was coined in 1956, at an event called the Dartmouth workshop. It was a gathering of the field’s founders, researchers who would later have a huge impact, including John McCarthy, Claude Shannon, Marvin Minsky, Herbert Simon, Allen Newell, Arthur Samuel, Ray Solomonoff, and W.S. McCulloch.
The simple and ambitious definition for artificial intelligence, included in the proposal for the workshop, is illuminating: ‘making a machine behave in ways that would be called intelligent if a human were so behaving’. These pioneers were making the assumption that ‘every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it’. This assumption turned out to be patently false and led to unrealistic expectations and forecasts. Fifty years later, McCarthy himself stated that ‘it was harder than we thought’.
Modern definitions of intelligence are of distinctly different flavour than the original one: ‘Intelligence is the quality that enables an entity to function appropriately and with foresight in its environment’ (Nilsson). Some even speak of rationality, rather than intelligence: ‘doing the right thing, given what it knows’ (Russell and Norvig).
In the full article, which you can read in the free PDF copy of the issue, George looks at:
In your free PDF of Hello World issue 12, you’ll also find:
You can also read many of these articles online on the Hello World website.
In Hello World issue 16, the focus is on all things data science and data literacy for your learners. As always, you can download a free copy of the issue. And on our Hello World podcast, we chat with practicing computing educators about how they bring AI, AI ethics, machine learning, and data science to the young people they teach.
If you want a practical introduction to the basics of machine learning and how to use it, take our free online course.
There are still many open questions about what good AI and data science education looks like for young people. To learn more, you can watch our panel discussion about the topic, and join our monthly seminar series to hear insights from computing education researchers around the world.
We are also collating a growing list of educational resources about these topics based on our research seminars, seminar participants’ recommendations, and our own work. Find the resource list here.
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Between September 2021 and March 2022, we’re partnering with The Alan Turing Institute to host a series of free research seminars about how to teach AI and data science to young people.
In the second seminar of the series, we were excited to hear from Professor Carsten Schulte, Yannik Fleischer, and Lukas Höper from the University of Paderborn, Germany, who presented on the topic of teaching AI and machine learning (ML) from a data-centric perspective. Their talk raised the question of whether and how AI and ML should be taught differently from other themes in the computer science curriculum at school.
The rationale behind the speakers’ work is a concept they call hybrid interaction system, referring to the way that humans and machines interact. To explain this concept, Carsten referred to a 2019 article published in Nature by Iyad Rahwan and colleagues: Machine hehaviour. The article’s authors propose that the study of AI agents (complex and simple algorithms that make decisions) should be a separate, cross-disciplinary field of study, because of the ubiquity and complexity of AI systems, and because these systems can have both beneficial and detrimental impacts on humanity, which can be difficult to evaluate. (Our previous seminar by Mhairi Aitken highlighted some of these impacts.) The authors state that to study this field, we need to draw on scientific practices from across different fields, as shown below:
In establishing their argument, the authors compare the study of animal behaviour and machine behaviour, citing that both fields consider aspects such as mechanism, development, evolution and function. They describe how part of this proposed machine behaviour field may focus on studying individual machines’ behaviour, while collective machines and what they call ‘hybrid human-machine behaviour’ can also be studied. By focusing on the complexities of the interactions between machines and humans, we can think both about machines shaping human behaviour and humans shaping machine behaviour, and a sort of ‘co-behaviour’ as they work together. Thus, the authors conclude that machine behaviour is an interdisciplinary area that we should study in a different way to computer science.
Carsten and his team said that, as educators, we will need to draw on the parameters and frameworks of this machine behaviour field to be able to effectively teach AI and machine learning in school. They argue that our approach should be centred on data, rather than on code. I believe this is a challenge to those of us developing tools and resources to support young people, and that we should be open to these ideas as we forge ahead in our work in this area.
In the interpretation of computational thinking popularised in 2006 by Jeanette Wing, she introduces computational thinking as being about ‘ideas, not artefacts’. When we, the computing education community, started to think about computational thinking, we moved from focusing on specific technology — and how to understand and use it — to the ideas or principles underlying the domain. The challenge now is: have we gone too far in that direction?
Carsten argued that, if we are to understand machine behaviour, and in particular, human-machine co-behaviour, which he refers to as the hybrid interaction system, then we need to be studying artefacts as well as ideas.
Throughout the seminar, the speakers reminded us to keep in mind artefacts, issues of bias, the role of data, and potential implications for the way we teach.
In addition, Carsten highlighted a number of differences between learning ML and learning other areas of computer science, including traditional programming:
These differences imply that our teaching should adapt too.
In Germany, education is devolved to state governments. Although computer science (known as informatics) was only last year introduced as a mandatory subject in lower secondary schools in North Rhine-Westphalia, where Paderborn is located, it has been taught at the upper secondary levels for many years. ProDaBi is a project that researchers have been running at Paderborn University since 2017, with the aim of developing a secondary school curriculum around data science, AI, and ML.
The ProDaBi curriculum includes:
Although the ProDaBi project site is in German, an English translation is available.
Our speakers described example activities from three of the modules:
There was a lot to digest in this seminar: challenging ideas and some new concepts, for me anyway. An important takeaway for me was how much we do not yet know about the concepts and skills we should be teaching in school around AI and ML, and about the approaches that we should be using to teach them effectively. Research such as that being carried out in Paderborn, demonstrating a data-centric approach, can really augment our understanding, and I’m looking forward to following the work of Carsten and his team.
Carsten and colleagues ended with this summary and discussion point for the audience:
“‘AI education’ requires developing an adequate picture of the hybrid interaction system — a kind of data-driven, emergent ecosystem which needs to be made explicitly to understand the transformative role as well as the technological basics of these artificial intelligence tools and how they are related to data science.”
You can catch up on the seminar, including the Q&A with Carsten and his colleagues, here:
This seminar really extended our thinking about AI education, and we look forward to introducing new perspectives from different researchers each month. At our next seminar on Tuesday 2 November at 17:00–18:30 BST / 12:00–13:30 EDT / 9:00–10:30 PDT / 18:00–19:30 CEST, we will welcome Professor Matti Tedre and Henriikka Vartiainen (University of Eastern Finland). The two Finnish researchers will talk about emerging trajectories in ML education for K-12. We look forward to meeting you there.
Carsten and their colleagues are also running a series of seminars on AI and data science: you can find out about these on their registration page.
You can increase your own understanding of machine learning by joining our latest free online course!
[1] Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J. F., Breazeal, C., … & Wellman, M. (2019). Machine behaviour. Nature, 568(7753), 477-486.
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A recent Forbes article reported that over the last four years, the use of artificial intelligence (AI) tools in many business sectors has grown by 270%. AI has a history dating back to Alan Turing’s work in the 1940s, and we can define AI as the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.
Four key areas of AI are machine learning, robotics, computer vision, and natural language processing. Other advances in computing technology mean we can now store and efficiently analyse colossal amounts of data (big data); consequently, data science was formed as an interdisciplinary field combining mathematics, statistics, and computer science. Data science is often presented as intertwined with machine learning, as data scientists commonly use machine learning techniques in their analysis.
AI and data science have recently received huge amounts of attention in the media, as machine learning systems are now used to make decisions in areas such as healthcare, finance, and employment. These AI technologies cause many ethical issues, for example as explored in the film Coded Bias. This film describes the fallout of researcher Joy Buolamwini’s discovery that facial recognition systems do not identify dark-skinned faces accurately, and her journey to push for the first-ever piece of legislation in the USA to govern against bias in the algorithms that impact our lives. Many other ethical issues concerning AI exist and, as highlighted by UNESCO’s examples of AI’s ethical dilemmas, they impact each and every one of us.
So how do such advances in technology impact the education of young people? In the UK, a recent Royal Society report on machine learning recommended that schools should “ensure that key concepts in machine learning are taught to those who will be users, developers, and citizens” — in other words, every child. The AI Roadmap published by the UK AI Council in 2020 declared that “a comprehensive programme aimed at all teachers and with a clear deadline for completion would enable every teacher confidently to get to grips with AI concepts in ways that are relevant to their own teaching.” As of yet, very few countries have incorporated any study of AI and data science in their school curricula or computing programmes of study.
Here at the Raspberry Pi Foundation, AI, machine learning, and data science are important topics both in our learning resources for young people and educators, and in our programme of research. So we are delighted to announce that starting this autumn we are hosting six free, online seminars on the topic of AI, machine learning, and data science education, in partnership with The Alan Turing Institute.
The Alan Turing Institute is the UK’s national institute for data science and artificial intelligence and does pioneering work in data science research and education. The Institute conducts many different strands of research in this area and has a special interest group focused on data science education. As such, our partnership around the seminar series enables us to explore our mutual interest in the needs of young people relating to these technologies.
This promises to be an outstanding series drawing from international experts who will share examples of pedagogic best practice […].
Dr Matt Forshaw, The Alan Turing Institute
Dr Matt Forshaw, National Skills Lead at The Alan Turing Institute and Senior Lecturer in Data Science at Newcastle University, says: “We are delighted to partner with the Raspberry Pi Foundation to bring you this seminar series on AI, machine learning, and data science. This promises to be an outstanding series drawing from international experts who will share examples of pedagogic best practice and cover critical topics in education, highlighting ethical, fair, and safe use of these emerging technologies.”
