Tag Archives: research seminar

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

via Raspberry Pi

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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Physical programming for children with visual disabilities

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When Stack Overflow conducted a survey of 64,000 software engineers, it found that 1% of their respondents were blind — a far higher percentage than among the total population. Yet it is far from easy for young people with visual disabilities to engage in learning programming in school. In this month’s seminar, Dr Cecily Morrison of Microsoft Research Cambridge shared some of her work in this area. Her talk highlighted the difficulties that children learning to program face if they are blind or have low vision, and the affordances of physical programming tools, in particular Code Jumper.

Cecily Morrison.
Dr Cecily Morrison

In her work as a Principal Researcher, Cecily focuses on designing inclusive experiences for people who are blind or have low vision, and she is leading the team that designed Code Jumper (known as Project Torino during its development). She is currently engaged in developing assistive agent technology in Project Tokyo, and she was recently awarded an MBE for her services to inclusive design.

Block-based programming is inaccessible for children with visual disabilities

Block-based programming has become the norm for primary school-aged children who are learning to program, and a variety of freely available environments exist, e.g. Scratch and Blockly. These tools have lots of advantages: discoverability of commands; no syntax errors; and live, imaginative visualisations. But how do you use Scratch if you are blind or have low vision and cannot see the screen?

A girl with her Scratch coding project on a desktop computer.
Block-based programming environments are commonly used to teach children about programming.

There are tools that ‘read out’ code in blocks-based environments but — as we experienced in the seminar — their audio output may not readily facilitate understanding. Listening to one line of code at a time can be difficult, for example when trying to understand a loop (let alone a nested loop!). It puts significant demand on listeners’ memory, and children may lack the conceptual cognitive structures to process the audio information. In addition, using screen-based programming environments involves other challenges for blind children: they need to master touch typing, memorise keyboard shortcuts, and understand file systems.

Project Torino to Code Jumper

To address these challenges, Cecily’s team at Microsoft Research started to develop a physical programming tool for primary-aged learners, in a project known as Project Torino. The project started in 2015, and the tool was developed iteratively over the next four years. The team’s goal for this research project was always to generate a tool that is useful and available to all young learners who are blind or have low vision. Thus, in 2019 the research and technology was transferred to the American Printing House for the Blind, and the Project Torino tool was renamed Code Jumper.

A boy creates a computer program using the Torino tool. There are several Torino pods attached to each other and the boy is using his hands to follow the sequence of the program as it runs.
As learners listen to the physical programming tool’s program output, they can can follow the execution of the program using their hands.

In the seminar Cecily described the iterative development of the physical programming tool. It consists of a number of physical pods, including a play pod, rest pod, loop pod, and selection pod. The young learner can feel the difference between the pods by touch and link them together in the right sequence to construct a program. They then use a central pod, known as the hub, to play an audio output of the program they have created. Using this tool they can code tunes, songs, and stories using ready-made sound sets or sounds that they record themselves.

Dials on the pods allow learners to change the parameter values for each program statement, e.g. the number of times to loop. The parameters can also be changed programmatically through the insertion of ‘plugs’ into the dials. For example, a ‘random’ plug can get a random sound to play.

A use case example is coding the song Row, row, row your boat, which is a common nursery rhyme in the UK and USA. By attaching different pods and using the dials, a learner can use a loop to play “row” three times, and then can add pods for the sounds for “your boat”. Constructing a program like this helps the young programmer learn about sequencing and loops.

Several threads can be attached to the central hub, as in the image below, so that children can learn to use multi-threaded programming, as they can in block-based programming environments such as Scratch. The seminar recording below includes some examples of Code Jumper in action!

A diagram of a multi-thread program built with Project Torino, and the equivalent code blocks program.
Code Jumper supports multi-threaded programming.

Five design principles

Cecily described five design principles that her Microsoft Research team used while developing this physical programming tool:

  1. Persistent program behaviour — When you listen to a program one block/line at a time, it’s hard to get a sense of what it does. Therefore, an important requirement in the design process was that the tool should allow the user to experience the program as a whole. With Code Jumper, the young person can use their hands to follow the program as it executes.
  2. Liveness — This refers to the responsiveness of the tool. It was important to have instant feedback when programming: with Code Jumper, as soon as you touch one of the pods, you get a response.
  3. Low floor, high ceiling — This means the tool is accessible to absolute beginners, but it also offers the opportunity to write more complex programs and develop more advanced skills. 
  4. Works across visual abilities — The tool can be used by children with and without vision, and it was designed to be used by learners with multiple disabilities as well as those with low vision. 
  5. Enables progression — The tool can support learners moving from a physical language to a textual language, by enabling them to listen or read their code as they follow its execution.

