Tag Archives: computing education

New free resources for young people to become independent digital makers

via Raspberry Pi

Our mission at the Raspberry Pi Foundation is to help learners get creative with technology and develop the skills and confidence they need to make things that matter to them using code and physical computing. One of the ways in which we do this is by offering learners a catalogue of more than 250 free digital making projects! Some of them have been translated into 30 languages, and they can be used with or without a Raspberry Pi computer.

Over the last 18 months, we’ve been developing an all-new format for these educational projects, designed to better support young people who want to learn coding, whether at home or in a coding club, on their digital making journey.

An illustration of the 3-2-1 structure of the new Raspberry Pi Foundation coding project paths.
Our new free learning content for young people who want to create with technology has a 3-2-1 structure (click the image to enlarge)

Supporting learners to become independent tech creators

In the design process of the new project format, we combined:

  • Leading research
  • Experience of what works in Code Clubs, CoderDojos, and other Raspberry Pi programmes
  • Feedback from the community

While designing the new format for our free projects, we found that, as well as support and opportunities to practise while acquiring new skills and knowledge, learners need a learning journey that lets them gradually develop and demonstrate increasing independence.

Therefore, each of our new learning paths is designed to scaffold learners’ success in the early stages, and then lets them build upon this learning by providing them with more open-ended tasks and inspirational ideas that learners can adapt or work from. Each learning path is made up of six projects, and the projects become less structured as learners progress along the path. This allows learners to practise their newly acquired skills and use their creativity and interests to make projects that matter to them. In this way, learners develop more and more independence, and when they reach the final project in the path, they are presented with a simple project brief. By this time they have the skills, practice, and confidence to meet this brief any way they choose!

The four new paths we’re sharing with you today focus on the Scratch language (including a physical computing path!), with a Python and a web development path coming very soon, and even more learning content in development.

Our new path structure for learning coding and digital making

When a learner is ready to develop a new set of coding skills, they choose one of our new paths to embark on. Each path is made up of three different types of projects in a 3-2-1 structure:

  • The first three Explore projects introduce learners to a set of skills and knowledge, and provide step-by-step instructions to help learners develop initial confidence. Throughout these projects, learners have lots of opportunity to personalise and tinker with what they’re creating.
  • The next two Design projects are opportunities for learners to practise the skills they learned in the previous Explore projects, and to express themselves creatively. Learners are guided through creating their own version of a type of project (such as a musical instrument, an interactive pet, or a website to support a local event), and they are given code examples to choose, combine, and customise. No new skills are introduced in these projects, so that learners can focus on practising and on designing and creating a project based on their own preferences and interests.
  • In the final one Invent project, learners focus on completing a project to meet a project brief for a particular audience. The project brief is written so that they can meet it using the skills they’ve learned by following the path up to this point. Learners are provided with reference material, but are free to decide which skills to use. They need to plan their project and decide on the order to carry out tasks.

As a result of working through a path, learners are empowered to make their own ideas and create solutions to situations they or their communities face, with increased independence. And in order to develop more skills, learners can work through more paths, giving them even more choice about what they create in the future.

More features for an augmented learning experience

We’ve also introduced some new features to add interactivity, choice, and authenticity to each project in a path:

  • Real-world info box-outs provide interesting and relevant facts about the skills and knowledge being taught.
  • Design decision points allow learners to make choices about how their project looks and what it does, based on their preferences and interests.
  • Debugging tips throughout each project give learners guidance for finding and fixing common coding mistakes.
  • Project reflection steps solidify new knowledge and provide opportunities for mastery by letting learners revisit the important learnings from the project. Common misconceptions are highlighted, and learners are guided to the correct answer.
  • At the start of each project, learners can interact with example creations from the community, and at the end of a project, they are encouraged to share what they’ve made. Thus, learners can find inspiration in the creations of their peers and receive constructive feedback on their own projects.
  • An open-ended upgrade step at the end of each project offers inspiration for young people to give them ideas for ways in which they could continue to improve upon their project in the future.

Access the new free learning content now

You can discover our new paths on our projects site right now!

We’ll be adding more content regularly, including completely new Python programming and web development paths coming very soon!

As always, we’d love to know what you think: here’s a feedback form for you to share comments you have about our new content!

For feedback specific to an individual project, you can use the feedback link in the footer of that project’s page as usual.