At our computing education research seminars, we hear from a range of experts in the field and build an international community of researchers, practitioners, and educators interested in this important area. Our new free series of seminars runs from September 2021 to February 2022, with some excellent and inspirational speakers:
All seminars start at 17:00 UK time (18:00 Central European Time, 12 noon Eastern Time, 9:00 Pacific Time) and take place in an online format, with a presentation, breakout discussion groups, and a whole-group Q&A.
Sign up now and we’ll send you the link to join on the day of each seminar — don’t forget to put the dates in your diary!
In the meantime, you can explore some of our educational resources related to machine learning and data science:
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In our brand-new issue of Hello World magazine, editor Gemma Coleman speaks to Kate Farrell from Data Education in Schools to discuss the importance of teaching data to help students navigate the world.
When I was searching for contributors for this issue of Hello World, a pattern quickly began to emerge: “Data? You want to speak to Kate.” Kate Farrell is director of curriculum development and professional learning on the Data Education in Schools project, part of the Data-Driven Innovation Skills Gateway in Scotland. With the project developing teaching materials, professional development, and even qualifications for schools that want to teach data education to learners aged 3–18, “It’s not the kind of role that fits easily on a business card,” she laughs.
The project started in 2019, with the team looking at the Scottish curriculum and mapping out where data could be embedded and how it could be used to support various subjects. “We know that teachers are under stress and won’t be able to deliver extra stuff, so we’re looking to understand how we get better at doing data literacy within the rest of the curriculum,” Kate explains. “How do we provide and support opportunities to look at data in the rest of the curriculum in cool new ways?”
“We like taking topics that you wouldn’t instantly think are about data science.”
The team runs monthly seminars drawing upon this theme, to help teachers see its applicability across all subjects. “We like taking topics that you wouldn’t instantly think are about data science. Yes, the sciences, computer science, and maths are where you would expect it, but there are huge amounts of data and data use in geography, music, social studies, and even PE.”
One example is the DataFit series of lessons for upper primary and lower secondary students, with a mission to simultaneously increase data literacy and physical activity literacy. This includes an introduction to activity-monitoring devices, such as step counters on phones. The lesson has the twin aims of teaching students how monitoring steps or sleep activity can be a positive thing, and also encouraging them to reflect on how they feel about their phone collecting their personal data.
“A lot of students don’t realise their phone is keeping track of their step count, just by virtue of it sitting in their pockets,” Kate muses. “It’s been interesting to see just how little some learners know about the data that’s being kept and tracked about them.”
Data Education in Schools ran a similarly themed workshop for students aged 10–11, with a series of events in an imagined Data Town being examined, to investigate how data can impact our lives. The day started by giving each student a cardboard mobile phone on which they could install apps in the form of stickers if they gave the town certain pieces of information about themselves, such as their favourite colour or football team. “Some kids would just install anything, give up any data, because they wanted the stickers – just like many kids will just download any app,” Kate explains. The apps and associated products then developed as they gathered more data, which was then presented back to the students. The purpose was to get students to reflect on how they felt about the products and how they used their data.
“[…] a series of ‘aha’ moments for students, as they realised what sharing their data meant.”
Later in the workshop, the mayor of Data Town announced that the town had sold the data to an advertising company who wanted to know people’s favourite colour, and to a gym who wanted to know their fitness data to help them decide the location of a new branch. “This meant a series of ‘aha’ moments for students, as they realised what sharing their data meant. Some of the kids who had opted not to collect the stickers were suddenly very smug!”
The project keeps a balance in the story it tells about data, with teaching materials encompassing both the risks of data collection and the huge benefits it can bring. “That is our main aim: how can we help learners use data to make their lives and the lives of their communities better — data for social good.” In the Data Town workshop, students also chose to share data with hospitals and researchers, and later found that this had helped them to develop new medicines. “We didn’t just want to send across the message that sharing data is bad. Yes, you can share your data, but be aware who you’re sharing it with, who you’re trusting with it.”
“How can we help learners use data to make their lives and the lives of their communities better?”
The materials that Data Education in Schools has produced use a framework called PPDAC: Problem, Plan, Data, Analysis, and Conclusion. This is an established approach to statistical literacy, and using this data problem-solving cycle in a real-world context is a powerful way to engage learners with data topics. “The aim is to empower students with the tools to be campaigning, to be making real-world changes to their lives and their communities using data.”
Kate gives a simple example of how a class could look at how much plastic their canteen is using, collecting the data on plastic products and then using that data to make the case to reduce their plastic consumption.
The project has also worked with Scottish exam board SQA to develop a National Progression Award in Data Science; they believe it is the world’s first data science school qualification. The award is aimed at upper secondary students, colleges, and workplaces as an introductory qualification in data science. It carries the same ethos as their materials for younger learners: to help students understand how data is used in society, both negatively and positively, and develop skills to help them make better decisions.
“We need learners to be able to look at the news, and their social media stream, and question what they’re looking at, or ask: where is the evidence?”
“I want people to realise that although data science sounds scary, it’s so important to learners’ lives these days. We’ve seen it with the pandemic. Being able to interpret and analyse data is hugely important. We need learners to be able to look at the news, and their social media stream, and question what they’re looking at, or ask: where is the evidence? This is so important, whether or not they go on to become a data scientist… although we’d love it if they did!”
Issue 16 of Hello World focuses on data science and data literacy; it is full of teaching ideas and inspiration to help you and your students use data to make decisions and to make sense of the world. Also in this issue:
Subscribe now to get each new digital issue straight to your inbox! And if you’re based in the UK and do paid or unpaid work in education, you can subscribe for free print issues.
PS Have you listened to our Hello World podcast yet? Episode 4 has just come out, and it’s great! Listen and subscribe wherever you get your podcasts.
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Over the nine-ish years since the release of our first model, we’ve watched grow a thriving global community of Raspberry Pi enthusiasts, hobbyists, and educators. But did you know that Raspberry Pi is also increasingly used in scientific research?
Dr Jolle Jolles, a behavioural ecologist at the Center for Ecological Research and Forestry Applications (CREAF) near Barcelona, Spain, and a passionate Raspberry Pi user, has recently published a detailed review of the uptake of Raspberry Pi in biological sciences. He found that well over a hundred published studies have made use of Raspberry Pi hardware in some way.
The list of applications is almost endless. Here are just a few:
Jolle’s review shows that use of Raspberry Pi is on the up, with more studies documenting the use of Raspberry Pi hardware every year, but he’s keen to see it employed even more widely.
“It is really great to see the broad range of applications that already exist, with Raspberry Pi’s helping biologists in the lab, the field, and in the classroom. However, Raspberry Pi is still not the common research tool that it could be”.
Jolle Jolles
To stimulate the uptake of Raspberry Pi and help researchers integrate it into their work, the review paper offers guidelines and recommendations. Jolle also maintains a dedicated website with over 30 tutorials: raspberrypi-guide.github.io
“I believe low-cost micro-computers like the Raspberry Pi are a powerful tool that can help transform and democratize scientific research, and will ultimately help push the boundaries of science.”
Jolle Jolles
The paper, Broad-scale Applications of the Raspberry Pi: A Review and Guide for Biologists, is currently under review, but a preprint is available here.
Jolle has also previously published a very handy software package especially with biological scientists in mind. It’s called pirecorder and helps with automated image and video recording using Raspberry Pi. You can check it out here: https://github.com/JolleJolles/pirecorder.
You can keep up with Jolle on Instagram, where he documents all the dreamy outdoor projects he’s working on.
Drop a comment below if you’ve seen an interesting scientific application of Raspberry Pi, at work, on TV, or maybe just in your imagination while you wait to find the time to build it!
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We are delighted to announce that we’re expanding our free Isaac Computer Science online learning platform in response to overwhelming demand from teachers and students for us to cover GCSE content.
Thanks to our contract with England’s Department for Education which is funding our work as part of the National Centre for Computing Education (NCCE) consortium, we’ve been able to collaborate with the University of Cambridge’s Department of Computer Science and Technology to build the Isaac Computer Science platform, and to create an events programme, for A level students and teachers. Now we will use this existing funding to also provide content and events for learning and teaching GCSE computer science.
With content designed by our expert team of computer science teachers and researchers, the Isaac Computer Science platform is already being used by 2000 teachers and 18,000 students at A level. The platform houses a rich set of interactive study materials and reflective questions, providing full coverage of exam specifications.
Within the Teach Computing Curriculum we built as part of our NCCE work, we’ve already created free classroom resources to support teachers with the delivery of GCSE computer science (as well as the rest of the English computing curriculum from Key Stages 1 to 4). Expanding the Isaac Computer Science platform to offer interactive learning content to GCSE students, and running events specifically for GCSE students, will perfectly complement the Teach Computing Curriculum and support learners to continue their computing education beyond GCSE.
