Commissioned by the Department for Education in England and led by the Raspberry Pi Foundation as part of our National Centre for Computing Education work, the GBIC programme was a collaborative effort involving the Behavioural Insights Team, Apps for Good, and the WISE Campaign.

Gender Balance in Computing ran from 2019 to 2022 and comprised seven studies relating to five different research areas:

• Teaching Approach:
  • Peer Instruction: Collaborative teaching approach in lower secondary
  • Pair Programming: Working collaboratively in upper primary
  • Storytelling: Connecting computing to storytelling with 6- to 7-year-olds
• Belonging: Supporting learners to feel that they “belong” in computer science
• Non-formal Learning: Establishing the connections between in-school and out-of-school computing
• Relevance: Making computing relatable to everyday life
• Subject Choice: How computer science is presented to young people as a subject choice 

In December we published the last of seven reports describing the results of the programme. In this blog post I summarise our overall findings and reflect on what we’ve learned through doing this research.

Gender balance in computing is not a new problem

I was fascinated to read a paper by Deborah Butler from 2000 which starts by summarising themes from research into gender balance in computing from the 1980s and 1990s, for example that boys may have access to more role models in computing and may receive more encouragement to pursue the subject, and that software may be developed with a bias towards interests traditionally considered to be male. Butler’s paper summarises research from at least two decades ago — have we really made progress?

In England, it’s true that making Computing a mandatory subject from age 5 means we have taken great strides forward; the need for young people to make a choice about studying the subject only arises at age 14. However, statistics for England’s externally assessed high-stakes Computer Science courses taken at ages 14–16 (GCSE) and 16–18 (A level) clearly show that, although there is a small upwards trend in the proportion of female students, particularly for A level, gender balance among the students achieving GCSE/A level qualifications remains an issue:

What did we do in the Gender Balance in Computing programme?

In GBIC, we carried out a range of research studies involving more than 14,500 pupils and 725 teachers in England. Implementation teams came from the Foundation, Apps For Good, the WISE Campaign, and the Behavioural Insights Team (BIT). A separate team at BIT acted as the independent evaluators of all the studies.

In total we conducted the following studies:

• Two feasibility studies: Storytelling; Relevance, which led to a full randomised controlled trial (RCT)
• Five RCTs: Belonging; Peer Instruction; Pair Programming; Relevance, which was preceded by a feasibility study; Non-formal Learning (primary)
• One quasi-experimental study: Non-formal Learning (secondary)
• One exploratory research study: Subject Choice (Subject choice evenings and option booklets)

Each study (apart from the exploratory research study) involved a 12-week intervention in schools. Bespoke materials were developed for all the studies, and teachers received training on how to deliver the intervention they were a part of. For the RCTs, randomisation was done at school level: schools were randomly divided into treatment and control groups. The independent evaluators collected both quantitative and qualitative data to ensure that we gained comprehensive insights from the schools’ experiences of the interventions. The evaluators’ reports and our associated blog posts give full details of each study.

The impact of the pandemic

The research programme ran from 2019 to 2022, and as it was based in schools, we faced a lot of challenges due to the coronavirus pandemic. Many research programmes meant to take place in school were cancelled as soon as schools shut during the pandemic.

Although we were fortunate that GBIC was allowed to continue, we were not allowed to extend the end date of the programme. Thus our studies were compressed into the period after schools reopened and primarily delivered in the academic year 2021/2022. When schools were open again, the implementation of the studies was affected by teacher and pupil absences, and by schools necessarily focusing on making up some of the lost time for learning.

The overall results of Gender Balance in Computing

Quantitatively, none of the RCTs showed a statistically significant impact on the primary outcome measured, which was different in different trials but related to either learners’ attitudes to computer science or their intention to study computer science. Most of the RCTs showed a positive impact that fell just short of statistical significance. The evaluators went to great lengths to control for pandemic-related attrition, and the implementation teams worked hard to support teachers in still delivering the interventions as designed, but attrition and disruptions due to the pandemic may have played a part in the results.

The qualitative research results were more encouraging. Teachers were enthusiastic about the approaches we had chosen in order to address known barriers to gender balance, and the qualitative data indicated that pupils reacted positively to the interventions. One key theme across the Teaching Approach (and other) studies was that girls valued collaboration and teamwork. The data also offered insights that enable us to improve on the interventions.

We designed the studies so they could act as pilots that may be rolled out at a national scale. While we have gained sufficient understanding of what works to be able to run the interventions at a larger scale, two particular learnings shape our view of what a large-scale study should look like:

1. A single intervention may not be enough to have an impact

The GBIC results highlight that there is no quick fix and suggest that we should combine some of the approaches we’ve been trialling to provide a more holistic approach to teaching Computing in an equitable way. We would recommend that schools adopt several of the approaches we’ve tested; the materials associated with each intervention are freely available (see our blog posts for links).

2. Age matters

One of the very interesting overall findings from this research programme was the difference in intent to study Computing between primary school and secondary school learners; fewer secondary school learners reported intent to study the subject further. This difference was observed for both girls and boys, but was more marked for girls, as shown in the graph below. This suggests that we need to double down on supporting children, especially girls, to maintain their interest in Computing as they enter secondary school at age 11. It also points to a need for more longitudinal research to understand more about the transition period from primary to secondary school and how it impacts children’s engagement with computer science and technology in general.

What’s next?

We think that more time (in excess of 12 weeks) is needed to both deliver the interventions and measure their outcome, as the change in learners’ attitudes may be slow to appear, and we’re hoping to engage in more longitudinal research moving forward.

We know that an understanding of computer science can improve young people’s access to highly skilled jobs involving technology and their understanding of societal issues, and we need that to be available to all. However, gender balance relating to computing and technology is a deeply structural issue that has existed for decades throughout the computing education and workplace ecosystem. That’s why we intend to pursue more work around a holistic approach to improving gender balance, aligning with our ongoing research into making computing education culturally relevant.

Stay in touch

We are very keen to continue to build on our research on gender balance in computing. If you’d like to support us in any way, we’d love to hear from you. To explore the research projects we’re currently involved in, check out our research pages and visit the website of the Raspberry Pi Computing Education Research Centre at the University of Cambridge.