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What’s a kangaroo?! AI ethics lessons for and from the younger generation

via Raspberry Pi

Between September 2021 and March 2022, we’re partnering with The Alan Turing Institute to host speakers from the UK, Finland, Germany, and the USA presenting a series of free research seminars about AI and data science education for young people. These rapidly developing technologies have a huge and growing impact on our lives, so it’s important for young people to understand them both from a technical and a societal perspective, and for educators to learn how to best support them to gain this understanding.

Mhairi Aitken.

In our first seminar we were beyond delighted to hear from Dr Mhairi Aitken, Ethics Fellow at The Alan Turing Institute. Mhairi is a sociologist whose research examines social and ethical dimensions of digital innovation, particularly relating to uses of data and AI. You can catch up on her full presentation and the Q&A with her in the video below.

Why we need AI ethics

The increased use of AI in society and industry is bringing some amazing benefits. In healthcare for example, AI can facilitate early diagnosis of life-threatening conditions and provide more accurate surgery through robotics. AI technology is also already being used in housing, financial services, social services, retail, and marketing. Concerns have been raised about the ethical implications of some aspects of these technologies, and Mhairi gave examples of a number of controversies to introduce us to the topic.

“Ethics considers not what we can do but rather what we should do — and what we should not do.”

Mhairi Aitken

One such controversy in England took place during the coronavirus pandemic, when an AI system was used to make decisions about school grades awarded to students. The system’s algorithm drew on grades awarded in previous years to other students of a school to upgrade or downgrade grades given by teachers; this was seen as deeply unfair and raised public consciousness of the real-life impact that AI decision-making systems can have.

An AI system was used in England last year to make decisions about school grades awarded to students — this was seen as deeply unfair.

Another high-profile controversy was caused by biased machine learning-based facial recognition systems and explored in Shalini Kantayya’s documentary Coded Bias. Such facial recognition systems have been shown to be much better at recognising a white male face than a black female one, demonstrating the inequitable impact of the technology.

What should AI be used for?

There is a clear need to consider both the positive and negative impacts of AI in society. Mhairi stressed that using AI effectively and ethically is not just about mitigating negative impacts but also about maximising benefits. She told us that bringing ethics into the discussion means that we start to move on from what AI applications can do to what they should and should not do. To outline how ethics can be applied to AI, Mhairi first outlined four key ethical principles:

  • Beneficence (do good)
  • Nonmaleficence (do no harm)
  • Autonomy
  • Justice

Mhairi shared a number of concrete questions that ethics raise about new technologies including AI: 

  • How do we ensure the benefits of new technologies are experienced equitably across society?
  • Do AI systems lead to discriminatory practices and outcomes?
  • Do new forms of data collection and monitoring threaten individuals’ privacy?
  • Do new forms of monitoring lead to a Big Brother society?
  • To what extent are individuals in control of the ways they interact with AI technologies or how these technologies impact their lives?
  • How can we protect against unjust outcomes, ensuring AI technologies do not exacerbate existing inequalities or reinforce prejudices?
  • How do we ensure diverse perspectives and interests are reflected in the design, development, and deployment of AI systems? 

Who gets to inform AI systems? The kangaroo metaphor

To mitigate negative impacts and maximise benefits of an AI system in practice, it’s crucial to consider the context in which the system is developed and used. Mhairi illustrated this point using the story of an autonomous vehicle, a self-driving car, developed in Sweden in 2017. It had been thoroughly safety-tested in the country, including tests of its ability to recognise wild animals that may cross its path, for example elk and moose. However, when the car was used in Australia, it was not able to recognise kangaroos that hopped into the road! Because the system had not been tested with kangaroos during its development, it did not know what they were. As a result, the self-driving car’s safety and reliability significantly decreased when it was taken out of the context in which it had been developed, jeopardising people and kangaroos.

A parent kangaroo with a young kangaroo in its pouch stands on grass.
Mitigating negative impacts and maximising benefits of AI systems requires actively involving the perspectives of groups that may be affected by the system — ‘kangoroos’ in Mhairi’s metaphor.