We’ll use our tried and tested process of content design, implementation of student and teacher feedback, and continual improvements based on evidence from platform usage data, to produce an educational offering for GCSE computer science that is of the highest quality.
Isaac Computer Science GCSE will support students and teachers of GCSE computer science across the OCR, AQA, Pearson Edexcel, Eduqas, and WJEC exam bodies, covering the whole of the national curriculum. The content will be aimed at ages 14 to 16, and it will be suitable for students of all experience levels and backgrounds — from those who have studied little computer science at Key Stage 3 and are simply interested, to those who are already set to pursue a career related to computer science.
Students will be able to:
Teachers will be able to:
As part of Isaac Computer Science GCSE, we’ll also organise an events programme for GCSE students to get support with specific topics, as well as inspiration about opportunities to continue their computer science education beyond GCSE into A level and higher education or employment.
For teachers, we’ll continue to provide a wide spectrum of free CPD training events and courses through the National Centre for Computing Education.
As is the case for the Isaac Computer Science A level content, we’ll create content for this project to suit the English national curriculum and exam bodies. However, anyone anywhere in the world will be able to access and use the platform for free. The content will be published under an Open Government License v3.0.
Our launch will be in January of 2022, with the full suite of content available by September of 2022.
We’ll be putting out calls to the teaching community in England, asking for your help to guide the design and quality assurance of the Isaac Computer Science GCSE materials.
Follow Isaac Computer Science on social media and sign up on the Isaac Computer Science platform to be the first to hear news!
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Look at our lovely friends over at This is not Rocket Science (TiNRS) – they’ve wasted no time at all in jumping in with our new chips. In this guest post, Stijn of TiNRS shares their fishily musical application of our new toy.
The new RP2040 chip by Raspberry Pi is amazing. When we got our hands on this beautiful little thing, we did what we always do with new chips and slapped on a Goldfish, our favourite acid bassline synthesiser (we make fish and chips, hahahaha).
While benchmarking the performance by copy/pasting instances of our entire Goldfish in search of the chip’s limits, we suddenly found ourselves with a polyphonic synth. We have since rewritten these multiple instances into a 16-voice Poly-Goldfish with 4 oscillators per voice. To celebrate we designed a PCB and brightly coloured frontpanel to give this new Goldfish some dedicated controls.
Bring-up was trivial due to the amazing documentation and the extremely flexible PIO-blocks. RP2040 is a dream to work with. Childlike giddiness ensued while lying on the carpet and programming in VSCode on a Raspberry Pi 400 talking directly to the RP2040. This is the way to release a chip into the world: with fantastic documentation, an open toolchain and plenty of examples of how to use everything.
Once these chips hit general availability we will probably share some designs on our Github. This chip is now part of our go-to set of tools to make cool stuff and will very bloody likely be inside our next three modules.
It fits perfectly in our Open Source attitude. Because of the easy, high quality, multi-platform, free and even beginner-friendly toolchain they have built around this chip, we can expand the accessibility to the insides of our designs. With these chips it is way easier for us to have you do things like adding your own algorithms, building extra modes or creating personal effects. We can lean on the quality of the Raspberry Pi platform and this amazing chip.
TiNRS approves.
Keep an eye on the TiNR blog for more adventures in technology. You can also find them on Twitter @rocket_not and on Instagram.
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A sci-fi writer wanted to add some realism to his fiction. The result: a Raspberry Pi-based Martian timepiece. Rosie Hattersley clocks in from the latest issue of The MagPi Magazine.
Ever since he first clapped eyes on Mars through the eyepiece of a telescope, Philip Ide has been obsessed with the Red Planet. He’s written several books based there and, many moons ago, set up a webpage showing the weather on Mars. This summer, Phil adapted his weather monitor and created a Raspberry Pi-powered Mars Clock.
After writing several clocks for his Mars Weather page, Phil wanted to make a physical clock: “something that could sit on my desk or such like, and tell the time on Mars.” It was to tell the time at any location on Mars, with presets for interesting locations “plus the sites of all the missions that made it to the surface – whether they pancaked or not.”
Another prerequisite was that the clock had to check for new mission file updates and IERS bulletins to see if a new leap second had been factored into Universal Coordinated Time.
“Martian seconds are longer,” explains Phil, “so everything was pointing at software rather than a mechanical device. Raspberry Pi was a shoo-in for the job”. However, he’d never used one.
“I’d written some software for calculating orbits and one of the target platforms was Raspberry Pi. I’d never actually seen it run on a Raspberry Pi but I knew it worked, so the door was already open.” He was able to check his data against a benchmark NASA provided. Knowing that the clocks on his Mars Weather page were accurate meant that Phil could focus on getting to grips with his new single-board computer.
He chose a 2GB Raspberry Pi 4 and official-inch touchscreen with a SmartiPi Touch 2 case. “Angles are everything,” he reasons. He also added a fan to lower the CPU temperature and extend the hardware’s life. Along with a power lead, the whole setup cost £130 from The Pi Hut.
Since his Mars Clock generates a lot of data, he made it skinnable so the user can choose which pieces of information to view at any one time. It can display two types of map – Viking or MOLA – depending on the co-ordinates for the clock. NASA provides a web map-tile service with many different data sets for Mars, so it should be possible to make the background an interactive map, allowing you to zoom in/out and scroll around. Getting these to work proved rather a headache as he hit incompatibilities with the libraries.
Phil wrote most of the software himself, with the exception of libraries for the keyboard and FTP which he pulled from GitHub. Here’s all the code.
He used JavaScript running on the Node.js/Electron framework. “This made for rapid development and is cross-platform, so I could write and test it on Windows and then move it to the Raspberry Pi,” he says. With the basic code written, Phil set about paring it back, reducing the number and duration of CPU time-slices the clock needed when running. “I like optimised software,” he explains.
His decades as a computer programmer meant other aspects were straightforward. The hardware is more than capable, he says of his first ever experience of Raspberry Pi, and the SmartiPi case makers had done a brilliant job. Everything fit together and in just a few minutes his Raspberry Pi was working.
Since completing his Mars Clock Phil has added a pi-hole and a NAS to his Raspberry Pi setup and says his confidence using them is such that he’s now contemplating challenging himself to build an orrery (a mechanical model of the solar system). “I have decades of programming experience, but I was still learning new things as the project progressed,” he says. “The nerd factor of any given object increases exponentially if you make it yourself.”
Check out page 26 in the latest issue of The MagPi Magazine for a step-by-step and to learn more about the maker, Phillip. You can read a PDF copy for free on The MagPi Magazine website if you’re not already a subscriber.
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We’re pleased to share that Dr Sue Sentance, our Chief Learning Officer, is receiving a Suffrage Science award for Mathematics and Computing today.
The Suffrage Science award scheme celebrates women in science. Sue is being recognised for her achievements in computer science and computing education research, and for her work promoting computing to the next generation.
Sue is an experienced teacher and teacher educator with an academic background in artificial intelligence, computer science, and education. She has made a substantial contribution to research in computing education in school over the last ten years, publishing widely on the teaching of programming, teacher professional development, physical computing, and curriculum change. In 2017 Sue received the BERA Public Engagement and Impact Award for her services to computing education. Part of Sue’s role at the Raspberry Pi Foundation is leading our Gender Balance in Computing research programme, which investigates ways to increase the number of girls and young women taking up computing at school level.
As Dr Hannah Dee, the previous award recipient who nominated Sue, says: “[…] The work she does is important — researchers need to look at what happens in schools, particularly when we consider gender. Girls are put off computing long before they get to universities, and an understanding of how children learn about computing and the ways in which we can support girls in tech is going to be vital to reverse this trend.”
Sue says, “I’m delighted and honoured that Hannah nominated me for this award, and to share this honour with other women also dedicated to furthering the fields of mathematics, computing, life sciences, and engineering. It’s been great to see research around computing in school start to gather pace (and also rigour) around the world over the last few years, and to play a part in that. There is still so much to do — many countries have now introduced computing or computer science into their school curricula as a mandatory subject, and we need to understand better how to make the subject fully accessible to all, and to inspire and motivate the next generation.”
Aside from her role in the Gender Balance in Computing research programme, Sue has led our work as part of the consortium behind the National Centre for Computing Education and is now our senior adviser on computing subject knowledge, pedagogy, and the Foundation’s computing education research projects. Sue also leads the programme of our ongoing computing education research seminar series, where academics and educators from all over the world come together online to hear about and discuss some of the latest work in the field.
We are currently inviting primary and secondary schools in England to take part in the Gender Balance in Computing project.
Congratulations from all your colleagues at the Foundation, Sue!
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Meet Callum Fawcett, who shares his journey from tinkering with the first Raspberry Pi while he was at school, to a Master’s degree in computer science and a real-life job in programming. We also get to see some of the awesome projects he’s made along the way.
Most of my learning was done through two sources. I learnt Linux and how the terminal worked using online resources such as Stack Overflow. I would have a problem that I needed to solve, look up solutions online, and try out commands that I found. This was perhaps the hardest part of learning how to use a Raspberry Pi, as it was something I had never done before, but it really helped me in later years when I would use Linux more than Windows. For learning programming, I preferred to use books. I had a book for C++ and a book for Python that I would work through. These were game-based books, so many of the fun projects that I did were simple text-based games where you typed in responses to questions.