The ultimate aim of Code Jumper is to open career opportunities in technology.

Evaluation of the tool

As part of Cecily’s research project, her team undertook a nationwide trial to evaluate the effectiveness of Project Torino, with 75 children and 30 teachers. The trial involved a diverse group of students with a wide range of cognitive skills, and the teachers mostly didn’t have much computing experience.

The team developed a curriculum and sent the teachers full course materials along with Torino kits and laptops. A validated instrument was used to measure engagement and motivation, along with teacher-reported learning outcomes.

In the findings from the trial, all teachers (100%) said that they would like to continue using Torino. Students were also very engaged by the project. Students’ self-efficacy in coding grew substantially after exposure to Torino, with a change in the median score from 2 to 4 (of 5) and large effect size (r = -0.730).

100% of teachers agreed or strongly agreed that they would like to use Torino to teach coding in the future. A table shows other results: The mean score for "I think Torino is a good tool for teaching coding for visually impaired children" was 4.9, for "I found some of the computing concepts hard to understand", it was 2.4, for "Teaching with Torino helped me to improve my own computing subject knowledge" it was 4.2 and for "The teacher guide was hard to follow" it was 1.7.
Cecily presented findings from the Torino trial showing the teachers’ responses to the assessment questionnaire.

Among the qualitative data the team collected, the teacher-reported outcomes included comments about the young people’s use of programming vocabulary (see our previous seminar on the importance of talk in learning to program), and how they improved their problem solving skills. Some teachers also commented on the fact that the physical computing tool generated an inclusive environment in the classroom, as it allowed sighted and non-sighted children to work together.

Overall, our seminar audience found this a very interesting and engaging topic and had lots of questions for Cecily in the question-and-answer session. There is obviously much more to do to ensure that computing is accessible to all children, regardless of any disability or impairment. Research projects such as the one Cecily presented generate useful output in terms of tools for use in the classroom or home, and they also challenge us to think about all our learning materials and their accessibility.

This paper contains more information about the trial. Download Cecily’s annotated slides here, and watch or listen to her presentation:

Join our next seminar

Between January and July 2021, we’re partnering with the Royal Academy of Engineering to host speakers from the UK and USA to give a series of research seminars focused on diversity and inclusion. By diversity, we mean any dimension that can be used to differentiate groups and people from one another. This might be, for example, age, gender, socio-economic status, disability, ethnicity, religion, nationality, or sexuality. The aim of inclusion is to embrace all people irrespective of difference.

In our next research seminar on Tuesday 1 June at 17:00–18:30 BST / 12:00–13:30 EDT / 9:00–10:30 PDT / 18:00–19:30 CEST, we’ll welcome Dr Hayley Leonard and Thom Kunkeler from the Raspberry Pi Foundation team. They will be talking about ‘Why the digital divide does not stop at access: understanding the complex interactions between socioeconomic disadvantage and computing education’.

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

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

You can now download the first volume of our seminar proceedings, with contributions from our previous guest speakers.

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What makes an impact on gender balance in computing education? Answers from experts

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The latest event in the Raspberry Pi Foundation series of research seminars was our first panel discussion, with formal and non-formal learning opportunities in computing education and their impact on gender balance as its theme.

The panel was chaired by Dr Yota Dimitriadi, Associate Professor of Computing at the University of Reading, who was joined by four expert speakers: Dr Jill Denner, Senior Research Scientist at ETR; Amali de Alwis MBE, Managing Director at Microsoft Startups and Founder of Code First: Girls; Pete Marshman FCCT, NCCE Computing Hub Leader at Park House School; and Carrie Anne Philbin MBE, Director of Educator Support here at the Raspberry Pi Foundation. The event opened with lightning talks from all speakers, followed by an interactive question-and-answer section. Our audience learned from a blend of research insights and lived experiences about practical ways to promote gender balance in both formal and non-formal computing education.

A girl and boy in India learning at a computer

Broadening the tech sector employee pool and empowering all students to see computing as a life-changing, fulfilling subject remains an enduring issue in many countries around the world. In England, the proportion of girls choosing formal qualifications in computer science is slowly increasing, and a number of initiatives support the uptake of computing as a career for girls and women. Nevertheless, much remains to be done in order to present computing as an appealing option for girls. In this blog post, I present three key themes which were covered during the panel session. You can find the recording of the event at the bottom of the post.

