Tag Archives: computing education

Computer science education is a global challenge

Post Syndicated from Sue Sentance original https://www.raspberrypi.org/blog/brookings-report-global-computer-science-education-policy/

For the last two years, I’ve been one of the advisors to the Center for Universal Education at the Brookings Institution, a US-based think tank, on their project to survey formal computing education systems across the world. The resulting education policy report, Building skills for life: How to expand and improve computer science education around the world, pulls together the findings of their research. I’ll highlight key lessons policymakers and educators can benefit from, and what elements I think have been missed.

Woman teacher and female students at a computer

Why a global challenge?

Work on this new Brookings report was motivated by the belief that if our goal is to create an equitable, global society, then we need computer science (CS) in school to be accessible around the world; countries need to educate their citizens about computer science, both to strengthen their economic situation and to tackle inequality between countries. The report states that “global development gaps will only be expected to widen if low-income countries’ investments in these domains falter while high-income countries continue to move ahead” (p. 12).

Student using a Raspberry Pi computer

The report makes an important contribution to our understanding of computer science education policy, providing a global overview as well as in-depth case studies of education policies around the world. The case studies look at 11 countries and territories, including England, South Africa, British Columbia, Chile, Uruguay, and Thailand. The map below shows an overview of the Brookings researchers’ findings. It indicates whether computer science is a mandatory or elective subject, whether it is taught in primary or secondary schools, and whether it is taught as a discrete subject or across the curriculum.

A world map showing countries' situation in terms of computing education policy.
Computer science education across the world. Figure courtesy of Brookings Institution (click to enlarge).

It’s a patchy picture, demonstrating both countries’ level of capacity to deliver computer science education and the different approaches countries have taken. Analysis in the Brookings report shows a correlation between a country’s economic position and implementation of computer science in schools: no low-income countries have implemented it at all, while over 20% of high-income countries have mandatory computer science education at both primary and secondary level. 

Capacity building: IT infrastructure and beyond

Given these disparities, there is a significant focus in the report on what IT infrastructure countries need in order to deliver computer science education. This infrastructure needs to be preceded by investment (funds to afford it) and policy (a clear statement of intent and an implementation plan). Many countries that the Brookings report describes as having no computer science education may still be struggling to put these in place.

A young woman codes in a computing classroom.

The recently developed CAPE (capacity, access, participation, experience) framework offers another way of assessing disparities in education. To have capacity to make computer science part of formal education, a country needs to put in place the following elements:

My view is that countries that are at the beginning of this process need to focus on IT infrastructure, but also on the other elements of capacity. The Brookings report touches on these elements of capacity as well. Once these are in place in a country, the focus can shift to the next level: access for learners.

Comparing countries — what policies are in place?

In their report, the Brookings researchers identify seven complementary policy actions that a country can take to facilitate implementation of computer science education:

  1. Introduction of ICT (information and communications technology) education programmes
  2. Requirement for CS in primary education
  3. Requirement for CS in secondary education
  4. Introduction of in-service CS teacher education programmes
  5. Introduction of pre-service teacher CS education programmes
  6. Setup of a specialised centre or institution focused on CS education research and training
  7. Regular funding allocated to CS education by the legislative branch of government

The figure below compares the 11 case-study regions in terms of how many of the seven policy actions have been taken, what IT infrastructure is in place, and when the process of implementing CS education started.

A graph showing the trajectory of 11 regions of the world in terms of computing education policy.
Trajectories of regions in the 11 case studies. Figure courtesy of Brookings Institution (click to enlarge).

England is the only country that has taken all seven of the identified policy actions, having already had nation-wide IT infrastructure and broadband connectivity in place. Chile, Thailand, and Uruguay have made impressive progress, both on infrastructure development and on policy actions. However, it’s clear that making progress takes many years — Chile started in 1992, and Uruguay in 2007 —  and requires a considerable amount of investment and government policy direction.

Computing education policy in England

The first case study that Brookings produced for this report, back in 2019, related to England. Over the last 8 years in England, we have seen the development of computing education in the curriculum as a mandatory subject in primary and secondary schools. Initially, funding for teacher education was limited, but in 2018, the government provided £80 million of funding to us and a consortium of partners to establish the National Centre for Computing Education (NCCE). Thus, in-service teacher education in computing has been given more priority in England than probably anywhere else in the world.

Three young people learn coding at laptops supported by a volunteer at a CoderDojo session.

Alongside teacher education, the funding also covered our development of classroom resources to cover the whole CS curriculum, and of Isaac Computer Science, our online platform for 14- to 18-year-olds learning computer science. We’re also working on a £2m government-funded research project looking at approaches to improving the gender balance in computing in English schools, which is due to report results next year.