The first robot Callum made using a Raspberry Pi
By far the coolest project I did with the Raspberry Pi was to build a small robot (shown above). This was a joint project between myself and my dad. He sorted out the electronics and I programmed the robot. It was a great opportunity to learn about robotics and refine my programming skills. By the end, the robot was capable of moving around by itself, driving into objects, and then reversing and trying a new direction. It was almost like an unintelligent Roomba that couldn’t hoover, but I spent many hours improving small bits and pieces to make it as easy to use as possible. My one wish that I never managed to achieve with my robot was allowing it to map out its surroundings. This was a very ambitious project at the time, since I was still quite inexperienced in programming. The biggest problem with this was calibrating the robot’s turning circle, which was never consistent so it was very hard to have the robot know where in the room it was.
Another fun project that I worked on used the Sense HAT developed for the Astro Pi computers for use on the International Space Station. Using this, I was able to make a memory maze game (shown below), in which a player is shown a maze for several seconds and then has to navigate that maze from memory by shaking the device. This was my first introduction to using more interactive types of input, and this eventually led to my final-year project, which used these interesting interactions to develop another way of teaching.
I have now just finished my Master’s degree in computer science at the University of Bristol. Before going to university, I had no experience of being taught programming in a formal environment. It was not a taught subject at my secondary school or sixth form. I wanted to get more people at my school interested in this area of study though, which I did by running a coding club for people. I would help others debug their code and discuss interesting problems with them. The reason that I chose to study computer science is largely because of my experiences with Raspberry Pi and other programming I did in my own time during my teenage years. I likely would have studied history if it weren’t for the programming I had done by myself making robots and other games.
Raspberry Pi has continued to play a part in my degree and extra-curricular activities; I used them in two large projects during my time at university and used a similar device in my final project. My robot experience also helped me to enter my university’s ‘Robot Wars’ competition which, though we never won, was a lot of fun.
Having a Raspberry Pi is always useful during a hackathon, because it’s such a versatile component. Tech like Raspberry Pi will always be useful for beginners to learn the basics of programming and electronics, but these computers are also becoming more and more useful for people with more experience to make fun and useful projects. I could see tech like Raspberry Pi being used in the future to help quickly prototype many types of electronic devices and, as they become more powerful, even being used as an affordable way of controlling many types of robots, which will become more common in the future.
Our guest blogger Callum
Now I am going on to work on programming robot control systems at Ocado Technology. My experiences of robot building during my years before university played a large part in this decision. Already, robots are becoming a huge part of society, and I think they are only going to become more prominent in the future. Automation through robots and artificial intelligence will become one of the most important tools for humanity during the 21st century, and I look forward to being a part of that process. If it weren’t for learning through Raspberry Pi, I certainly wouldn’t be in this position.
Cheers for your story, Callum! Has tinkering with our tiny computer inspired your educational or professional choices? Let us know in the comments below.
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Did you know: the first machine to break the exaflop barrier (one quintillion floating‑point operations per second) wasn’t a huge dedicated IBM supercomputer, but a bunch of interconnected PCs with ordinary CPUs and gaming GPUs.
With that in mind, welcome to the Folding@home project, which is targeting its enormous power at COVID-19 research. It’s effectively the world’s fastest supercomputer, and your PC can be a part of it.
The Folding@home project is now targeting COVID-19 research
Put simply, Folding@home runs hugely complicated simulations of protein molecules for medical research. They would usually take hundreds of years for a typical computer to process. However, by breaking them up into smaller work units, and farming them out to thousands of independent machines on the Internet, it’s possible to run simulations that would be impossible to run experimentally.
Back in 2004, Custom PC magazine started its own Folding@home team. The team is currently sitting at number 12 on the world leaderboard and we’re still going strong. If you have a PC, you can join us (or indeed any Folding@home team) and put your spare clock cycles towards COVID-19 research.
Getting your machine folding is simple. First, download the client. Your username can be whatever you like, and you’ll need to put in team number 35947 to fold for the Custom PC & bit-tech team. If you want your PC to work on COVID-19 research, select ‘COVID-19’ in the ‘I support research finding’ pulldown menu.
Enter team number 35947 to fold for the Custom PC & bit-tech team
You’ll get the most points per Watt from GPU folding, but your CPU can also perform valuable research that can’t be done on your GPU. ‘There are actually some things we can do on CPUs that we can’t do on GPUs,’ said Professor Greg Bowman, Director of Folding@home, speaking to Custom PC in the latest issue.
‘With the current pandemic in mind, one of the things we’re doing is what are called “free energy calculations”. We’re simulating proteins with small molecules that we think might be useful starting points for developing therapeutics, for example.’
If you want your PC to work on COVID-19 research, select ‘COVID-19’ in the ‘I support research finding’ pulldown menu
Bear in mind that enabling folding on your machine will increase power consumption. For reference, we set up folding on a Ryzen 7 2700X rig with a GeForce GTX 1070 Ti. The machine consumes around 70W when idle. That figure increases to 214W when folding on the CPU and around 320W when folding on the GPU as well. If you fold a lot, you’ll see an increase in your electricity bill, so keep an eye on it.
Could we also see Folding@home running on Arm machines, such as Raspberry Pi? ‘Oh I would love to have Folding@home running on Arm,’ says Bowman. ‘I mean they’re used in Raspberry Pis and lots of phones, so I think this would be a great future direction. We’re actually in contact with some folks to explore getting Folding@home running on Arm in the near future.’
In the meantime, you can still recruit your Raspberry Pi for the cause by participating in Rosetta@home, a similar project also working to help the fight against COVID-19. For more information, visit the Rosetta@home website.
You’ll also find a full feature about Folding@home and its COVID-19 research in Issue 202 of Custom PC, available from the Raspberry Pi Press online store.
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Raspberry Pi devices are often used by scientists, especially in biology to capture and analyse data, and a particularly striking – and sobering – project has made the news this week. Researchers at UMass Amherst have created FluSense, a dictionary-sized piece of equipment comprising a cheap microphone array, a thermal sensor, an Intel Movidius 2 neural computing engine, and a Raspberry Pi. FluSense monitors crowd sounds to forecast outbreaks of viral respiratory disease like seasonal flu; naturally, the headlines about their work have focused on its potential relevance to the COVID-19 pandemic.
Forsad Al Hossain and Tauhidur Rahman with the FluSense device. Image courtesy of the University of Massachusetts Amherst
The device can distinguish coughing from other sounds. When cough data is combined with information about the size of the crowd in a location, it can provide an index predicting how many people are likely to be experiencing flu symptoms.
It was successfully tested in in four health clinic waiting rooms, and now, PhD student Forsad Al Hossain and his adviser, assistant professor Tauhidur Rahman, plan to roll FluSense out in other large spaces to capture data on a larger scale and strengthen the device’s capabilities. Privacy concerns are mitigated by heavy encryption, and Al Hossain and Rahman explain that the emphasis is on aggregating data, not identifying sickness in any single patient.
The researchers believe the secret to FluSense’s success lies in how much of the processing work is done locally, via the neural computing engine and Raspberry Pi: “Symptom information is sent wirelessly to the lab for collation, of course, but the heavy lifting is accomplished at the edge.”
Image courtesy of the University of Massachusetts Amherst
FluSense offers a different set of advantages to other tools, such as the extremely popular self-reporting app developed by researchers at Kings College Hospital in London, UK, together with startup Zoe. Approaches like this rely on the public to sign up, and that’s likely to skew the data they gather, because people in some demographic groups are more likely than others to be motivated and able to participate. FluSense can be installed to capture data passively from groups across the entire population. This could be particularly helpful to underprivileged groups who are less likely to have access to healthcare.
Makers, engineers, and scientists across the world are rising to the challenge of tackling COVID-19. One notable initiative is the Montreal General Hospital Foundation’s challenge to quickly design a low-cost, easy to use ventilator which can be built locally to serve patients, with a prize of CAD $200,000 on offer. The winning designs will be made available to download for free.
There is, of course, loads of chatter on the Raspberry Pi forum about the role computing has in beating the virus. We particularly liked this PSA letting you know how to free up some of your unused processing power for those researching treatments.
Screenshot via @deeplocal on Instagram
And to end on a cheering note, we *heart* this project from @deeplocal on Instagram. They’ve created a Raspberry Pi-powered soap dispenser which will play 20 seconds of your favourite song to keep you at the sink and make sure you’re washing your hands for long enough to properly protect yourself.
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The Edwards Lab at the University of Reading has developed a flexible, low-cost, open source lab robot for capturing images of microbiology samples with a Raspberry Pi camera module. It’s called POLIR, for Raspberry Pi camera Open-source Laboratory Imaging Robot. Here’s a timelapse video of them assembling it.