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Genevieve has a background in classical ballet and was also a computing teacher for several years before starting Ready Salted Code, an educational initiative around data-driven dance. She is now coming to the end of her doctoral studies at the University of Cambridge, in which she focuses on raising awareness of data ethics using ballet and brainwave data as narrative tools, working with student Computing teachers.

Why dance and computing?

You may be surprised that there are links between dance, particularly ballet, and computing. Genevieve explained that classical ballet has a strict repetitive routine, using rule-based choreography and algorithms. Her work on data-driven dance had started at the time of the announcement of the new Computing curriculum in England, when she realised the lack of gender balance in her computing classroom. As an expert in both ballet and computing, she was driven by a desire to share the more creative elements of computing with her learners.

Genevieve has been working with a technologist and a choreographer for several years to develop ballets that generate biometric data and include visualisation of such data — hence her term ‘data-driven dance’. This has led to her developing a second focus in her PhD work on how Computing students can discuss questions of ethics based on the kind of biometric and brainwave data that Genevieve is collecting in her research. Students need to learn about the ethical issues surrounding data as part of their Computing studies, and Genevieve has been working with student teachers to explore ways in which her research can be used to give examples of data ethics issues in the Computing curriculum.

Collecting data during dances

Throughout her talk, Genevieve described several examples of dances she had created. One example was [arra]stre, a project that involved a live performance of a dance, plus a series of workshops breaking down the computer science theory behind the performance, including data visualisation, wearable technology, and images triggered by the dancers’ data.

Much of Genevieve’s seminar was focused on the technologies used to capture movement data from the dancers and the challenges this involves. For example, some existing biometric tools don’t capture foot movement — which is crucial in dance — and also can’t capture movements when dancers are in the air. For some of Genevieve’s projects, dancers also wear headsets that allow collection of brainwave data.

Due to interruptions to her research design caused by the COVID-19 pandemic, much of Genevieve’s PhD research took place online via video calls. New tools had to be created to capture dance performances within a digital online setting. Her research uses webcams and mobile phones to record the biometric data of dancers at 60 frames per second. A number of processes are then followed to create a digital representation of the dance: isolating the dancer in the raw video; tracking the skeleton data; using post pose estimation machine learning algorithms; and using additional software to map the joints to the correct place and rotation.

Are your brainwaves personal data?

It’s clear from Genevieve’s research that she is collecting a lot of data from her research participants, particularly the dancers. The projects include collecting both biometric data and brainwave data. Ethical issues tied to brainwave data are part of the field of neuroethics, which comprises the ethical questions raised by our increasing understanding of the biology of the human brain.

Teaching learners to be mindful about how to work with personal data is at the core of the work that Genevieve is doing now. She mentioned that there are a number of ethics frameworks that can be used in this area, and highlighted the UK government’s Data Ethics Framework as being particularly straightforward with its three guiding principles of transparency, accountability, and fairness. Frameworks such as this can help to guide a classroom discussion around the security of the data, and whether the data can be used in discriminatory ways.

Data ethics provides lots of material for discussion in Computing classrooms. To exemplify this, Genevieve recorded her own brainwaves during dance, research, and rest activities, and then shared the data during workshops with student computing teachers. In our seminar Genevieve showed two visualisations of her own brainwave data (see the images above) and discussed how the student computing teachers in her workshops had felt that one was more “personal” than the other. The same brainwave data can be presented as a spreadsheet, or a moving graph, or an image. Student computing teachers felt that the graph data (shown above) felt more medical, and more like permanent personal data than the visualisation (shown above), but that the actual raw spreadsheet data felt the most personal and intrusive.

Watch the recording of Genevieve’s seminar to see her full talk:

You can also access her slides and the links she shared in her talk.

More to explore

There are a variety of online tools you can use to explore augmented reality: for example try out Posenet with the camera of your device.

Genevieve’s seminar used the title ME++, which refers to the data self and the human self: both are important and of equal value. Genevieve’s use of this term is inspired by William J. Mitchell’s book Me++: The Cyborg Self and the Networked City. Within his framing, the I in the digital world is more than the I of the physical world and highlights the posthuman boundary-blurring of the human and non-human. 

Genevieve’s work is also inspired by Luciani Floridi’s philosophical work, and his book Ethics of Information might be something you want to investigate further. You can also read ME++ Data Ethics of Biometrics Through Ballet and AR, a paper by Genevieve about her doctoral work

Join our next seminar

In our final two seminars for this year we are exploring further aspects of cross-disciplinary computing. Just this week, Conrad Wolfram of Wolfram Technologies joined us to present his ideas on maths and a core computational curriculum. We will share a summary and recording of his talk soon.

On 8 November, Tracy Gardner and Rebecca Franks from the Raspberry Pi Foundation team will close out this series by presenting work we have been doing on computing education in non-formal settings. Sign up now to join us for this session:

We will shortly be announcing the theme of a brand-new series of seminars starting in January 2023.  

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Computing education research that focuses specifically on young people is relatively new, particularly in contrast to established research disciplines such as those focused on mathematics or science education. However, computing is now a mandatory part of the curriculum in several countries, and being taken up in education globally, so we need to rigorously investigate the learning and teaching of this subject, and do so in conjunction with schools and teachers.

You’re invited to our in-person launch event

To celebrate the official launch of the Raspberry Pi Computing Education Research Centre, we will be holding an in-person event in Cambridge, UK on Weds 20 July from 15.00. This event is free and open to all: if you are interested in computing education research, we invite you to register for a ticket to attend. By coming together in person, we want to help strengthen a collaborative community of researchers, teachers, and other education practitioners.

The launch event is your opportunity to meet and mingle with members of the Centre’s research team and listen to a series of short talks. We are delighted that Prof. Mark Guzdial (University of Michigan), who many readers will be familiar with, will be travelling from the US to join us in cutting the ribbon. Mark has worked in computer science education for decades and won many awards for his research, so I can’t think of anybody better to be our guest speaker. Our other speakers are Prof. Alastair Beresford from the Department of Computer Science and Technology, and Carrie Anne Philbin MBE, our Director of Educator Support at the Foundation.

The event will take place at the Department of Computer Science and Technology in Cambridge. It will start at 15.00 with a reception where you’ll have the chance to talk to researchers and see the work we’ve been doing. We will then hear from our speakers, before wrapping up at 17.30. You can find more details about the event location on the ticket registration page.