The future of education policy in the UK as it relates to AI technologies is the topic of an upcoming panel discussion I’m inviting you to attend.

school-aged girls and a teacher using a computer together.

The Brookings report highlights the way in which the English government worked with non-profit organisations, including us here at the Raspberry Pi Foundation, to deliver on the seven policy actions. Partnerships and engagement with stakeholders appear to be key to effectively implementing computer science education within a country. 

Lessons learned, lessons missed

What can we learn from the Brookings report’s helicopter view of 11 case studies? How can we ensure that computer science education is going to be accessible for all children? The Brookings researchers draw our six lessons learned in their report, which I have taken the liberty of rewording and shortening here:

  1. Create demand
  2. Make it mandatory
  3. Train teachers
  4. Start early
  5. Work in partnership
  6. Make it engaging

In the report, the sixth lesson is phrased as, “When taught in an interactive, hands-on way, CS education builds skills for life.” The Brookings researchers conclude that focusing on project-based learning and maker spaces is the way for schools to achieve this, which I don’t find convincing. The problem with project-based learning in maker spaces is one of scale: in my experience, this approach only works well in a non-formal, small-scale setting. The other reason is that maker spaces, while being very engaging, are also very expensive. Therefore, I don’t see them as a practicable aspect of a nationally rolled-out, mandatory, formal curriculum.

When we teach computer science, it is important that we encourage young people to ask questions about ethics, power, privilege, and social justice.

Sue Sentance

We have other ways to make computer science engaging to all learners, using a breadth of pedagogical approaches. In particular, we should focus on cultural relevance, an aspect of education the Brookings report does not centre. Culturally relevant pedagogy is a framework for teaching that emphasises the importance of incorporating and valuing all learners’ knowledge, heritage, and ways of learning, and promotes the development of learners’ critical consciousness of the world. When we teach computer science, it is important that we encourage young people to ask questions about ethics, power, privilege, and social justice.

Three teenage boys do coding at a shared computer during a computer science lesson.

The Brookings report states that we need to develop and use evidence on how to teach computer science, and I agree with this. But to properly support teachers and learners, we need to offer them a range of approaches to teaching computing, rather than just focusing on one, such as project-based learning, however valuable that approach may be in some settings. Through the NCCE, we have embedded twelve pedagogical principles in the Teach Computing Curriculum, which is being rolled out to six million learners in England’s schools. In time, through this initiative, we will gain firm evidence on what the most effective approaches are for teaching computer science to all students in primary and secondary schools.

Moving forward together

I believe the Brookings Institution’s report has a huge contribution to make as countries around the world seek to introduce computer science in their classrooms. As we can conclude from the patchiness of the CS education world map, there is still much work to be done. I feel fortunate to be living in a country that has been able and motivated to prioritise computer science education, and I think that partnerships and working across stakeholder groups, particularly with schools and teachers, have played a large part in the progress we have made.

To my mind, the challenge now is to find ways in which countries can work together towards more equity in computer science education around the world. The findings in this report will help us make that happen.

PS We invite you to join us on 16 November for our online panel discussion on what the future of the UK’s education policy needs to look like to enable young people to navigate and shape AI technologies. Our speakers include UK Minister Chris Philp, our CEO Philip Colligan, and two young people currently in education. Tabitha Goldstaub, Chair of the UK government’s AI Council, will be chairing the discussion.

Sign up for your free ticket today and submit your questions to our panel!

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Hello World’s first-ever special edition is here!

Post Syndicated from Gemma Coleman original https://www.raspberrypi.org/blog/hello-world-big-book-of-computing-pedagogy/

Hello World, our free magazine for computing and digital making educators, has just published its very first special edition: The Big Book of Computing Pedagogy!

“When I started to peruse the draft for The Big Book of Computing Pedagogy, I was simply stunned.”

Monica McGill, founder & CEO of CSEDResearch.org

Cover of The Big Book of Computing Pedagogy.

This special edition focuses on practical approaches to teaching computing in the classroom, and includes some of our favourite pedagogically themed articles from previous issues of Hello World, as well as a few never-seen-before pieces. It is structured around twelve pedagogical principles, first developed by us as part of our work related to the National Centre for Computing Education in England. These twelve principles are based on up-to-date research around the best ways of approaching the teaching and learning of computing.

A girl doing a physical computing project with Raspberry Pi hardware.