The robot is useful for all kinds of microbiology imaging, but at the moment the lab is using it to measure antimicrobial resistance in bacteria. They’re doing this by detecting the colour change in a dye called resazurin, which changes from blue to pink in the presence of metabolically active cells: if bacteria incubated with antibiotics grow, their metabolic activity causes the dye to turn pink. However, if the antibiotics stop or impede the growth of the bacteria, their lower levels of metabolic activity will cause less colour change, or none at all. In the photo below, the colourful microtitre plate holds bacterial samples with and without resistance to the antibiotics against which they’re being tested.
POLIR, an open source 3D printer-based Raspberry Pi lab imaging robot
The researchers adapted existing open source 3D printer designs and used v-slot aluminium extrusion (this stuff) with custom 3D-printed joints to make a frame. Instead of a printer extrusion head, a Raspberry Pi and camera module are mounted on the frame. An Arduino running open-source Repetier software controls x-y-z stepper motors to adjust the position of the computer and camera.
Front and top views of POLIR
Open-source OctoPrint software controls the camera position by supplying scripts from the Raspberry Pi to the Arduino. OctoPrint also allows remote access and control, which gives researchers flexibility in when they run experiments and check progress. Images are acquired using a Python script configured with the appropriate settings (eg image exposure), and are stored on the Raspberry Pi’s SD card. From there, they can be accessed via FTP.
Off-the-shelf lab automation systems are extremely expensive and remain out of the reach of most research groups. POLIR cost just £600.
The system has a number of advantages over higher-cost off-the-shelf imaging systems. One is its flexibility: the robot can image a range of sample formats, including agar plates like those in the video above, microtitre plates like the one in the first photograph, and microfluidic “lab-on-a-comb” devices. A comb looks much like a small, narrow rectangle of clear plastic with striations running down its length; each striation is a microcapillary with capacity for a 1μl sample, and each comb has ten microcapillaries. These microfluidic devices let scientists run experiments on a large number of samples at once, while using a minimum of space on a lab bench, in an incubator, or in an imaging robot like POLIR.
POLIR accommodates 2160 individual capillaries and a 96 well plate, with room to spare
For lab-on-a-comb images, POLIR gives the Reading team four times the spatial resolution they get with a static camera. The moveable Raspberry Pi camera with a short focus yields images with 6 pixels per capillary, compared to 1.5 pixels per capillary using a $700 static Canon camera with a macro lens.
Because POLIR is automated, it brings higher temporal resolution within reach, too. A non-automated system, by contrast, can only be used for timelapse imaging if a researcher repeatedly intervenes at fixed time intervals. Capturing kinetic data with timelapse imaging is valuable because it can be significant if different samples reach the same endpoint but at different rates, and because some dyes can give a transient signal that would be missed by an endpoint measurement alone.
Dr Alexander Edwards of the University of Reading comments:
We built the robot with a simple purpose, to make antimicrobial resistance testing more robust without resorting to expensive and highly specialised lab equipment […] The beauty of the POLIR kit is that it’s based on open source designs and we have likewise published our own designs and modifications, allowing everyone and anyone to benefit from the original design and the modifications in other contexts. We believe that open source hardware is a game changer that will revolutionise microbiological and other life science lab work by increasing data production whilst reducing hands-on labour time in the lab.
You can find POLIR on GitLab here. You can also read more, and browse more figures, in the team’s open-access paper, Exploiting open source 3D printer architecture for laboratory robotics to automate high-throughput time-lapse imaging for analytical microbiology.
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Homes in Madrid, Dublin, Cardiff, Ljubljana, and Leuven are participating in the Citizens Observing UrbaN Transport (WeCount) project, a European Commission–funded research project investigating sustainable economic growth.
1,500 Raspberry Pi traffic sensors will be distributed to homes in the five cities to gather data on traffic conditions. Every hour, the devices will upload information to publically accessible cloud storage. The team behind WeCount says:
Following this approach, we will be able to quantify local road transport (cars, heavy goods vehicles, active travel modes, and speed), produce scientific knowledge in the field of mobility and environmental pollution, and co-design informed solutions to tackle a variety of road transport challenges.
“With air pollution being blamed for 500,000 premature deaths across the continent in 2018,” states a BBC News article about the project, “the experts running the survey hope their results can be used to make cities healthier places to live.” Says the WeCount team:
[T]he project will provide cost-effective data for local authorities, at a far greater temporal and spatial scale than what would be possible in classic traffic counting campaigns, thereby opening up new opportunities for transportation policy making and research.
Find more information about the WeCount project on the BBC News website and on the the CORDIS website.
The small form factor and low cost of Raspberry Pi mean it’s the ideal brain for citizen science projects across the globe, including our own Raspberry Pi Oracle Weather Station.
While the original Oracle Weather Station programme involved only school groups from across the world, we’ve published freely accessible online guides to building your own Raspberry Pi weather station, and to uploading weather data to the Initial State platform.
Another wonderful Raspberry Pi–powered citizen science project is Penguin Watch, which asks the public to, you guessed it, watch penguins. Time-lapse footage — obtained in the Antarctic by Raspberry Pi Camera Modules connected to Raspberry Pi Zeros — is uploaded to the Penguin Watch website, and anyone in the world can go online to highlight penguins in the footage, helping the research team to monitor the penguin population in these locations.
Setting up. Credit: Alasdair Davies, ZSL
Penguin Watch is highly addictive and it’s for a great cause, so be sure to check it out.
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In Hello World issue 11, Pen Holland and Sarah Wyse discuss how educators and students can get closer to the natural world while honing maths and computing skills. Using a Raspberry Pi, you too can join this citizen science collaboration.
Connectedness to nature as measured by the Nature Connection Index is currently the lowest in young people aged 16-24, with everyone aged 8-34 reporting lower connectedness, compared to the 35+ age groups.
Although there is some positive correlation between individuals living in the same households, parents are now less likely to raise their children where they grew up themselves, and as such they may be less knowledgeable about local species. Connecting with nature does not have to mean a trip out into the wilds: urban ecology is increasingly popular in research, and even the most determined of city dwellers is likely to pass a municipal tree or two during their day.
The positive association between connectedness to nature and wellbeing should encourage us all to appreciate and explore our local environments. However, being at one with the natural world doesn’t preclude an abundance of enjoyable science and technology. For example, the authors’ overriding memory of GCSE maths involves triangles – a lot of triangles – combined with frequent musings over how this could possibly ever be useful in the real world. Fast forward 20 years, and we’ve spent more time than we’d like to count surrounded by triangles, chanting ‘SOH CAH TOA’ in the name of ecology.
The Seed Eater project arose from research into how fast winged seeds (samaras) fall, in order to predict how far they might travel across a landscape, and hence understand how quickly populations of invasive trees might spread. In the past, ecologists have measured the terminal velocity of seeds using stopwatches and lasers, but stopwatches are inaccurate, and lasers are expensive.
Timestamped images in which the seed appears tell us the time taken for it to fall through the field of view (A). The distance at which the seed lands from the wall (B) and the viewing angle of the camera (C) are used to calculate distance travelled by the seed while in view. Finally, the speed at which the seed is travelling can be calculated as distance/time.
Enter stage left, Pieter the Seed Eater; a low-cost device fitted with a Raspberry Pi computer and camera that captures a sequence of images, assesses which timestamped images contain a falling seed, and then calculates how far the seed fell, and hence how fast it was travelling.
Pieter the Seed Eater was introduced in issue 10 of Hello World, and if you missed that, you can download a free PDF copy of the magazine from the website.
Pieter the Seed Eater was designed to measure the terminal velocity of pine (Pinus species) seeds from invasive trees in New Zealand, with a particular interest in the variation in falling speeds among seeds from the same cones, between different cones on the same tree, between trees in the same population, and between populations across the landscape. His diet is now expanding to take in a whole range of pine species, but there are many other species of tree around the world that also have winged seeds, in a variety of fascinating shapes.
To help emphasise the connections between nature and STEM, and because Pieter doesn’t have time to eat all the seeds, we are making cross-curricular resources available to support teaching activities. These range from tree identification and seed collection, through seed dispersal experiments and Seed Eater engineering, to terminal velocity measurements and understanding population spread.
There are several ways to measure tree height, which can be a stimulating discussion and activity. Fire arrows attached to string over high branches, go exploring on Google street view, or use trigonometry, making measurements in a variety of simple or sophisticated ways. Are they all equally accurate? Would they all work on isolated trees and in a dense forest?
These draw on links from elsewhere (for example, the tree identification keys provided by the Natural History Museum, and helicopter seed templates hosted by STEM Learning UK), as well as new material designed specifically for Pieter the Seed Eater, and more general cross-curricular activities related to ecology. In addition, participants can contribute their data to an online database and explore questions about their data using visualisation tools for dispersal equations and population spread.
The teaching resources fall into four main categories:
Each section contains background information, suggested activities for groups and individuals, data recording sheets, and stretch activities for students to carry out in class or at home. The resources are provided as Google slides under a Creative Commons license so that you can edit and adapt them for your own educational needs, with links to the National Curriculum highlighted throughout (thanks to Mary Howell, professional development leader at STEM Learning UK) and interactive graphics hosted online to help understand some of the concepts and equations more easily. Python code for the Seed Eater can be downloaded or written from scratch (or in Scratch!), so that you can set up the device or let students engineer it from first principles. It will need some calibration, but that is all part of the learning experience, and the resources come with some troubleshooting ideas to get started.