Our research at the Centre

The aim of the Raspberry Pi Computing Education Research Centre is to increase our understanding of teaching and learning computing, computer science, and associated subjects, with a particular focus on young people who are from backgrounds that are traditionally under-represented in the field of computing or who experience educational disadvantage.

We have been establishing the Centre over the last nine months. In October, I was appointed Director, and in December, we were awarded funding by Google for a one-year research project on culturally relevant computing teaching, following on from a project at the Raspberry Pi Foundation. The Centre’s research team is uniquely positioned, straddling both the University and the Foundation. Our two organisations complement each other very well: the University is one of the highest-ranking universities in the world and renowned for its leading-edge academic research, and the Raspberry Pi Foundation works with schools, educators, and learners globally to pursue its mission to put the power of computing into the hands of young people.

In our research at the Centre, we will make sure that:

1. We collaborate closely with teachers and schools when implementing and evaluating research projects
2. We publish research results in a number of different formats, as promptly as we can and without a paywall
3. We translate research findings into practice across the Foundation’s extensive programmes and with our partners

We are excited to work with a large community of teachers and researchers, and we look forward to meeting you at the launch event.

Stay up to date

At the end of June, we’ll be launching a new website for the Centre at computingeducationresearch.org. This will be the place for you to find out more about our projects and events, and to sign up to our newsletter. For announcements on social media, follow the Raspberry Pi Foundation on Twitter or Linkedin.

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Mark has worked in computer science (CS) education for decades and won many awards for his research, including the prestigious ACM SIGCSE Outstanding Contribution to Computing Education award in 2019. He has written literally hundreds of papers about CS education, and he authors an extremely popular computing education research blog that keeps us all up to date with what is going on in the field.

In his talk, Mark focused on his recent work around developing task-specific programming (TSP) languages, with which teachers can add a teaspoon (also abbreviated TSP) of programming to a wide variety of subject areas in schools. Mark’s overarching thesis is that if we want everyone to have some exposure to CS, then we need to integrate it into a range of subjects across the school curriculum. And he explained that this idea of “adding a teaspoon” embraces some core principles; for TSP languages to be successful, they need to:

• Meet the teachers’ needs
• Be relevant to the context or lesson in which it appears
• Be technically easy to get to grips with

Mark neatly summarised this as ‘being both usable and useful’. 

Historical views on why we should all learn computer science

We can learn a lot from going back in time and reflecting on the history of computing. Mark started his talk by sharing the views of some of the eminent computer scientists of the early days of the subject. C. P. Snow maintained, way back in 1961, that all students should study CS, because it was too important to be left to a small handful of people.

Alan Perlis, also in 1961, argued that everyone at university should study one course in CS rather than a topic such as calculus. His reason was that CS is about process, and thus gives students tools that they can use to change the world around them. I’d never heard of this work from the 1960s before, and it suggests incredible foresight. Perhaps we don’t need to even have the debate of whether computer science is for everyone — it seems it always was!

What’s the problem with the current situation?

In many of our seminars over the last two years, we have heard about the need to broaden participation in computing in school. Although in England, computing is mandatory for ages 5 to 16 (in theory, in practice it’s offered to all children from age 5 to 14), other countries don’t have any computing for younger children. And once computing becomes optional, numbers drop, wherever you are.

Mark shared with us that in US high schools, only 4.7% of students are enrolled in a CS course. However, students are studying other subjects, which brought him to the conclusion that CS should be introduced where the students already are. For example, Mark described that, at the Advanced Placement (AP) level in the US, many more students choose to take history than CS (399,000 vs 114,000) and the History AP cohort has more even gender balance, and a higher proportion of Black and Hispanic students. 

The teaspoon approach to broadening participation

A solution to low uptake of CS being proposed by Mark and his colleagues is to add a little computing to other subjects, and in his talk he gave us some examples from history and mathematics, both subjects taken by a high proportion of US students. His focus is on high school, meaning learners aged 14 and upwards (upper secondary in Europe, or key stage 4 and 5 in England). To introduce a teaspoon of CS to other subjects, Mark’s research group builds tools using a participatory design approach; his group collaborates with teachers in schools to identify the needs of the teachers and students and design and iterate TSP languages in conjunction with them.

Mark demonstrated a number of TSP language prototypes his group has been building for use in particular contexts. The prototypes seem like simple apps, but can be classified as languages because they specify a process for a computational agent to execute. These small languages are designed to be used at a specific point in the lesson and should be learnable in ten minutes. For example, students can use a small ‘app’ specific to their topic, look at a script that generates a visualisation, and change some variables to find out how they impact the output. Students may also be able to access some program code, edit it, and see the impact of their edits. In this way, they discover through practical examples the way computer programs work, and how they can use CS principles to help build an understanding of the subject area they are currently studying. If the language is never used again, the learning cost was low enough that it was worth the value of adding computation to the one lesson.

Try TSP languages yourself

You can try out the TSP language prototypes Mark shared yourself, which will give you a good idea of how much a teaspoon is!

DV4L: For history students, the team and participating teachers have created a prototype called DV4L, which visualises historical data. The default example script shows population growth in Africa. Students can change some of the variables in the script to explore data related to other countries and other historical periods. A example lesson activity illustrates how a teacher might incorporate this TSP language into a lesson.

Pixel Equations: Mathematics and engineering students can use the Pixel Equations tool to learn about the way that pictures are made up of individual pixels. This can be introduced into lessons using a variety of contexts. One example lesson activity looks at images in the contexts of maps. This prototype is available in English and Spanish. 

Counting Sheets: Another example given by Mark was Counting Sheets, an interactive tool to support the exploration of counting problems, such as how many possible patterns can come from flipping three coins. 

Have a go yourself. What subjects could you imagine adding a teaspoon of computing to?

Join our next free research seminar

We’d love you to join us for the next seminar in our series on cross-disciplinary computing. On 7 June, we will hear from Pratim Sengupta, of the University of Calgary, Canada. He has conducted studies in science classrooms and non-formal learning environments, focusing on providing open and engaging experiences for anyone to explore code. Pratim will share his thoughts on the ways that more of us can become involved with code when we open up its richness and depth to a wider audience. He will also introduce us to his ideas about countering technocentrism, a key focus of his new book.