Grounded in research and practice

Computing education is still relatively new, and it’s a field that’s constantly changing and adapting. Despite leaving school less than ten years ago, I remember my days in the computer lab being limited to learning about how to add animations on PowerPoints and trying out basic Excel formulas (and yes, there was still the odd mouse with a ball knocking about!).

A tweet praising The Big Book of Computing Pedagogy.
The Big Book of Computing Pedagogy — a big hit with educators!

Computing education research is even younger, and we are proud to be an important part of this growing space. As an organisation, we engage in rigorous original research around computing education and learning for young people, and we share all of our research work through blogs, reports, research seminars, and academic publications. We’re particularly proud to have partnered with the University of Cambridge to establish the Raspberry Pi Computing Education Research Centre. 

12 principles of computing pedagogy: lead with concepts; structure lessons; make concrete; unplug, unpack, repack; work together; read and explore code first; foster program comprehension; model everything; challenge misconceptions; create projects; get hands-on; add variety.
Our special edition of Hello World is organised around twelve pedagogical principles.

The Big Book of Computing Pedagogy represents another way in which we bring research and practice to computing educators in an accessible and engaging way. The book aims to be an educator’s companion to learning about tried and tested approaches to teaching computing.

A tweet praising The Big Book of Computing Pedagogy.
The perfect morning read for computing educators.

It includes articles on techniques for fostering program comprehension, advice for bringing physical computing to your classroom, and introductions to frameworks for structuring your computing lessons. As with all Hello World content, we’re bridging the gap between research and practice by giving you accessible chunks of research, followed by stories of trusty educators who have tried out the approaches in their classroom or educational space.

A tweet praising The Big Book of Computing Pedagogy.
Teachers are jumping for joy at this special edition.

Monica McGill, founder and CEO of CSEDResearch.org, says about Hello World’s latest offering, “When I started to peruse the draft for The Big Book of Computing Pedagogy, I was simply stunned. I found the ready-to-consume content to be solidly based on research evidence and tried-and-true best practices from teachers themselves. This resource provides valuable insights into introducing computing to students via unplugged activities, integrating the Predict–Run–Investigate–Modify–Make (PRIMM) pedagogical model, and introducing physical devices for computing — all written in a way that teachers can adopt and use in their own classrooms.”

We’ve been thrilled to see the reaction of educators to this special edition, with many teachers already using it as a reference guide and for a spot of CPD. Why not join them and download it for free today?

Download The Big Book of Computing Pedagogy now

Subscribe now to get each new Hello World — whether regular issue or special edition — straight to your digital inbox, for free! And if you’re based in the UK and do paid or unpaid work in education, you can subscribe for free print issues.

PS Have you listened to our Hello World podcast yet? A new episode has just come out, and it’s great! Listen and subscribe wherever you get your podcasts.

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Take part in the UK Bebras Challenge 2021 for schools!

Post Syndicated from Duncan Maidens original https://www.raspberrypi.org/blog/uk-bebras-challenge-2021-for-schools/

The annual UK Bebras Computational Thinking Challenge is back to provide fun, brain-teasing puzzles for schools from 8 to 19 November!

The UK Bebras Challenge 2021 runs from 8 to 19 November.

In the free Bebras Challenge, your students get to practise their computational thinking skills while solving a set of accessible, puzzling, and engaging tasks over 40 minutes. It’s tailored for age groups from 6 to 18.

“I just want to say how much the children are enjoying this competition. It is the first year we have entered, and I have students aged 8 to 11 participating in my Computing lessons, with some of our older students also taking on the challenges. It is really helping to challenge their thinking, and they are showing great determination to try and complete each task!”

– A UK-based teacher

Ten key facts about Bebras

  1. It’s free!
  2. The challenge takes place in school, and it’s a great whole-school activity
  3. It’s open to learners aged 6 to 18, with activities for different age groups
  4. The challenge is made up of a set of short tasks, and completing it takes 40 minutes
  5. The closing date for registering your school is 4 November
  6. Your learners need to complete the challenge between 8 and 19 November 2021
  7. All the marking is done for you (hurrah!)
  8. You’ll receive the results and answers the week after the challenge ends, so you can go through them with your learners and help them learn more
  9. The tasks are logical thinking puzzles, so taking part does not require any computing knowledge
  10. There are practice questions you can use to help your learners prepare for the challenge, and throughout the year to help them practice their computational thinking

Do you want to support your learners to take on the Bebras Challenge? Then register your school today!

I want to register my school for Bebras

Remember to sign up by 4 November!

The benefits of Bebras

Bebras is an international challenge that started in Lithuania in 2004 and has grown into a worldwide event. The UK became involved in Bebras for the first time in 2013, and the number of participating students has increased from 21,000 in the first year to more than half a million over the last two years! Internationally, nearly 2.5 million learners took part in 2020 despite the disruptions to schools.