Relevant resources are available here. These are currently aimed at Key Stage 3 (age 11-14) and 4 (14-16), but will be developed and extended as time passes, feedback is incorporated, and new requests are made.
Ultimately, we would like to reach Key Stage 1 to sixth form and beyond, and develop the project into a citizen science collaboration in which people around the world share information about their local trees and seeds with the global community.
We welcome feedback and engagement with the project from anyone who is interested in taking part – get in touch via Twitter or email pen.holland@york.ac.uk.
Hello World is available now as a FREE PDF download. UK-based educators can also subscribe to receive Hello World directly to their door in all its shiny printed goodness. Visit the Hello World website for more information.
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Engineering has always been important, but never more so than now, as we face global challenges and need more brilliant young minds to solve them. Tim Peake, ESA astronaut and one of our Members, knows this well, and is a big advocate of engineering, and of STEM more broadly.
That’s why during his time aboard the International Space Station for the Principia mission, Tim was involved in the deployment of two Astro Pis, special Raspberry Pi computers that have been living on the ISS ever since, making it possible for us to run our annual European Astro Pi Challenge.
Tim spoke about the European Astro Pi Challenge at today’s award ceremony
Tim played a huge part in the first Astro Pi Challenge, and he has helped us spread the word about Astro Pi and the work of the Raspberry Pi Foundation ever since.
Earlier this year, Tim was awarded the 2019 Royal Academy of Engineering Rooke Award for his work promoting engineering to the public, following a nomination by Raspberry Pi co-founder and Fellow of the Academy Pete Lomas. Pete says:
“As part of Tim Peake’s Principia mission, he personally spearheaded the largest education and outreach initiative ever undertaken by an ESA astronaut. Tim actively connects space exploration with the requirement for space engineering.
As a founder of Raspberry Pi, I was thrilled that Tim acted as a personal ambassador for the Astro Pi programme. This gives young people across Europe the opportunity to develop their computing skills by writing computer programs that run on the specially adapted Raspberry Pi computers onboard the ISS.” – Pete Lomas
Today, Tim received the Rooke Award in person, at a celebratory event held at the Science Museum in London.
Royal Academy of Engineering CEO Dr Hayaatun Sillem presents Tim with the 2019 Rooke Award for public engagement with engineering, in recognition of his nationwide promotion of engineering and space
Four hundred young people got to attend the event with him, including two winning Astro Pi teams. Congratulations to Tim, and congratulations to those Astro Pi winners who got to meet a real-life astronaut!
Since Tim’s mission on the ISS, the Astro Pi Challenge has evolved, and in collaboration with ESA Education, we now offer it in the form of two missions for young people every year:
…then help the young people in your life participate! Mission Zero is really simple and requires no prior coding knowledge, neither from you, nor from the young people in your team. Or your team could take part in Mission Space Lab — you’ve still got 10 days to send us your team’s experiment idea! And then, who knows, maybe your team will get to meet Tim Peake one day… or even become astronauts themselves!
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Plant scientists and agronomists use growth chambers to provide consistent growing conditions for the plants they study. This reduces confounding variables – inconsistent temperature or light levels, for example – that could render the results of their experiments less meaningful. To make sure that conditions really are consistent both within and between growth chambers, which minimises experimental bias and ensures that experiments are reproducible, it’s helpful to monitor and record environmental variables in the chambers.
Arabidopsis thaliana in a growth chamber on the International Space Station. Many experimental plants are less well monitored than these ones.
(“Arabidopsis thaliana plants […]” by Rawpixel Ltd (original by NASA) / CC BY 2.0)
In a recent paper in Applications in Plant Sciences, Brandin Grindstaff and colleagues at the universities of Missouri and Arizona describe how they developed Growth Monitor pi, or GMpi: an affordable growth chamber monitor that provides wider functionality than other devices. As well as sensing growth conditions, it sends the gathered data to cloud storage, captures images, and generates alerts to inform scientists when conditions drift outside of an acceptable range.
Appl Plant Sci. 2019 Aug; 7(8): e11280. Figure 1, © 2019 Grindstaff et al. / CC BY 4.0
The authors emphasise – and we heartily agree – that you don’t need expertise with software and computing to build, use, and adapt a system like this. They’ve written a detailed protocol and made available all the necessary software for any researcher to build GMpi, and they note that commercial solutions with similar functionality range in price from $10,000 to $1,000,000 – something of an incentive to give the DIY approach a go.
GMpi uses a Raspberry Pi Model 3B+, to which are connected temperature-humidity and light sensors from our friends at Adafruit, as well as a Raspberry Pi Camera Module.
Appl Plant Sci. 2019 Aug; 7(8): e11280. Figure 2, © 2019 Grindstaff et al. / CC BY 4.0
The team used open-source app Rclone to upload sensor data to a cloud service, choosing Google Drive since it’s available for free. To alert users when growing conditions fall outside of a set range, they use the incoming webhooks app to generate notifications in a Slack channel. Sensor operation, data gathering, and remote monitoring are supported by a combination of software that’s available for free from the open-source community and software the authors developed themselves. Their package GMPi_Pack is available on GitHub.
Appl Plant Sci. 2019 Aug; 7(8): e11280. Figure 3, © 2019 Grindstaff et al. / CC BY 4.0
With a bill of materials amounting to something in the region of $200, GMpi is another excellent example of affordable, accessible, customisable open labware that’s available to researchers and students. If you want to find out how to build GMpi for your lab, or just for your greenhouse, Affordable remote monitoring of plant growth in facilities using Raspberry Pi computers by Brandin et al. is available on PubMed Central, and it includes appendices with clear and detailed set-up instructions for the whole system.
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Low-cost open labware is a good thing in the world, and I was particularly pleased when micropalaeontologist Martin Tetard got in touch about the Raspberry Pi-based microscope he is developing. The project is called microscoPI (what else?), and it can capture, process, and store images and image analysis results. Martin is engaged in climate research: he uses microscopy to study tiny fossil remains, from which he gleans information about the environmental conditions that prevailed in the far-distant past.
microscoPI: a microcomputer-assisted microscope
microscoPI a project that aims to design a multipurpose, open-source and inexpensive micro-computer-assisted microscope (Raspberry PI 3). This microscope can automatically take images, process them, and save them altogether with the results of image analyses on a flash drive. It it multipurpose as it can be used on various kinds of images (e.g.
Martin repurposed an old microscope with a Z-axis adjustable stage for accurate focusing, and sourced an inexpensive X/Y movable stage to allow more accurate horizontal positioning of samples under the camera. He emptied the head of the scope to install a Raspberry Pi Camera Module, and he uses an M12 lens adapter to attach lenses suitable for single-specimen close-ups or for imaging several specimens at once. A Raspberry Pi 3B sits above the head of the microscope, and a 3.5-inch TFT touchscreen mounted on top of the Raspberry Pi allows the user to check images as they are captured and processed.
The Raspberry Pi runs our free operating system, Raspbian, and free image-processing software ImageJ. Martin and his colleagues use a number of plugins, some developed themselves and some by others, to support the specific requirements of their research. With this software, microscoPI can capture and analyse microfossil images automatically: it can count particles, including tiny specimens that are touching, analyse their shape and size, and save images and results before prompting the user for the name of the next sample.
microscoPI is compact – less than 30cm in height – and it’s powered by a battery bank secured under the base of the microscope, so it’s easily portable. The entire build comes in at under 160 Euros. You can find out more, and get in touch with Martin, on the microscoPI website.
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We are delighted to co-launch Isaac Computer Science, a new online platform for teachers and students of A level Computer Science.
Introducing Isaac Computer Science
Introducing the new Isaac Computer Science online learning platform and calendar of free events for students and teachers. Be the first to know about new features and content on the platform: Twitter – ncce.io/ytqstw Instagram – ncce.io/ytqsig Facebook – ncce.io/ytqsfb If you are a teacher, you may also be interested in our free online training courses for GCSE Computer Science teachers.
The project is a collaboration between the Raspberry Pi Foundation and the University of Cambridge, and is funded by the Department for Education’s National Centre for Computing Education programme.
Isaac Computer Science gives you access to a huge range of online learning materials for the classroom, homework, and revision — all for free.
The platform’s resources are mapped to the A level specifications in England (including the AQA and OCR exam boards). You’ll be able to set assignments for your students, have the platform mark it for you, and be confident that the content is relevant and high quality. We are confident that this will save you time in planning lessons and setting homework.
“Computer Science is a relatively small subject area and teachers across the country often work alone without the support of colleagues. Isaac Computer Science will build a teaching and learning community to support teachers at all levels and will offer invaluable support to A level students in their learning journey. As an experienced teacher, I am very excited to have the opportunity to work on this project.”
– Diane Dowling, Isaac Computer Science Learning Manager and former teacher
And that’s not all! To further support you, we are also running free student workshops and teacher CPD events at universities and schools around England. Tickets for the events are available to book through the Isaac Computer Science website.