Mhairi used the kangaroo example as a metaphor to illustrate ethical issues around AI: the creators of an AI system make certain assumptions about what an AI system needs to know and how it needs to operate; these assumptions always reflect the positions, perspectives, and biases of the people and organisations that develop and train the system. Therefore, AI creators need to include metaphorical ‘kangaroos’ in the design and development of an AI system to ensure that their perspectives inform the system. Mhairi highlighted children as an important group of ‘kangaroos’. 

AI in children’s lives

AI may have far-reaching consequences in children’s lives, where it’s being used for decision-making around access to resources and support. Mhairi explained the impact that AI systems are already having on young people’s lives through these systems’ deployment in children’s education, in apps that children use, and in children’s lives as consumers.

A young child sits at a table using a tablet.
AI systems are already having an impact on children’s lives.

Children can be taught not only that AI impacts their lives, but also that it can get things wrong and that it reflects human interests and biases. However, Mhairi was keen to emphasise that we need to find out what children know and want to know before we make assumptions about what they should be taught. Moreover, engaging children in discussions about AI is not only about them learning about AI, it’s also about ethical practice: what can people making decisions about AI learn from children by listening to their views and perspectives?

AI research that listens to children

UNICEF, the United Nations Children’s Fund, has expressed concerns about the impact of new AI technologies used on children and young people. They have developed the UNICEF Requirements for Child-Centred AI.

Unicef Requirements for Child-Centred AI: Support childrenʼs development and well-being. Ensure inclusion of and for children. Prioritise fairness and non-discrimination for children. Protect childrenʼs data and privacy. Ensure safety for children. Provide transparency, explainability, and accountability for children. Empower governments and businesses with knowledge of AI and childrenʼs rights. Prepare children for present and future developments in AI. Create an enabling environment for child-centred AI. Engage in digital cooperation.
UNICEF’s requirements for child-centred AI, as presented by Mhairi. Click to enlarge.

Together with UNICEF, Mhairi and her colleagues working on the Ethics Theme in the Public Policy Programme at The Alan Turing Institute are engaged in new research to pilot UNICEF’s Child-Centred Requirements for AI, and to examine how these impact public sector uses of AI. A key aspect of this research is to hear from children themselves and to develop approaches to engage children to inform future ethical practices relating to AI in the public sector. The researchers hope to find out how we can best engage children and ensure that their voices are at the heart of the discussion about AI and ethics.

We all learned a tremendous amount from Mhairi and her work on this important topic. After her presentation, we had a lively discussion where many of the participants relayed the conversations they had had about AI ethics and shared their own concerns and experiences and many links to resources. The Q&A with Mhairi is included in the video recording.

What we love about our research seminars is that everyone attending can share their thoughts, and as a result we learn so much from attendees as well as from our speakers!

It’s impossible to cover more than a tiny fraction of the seminar here, so I do urge you to take the time to watch the seminar recording. You can also catch up on our previous seminars through our blogs and videos.

Join our next seminar

We have six more seminars in our free series on AI, machine learning, and data science education, taking place every first Tuesday of the month. At our next seminar on Tuesday 5 October 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 Carsten Schulte, Yannik Fleischer, and Lukas Höper from the University of Paderborn, Germany, who will be presenting on the topic of teaching AI and machine learning (ML) from a data-centric perspective (find out more here). Their talk will raise the questions of whether and how AI and ML should be taught differently from other themes in the computer science curriculum at school.

Sign up now and we’ll send you the link to join on the day of the seminar — don’t forget to put the date in your diary.

I look forward to meeting you there!

In the meantime, we’re offering a brand-new, free online course that introduces machine learning with a practical focus — ideal for educators and anyone interested in exploring AI technology for the first time.

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Free computer science courseware and hardware for American educators

via Raspberry Pi

Today we’re announcing two brand-new, fantastic, free online courses for educators in the USA. And to kickstart their learning journey, we are giving qualified US-based educators the chance to get a free Raspberry Pi Pico microcontroller hardware kit. This is all thanks to our partners at Infosys Foundation USA, who are committed to expanding access to computer science and maker education in public schools across the United States.

In a classroom, a teacher and a student look at a computer screen while the student types on the keyboard.
Bring computer science to your students with the help of our new free online courses.

You can find both new courses on the Pathfinders Online Institute platform, which supports US classroom educators to bring high-quality computer science and maker education content to their kindergarten through 12th grade students. And best of all, the platform is completely free!