On the left, a drawing of a bracelet made of stars and moons.
On the left, a bracelet design from an activity for ages 10–12. On the right, a password checker from an activity for ages 14–16.

Bebras, brought to you in the UK by us and Oxford University, is a great way to give your learners of all age groups a taste of the principles behind computing by engaging them in fun problem-solving activities. The challenge results highlight computing principles, so Bebras can be educational for you as a teacher too.

Throughout the year, questions from previous years of the challenge are available to registered teachers on the bebras.uk website, where you can create self-marking quizzes to help you deliver the computational thinking part of the curriculum for your classes.

You can register your school at bebras.uk/admin.

Learn more about our work to support learners with computational thinking.

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Should we teach AI and ML differently to other areas of computer science? A challenge

Post Syndicated from Sue Sentance original https://www.raspberrypi.org/blog/research-seminar-data-centric-ai-ml-teaching-in-school/

Between September 2021 and March 2022, we’re partnering with The Alan Turing Institute to host a series of free research seminars about how to teach AI and data science to young people.

In the second seminar of the series, we were excited to hear from Professor Carsten Schulte, Yannik Fleischer, and Lukas Höper from the University of Paderborn, Germany, who presented on the topic of teaching AI and machine learning (ML) from a data-centric perspective. Their talk raised the question of whether and how AI and ML should be taught differently from other themes in the computer science curriculum at school.

  • Carsten Schulte.
  • Yannik Fleischer.
  • Lukas Höper.

Machine behaviour — a new field of study?

The rationale behind the speakers’ work is a concept they call hybrid interaction system, referring to the way that humans and machines interact. To explain this concept, Carsten referred to an 2019 article published in Nature by Iyad Rahwan and colleagues: Machine hehaviour. The article’s authors propose that the study of AI agents (complex and simple algorithms that make decisions) should be a separate, cross-disciplinary field of study, because of the ubiquity and complexity of AI systems, and because these systems can have both beneficial and detrimental impacts on humanity, which can be difficult to evaluate. (Our previous seminar by Mhairi Aitken highlighted some of these impacts.) The authors state that to study this field, we need to draw on scientific practices from across different fields, as shown below:

Machine behaviour as a field sits at the intersection of AI engineering and behavioural science. Quantitative evidence from machine behaviour studies feeds into the study of the impact of technology, which in turn feeds questions and practices into engineering and behavioural science.
The interdisciplinarity of machine behaviour. (Image taken from Rahwan et al [1])

In establishing their argument, the authors compare the study of animal behaviour and machine behaviour, citing that both fields consider aspects such as mechanism, development, evolution and function. They describe how part of this proposed machine behaviour field may focus on studying individual machines’ behaviour, while collective machines and what they call ‘hybrid human-machine behaviour’ can also be studied. By focusing on the complexities of the interactions between machines and humans, we can think both about machines shaping human behaviour and humans shaping machine behaviour, and a sort of ‘co-behaviour’ as they work together. Thus, the authors conclude that machine behaviour is an interdisciplinary area that we should study in a different way to computer science.

Carsten and his team said that, as educators, we will need to draw on the parameters and frameworks of this machine behaviour field to be able to effectively teach AI and machine learning in school. They argue that our approach should be centred on data, rather than on code. I believe this is a challenge to those of us developing tools and resources to support young people, and that we should be open to these ideas as we forge ahead in our work in this area.

Ideas or artefacts?

In the interpretation of computational thinking popularised in 2006 by Jeanette Wing, she introduces computational thinking as being about ‘ideas, not artefacts’. When we, the computing education community, started to think about computational thinking, we moved from focusing on specific technology — and how to understand and use it — to the ideas or principles underlying the domain. The challenge now is: have we gone too far in that direction?

Carsten argued that, if we are to understand machine behaviour, and in particular, human-machine co-behaviour, which he refers to as the hybrid interaction system, then we need to be studying   artefacts as well as ideas.

Throughout the seminar, the speakers reminded us to keep in mind artefacts, issues of bias, the role of data, and potential implications for the way we teach.

Studying machine learning: a different focus

In addition, Carsten highlighted a number of differences between learning ML and learning other areas of computer science, including traditional programming:

  1. The process of problem-solving is different. Traditionally, we might try to understand the problem, derive a solution in terms of an algorithm, then understand the solution. In ML, the data shapes the model, and we do not need a deep understanding of either the problem or the solution.
  2. Our tolerance of inaccuracy is different. Traditionally, we teach young people to design programs that lead to an accurate solution. However, the nature of ML means that there will be an error rate, which we strive to minimise. 
  3. The role of code is different. Rather than the code doing the work as in traditional programming, the code is only a small part of a real-world ML system. 