Theme 1: Putting computing in context

Students often describe computing as a very abstract, academic subject. Dr Jill Denner shared that research has shown a promising approach to altering this perception: connecting the content of computing lessons to people’s everyday lives. Learners’ need for contextual lessons was reiterated by Pete Marshman. In his teaching, Pete has observed that the very first lesson in Year 7 (11-year-olds) is crucial, because students form opinions about computing immediately. Pete devised a lesson that uses collaborative play and pixel art to introduce steganography, a cybersecurity technique for hiding data in plain sight within an ordinary file or message.

Description of a computing lesson that uses collaborative play and pixel art to introduce steganography.
Pete’s very first lesson for 11-year-old students gives them a real-world context for computing

Computing education research has much more to uncover about how computing can be presented as a relevant subject in formal education. In this vein, Carrie Anne Philbin gave an overview of the Relevance strand of the ground-breaking Gender Balance in Computing research programme (co-led by the Foundation). The programme’s Relevance strand will explore the impact of linking computing to real-world problem-solving, working with Year 8 pupils in more than 180 secondary schools in England.

Theme 2: Giving everyone a sense of belonging 

A second theme that emerged during the panel discussion was to who belongs in computing, more specifically which groups self-identify as belonging in computing. Computing suffers from the perception of brilliance bias amongst students: they often feel that they need genius-like abilities in order to succeed with their computing studies, and that such abilities are most commonly exhibited by men. Amali de Alwis turned this concept upside down when she described the “human-centred design” of Code First: Girls courses. Women attending these courses learn from a volunteer with a group of peers and become part of a community where members support each other towards brilliance. Jill echoed this when she spoke about the need to challenge stereotypes, embed diversity in educational materials, and continue to educate teachers to create computing classrooms where girls feel that they belong.

Four young women of colour code at computers
You can find out more about embedding diversity in computing lessons from our past research seminar about equity-focused teaching.

In the Belonging strand of the Gender Balance in Computing programme, the researchers will look closely at the attitudes of both boys and girls towards computing, and Carrie Annie explained that giving learners the chance to talk to female role models from the tech sector may cause a measurable shift in their attitudes to the subject. Pete highlighted practical steps that every school can take by using internal role models drawn from the student body to inspire other pupils and produce influential peer-to-peer interactions. As Jill remarked so succinctly, educators need “to tell all students they belong in computer science”.

Theme 3: Presenting learners with role models and advocates

Finally, we heard about the role that adult and course leader expectations play in shaping young people’s attitudes towards computing. Eccles’ expectancy-value theory suggests that when girls and women make choices about a subject (or career), they are influenced by the perceptions that others hold about that subject. If parents, teachers, and course leaders unconsciously discourage girls from considering computing, then girls will take notice of this. However, adults also have opportunities to underline that they see the value of computing, as for example a parent from Pete’s school did by accompanying a school trip to Google’s offices. In non-formal learning spaces, educators can share insights about their own approaches to problem-solving in computing, such as learning from others’ code on GitHub. Amali believes that sharing this type of common workplace practice shows that in the tech sector you are not expected to be able to solve every problem alone, which helps girls and women feel that they can succeed in a computing career.

Young women in hijab smiles while holding up a laptop displaying code she has written
For learners it’s very important to have role models, such as the inspiring young programmer Dalia Awad, who was a guest on our Digital Making at Home live stream recently.

Final takeaways

The drop-off in female participation in computing between formal education and the workplace has often been presented as a leaky pipeline. This deficit-based model suggests that solutions need to be aimed at fixing the leaks in the pipeline, such as providing interventions at specific stages when girls make decisions about formal qualifications or careers. An alternative viewpoint and important takeaway from the panel was this: as a community of educators and researchers, we need to focus our efforts on identifying the unconscious bias that exists in computing education, so that we can dismantle the barriers that this bias has created and ensure girls have access to equitable computing education at all stages of their learning.

One male and two female teenagers at a computer

During the question-and answer-session, Dr Yota Dimitriadi skillfully drew out and linked some key factors to encourage girls and women to flourish in computing. The audience heard about the need for advocates at all levels in schools to support careful and thoughtful timetabling of computing lessons. Questions about overcoming negative learning experiences and succeeding later in life elicited thoughts from the panel about how non-formal learning can break down learners’ preconceived ideas about computing and show that it’s never too late to learn.