And finally… save another date!

We will shortly be sharing details about the official in-person launch event of the Raspberry Pi Computing Education Research Centre at the University of Cambridge on 20 July 2022. And guess who is going to be coming to Cambridge, UK, from Michigan to officially cut the ribbon for us? That’s right, Mark Guzdial. More information coming soon on how you can sign up to join us for free at this launch event.

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In the final seminar of the series, we were excited to hear from Stefania Druga from the University of Washington, who presented on the topic of AI literacy for families. Stefania’s talk highlighted the importance of families in supporting children to develop AI literacy. Her talk was a perfect conclusion to the series and very well-received by our audience.

Stefania is a third-year PhD student who has been working on AI literacy in families, and since 2017 she has conducted a series of studies that she presented in her seminar talk. She presented some new work to us that was to be formally shared at the HCI conference in April, and we were very pleased to have a sneak preview of these results. It was a fascinating talk about the ways in which the interactions between parents and children using AI-based devices in the home, and the discussions they have while learning together, can facilitate an appreciation of the affordances of AI systems, and critical thinking about their limitations and fallibilities. You’ll find my summary as well as the seminar recording below.

“AI literacy practices and skills led some families to consider making meaningful use of AI devices they already have in their homes and redesign their interactions with them. These findings suggest that family has the potential to act as a third space for AI learning.”

– Stefania Druga

AI literacy: Growing up with AI systems, growing used to them

Back in 2017, interest in Alexa and other so-called ‘smart’, AI-based devices was just developing in the public, and such devices would have been very novel to most people. That year, Stefania and colleagues conducted a first pilot study of children’s and their parents’ interactions with ‘smart’ devices, including robots, talking dolls, and the sort of voice assistants we are used to now.

Working directly with families, the researchers explored the level of understanding that children had about ‘smart’ devices, and were surprised by the level of insight very young children had into the potential of this type of technology.

In this AI literacy pilot study, Stefania and her colleagues found that:

• Children perceived AI-based agents (i.e. ‘smart’ devices) as friendly and truthful
• They treated different devices (e.g. two different Alexas) as completely independent
• How ‘smart’ they found the device was dependent on age, with older children more likely to describe devices as ‘smart’

AI literacy: Influence of parents’ perceptions, influence of talking dolls

Stefania’s next study, undertaken in 2018, showed that parents’ perceptions of the implications and potential of ‘smart’ devices shaped what their children thought. Even when parents and children were interviewed separately, if the parent thought that, for example, robots were smarter than humans, then the child did too.

Another part of this study showed that talking dolls could influence children’s moral decisions (e.g. “Should I give a child a pillow?”). In some cases, these ‘smart’ toys would influence the child more than another human. Some ‘smart’ dolls have been banned in some European countries because of security concerns. In the light of these concerns, Stefania pointed out how important it is to help children develop a critical understanding of the potential of AI-based technology, and what its fallibility and the limits of its guidance are.

AI literacy: Programming ‘smart’ devices, algorithmic bias

Another study Stefania discussed involved children who programmed ‘smart’ devices. She used the children’s drawings to find out about their mental models of how the technology worked.

She found that when children had the opportunity to train machine learning models or ‘smart’ devices, they became more sceptical about the appropriate use of these technologies and asked better questions about when and for what they should be used. Another finding was that children and adults had different ideas about algorithmic bias, particularly relating to the meaning of fairness.

AI literacy: Kinaesthetic activities, sharing discussions

The final study Stefania talked about was conducted with families online during the pandemic, when children were learning at home. 15 families, with in total 18 children (ages 5 to 11) and 16 parents, participated in five weekly sessions. A number of learning activities to demonstrate features of AI made up each of the sessions. These are all available at aiplayground.me.

The fact that children and parents, or other family members, worked through the activities together seemed to generate fruitful discussions about the usefulness of AI-based technology. Many families were concerned about privacy and what was happening to their personal data when they were using ‘smart’ devices, and also expressed frustration with voice assistants that couldn’t always understand the way they spoke.

In one of the sessions, with a focus on machine learning, families were introduced to a kinaesthetic activity involving moving around their home to train a model. Through this activity, parents and children had more insight into the constraints facing machine learning. They used props in the home to experiment and find out ways of training the model better. In another session, families were encouraged to design their own devices on paper, and Stefania showed some examples of designs children had drawn.

This study identified a number of different roles that parents or other adults played in supporting children’s learning about AI, and found that embodied and tangible activities worked well for encouraging joint work between children and their families.

Find out more

You can catch up with Stefania’s seminar below in the video, and download her presentation slides.

More about Stefania’s work can be learned in her paper on children’s training of ML models and also in her latest paper about the five weekly AI literacy sessions with families.

Recordings and slides of all our previous seminars on AI education are available online for you, and you can see the list of AI education resources we’ve put together based on recommendations from seminar speakers and participants.

Join our next free research seminar

We are delighted to start a new seminar series on cross-disciplinary computing, with seminars in May, June, July, and September to look forward to. It’s not long now before we begin: Mark Guzdial will speak to us about task-specific programming languages (TSP) in history and mathematics classes on 3 May, 17.00 to 18.30pm local UK time. I can’t wait!

Sign up to receive the Zoom details for the seminar with Mark:

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Crossing disciplinary boundaries

What do we mean by cross-disciplinary computing? Through this upcoming seminar series, we want to embrace the intersections and interactions of computing with all aspects of learning and life, and think about how they can help us teach young people. The researchers we’ve invited as our speakers will help us shed light on cross-disciplinary areas of computing through the breadth of their presentations.

At the Raspberry Pi Foundation our mission is to make computing accessible to all children and young people everywhere, and because computing and technology appear in all aspects of our and young people’s lives, in this series of seminars we will consider what computing education looks like in a multiplicity of environments.

Mark Guzdial on computing in history and mathematics

We start the new series on 3 May, and are beyond delighted to be kicking off with a talk from Mark Guzdial (University of Michigan). Mark has worked in computer science education for decades and won many awards for his research, including the prestigious ACM SIGCSE Outstanding Contribution to Computing Education award in 2019. Mark has written hundreds of papers about computer science education, and he authors an extremely popular computing education research blog that keeps us all up to date with what is going on in the field.