Learn how to teach the essentials of programming

The first course we’ve created for you is called Programming essentials in Scratch. It supports teachers to introduce the essentials of programming to fourth to eighth grade students. The course covers the key concepts of programming, such as variables, selection, and iteration. In addition to learning how to teach programming effectively, teachers will also discover how to inspire their students and help them create music, interactive quizzes, dance animations, and more.

A girl sits by a desktop computer, with her Scratch coding project showing on the screen.
Scratch is a block-based programming language and ideal for teaching key programming concepts.

Discover how to teach physical computing

Our second new course for you is called Design, build, and code a rover with Raspberry Pi Pico. It gives teachers of fourth to eighth grade students everything they need to start teaching physical computing in their classroom. Teachers will develop their students’ knowledge of the subject by using basic circuits, coding a Raspberry Pi Pico microcontroller to work with motors and LEDs, and designing algorithms to navigate a rover through a maze. By the end of the course, teachers will have all the resources they need to inspire students and help them explore practical programming, system design, and prototyping.

On a wooden desktop, electronic components, a Raspberry Pi Pico, and a motor next to a keyboard.
Take our free course to learn how to build and code a rover with your students.

Get one of 1,000 free hardware kits

And thanks to the generous support of Infosys Foundation USA, we’re able to provide qualified educators with a FREE kit of materials to participate in the Design, build, and code a rover with Raspberry Pi Pico course. We’re especially excited about this because the kit includes our first-ever microcontroller, Raspberry Pi Pico. This offer is available to 1,000 US-based K–12 public or charter school teachers on a first-come, first-served basis.

To claim your kit, just create a free account on Pathfinders Online Institute and start the course. On the first page of the course, you’ll receive instructions on how to apply for a free kit.

A soldered Raspberry Pi Pico on a breadboard.
The first 1,000 qualified educators who sign up for Design, build, and code a rover with Raspberry Pi Pico receive all a free hardware kit.

If you’re not a qualified educator, or if you’ve missed out on the opportunity to get the free hardware, we still welcome you to join the course! You can find the materials yourself, or purchase the kit from our partners at PiShop.us.

Thank you to Infosys Foundation USA

All of us at the Raspberry Pi Foundation want to thank the Infosys Foundation USA team for collaborating with us on this new resource and learning opportunity for educators. We appreciate and share their commitment to support computer science and maker education.

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Educating young people in AI, machine learning, and data science: new seminar series

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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.

A woman explains a graph on a computer screen to two men.
Recent advances in computing technology have accelerated the rate at which AI and data science tools are coming to be used.

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.

Venn diagram showing the overlaps between computer science, AI, machine learning, statistics, and data science.
Computer science, AI, statistics, machine learning, and data science are overlapping fields. (Diagram from our forthcoming free online course about machine learning for educators)

AI impacts everyone, so we need to teach young people about it

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.

Three female teenagers and a teacher use a computer together.
We need to make sure that young people understand AI technologies and how they impact society and individuals.

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.

A teacher and a student work on a coding task at a laptop.
Our seminar speakers will share findings on how teachers can help their learners get to grips with AI concepts.

Partnering with The Alan Turing Institute for a new seminar series

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.

A woman teacher presents to an audience in a classroom.
Everyone with an interest in computing education research is welcome at our seminars, from researchers to educators and students!

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.”

Our free seminar series about AI, machine learning, and data science

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:

  • Tues 7 September: Dr Mhairi Aitken from The Alan Turing Institute will share a talk about AI ethics, setting out key ethical principles and how they apply to AI before discussing the ways in which these relate to children and young people.
  • Tues 5 October: Professor Carsten Schulte, Yannik Fleischer, and Lukas Höper from Paderborn University in Germany will use a series of examples from their ProDaBi programme to explore whether and how AI and machine learning should be taught differently from other topics in the computer science curriculum at school. The speakers will suggest that these topics require a paradigm shift for some teachers, and that this shift has to do with the changed role of algorithms and data, and of the societal context.
  • Tues 3 November: Professor Matti Tedre and Dr Henriikka Vartiainen from the University of Eastern Finland will focus on machine learning in the school curriculum. Their talk will map the emerging trajectories in educational practice, theory, and technology related to teaching machine learning in K-12 education.
  • Tues 7 December: Professor Rose Luckin from University College London will be looking at the breadth of issues impacting the teaching and learning of AI.
  • Tues 11 January: We’re delighted that Dr Dave Touretzky and Dr Fred Martin (Carnegie Mellon University and University of Massachusetts Lowell, respectively) from the AI4K12 Initiative in the USA will present some of the key insights into AI that the researchers hope children will acquire, and how they see K-12 AI education evolving over the next few years.
  • Tues 1 February: Speaker to be confirmed