Watch the recording of the event here:

More research is urgently needed

A recent report from Engineering UK suggests that one possible impact of the coronavirus pandemic is a widening of the existing gender gap in young people’s engineering or technology career aspirations. That means the need to promote gender-equitable learning spaces in both formal and non-formal computing education is even more pressing now.

Research to provide evidence-informed solutions will be absolutely crucial to shifting the gender balance in computing. The Raspberry Pi Foundation is a lead organisation in the Gender Balance in Computing research programme, funded by the Department for Education to identify scalable approaches to improving the gender balance in computing. We are currently recruiting primary and secondary schools in England to take part in trials starting in September 2021 and January 2022. Sign up or find information to share with your networks

Next up in our free series

In our next research seminar on Tuesday 1 June at 17:00–18:30 BST / 12:00–13:30 EDT / 9:00–10:30 PDT / 18:00–19:30 CEST, we’ll welcome Dr Hayley Leonard and Thom Kunkeler from the Raspberry Pi Foundation team. They will be talking about ‘Why the digital divide does not stop at access: understanding the complex interactions between socioeconomic disadvantage and computing education’. To join this free event, click below and sign up with your name and email address:

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

You can now download the first volume of our seminar proceedings, with contributions from our previous guest speakers.

The post What makes an impact on gender balance in computing education? Answers from experts appeared first on Raspberry Pi.

How can we design inclusive and accessible curricula for computer science?

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After a brief hiatus over the Easter period, we are excited to be back with our series of online research seminars focused on diversity and inclusion, where in partnership with the Royal Academy of Engineering, we host researchers from the UK and USA. By diversity, we mean any dimension that can be used to differentiate groups and people from one another. This might be, for example, age, gender, socio-economic status, disability, ethnicity, religion, nationality, or sexuality. The aim of inclusion is to embrace all people irrespective of difference.

Maya Israel

This month we welcomed Dr Maya Israel, who heads the Creative Technology Research Lab at the University of Florida. She spoke to us about designing inclusive learning experiences in computer science (CS) that cater for learners with a wide range of educational needs.

Underrepresentation of computer science students with additional needs

Maya introduced her work by explaining that the primary goal of her research is to “increase access to CS education for students with disabilities and others at risk for academic failure”. To illustrate this, she shared some preliminary findings (paper in preparation) from the analysis of data from one US school district.

A computing classroom filled with learners.
By designing activities that support students with additional educational needs, we can improve the understanding and proficiency of all of our students.

Her results showed that only around 22–25% of elementary school students with additional needs (including students with learning disabilities, speech or language impairments, emotional disturbances, or learners on the autistic spectrum) accessed CS classes. Even more worryingly, by high school only 5–7% of students with additional needs accessed CS classes (for students on the autistic spectrum the decline in access was less steep, to around 12%).

Maya made the important point that many educators and school leaders may ascribe this lack of representation to students’ disabilities being a barrier to success, rather than to the design of curricula and instruction methods being a barrier to these students accessing and succeeding in CS education.

What barriers to inclusion are there for students with additional needs?

Maya detailed the systems approach she uses in her work to think about external barriers to inclusion in CS education:

  • At the classroom level — such as teachers’ understanding of learner variability and instructional approaches
  • At the school level — perhaps CS classes clash with additional classes that the learner requires for extra support with other subjects
  • At the systemic level — whether the tools and curricula in use are accessible

As an example, Maya pointed out that many of the programming platforms used in CS education are not fully accessible to all learners; each platform has unique accessibility issues.

A venn diagram illustrating that the work to increase access to CS education for students with disabilities and others at risk for academic failure cannot occur if we do not examine barriers to inclusion in a systematic way. The venn diagram consists of four fully overlapping circles. The outermost is represents systemic barriers. The next one represents school-level barriers. The third one represents classroom barriers. The innermost one represents the resulting limited inclusion.

This is not to say that students with additional needs have no internal barriers to succeeding in CS (these may include difficulties with understanding code, debugging, planning, and dealing with frustration). Maya told us about a study in which the researchers used the Collaborative Computing Observation Instrument (C-COI), which allows analysis of video footage recorded during collaborative programming exercises to identify student challenges and strategies. The study found various strategies for debugging and highlighted a particular need for supporting students in transitioning from a trial-and-error approach to more systematic testing. The C-COI has a lot of potential for understanding student-level barriers to learning, and it will also be able to give insight into the external barriers to inclusion.