These differences imply that our teaching should adapt too.

A graphic demonstrating that in machine learning as compared to other areas of computer science, the process of problem-solving, tolerance of inaccuracy, and role of code is different.
Click to enlarge.

ProDaBi: a programme for teaching AI, data science, and ML in secondary school

In Germany, education is devolved to state governments. Although computer science (known as informatics) was only last year introduced as a mandatory subject in lower secondary schools in North Rhine-Westphalia, where Paderborn is located, it has been taught at the upper secondary levels for many years. ProDaBi is a project that researchers have been running at Paderborn University since 2017, with the aim of developing a secondary school curriculum around data science, AI, and ML.

The ProDaBi curriculum includes:

  • Two modules for 11- to 12-year-olds covering decision trees and data awareness (ethical aspects), introduced this year
  • A short course for 13-year-olds covering aspects of artificial intelligence, through the game Hexapawn
  • A set of modules for 14- to 15-year-olds, covering data science, data exploration, decision trees, neural networks, and data awareness (ethical aspects), using Jupyter notebooks
  • A project-based course for 18-year-olds, including the above topics at a more advanced level, using Codap and Jupyter notebooks to develop practical skills through projects; this course has been running the longest and is currently in its fourth iteration

Although the ProDaBi project site is in German, an English translation is available.

Learning modules developed as part of the ProDaBi project.
Modules developed as part of the ProDaBi project

Our speakers described example activities from three of the modules:

  • Hexapawn, a two-player game inspired by the work of Donald Michie in 1961. The purpose of this activity is to support learners in reflecting on the way the machine learns. Children can then relate the activity to the behavior of AI agents such as autonomous cars. An English version of the activity is available. 
  • Data cards, a series of activities to teach about decision trees. The cards are designed in a ‘Top Trumps’ style, and based on food items, with unplugged and digital elements. 
  • Data awareness, a module focusing on the amount of data an individual can generate as they move through a city, in this case through the mobile phone network. Children are encouraged to reflect on personal data in the context of the interaction between the human and data-driven artefact, and how their view of the world influences their interpretation of the data that they are given.

Questioning how we should teach AI and ML at school

There was a lot to digest in this seminar: challenging ideas and some new concepts, for me anyway. An important takeaway for me was how much we do not yet know about the concepts and skills we should be teaching in school around AI and ML, and about the approaches that we should be using to teach them effectively. Research such as that being carried out in Paderborn, demonstrating a data-centric approach, can really augment our understanding, and I’m looking forward to following the work of Carsten and his team.

Carsten and colleagues ended with this summary and discussion point for the audience:

“‘AI education’ requires developing an adequate picture of the hybrid interaction system — a kind of data-driven, emergent ecosystem which needs to be made explicitly to understand the transformative role as well as the technological basics of these artificial intelligence tools and how they are related to data science.”

You can catch up on the seminar, including the Q&A with Carsten and his colleagues, here:

Join our next seminar

This seminar really extended our thinking about AI education, and we look forward to introducing new perspectives from different researchers each month. At our next seminar on Tuesday 2 November at 17:00–18:30 BST / 12:00–13:30 EDT / 9:00–10:30 PDT / 18:00–19:30 CEST, we will welcome Professor Matti Tedre and Henriikka Vartiainen (University of Eastern Finland). The two Finnish researchers will talk about emerging trajectories in ML education for K-12. We look forward to meeting you there.

I want to sign up for the next seminar

Carsten and their colleagues are also running a series of seminars on AI and data science: you can find out about these on their registration page.

You can increase your own understanding of machine learning by joining our latest free online course!

[1] Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J. F., Breazeal, C., … & Wellman, M. (2019). Machine behaviour. Nature, 568(7753), 477-486.

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New free resources for young people to become independent digital makers

Post Syndicated from Rik Cross original https://www.raspberrypi.org/blog/free-coding-resources-children-young-people-digital-making-independence/

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

  • Two girls code at a laptop.
  • A child codes at a desktop computer.

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

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

Supporting learners to become independent tech creators

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

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

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

  • Smiling learners in a computing classroom
  • A young girl solders something at a worktop while a man looks over her shoulder.

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

The new content structure

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

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

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

  • A boy working on a Raspberry Pi robot buggy
  • At a Coolest Projects event, wo girls show off the digital making projects they've coded.