“Isaac Computer Science helped equip me with the skills to teach A level, and ran a great workshop at one of their recent Discovery events using the micro:bit and the Kitronik :MOVE mini. This is a session that I’ll definitely be using again and again.”
– James Spencer, Computer Science teacher at St Martin’s School
A teacher works with her students at our recent Discovery event in Cambridge.
Isaac Computer Science provides:
Isaac Computer Science allows you to:
Start using Isaac Computer Science today:
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Today is GCSE results day, and with it comes the usual amount of excitement and trepidation as thousands of young people in the UK find out whether they got the grades they wanted. So here’s a massive CONGRATULATIONS from everyone at the Raspberry Pi Foundation to all the students out there who have worked so hard to get their GCSEs, A levels, BTECs, IBs, and a host of other qualifications.
We also want to highlight the efforts of the amazing teachers who have spent countless hours thinking up new ways to bring their subjects to life and inspire the next generation.
Looking at the initial data from the Department for Education, it’s clear that:
From L to R: Rebecca Franks, Allen Heard, Ben Garside, Carrie Anne Philbin
I caught up with four former teachers on our team to reflect on these findings and their own experiences of results days…
What thoughts and emotions are going through your head as a teacher on results day?
Ben: It’s certainly a nerve-wracking time! You hope that your students have reached the potential that you know that they are capable of. You log onto the computer the second you wake up to see if you’ve got access to the exam boards results page yet. It was always great being there to see their faces, to give them a high five, and to support them with working out their options going forward.
Rebecca: I think that head teachers want you to be worried about targets and whether you’ve met them, but as a teacher, when you look at each individual students’ results, you see their journey, and you know how much effort they’ve put in. You are just really proud of how well they have done, and it’s lovely to have those post-results conversations and celebrate with them. It makes it all worth it.
Allen: I liken the feeling to that of an expectant father! You have done as much as you can to make sure things run smoothly, you’ve tried to keep all those involved calm, and now the moment is here and you just want everything to be OK.
Carrie Anne: As a teacher, I always felt both nerves and excitement for results day, probably more so than my students did. Sleepless nights in the run-up to the big day were common! But I always enjoyed seeing my students, who I’d worked with since they were youngsters, see the culmination of their hard work into something useful. I always felt proud of them for how far they’d come.
There has been an increased uptake of students taking computing-related subjects at GCSE since last year. What do you think about this?
Ben: It’s great news and shows that schools are realising how important the subject is to prepare our young people for the future workplace.
Carrie Anne: It’s a sign that our message — that all students should have access to a Computing qualification of rigour, and that there is a willing and ready audience hungry for the opportunity to study Computing at a deeper level — is making traction. My hope is to see this number increase as teachers take part in the free National Centre for Computing Education professional development and certification over the coming years.
Rebecca: I think it’s a step in the right direction, but we definitely have a long way to go. We must make sure that computing is at the forefront of any curriculum model in our secondary schools, which is why the National Centre for Computing Education is so important. In particular, we must support schools in ensuring that KS3 computing is given the time it needs to give students the grounding for GCSE.
Allen: I agree with Rebecca: more needs to be done about teacher training and helping schools see the overall benefit to students in undertaking such subjects. Schools that are investing time in nurturing these subjects in their curriculum provision are seeing them become more popular and enjoying success. Patience is the key for senior leadership teams, and teachers need support and to have confidence in their ability to continue to deliver the subject.
Why is it important that more students learn about computing?
Rebecca: Computing feeds into so much of our everyday lives, and we must prepare our young people for a world that doesn’t exist yet. Computing teaches you logical thinking and problem-solving. These skills are transferable and can be used in all sorts of situations. Computing also teaches you essential digital literacy skills that can help you keep safe whilst using online tools.
Ben: For me, it’s really important that young people pick this subject to help them understand the world around them. They’ll hopefully then be able to see the potential of computing as a power for good and harness it, rather than becoming passive consumers of technology.
Carrie Anne: Following on from what Ben said, I also think it’s important that technology developed in the future reflects the people and industries using it. The tech industry needs to become more diverse in its workforce, and non-technical fields will begin to use more technology in the coming years. If we equip young people with a grounding in computing, they will be equipped to enter these fields and find solutions to technical solutions without relying on a small technical elite.
Imagine I’m a GCSE student who has just passed my Computer Science exams. What resources should I look at if I want to learn more about computing with the Raspberry Pi Foundation for free?
Rebecca: Isaac Computer Science would be the best place to start, because it supports students through their A level Computer Science. If you wanted to experiment and try some physical computing, then you could take a look at the Projects page of the Raspberry Pi Foundation website. You can filter this page by ‘Software type: Python’ and find some ideas to keep you occupied!
Allen: First and foremost, I would advise you to keep your hard-earned coding skills on point, as moving on to the next level of complexity can be a shock. Now is the time to start building on your already sound knowledge and get prepared for A level Computer Science in September. Isaac Computer Science would be a great place to start to undertake some further learning over the summer and prime yourself for further study.
Ben: Same as Rebecca and Allen, I’d be telling you to get started with Isaac Computer Science too. The resources that are being provided for free are second to none, and will really help you get a good feel for what A level Computer Science is all about.
Carrie Anne: Beyond the Raspberry Pi projects site and Isaac Computer Science, I’d recommend getting some face-to-face experience. Every year the Python community holds a conference that’s open to everyone. It’s a great opportunity to meet new people and learn new skills. PyConUK 2019 is taking place in September and has bursaries to support people in full-time education to attend.
We’ve been working on providing support for secondary and GCSE teachers as part of the National Centre for Computing Education this year. Could you talk about the support we’ve got available?
Allen: We’re producing resources to cover the whole range of topics that appear in all the Computing/Computer Science specifications. The aim of these resources is to provide teachers — both experienced and new to the subject — with the support they need to deliver quality, engaging lessons. Founded on sound pedagogical principles and created by a number of well-established teachers, these resources will help reduce workload and increase productivity for teachers, and increase engagement of students. This will ultimately result in some fantastic out-turns for schools, as well as developing confident computing teachers along the way.
Rebecca: As Allen explained, we are busy creating new, free teaching resources for KS3 and GCSE. The units will cover the national curriculum and beyond, and the lessons will be fully resourced. They will be accessible to teachers with varying levels of experience, and there will be lots of support along the way through online courses and face-to-face training if teachers want to know more. Teachers can already take our ‘CS Accelerator’ programme, which is extremely popular and has excellent reviews.
Thanks for your time, everyone!
How was your GCSE results day? Are your students, or young people you know, receiving their results today? Tell us about it in the comments below.
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In an effort to save themselves and fellow biologists hours of time each week, Team IoHeat are currently prototyping a device that allows solutions to be heated while they are still in cold storage.
The IoHeat team didn’t provide any photos with their project writeup, so here’s a picture of a bored biologist that I found online
As they explain in their prototype write-up:
As scientists working with living organisms (from single cells to tissue samples), we are often required to return to work outside of normal hours to maintain our specimens. In many cases, the compounds and solutions we are using in our line of work are stored at 4°C and need to reach 37°C before they can be used. So far, in order to do this we need to return to our workplace early, incubate our solutions at 37°C for 1–2h, depending on the required volume, and then use them in processes that often take a few minutes. It is clear that there is a lot of room here to improve our efficiency.
These hours wasted on waiting for solutions to heat up could be better spent elsewhere, so the team is building a Raspberry Pi–powered device that will allow them to control the heating process remotely.
We are aiming to built a small incubator that we can store in a cold room/fridge, and that can be activated remotely to warm up to a defined temperature. This incubator will enable us to safely store our reagents at low temperature and warm them up remotely before we need to use them, saving an estimate of 12h per week per user.
This is a great project idea, and they’ve already prototyped it using a Raspberry Pi, heating element, and fan. Temperature and humidity sensors connected to the Raspberry Pi monitor conditions inside the incubator, and the prototype can be controlled via Telegram.
Find out more about the project on Hackster.
We’ve got more than one biologist on the Raspberry Pi staff, so we have a personal appreciation for the effort behind this project, and we look forward to seeing how IoHeat progresses in the future.
The post Saving biologists’ time with Raspberry Pi appeared first on Raspberry Pi.
Today, ESA Education and the Raspberry Pi Foundation are proud to celebrate the International Day of Women and Girls in Science! In support of this occasion and to encourage young women to enter a career in STEM (science, technology, engineering, mathematics), CSA astronaut Jenni Sidey discusses why she believes computing and digital making skills are so important, and tells us about the role models that inspired her.
Jenni Sidey inspires young women in science with Astro Pi
Today, ESA Education and the Raspberry Pi Foundation are proud to celebrate the International Day of Women and Girls in Science! In support of this occasion and to encourage young women to enter a career in STEM (science, technology, engineering, mathematics), CSA astronaut Jenni Sidey discusses why she believes computing and digital making skills are so important, and tells us about the role models that inspired her.
The International Day of Women and Girls in Science is part of the United Nations’ plan to achieve their 2030 Agenda for Sustainable Development. According to current UNESCO data, less than 30% of researchers in STEM are female and only 30% of young women are selecting STEM-related subjects in higher education
That’s why part of the UN’s 2030 Agenda is to promote full and equal access to and participation in science for women and girls. And to help young women and girls develop their computing and digital making skills, we want to encourage their participation in the European Astro Pi Challenge!