Recently, he has been researching the ways in which programming education can be integrated into other subjects, so he is a perfect speaker to start us thinking about our theme of cross-disciplinary computing. His talk will focus on how we can add a teaspoon of computing to history and mathematics classes.

Pratim Sengupta on countering technocentrism

On 7 June, our speaker will be Pratim Sengupta (University of Calgary), who I feel will really challenge us to think about programming and computing education in a new way. He has conducted studies in science classrooms and non-formal learning environments which focus on providing open and engaging experiences for the public to explore code, for example through the Voice your Celebration installation. Recently, he has co-authored a book called Voicing Code in STEM: A Dialogical Imagination (MIT Press, availabe open access).

In Pratim’s talk, he will share his thoughts about the ways that more of us can become involved with code through opening up its richness and depth to a wider public audience, and he will introduce us to his ideas about countering technocentrism, a key focus of his new book. I’m so looking forward to being challenged by this talk.

Yasmin Kafai on curriculum design with e-textiles

On 12 July, we will hear from Yasmin Kafai (University of Pennsylvania), who is another legend in computing education in my eyes. Yasmin started her long career in computing education with Seymour Papert, internationally known for his work on Logo and on constructionism as a theoretical lens for understanding the way we learn computing. Yasmin was part of the team that created Scratch, and for many years now has been working on projects revolving around digital making, electronic textiles, and computational participation.

In Yasmin’s talk she will present, alongside a panel of teachers she’s been collaborating with, some of their work to develop a high school curriculum that uses electronic textiles to introduce students to computer science. This promises to be a really engaging and interactive seminar.

Genevieve Smith-Nunes on exploring data ethics

In August we will take a holiday, to return on 6 September to hear from the inspirational Genevieve Smith-Nunes (University of Cambridge), whose research is focused on dance and computing, in particular data-driven dance. Her work helps us to focus on the possibilities of creative computing, but also to think about the ethics of applications that involve vast amounts of data.

Genevieve’s talk will prompt us to think about some really important questions: Is there a difference in sense of self (identity) between the human and the virtual? How does sharing your personal biometric data make you feel? How can biometric and immersive development tools be used in the computing classroom to raise awareness of data ethics? Impossible to miss!

Update: Seminars in October and November

• On 4 October, Conrad Wolfram (Wolfram Research) will give a talk on computational literacy in mathematics
• On 8 November, Tracy Gardner and Rebecca Franks (Raspberry Pi Foundation) will present about computing education in non-formal settings

Sign up now to attend the seminars

Do enter all these dates in your diary so you don’t miss out on participating — we are very excited about this series. Sign up below, and ahead of every seminar, we will send you the information for joining.

As usual, the seminars will take place online on a Tuesday at 17:00 to 18:30 local UK time.

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We can’t talk about AI education — or computer science education more generally — without considering the context in which we deliver it, and the societal issues surrounding computing, AI, and data. For this International Women’s Day, I’m writing about the intersection of AI and gender, particularly with respect to gender bias in machine learning.

The quest for gender equality

Gender inequality is everywhere, and researchers, activists, and initiatives, and governments themselves, have struggled since the 1960s to tackle it. As women and girls around the world continue to suffer from discrimination, the United Nations has pledged, in its Sustainable Development Goals, to achieve gender equality and to empower all women and girls.

While progress has been made, new developments in technology may be threatening to undo this. As Susan Leavy, a machine learning researcher from the Insight Centre for Data Analytics, puts it:

Artificial intelligence is increasingly influencing the opinions and behaviour of people in everyday life. However, the over-representation of men in the design of these technologies could quietly undo decades of advances in gender equality.

Susan Leavy, 2018 [1]

Gender-biased data

In her 2019 award-winning book Invisible Women: Exploring Data Bias in a World Designed for Men [2], Caroline Criado Perez discusses the effects of gender-biased data. She describes, for example, how the designs of cities, workplaces, smartphones, and even crash test dummies are all based on data gathered from men. She also discusses that medical research has historically been conducted by men, on male bodies.

Looking at this problem from a different angle, researcher Mayra Buvinic and her colleagues highlight that in most countries of the world, there are no sources of data that capture the differences between male and female participation in civil society organisations, or in local advisory or decision making bodies [3]. A lack of data about girls and women will surely impact decision making negatively. 

Bias in machine learning

Machine learning (ML) is a type of artificial intelligence technology that relies on vast datasets for training. ML is currently being use in various systems for automated decision making. Bias in datasets for training ML models can be caused in several ways. For example, datasets can be biased because they are incomplete or skewed (as is the case in datasets which lack data about women). Another example is that datasets can be biased because of the use of incorrect labels by people who annotate the data. Annotating data is necessary for supervised learning, where machine learning models are trained to categorise data into categories decided upon by people (e.g. pineapples and mangoes).

In order for a machine learning model to categorise new data appropriately, it needs to be trained with data that is gathered from everyone, and is, in the case of supervised learning, annotated without bias. Failing to do this creates a biased ML model. Bias has been demonstrated in different types of AI systems that have been released as products. For example:

Facial recognition: AI researcher Joy Buolamwini discovered that existing AI facial recognition systems do not identify dark-skinned and female faces accurately. Her discovery, and her work to push for the first-ever piece of legislation in the USA to govern against bias in the algorithms that impact our lives, is narrated in the 2020 documentary Coded Bias. 

Natural language processing: Imagine an AI system that is tasked with filling in the missing word in “Man is to king as woman is to X” comes up with “queen”. But what if the system completes “Man is to software developer as woman is to X” with “secretary” or some other word that reflects stereotypical views of gender and careers? AI models called word embeddings learn by identifying patterns in huge collections of texts. In addition to the structural patterns of the text language, word embeddings learn human biases expressed in the texts. You can read more about this issue in this Brookings Institute report. 

Not noticing

There is much debate about the level of bias in systems using artificial intelligence, and some AI researchers worry that this will cause distrust in machine learning systems. Thus, some scientists are keen to emphasise the breadth of their training data across the genders. However, other researchers point out that despite all good intentions, gender disparities are so entrenched in society that we literally are not aware of all of them. White and male dominance in our society may be so unconsciously prevalent that we don’t notice all its effects.