More features for an augmented learning experience

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

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

Access the new free learning content now

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

Let me see the new free learning paths

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

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

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

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

Post Syndicated from Sue Sentance original https://www.raspberrypi.org/blog/ai-ethics-lessons-education-children-research/

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 want to sign up for the next free seminar

I look forward to meeting you there!

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

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Free computer science courseware and hardware for American educators

Post Syndicated from original https://www.raspberrypi.org/blog/free-computer-science-courseware-hardware-for-american-educators/

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

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

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

Learn how to teach the essentials of programming

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

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

Discover how to teach physical computing

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

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

Get one of 1,000 free hardware kits

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

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

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

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

Thank you to Infosys Foundation USA

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

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Delivering a culturally relevant computing curriculum: new guide for teachers

Post Syndicated from Sue Sentance original https://www.raspberrypi.org/blog/culturally-relevant-computing-curriculum-guidelines-for-teachers/

In computing education, designing equitable and authentic learning experiences requires a conscious effort to take into account the characteristics of all learners and their social environments. Doing this allows teachers to address topics that are relevant to a diverse range of learners. To support computing and computer science teachers with this work, we’re now sharing a practical guide document for culturally responsive teaching in schools.

Download the guide as a PDF

Why we need to make computing culturally relevant

Making computing culturally relevant means that learners with a range of cultural identities will be able to identify with the examples chosen to illustrate computing concepts, to engage effectively with the teaching methods, and to feel empowered to use computing to address problems that are meaningful to them and their communities. This will enable a more diverse group of learners to feel that they belong in computing and encourage them to choose to continue with it as a discipline in qualifications and careers.

Such an approach can empower all our students and support their skills and understanding of the integral role that computing can play in promoting social justice.

Yota Dimitriadi, Associate Professor at the University of Reading, member of the project working group

We introduced our work on this new document to you previously here on the blog. Check out the prblog post to find out more about the project’s funding and background, and the external working group of teachers and academics we convened to develop the guide.

  • Two teenage girls do coding activities at their laptops in a classroom.
  • Three teenage boys do coding at a shared computer during a computer science lesson.

Some shared definitions

To get the project off to the best start possible once we had assembled the working group, we first spent time drawing on research from the USA and discussing within the working group to come to a shared understanding of key terms:

  • Culture: A person’s knowledge, beliefs, and understanding of the world, which are affected by multiple personal characteristics, as well as social and economic factors.
  • Culturally relevant pedagogy: A framework for teaching that emphasises the importance of incorporating and valuing all learners’ knowledge, ways of learning, and heritage, and that promotes critical consciousness in teachers and learners.
  • Culturally responsive teaching: A range of teaching practices that draw on learners’ personal experiences and cultural identities to make learning more relevant to them, and that support the development of critical consciousness.
  • Social justice: The extent to which all members of society have a fair and equal chance to participate in all aspects of social life, develop to their full potential, contribute to society, and be treated as equals.
  • Equity: The extent to which different groups in society have access to particular activities or resources. To ensure that opportunities for access and participation are equal across different groups.
  • A young girl and boy do a Scratch coding activity together at a desktop computer.
  • Two teenage boys do coding at a shared computer during a computer science lesson.

To bring in the voices of young people into the project, we asked teachers in the working group to consult with their learners to understand their perspectives on computing and how schools can engage more diverse groups of learners in elective computer science courses. The main reason that learners reported for being put off computing: complex or boring lessons of coding activities with a focus on theory rather than on practical outcomes. Many said that they were inspired by tasks such as producing their own games and suggested that early experiences in primary school and KS3 had been very important for their engagement in computing.

Curriculum, teaching approaches, and learning materials

The guide shows you that a culturally relevant pedagogy applies in three aspects of education, which we liken to a tree to indicate how these aspects connect to each other: the tree’s root system, the basis of culturally relevant pedagogy, is the focus of the curriculum; the tree’s trunk and branches are the teaching approaches taken to deliver the curriculum; the learning materials, represented by the tree’s crown of leaves, are the most widely visible aspect of computing lessons.

A tree with the roots labeled 'curriculum, the trunk labeled 'teaching approaches', and the crown labeled 'learning materials'.