How you can join our online seminars

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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The digital divide: interactions between socioeconomic disadvantage and computing education

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Digital technology is developing at pace, impacting us all. Most of us use screens and all kinds of computers much more than we did five years ago. The total number of apps downloaded globally each quarter has doubled since 2015, reflecting both increased smartphone penetration and the increasingly prominent role of apps in our lives. However, access to digital technology and the internet is not yet equal: there is still a ‘digital divide’, i.e. some people do not have as much access to digital technologies as others, if any at all.

This month we welcomed Dr Hayley Leonard and Thom Kunkeler at our research seminar series, to present findings on ‘Why the digital divide does not stop at access: understanding the complex interactions between socioeconomic disadvantage and computing education’. Both Hayley and Thom work as researchers at the Raspberry Pi Foundation, where we have a focus on increasing our understanding of computing education for all. They shared some results of a research project they’d carried out with a group of young people who benefitted from our Learn at Home campaign.

Digital inequality: beyond the dichotomy of access

Hayley introduced some of the existing research and thinking around digital inequality, and Thom presented the results of their research project. Setting the scene, Hayley explained that the term ‘digital divide’ can create a dichotomous have/have-not view of the world, as can the concept of a ‘gap’. However, the research presents a more nuanced picture. Rather than describing digital inequality as purely centred on access to technology, some researchers characterise three levels of the digital divide:

  • Level 1: Access
  • Level 2: Skills (digital skills, internet skills) and uses (what you do once you have access)
  • Level 3: Outcomes (what you achieve)

This characterisation is useful because it enables us to look beyond access and also towards what happens once people have access to technology. This is where our Learn At Home campaign came in.

The presenters gave a brief overview of the impact of the campaign, in which the Raspberry Pi Foundation has partnered with 80 youth and community organisations and to date, thanks to generous donors, has given 5100 Raspberry Pi desktop computer kits (including monitors, headphones, etc.) to young people in the UK who didn’t have the resources to buy their own computers.

Hayley Leonard presents an online slide describing the interview responses of recipients of Raspberry Pi desktop computer kits, which revolved around five themes: ease of homework completion; connecting with others; having their own device; new opportunities for learning; improved understanding of schoolwork.
Click on the image to enlarge it. Learn more in the first Learn at Home campaign impact report.

Computing, identity, and self-efficacy

As part of the Learn At Home campaign, Hayley and Thom conducted a pilot study of how young people from underserved communities feel about computing and their own digital skills. They interviewed and analysed responses of fifteen young people, who had received hardware through Learn At Home, about computing as a subject, their confidence with computing, stereotypes, and their future aspirations.

Thom Kunkeler presents an online slide describing the background and research question of the 'Learn at Home campaign' pilot study: underrepresentation, belonging, identity, archetypes, and the question "How do young people from underserved communities feel about computing and their own digital skills?".
Click on the image to enlarge it.

The notion of a ‘computer person’ was used in the interview questions, following work conducted by Billy Wong at the University of Reading, which found that young people experienced a difference between being a ‘computer person’ and ‘doing computing’. The study carried out by Hayley and Thom largely supports this finding. Thom described two major themes that emerged from their analysis: a mismatch between computing and interviewees’ own identities, and low self-indicated self-efficacy.

Showing that stereotypes still persist of what a ‘computer person’ is like, a 13-year-old female interviewee described them as “a bit smart. Very, very logical, because computers are very logical. Things like smart, clever, intelligent because computers are quite hard.” Four of the interviewees were also more likely to associate a ‘computer person’ with being male.

Thom Kunkeler presents an online slide of findings of the 'Learn at Home campaign' pilot study. The young people interviewed associated the term 'computing person' with the attributes smart, clever, intelligent, nerdy/geeky, problem-solving ability.
The young people interviewed associated a ‘computing person’ with the following characteristics: smart, clever, intelligent, nerdy/geeky, problem-solving ability. Click on the image to enlarge it.