The European Astro Pi Challenge is an ESA Education programme run in collaboration with the Raspberry Pi Foundation that offers students and young people the amazing opportunity to conduct scientific investigations in space! The challenge is to write computer programs for one of two Astro Pi units — Raspberry Pi computers on board the International Space Station.
Astro Pi’s Mission Zero is open until 20 March 2019, and this mission gives young people up to 14 years of age the chance to write a simple program to display a message to the astronauts on the ISS. No special equipment or prior coding skills are needed, and all participants that follow the mission rules are guaranteed to have their program run in space!
To help many more people take part in their native language, we’ve translated the Mission Zero resource, guidelines, and web page into 19 different languages! Head to our languages section to find your version of Mission Zero and take part.
If you have any questions regarding the European Astro Pi Challenge, email us at astropi@esa.int.
The post Jenni Sidey inspires young women in science with Astro Pi appeared first on Raspberry Pi.
In Hello World issue 7, Steven Weir introduces a Raspberry Pi into the classroom to monitor a classic science experiment.
A Raspberry Pi can be used to monitor the reaction between hydrochloric acid and sodium thiosulphate to complement a popular GCSE Chemistry practical.
The rate of reaction between hydrochloric acid and sodium thiosulphate is typically studied as part of GCSE Chemistry. The experiment involves measuring the time required for the reaction mixture to turn cloudy, due to the formation of sulphur as a precipitate. Students can then change the temperature or concentration of the reactants to study their effect on the rate of reaction. The time for the reaction mixture to turn cloudy is normally facilitated by recording the time a hand-drawn cross takes to become obscured when placed underneath a glass vessel holding the reaction mixture. This timing is prone to variability due to operator judgement of when the cross first becomes obscured. This variability can legitimately be discussed as part of the lesson. However, the element of operator judgement can be avoided using a Raspberry Pi-monitored chemical reactor.
Attached to a glass jar of approximate 80ml volume (the size is not critical) are two drinking straws, of which one houses a white LED (light-emitting diode) and the other a LDR (light-dependent resistor). The jar is covered in black tape to minimise intrusion of ambient light. The reactor is shown in Figure 1, along with details of other electrical components and connection instructions to a Raspberry Pi.
Figure 1
A: Reactor covered in black tape
B: Drinking straw attached to the reactor, with a further straw inserted housing a white LED
C: Drinking straw attached to the reactor, with a further straw inserted housing a LDR
D: 220Ω resistor to connect to the LED and GPIO 23
E: Wire to connect to ground
F: Wire to connect to 3.3v supply
G: 1µF capacitor to connect to ground
H: Crocodile clip to connect to GPIO 27 (NB: the other end of the wire is situated in between the capacitor and the LDR)
The Python code shown in Figure 2 should be run prior to addition of chemicals to the reactor. Instructions appear on the screen to prompt chemical additions and to start data collection.
Figure 2: Python code for the chemical reactor
Figure 3 shows the results from the experiment when 25ml 0.1M hydrochloric acid is reacted with 25ml 0.15M sodium thiosulphate at 20°C. The reaction is complete at the time the light transmission first reads 0, (i.e. complete obscuration of the light by the precipitate formation) — in this example, that time is 45.4s. For more advanced students, tangents can be drawn at various points on the curve, and gradients calculated to determine the maximum rate of reaction from various reaction conditions.
Figure 3: Graph showing the change in light transmission with time
Download your free copy of Hello World issue 7 today from the Hello World website, where you’ll also find all previous issues. And if you’re an educator in the UK, you’ll have the chance sign up to receive free hard copies to your door!
The post Raspberry Pi-monitored chemical reactor 💥 appeared first on Raspberry Pi.
In HackSpace magazine issue 14, out today, Cameron Norris writes about how citizen scientists at Tokyo Hackerspace took on the Fukushima nuclear disaster.
Safecast is an independent citizen science project that emerged in the wake of the Fukushima nuclear disaster to provide accurate, unbiased, and credible data on radiation exposure in Japan.
On 11 March 2011, an undersea earthquake off the Pacific coast of Thoku, Japan, caused the second-worst nuclear accident in the history of nuclear power generation, releasing almost 30% more radiation than the Chernobyl disaster in 1986.
The magnitude 9.0–9.1 earthquake resulted in a series of devastating tsunami waves that damaged the backup generator of Fukushima Daiichi Nuclear Power Plant. Without functioning cooling systems, the temperature of the plant’s many nuclear reactors steadily began to rise, eventually leading to a partial meltdown and several hydrogen gas explosions, launching nuclear fallout into the air and sea. Due to concerns over possible radiation exposure, the Japanese government established an 18-mile no-fly zone around the Fukushima plant, and approximately 232 square miles of land was evacuated.
However, citizens of Fukushima Prefecture living outside of the exclusion zone were faced with a serious problem: radiation exposure data wasn’t available to the public until almost two months after the meltdown occurred. Many residents felt they had been left to guess if dangerous levels of ionising radiation had contaminated their communities or not.
Alarmed by the situation, Dutch electrical engineer and computer scientist Pieter Franken, who was living in Tokyo with his family at the time, felt compelled to act. “After the massive wall of water, we had this invisible wall of radiation that was between myself and my family-in-law in the north of Japan, so that kind of triggered the start of Safecast,” says Pieter.
Pieter Franken, a Dutchman living in Japan, who helped start Safecast
Image credit: Joi Ito – CC BY 2.0
Pieter picked up an idea from Ray Ozzie, the former CTO of Microsoft, who suggested quickly gathering data by attaching Geiger counters – used for measuring radioactivity – to the outside of cars before driving around Fukushima. The only problem was that Geiger counters sold out almost globally in a matter of hours after the tsunami hit, making it even more difficult for Pieter and others on the ground to figure out exactly what was going on. The discussion between Pieter and his friends quickly changed from buying devices to instead building and distributing them to the people of Fukushima.
At Tokyo Hackerspace, Pieter – along with several others, including Joi Ito, the director of the MIT Media Lab, and Sean Bonner, an activist and journalist from Los Angeles – built a series of open-source tools for radiation mapping, to enable anyone to build their own pocket Geiger counter and easily share the data they collect. “Six days after having the idea, we had a working system. The next day we were off to Fukushima,” recalls Sean.
A bGeigie Nano removed from its Pelican hardshell
Safecast CC-BY-NC 4.0
A successful Kickstarter campaign raised $36,900 to provide the funding necessary to distribute hundreds of Geiger counters to the people of Japan, while training volunteers on how to use them. Today, Safecast has collected over 100 million data points and is home to the largest open dataset about environmental radiation in the world. All of the data is collected via the Safecast API and published free of charge in the public domain to an interactive map developed by Safecast and MIT Media Lab.
You can read the rest of this feature in HackSpace magazine issue 14, out today in Tesco, WHSmith, and all good independent UK newsagents.
Or you can buy HackSpace mag directly from us — worldwide delivery is available. And if you’d like to own a handy digital version of the magazine, you can also download a free PDF.
The post From HackSpace mag issue 14: DIY Geiger counters appeared first on Raspberry Pi.
The University of Michigan is home to the largest stadium in the USA (the second-largest in the world!). So what better place to test for spectator-induced seismic activity than The Big House?
University of Michigan geology professor Ben van der Pluijm decided to make waves by measuring the seismic activity produced during games at the university’s 107601 person-capacity stadium. Because earthquakes are (thankfully) very rare in the Midwest, and therefore very rarely experienced by van der Pluijm’s introductory geology class, he hoped this approach would make the movement of the Earth more accessible to his students.
“The bottom line was, I wanted something to show people that the Earth just shakes from all kinds of interactions,” explained van der Pluijm in his interview with The Michigan Daily. “All kinds of activity makes the Earth shake.”
To measure the seismic activity, van der Pluijm used a Raspberry Pi, placing it on a flat concrete surface within the stadium.
Van der Pluijm installed a small machine called a Raspberry Pi computer in the stadium. He said his only requirements were that it needed to be able to plug into the internet and set up on a concrete floor. “Then it sits there and does its thing,” he said. “In fact, it probably does its thing right now.”
He then sent freshman student Sahil Tolia to some games to record the moments of spectator movement and celebration, so that these could be compared with the seismic activity that the Pi registers.
We’re not sure whether Professor van der Pluijm plans on releasing his findings to the outside world, or whether he’ll keep them a close secret with his introductory students, but we hope for the former!
We’re not sure what other technology van der Pluijm uses in conjunction with the Raspberry Pi, but it’s fairly easy to create your own seismic activity reader using our board. You can purchase the Raspberry Shake, an add-on board for the Pi that has vertical and horizontal geophones, MEMs accelerometers, and omnidirectional differential pressure transducers. Or you can fashion something at home, for example by taking hints from this project by Carlo Cristini, which uses household items to register movement.
The post I feel the earth move under my feet (in Michigan) appeared first on Raspberry Pi.
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