Pathways to inclusion

Maya’s work has focused not only on identifying the problems with access, it also aims to develop solutions, which she terms pathways to inclusion. A standard approach to inclusion might involve designing curricula for the ‘average’ learner and then differentiating work for learners with additional needs. What is new and exciting about Maya’s approach is that it is based on the premise that there is no such person as an average learner, and rather that all learners have jagged profiles of strengths and weaknesses that contribute to their level of academic success.

In the seminar, Maya described ways in which CS curricula can be designed to be flexible and take into account the variability of all learners. To do this, she has been using the Universal Design for Learning (UDL) approach, adapting it specifically for CS and testing it in the classroom.

The three core concepts of Universal Design for Learning according to Maya Israel. 1, barriers exists in the learning environment. 2, variability is the norm, meaning learners have jagged learning profiles. 3, the goal is expert learning.

Why is Universal Design for Learning useful?

The UDL approach helps educators anticipate barriers to learning and plan activities to overcome them by focusing on providing different means of engagement, representation, and expression for learners in each lesson. Different types of activities are suggested to address each of these three areas. Maya and her team have adapted the general principles of UDL to a CS-specific context, providing teachers with clear checkpoints to consider when designing computing lessons; you can read more on this in this recent Hello World article.

Two young children code in Scratch on a laptop.

A practical UDL example Maya shared with us was using a series of scaffolded Scratch projects based on the ‘Use-Modify-Create’ approach. Students begin by playing and remixing code; then they try to debug the same program when it is not working; then they reconstruct code that has been deconstructed for the same program; and then finally, they try to expand the program to make the Scratch sprite do something of their choosing. All four Scratch project versions are available at the same time, so students can toggle between them as they learn. This helps them work more independently by reducing cognitive load and providing a range of scaffolded support.

This example illustrates that, by designing activities that support students with additional educational needs, we can improve the understanding and proficiency of all of our students.

Training teachers to support CS students with additional needs

Maya identified three groups of teachers who can benefit from training in either UDL or in supporting students with additional needs in CS:

  1. Special Education teachers who have knowledge of instructional strategies for students with additional needs but little experience/subject knowledge of computing
  2. Computing teachers who have subject knowledge but little experience of Special Education strategies
  3. Teachers who are new to computing and have little experience of Special Education

Maya and her team conducted research with all three of these teacher groups, where they provided professional development for the teachers with the aim to understand what elements of the training were most useful and important for teachers’ confidence and practice in supporting students with additional needs in CS. In this research project, they found that for the teachers, a key aspect of the training was having time to identify and discuss the barriers/challenges their students face, as well as potential strategies to overcome these. This process is a core element of the UDL approach, and may be very different to the standard method of planning lessons that teachers are used to.

A teacher attending Picademy teacher training laughs as she works through an activity.
Having time to identify and discuss the barriers/challenges students face, as well as potential strategies to overcome these, is key for teachers to design accessible curricula.

Another study by Maya’s team showed that an understanding of UDL in the context of CS was a key predictor of teacher confidence in teaching CS to students with additional needs (along with the number years spent teaching CS, and general confidence in teaching CS). Maya therefore believes that focusing on teachers’ understanding of the UDL approach and how they can apply it in CS will be the most important part of their future professional development training.

Final thoughts

Maya talked to us about the importance of intersectionality in supporting students who are learning CS, which aligns with a previous seminar given by Jakita O. Thomas. Specifically, Maya identified that UDL should fit into a wider approach of Intersectional Inclusive Computer Science Education, which encompasses UDL, culturally relevant and sustaining pedagogy, and translanguaging pedagogy/multilingual education. We hope to learn more about this topic area in upcoming seminars in our current series.

Four key takeaways from Maya Israel's research seminar: 1, include students with disabilities in K-12 CS education. They will succeed when given accessible, engaging activities. 2, consider goals, anticipated barriers, and the UDL principles when designing instructions for all learners. 3, disaggregate your data to see who is meeting instructional goals and who is not. 4, share successes of students with disabilities in CS education so we can start shifting the discourse to better inclusion.

You can download Maya’s presentation slides now, and we’ll share the video recording of her seminar on the same page soon. 

Attend the next online research seminar

The next seminar in the diversity and inclusion series will take place on Tuesday 4 May at 17:00–18:30 BST / 12:00–13:30 EDT / 9:00–10:30 PDT / 18:00–19:30 CEST. You’ll hear from Dr Cecily Morrison (Microsoft Research) about her research into computing for learners with visual impairments.

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

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

This was our 15th research seminar — you can find all the related blog posts here.

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