The majority of the young people in the study said that they could be this ’computer person’. Even for those who did not see themselves working with computers in the future, being a ’computer person’ was still a possibility: One interviewee said, “I feel like maybe I’m quite good at using a computer. I know my way around. Yes, you never know. I could be, eventually.”

Five of the young people indicated relatively low self-efficacy in computing, and thought there were more barriers to becoming a computer person, for example needing to be better at mathematics. 

In terms of future career goals, only two (White male) participants in the study considered computing as a career, with one (White female) interviewee understanding that choosing computing as a qualification might be important for her future career. This aligns with research into computer science (CS) qualification choice at age 14 in England, explored in a previous seminar, which highlighted the interaction between income, gender, and ethnicity: White girls from lower-income families were more likely to choose a CS qualification than White girls more from more affluent families, while very few Asian, Black, and Chinese girls from low-income backgrounds chose a CS qualification.

Evaluating computing education opportunities using the CAPE framework

An interesting aspect of this seminar was how Hayley and Thom situated their work in the relatively new CAPE framework, which describes different levels at which to evaluate computer science education opportunities. The CAPE framework highlights that capacity and access to computing (C and A in the framework) are only part of the challenge of making computer science education equitable; students’ participation (P) in and experience (E) of computing are key factors in keeping them engaged longer-term.

A diagram illustrating the CAPE framework for assessing computing education opportunities according to four aspects. 1, capacity, which relates to availability of resources. 2, access, which relates to whether learners have the opportunity to engage in the subject. 3, participation, which relates to whether learners choose to engage with the subject. 4, experience, which relates to what the outcome of learners' participation is.
Socioeconomic status (SES) can affect learner engagement with computing education at four levels set out in the CAPE framework.

As we develop computing education in the curriculum, we can use the CAPE framework to evaluate our provision. For example, where I’m writing from in England, we have the capacity to teach computing through the availability of professional development training for teachers, fully developed curriculum materials such as the Teach Computing Curriculum, and community support for teachers through organisations such as Computing at School and the National Centre for Computing Education. In terms of access we have an established national curriculum in the subject, but access to it has been interrupted for many due to the coronavirus pandemic. In terms of participation we know that gender and economic status can impact whether young people choose computer science as an elective subject post-14, and taking an intersectional view reveals that the issue of participation is more complex than that. Finally, according to our seminar speakers, young people’s experience of computing education can be impacted by their digital or technological capital, by their self-efficacy, and by the relevance of the subject to their career aspirations and goals. This analysis really enhances our understanding of digital inequality, as it moves us away from the have/have-not language of the digital divide and starts to unpack the complexity of the impacting factors. 

Although this was not covered in this month’s seminar, I also want to draw out that the CAPE framework also supports our understanding of global computing education: we may need to focus on capacity building in order to create a foundation for the other levels. Lots to think about! 

If you’d like to find out more about this project, you can read the paper that relates to the research and the impact report of the early phases of the Learn At Home initiative

If you missed the seminar, you can find the presentation slides on our seminars page and watch the recording of the researchers’ talk:

Join our next seminar

The next seminar will be the final one in the current series focused diversity and inclusion, which we’re co-hosting with the Royal Academy of Engineering. It will take place on Tuesday 13 July at 17:00–18:30 BST / 12:00–13:30 EDT / 9:00–10:30 PDT / 18:00–19:30 CEST, and we’ll welcome Prof Ron Eglash, a prominent researcher in the area of ethnocomputing. The title of Ron’s seminar is Computing for generative justice: decolonizing the circular economy.

To join this free event, click below and sign up with your name and email address:

We’ll email you the link and instructions. See you there!

This was our 17th research seminar — you can find all the related blog posts here, and download the first volume of our seminar proceedings with contributions from previous guest speakers.

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How do you use data to solve a real-world problem? | Hello World #16

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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.

Cover of Hello World magazine issue 16.
The big theme of issue 16 of Hello World is data science and data literacy, and on how to teach those topics to your students.

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.

Kate Farrell.
Kate Farrell

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!”

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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:

  • Key digital skills for young people with SEND
  • Top tips and case studies on how to run a successful computing club
  • Reflections on decolonising the computing curriculum
  • And more

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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.

The post How do you use data to solve a real-world problem? | Hello World #16 appeared first on Raspberry Pi.