As sociologist Pierre Bourdieu famously asserted in 1977: “What is essential goes without saying because it comes without saying: the tradition is silent, not least about itself as a tradition.” [4]. This view holds that people’s experiences are deeply, or completely, shaped by social conventions, even those conventions that are biased. That means we cannot be sure we have accounted for all disparities when collecting data.

What is being done in the AI sector to address bias?

Developers and researchers of AI systems have been trying to establish rules for how to avoid bias in AI models. An example rule set is given in an article in the Harvard Business Review, which describes the fact that speech recognition systems originally performed poorly for female speakers as opposed to male ones, because systems analysed and modelled speech for taller speakers with longer vocal cords and lower-pitched voices (typically men).

The article recommends four ways for people who work in machine learning to try to avoid gender bias:

• Ensure diversity in the training data (in the example from the article, including as many female audio samples as male ones)
• Ensure that a diverse group of people labels the training data
• Measure the accuracy of a ML model separately for different demographic categories to check whether the model is biased against some demographic categories
• Establish techniques to encourage ML models towards unbiased results

What can everybody else do?

The above points can help people in the AI industry, which is of course important — but what about the rest of us? It’s important to raise awareness of the issues around gender data bias and AI lest we find out too late that we are reintroducing gender inequalities we have fought so hard to remove. Awareness is a good start, and some other suggestions, drawn out from others’ work in this area are:

Improve the gender balance in the AI workforce

Having more women in AI and data science, particularly in both technical and leadership roles, will help to reduce gender bias. A 2020 report by the World Economic Forum (WEF) on gender parity found that women account for only 26% of data and AI positions in the workforce. The WEF suggests five ways in which the AI workforce gender balance could be addressed:

1. Support STEM education
2. Showcase female AI trailblazers
3. Mentor women for leadership roles
4. Create equal opportunities
5. Ensure a gender-equal reward system

Ensure the collection of and access to high-quality and up-to-date gender data

We need high-quality dataset on women and girls, with good coverage, including country coverage. Data needs to be comparable across countries in terms of concepts, definitions, and measures. Data should have both complexity and granularity, so it can be cross-tabulated and disaggregated, following the recommendations from the Data2x project on mapping gender data gaps.

Educate young people about AI

At the Raspberry Pi Foundation we believe that introducing some of the potential (positive and negative) impacts of AI systems to young people through their school education may help to build awareness and understanding at a young age. The jury is out on what exactly to teach in AI education, and how to teach it. But we think educating young people about new and future technologies can help them to see AI-related work opportunities as being open to all, and to develop critical and ethical thinking.

In our AI education seminars we heard a number of perspectives on this topic, and you can revisit the videos, presentation slides, and blog posts. We’ve also been curating a list of resources that can help to further AI education — although there is a long way to go until we understand this area fully. 

We’d love to hear your thoughts on this topic.

References

[1] Leavy, S. (2018). Gender bias in artificial intelligence: The need for diversity and gender theory in machine learning. Proceedings of the 1st International Workshop on Gender Equality in Software Engineering, 14–16.

[2] Perez, C. C. (2019). Invisible Women: Exploring Data Bias in a World Designed for Men. Random House.

[3] Buvinic M., Levine R. (2016). Closing the gender data gap. Significance 13(2):34–37 

[4] Bourdieu, P. (1977). Outline of a Theory of Practice (No. 16). Cambridge University Press. (p.167)

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The Technovation Challenge

Tara started Technovation, formerly called Iridescent, in 2007 with a family science programme in one school in Los Angeles. The nonprofit has grown hugely, and Technovation now runs computing education activities across the world. We heard from Tara that over 350,000 girls from more than 100 countries take part in their programmes, and that the nonprofit focuses particularly on empowering girls to become tech entrepreneurs. The girls, with support from industry volunteers, parents, and the Technovation curriculum, work in teams to solve real-world problems through an annual event called the Technovation Challenge. Working at scale with young people has given the Technovation team the opportunity to investigate the impact of their programmes as well as more generally learn what works in computing education. 

Tara’s talk was extremely engaging (you’ll find the recording below), with videos of young people who had participated in recent years. Technovation works with volunteers and organisations to reach young people in communities where opportunities may be lacking, focussing on low- and middle-income countries. Tara spoke about the 900 million teenage girls in the world, a  substantial number of whom live in countries where there is considerable inequality. 

To illustrate the impact of the programme, Tara gave a number of examples of projects that students had developed, including:

• An air quality sensor linked to messaging about climate change
• A support circle for girls living in domestic violence situation
• A project helping mothers communicate with their daughters
• Support for water collection in Kenya

Early on, the Technovation Challenge had involved the creation of mobile apps, but in recent years, the projects have focused on using AI technologies to solve problems. An key message that Tara wanted to get across was that the focus on real-world problems and teamwork was as important, if not more, than the technical skills the young people were developing.

Developing AI-related projects in teams

Technovation has designed an online curriculum to support teams, who may have no prior computing experience, to learn how to design an AI project. Students work through units on topics such as data analysis and building datasets. As well as the technical activities, young people also work through activities on problem-solving approaches, design, and system thinking to help them tackle a real-world problem that is relevant to them. The curriculum supports teams to identify problems in their community and find a path to prototype and share an invention to tackle that problem.

While working through the curriculum, teams develop AI models to address the problem that they have chosen. They then submit them to a global competition for beginners, juniors, and seniors. Many of the girls enjoy the Technovation Challenge so much that they come back year on year to further develop their team skills. 

AI Families: Children and parents using AI to solve problems

Technovation runs another programme, AI Families, that focuses on families working together to learn AI concepts and skills and use them to develop projects together. Families worked together with the help of educators to identify meaningful problems in their communities, and developed AI prototypes to address them.

There were 20,000 participants from under-resourced communities in 17 countries through 2018 and 2019. 70% of them were women (mothers and grandmothers) who wanted their children to participate; in this way the programme encouraged parents to be role models for their daughters, as well as enabling families to understand that AI is a tool that could be used to think about what problems in their community can be solved with the help of AI skills and principles. Tara was keen to emphasise that, given the importance of AI in the world, the more people know about it, the more impact they can make on their local communities.

Tara shared links to the curriculum to demonstrate what families in this programme would learn week by week. The AI modules use tools such as Machine Learning for Kids.