Each aspect plays an important role in culturally relevant pedagogy:

  • Within the curriculum, it is important to think about the contexts in which computing concepts are taught, and about you make connections with issues that are meaningful to your learners
  • Equitable teaching approaches, such as open-ended, inquiry-led activities and discussion-based collaborative tasks, are key if you want to provide opportunities for all your learners to express their ideas and their identities through computing
  • Finally, inclusive representations of a range of cultures, and making learning materials accessible, are both of great importance to ensure that all your learners feel that computing is relevant to them

You can download the guide on culturally relevant pedagogy for computing teachers now to explore the resources provided:

  • You’ll find a lot more information, practical tips, and links to resources to support you to implement culturally relevant pedagogy in all these aspects of your teaching
  • The document links to different available curricula, and we have highlighted materials we’ve created for the Teach Computing Curriculum that promote key aspects of the approach
  • We’ve also included links to academic papers and books if you want to learn more, as well as to videos and courses that you can use for professional development

What was being part of the working group like?

One of the teachers who was part of the working group is Joe Arday from Woodbridge High School in Essex, UK. Joe originally worked in the technology sector and has been teaching computing for ten years. We asked him about his experience of being part of the project and how he plans to use the guide in his own classroom practice:

“It has been an absolute privilege to play a part in working towards producing the guide that my own children will be beneficiaries of when they are studying the computing curriculum throughout their education. I have been able to reflect on how to further improve my teaching practice and pedagogy to ensure that the curriculum taught is culturally diverse and caters for all learners that I teach. (Also, having the opportunity to work with academics from both the UK and US has made me think about becoming an academic in the field of computing at some point in the future!)”

Computer science teacher Joe Arday.

Joe also says: “I plan to review the computing curriculum taught in my computing department and sit down with my colleagues to work on how we can implement the guide in our units of work for Key Stages 3 to 5. The guide will also help my department to work towards one of my school’s aims to encourage an anti-racism community and curriculum in my school.“

Continuing the work

We hope you find this resource useful for your own practice, and for conversations within your school and network of fellow educators! Please spread the word about the guide to anyone in your circles who you think might benefit.

We plan to keep working with learners on their perspectives on culturally relevant teaching, and to develop professional development opportunities for teachers, initially in conjunction with a small number of schools. As always with our research projects, we will investigate what works well and share all our findings widely and promptly.

  • A girl doing Scratch coding in a Code Club classroom
  • Two teenagers sit at laptops and do coding activities.

Many thanks to the teachers and academics in the working group for being wonderful collaborators, to the learners who contributed their time and ideas, and to Hayley Leonard and Diana Kirby from our team for all the time and energy they devoted to this project!

Working group

Joseph Arday, FCCT, Woodbridge High School, Essex, UK

Lynda Chinaka, University of Roehampton, UK

Mike Deutsch, Kids Code Jeunesse, Canada

Dr Yota Dimitriadi, University of Reading, UK

Amir Fakhoury, St Anne’s Catholic School and Sixth Form College, Hampshire, UK

Dr Samuel George, Ark St Alban’s Academy, West Midlands, UK

Professor Joanna Goode, University of Oregon, USA

Alain Ndabala, St George Catholic College, Hampshire, UK

Vanessa Olsen-Dry, North Cambridge Academy, Cambridgeshire, UK

Rohini Shah, Queens Park Community School, London, UK

Neelu Vasishth, Hampton Court House, Surrey, UK

The post Delivering a culturally relevant computing curriculum: new guide for teachers appeared first on Raspberry Pi.

Exploring how culture and computing intersect

Post Syndicated from Oliver Quinlan original https://www.raspberrypi.org/blog/culture-computing-stem-education-diversity-research-seminar/

It can be easy to think of science, technology, engineering, and maths (STEM) as fields that develop in a linear way, always progressing towards ever better solutions and approaches. Of course, alternative solutions are posed to all sorts of problems, but in western culture, those solutions that did not take hold are sometimes seen as the approaches that were ‘wrong’ or mistaken, and that eventually gave way to the ‘right’ approaches. A culture that includes the belief that there is only one ‘right’ way can be alienating to anyone who sees the world in a different way.

Ron Eglash.
Dr Ron Eglash, University of Michigan

Dr Ron Eglash from the University of Michigan explored the intersections of diverse cultural ideas and computing in his talk at the final research seminar in our series about diversity and inclusion (see below for the recorded video). His work and insights show us how we might think about diversity in computing as being dependent on the diversity of cultural concepts and beliefs that can underpin the subject. Ron also shared free resources for educators who want to help their students learn about STEM while exploring cultural ideas.

Where do our ideas about computing and STEM come from?

Ron’s talk explored the overlaps of technology, culture, and society. In his research work, Ron has facilitated collaborations across the world between STEM students and people from indigenous cultures, opening up computing to people who have different backgrounds and different ways of seeing the world and, in the process, revealing many complex assumptions that different cultures have about computing and technology.