The results of the AI Families project as investigated over 2018 and 2019 are reported in this paper.  The findings of the programme included:

• Learning needs to focus on more than just content; interviews showed that the learners needed to see the application to real-world applications
• Engaging parents and other family members can support retention and a sense of community, and support a culture of lifelong learning
• It takes around 3 to 5 years to iteratively develop fun, engaging, effective curriculum, training, and scalable programme delivery methods. This level of patience and commitment is needed from all community and industry partners and funders.

The research describes how the programme worked pre-pandemic. Tara highlighted that although the pandemic has prevented so much face-to-face team work, it has allowed some young people to access education online that they would not have otherwise had access to.

Many perspectives on AI education

Our goal is to listen to a variety of perspectives through this seminar series, and I felt that Tara really offered something fresh and engaging to our seminar audience, many of them (many of you!) regular attendees who we’ve got to know since we’ve been running the seminars. The seminar combined real-life stories with videos, as well as links to the curriculum used by Technovation to support learners of AI. The ‘question and answer’ session after the seminar focused on ways in which people could engage with the programme. On Twitter, one of the seminar participants declared this seminar “my favourite thus far in the series”.  It was indeed very inspirational.

As we near the end of this series, we can start to reflect on what we’ve been learning from all the various speakers, and I intend to do this more formally in a month or two as we prepare Volume 3 of our seminar proceedings. While Tara’s emphasis is on motivating children to want to learn the latest technologies because they can see what they can achieve with them, some of our other speakers have considered the actual concepts we should be teaching, whether we have to change our approach to teaching computer science if we include AI, and how we should engage young learners in the ethics of AI.

Join us for our next seminar

I’m really looking forward to our final seminar in the series, with Stefania Druga, on Tuesday 1 March at 17:00–18:30 GMT. Stefania, PhD candidate at the University of Washington Information School, will also focus on families. In her talk ‘Democratising AI education with and for families’, she will consider the ways that children engage with smart, AI-enabled devices that they are becoming part of their everyday lives. It’s a perfect way to finish this series, and we hope you’ll join us.

Thanks to our seminars series, we are developing a list of AI education resources that seminar speakers and attendees share with us, plus the free resources we are developing at the Foundation. Please do take a look.

You can find all blog posts relating to our previous seminars on this page.

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Complete our survey for computing teachers

Experiences shared by teachers on the ground can give important insights to educators and researchers as well as to policymakers, and can be used to understand both gaps in provision and what is working well. 

Today we launch a survey for computing teachers across Ireland and the UK. The purpose of this survey is to find out about the experiences of computing teachers across the UK and Ireland, including what you teach, your approaches to teaching, and professional development opportunities that you have found useful. You can access it by clicking one of these buttons:

The survey is:

• Open to all early years, primary, secondary, sixth-form, and further education teachers in Ireland, England, Northern Ireland, Scotland, and Wales who have taught any computing or computer science (even a tiny bit) in the last year
• Available in English, Welsh, Gaelic, and Irish/Gaeilge
• Anonymous, and we aim to make the data openly available, in line with our commitment to open-source data; the survey collects no personal data
• Designed to take you 20 to 25 minutes to complete

The survey will be open for four weeks, until 7 March. When you complete the survey, you’ll have the opportunity to enter a prize draw for a £50 book token per week, so if you complete the survey in the first week, you automatically get four chances to win a token!

We’re aiming for 1000 teachers to complete the survey, so please do fill it in and share it with your colleagues. If you can help us now, we’ll be able to share the survey findings on this website and other channels in the summer.

“Computing education in Ireland — as in many other countries — has changed so much in the last decade, and perhaps even more so in the last few years. Understanding teachers’ views is vital for so many reasons: to help develop, inform, and steer much-needed professional development; to inform policymakers on actions that will have positive effects for teachers working in the classroom; and to help researchers identify and conduct research in areas that will have real impact on and for teachers.”

– Keith Quille (Technological University Dublin), member of the research project team

What computing is taught in the UK and Ireland?

There are key differences in the provision of computer science and computing education across the UK and Ireland, not least what we all call the subject.

In England, the mandatory national curriculum subject is called Computing, but for learners electing to take qualifications such as GCSE and A level, the subject is called computer science. Computing is taught in all schools from age 5, and is a broad subject covering digital literacy as well as elements of computer science, such as algorithms and programming; networking; and computer architecture.

In Northern Ireland, the teaching curriculum involves developing Cross-Curricular Skills (CCS) and Thinking Skills and Personal Capabilities. This means that from the Early Years Foundation Stage to the end of key stage 3, “using ICT” is one of the three statutory CCS, alongside “communication” and “using mathematics”, which must be included in lessons. At GCSE and A level, the subject (for those who select it) is called Digital Technology, with GCSE students being able to choose between GCSE Digital Technology (Multimedia) and GCSE Digital Technology (Programming).

In Scotland, the ​​Curriculum for Excellence is divided into two phases: the broad general education (BGE) and the senior phase. In the BGE, from age 3 to 15 (the end of the third year of secondary school), all children and young people are entitled to a computing science curriculum as part of the Technologies framework. In S4 to S6, young people may choose to extend and deepen their learning in computing science through National and Higher qualification courses.

In Wales, computer science will be part of a new Science & Technology area of learning and experience for all learners aged 3-16. Digital competence is also a statutory cross-curricular skill alongside literacy and numeracy;  this includes Citizenship; Interacting and collaborating; Producing; and Data and computational thinking. Wales offers a new GCSE and A level Digital Technology, as well as GCSE and A level Computer Science.

Ireland has introduced the Computer Science for Leaving Certificate as an optional subject (age ranges typically from 15 to 18), after a pilot phase which began in 2018. The Leaving Certificate subject includes three strands: practices and principles; core concepts; and computer science in practice. At junior cycle level (age ranges typically from 12 to 15), an optional short course in coding is now available. The short course has three strands: Computer science introduction; Let’s get connected; and Coding at the next level. 

What is the survey?

The survey is a localised and slightly adapted version of METRECC, which is a comprehensive and validated survey tool developed in 2019 to benchmark and measure developments of the teaching and learning of computing in formal education systems around the world. METRECC stands for ‘MEasuring TeacheR Enacted Computing Curriculum’. The METRECC survey has ten categories of questions and is designed to be completed by practising computing teachers.

Using existing standardised survey instruments is good research practice, as it increases the reliability and validity of the results. In 2019, METRECC was used to survey teachers in England, Scotland, Ireland, Italy, Malta, Australia, and the USA. It was subsequently revised and has been used more recently to survey computing teachers in South Asia and in four countries in Africa.

With sufficient responses, we hope to be able to report on the resources and classroom practices of computing teachers, as well as on their access to professional development opportunities. This will enable us to not only compare the UK’s four devolved nations and Ireland, but also to report on aspects of the teaching of computing in general, and on how teachers perceive the teaching of the subject. As computing is a relatively new subject whatever country you are in, it’s crucial to gather and analyse this information so that we can develop our understanding of the teaching of computing. 

The research team

For this project, we are working as a team of researchers across the UK and Ireland. Together we have a breadth of experience around the development of computing as a school subject (using this broad term to also cover digital competencies and digital technology) in our respective countries. We also have experience of quantitative research and reporting, and we are aiming to publish the results in an academic journal as well as disseminate them to a wider audience. 

In alphabetical order, on the team are:

• Elizabeth Cole, who researches early years and primary programming education at the Centre for Computing Science Education (CCSE), University of Glasgow
• Tom Crick, who is Professor of Digital Education & Policy at Swansea University and has been involved in policy development around computing in Wales for many years
• Diana Kirby, who is a Programme Coordinator at the Raspberry Pi Foundation
• Nicola Looker, who is a Lecturer in Secondary Education at Edgehill University, and a PhD student at CCSE, University of Glasgow, researching programming pedagogy
• Keith Quille, who is a Senior Lecturer in Computing at Technological University Dublin
• Sue Sentance, who is the Director of the Raspberry Pi Computing Education Research Centre at University of Cambridge; and Chief Learning Officer at the Raspberry Pi Foundation

In addition, Dr Irene Bell, Stranmillis University College, Belfast, has been assisting the team to ensure that the survey is applicable for teachers in Northern Ireland. Keith, Sue, and Elizabeth were part of the original team that designed the survey in 2019.

How can I find out more?

On this page, you’ll see more information about the survey and our findings once we start analysing the data. You can bookmark the page, as we will keep it updated with the results of the survey and any subsequent publications.

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Across both organisations, we’ve got great ambitions for the Centre, and I’m delighted to have been appointed as its Director. It’s a great privilege to lead this work. 

What do we mean by culturally relevant pedagogy?

Culturally relevant pedagogy is a framework for teaching that emphasises the importance of incorporating and valuing all learners’ knowledge, ways of learning, and heritage. It promotes the development of learners’ critical consciousness of the world and encourages them to ask questions about ethics, power, privilege, and social justice. Culturally relevant pedagogy emphasises opportunities to address issues that are important to learners and their communities.

Culturally responsive teaching builds on the framework above to identify a range of teaching practices that can be implemented in the classroom. These include:

• Drawing on learners’ cultural knowledge and experiences to inform the curriculum
• Providing opportunities for learners to choose personally meaningful projects and express their own cultural identities
• Exploring issues of social justice and bias

The story so far

The overall objective of our work in this area is to further our understanding of ways to engage underrepresented groups in computing. In 2021, funded by a Special Projects Grant from ACM’s Special Interest Group in Computer Science Education (SIGCSE), we established a working group of teachers and academics who met up over the course of three months to explore and discuss culturally relevant pedagogy. The result was a collaboratively written set of practical guidelines about culturally relevant and responsive teaching for classroom educators.

The video below is an introduction for teachers who may not be familiar with the topic, showing the perspectives of three members of the working group and their students. You can also find other resources that resulted from this first phase of the work, and read our Special Projects Report.

We’re really excited that, having developed the guidelines, we can now focus on how culturally responsive computing teaching can be implemented in English schools through a new, related project supported by funding from Google. This funding continues Google’s commitment to grow the impact of computer science education in schools, which included a £1 million donation to support us and other organisations to develop online courses for teachers.

The next phase of work

In our new project, we want to learn from practitioners how culturally responsive computing teaching can be implemented in classrooms in England, by supporting teachers to plan activities, and listening carefully to their experiences in school. Our approach is similar to the Research-Practice-Partnership (RPP) approach used extensively in the USA to develop research in computing education; this approach hasn’t yet been used in the UK. In this way, we hope to further develop and improve the guidelines with exemplars and case studies, and to increase our understanding of teachers’ motivations and beliefs with respect to culturally responsive computing teaching.

The pilot phase of this project starts this month and will run until December 2022. During this phase, we will work with a small group of schools around London, Essex, and Cambridgeshire. Longer-term, we aim to scale up this work across the UK.

The project will be centred around two workshops held in participating teachers’ schools during the first half of the year. In the first workshop, teachers will work together with facilitators from the Foundation and the Raspberry Pi Computing Education Research Centre to discuss culturally responsive computing teaching and how to make use of the guidelines in adapting existing lessons and programmes of study. The second workshop will take place after the teachers have implemented the guidelines in their classroom, and it will be structured around a discussion of the teachers’ experiences and suggestions for iteration of the guidelines. We will also be using a visual research methodology to create a number of videos representing the new knowledge gleaned from all participants’ experiences of the project. We’re looking forward to sharing the results of the project later on in the year. 

We’re delighted that Dr Polly Card will be leading the work on this project at the Raspberry Pi Computing Education Research Centre, University of Cambridge, together with Saman Rizvi in the Foundation’s research team and Katie Vanderpere-Brown, Assistant Headteacher, Saffron Walden County High School, Essex and Computing Lead of the NCCE London, Hertfordshire and Essex Computing Hub.

More about equity, diversity, and inclusion in computing education

We hold monthly research seminars here at the Foundation, and in the first half of 2021, we invited speakers who focus on a range of topics relating to equity, diversity, and inclusion in computing education.

As well as holding seminars and building a community of interested people around them, we share the insights from speakers and attendees through video recordings of the sessions, blog posts, and the speakers’ presentation slides. We also publish a series of seminar proceedings with referenced chapters written by the speakers.

You can download your copy of the proceedings of the equity, diversity, and inclusion series now.  

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