Ron’s work challenges some of the assumptions in western culture about technological knowledge. He started his talk by showing the evolution of knowledge as a branching set of possibilities and ideas that societies choose to move forward with or leave behind. To illustrate, he gave examples of different concepts of mathematics that western society has taken on board, refined, or discarded throughout its history, demonstrating that there are different versions of mathematics we could have had but chose not to.

A branching diagram showing a very simplified historical relationship of the knowledge systems of Native American, Asian, African, and European people. Created by Ron Eglash.
A simplified view of the relationships of knowledge systems across the world, as shown by Ron in his talk.

These different choices in adoption and exploration of ideas, Ron continued, are more evident when one looks at the knowledge systems of different cultures side by side: different knowledge systems represent different paths that groups of people have chosen — not in totality but as the result of smaller decisions that select which ideas will be influential and which will be eliminated.

What ideas pattern our cultures?

One idea that western society has chosen, and that Ron highlighted for us, is the extraction of value. This is something we can see across this society, and it’s a powerful idea that fundamentally shapes how many of us think about the world. We extract value from the natural world in the way we exploit raw materials. We extract value from labour through the organisation of working arrangements that we have made the norm. And we extract value from social relationships through the online social media platforms, online games, and other digital tools that have so quickly become a central part of billions of people’s lives.

Traditional African art: by using patterns of recursive and non-linear scaling, artists intentionally symbolised the bottom-up and circular ideas permeating their culture.
Examples of indigenous visual art patterned by circular and bottom-up principles, as shown by Ron in his talk.

But western culture, with its particular knowledge system and core tenet of value extraction, represents just one possible way of social and technical development. In nature, systems do not extract value, they circulate it: value moves in a recursive loop as organisms grow, die, and are subsumed back into the ecosystem. Many indigenous cultures have developed within this framework of circulating value. The possible benefits of a circular economy are becoming a topic of discussion in western society, and we would do well to remember that this concept is not western in origin: other cultures have been practicing it for a long time, a point Ron made clear in his talk. And as Ron showed us through his research, the framework of circulating value permeates various indigenous cultures in ways that go beyond approaches such as sustainable agriculture, and thereby creates repeating, fractal patterns in cultural artefacts at different scales, from artworks, to the way settlements are organised, to philosophical ideas.

Close-up photo of an Angelica flowerhead.
Many natural phenomena show fractal patterns, for example this Angelica flowerhead, a sphere of spheres. (Photo by Chiswick Chap – Own work, CC BY-SA 3.0)

In nature, there are many examples of fractal geometry because of biological and chemical phenomena of bottom-up growth and replication. Ron shared images gathered during his research that highlight that fractal patterns are also clearly visible in, for example, traditional African art: by using visual patterns of recursive and non-linear scaling, artists intentionally symbolised the bottom-up and circular ideas permeating their culture. African cultural concepts of recursion and non-linearity, which were also brought to the Americas during the transatlantic slave trade, can be seen today in, for example, cornrow hair braiding, quilting, growing traditions, and spiritual practices.

Examples of hair braiding patterns informed by African cultural traditions.
Examples of hair braiding patterns informed by African cultural traditions, as shown by Ron in his talk.

Computing activities based on circulation of value

The links between indigenous cultural concepts and computing algorithms are many. To explore these in the context of education, Ron and his team have worked in collaboration with members of indigenous communities to develop Culturally Situated Design Tools (CSDT), a suite of computing and STEM activities and learning resources that allow young people of a range of ages to discover the relationship between computing and programming concepts and cultural ideas that trace back to indigenous cultures. The CSDT development process Ron described involved genuine collaboration: seeking ‘cultural permission’ from communities; deeply understanding the cultural concepts behind the artefacts that were being developed; and creating tools that not only allow students to explore traditional designs and artefacts but also give them the scope to design their own original artefacts and to actively contribute to communities’ cultural practices.

Screenshot from the Culturally Situated Design Tools website showing Cornrow Curves Tutorials.
Screenshot from the Culturally Situated Design Tools website showing Cornrow Curves Tutorials.

Ron underlined in his talk how important it is not to see activities like CSDT as a lure to ‘trick’ young people into engaging with STEM classes; the intention is not using them as a veneer to interest more young people in industries underpinned by an extractive world view. Instead, circular and bottom-up concepts are an alternative way of seeing how technology can be used to influence and construct the world.

Returning creative contributions

As such, an important aspect of the pedagogy of Culturally Situated Design Tools is returning creative contributions to the community whose concepts or artefacts are being explored in each activity. The aim is to create a generative cycle of STEM engagement, and Ron demonstrated how this can work by sharing more about a project he conducted with STEM students in Albany, NY. Students began the project by exploring cornrow design simulations. They brought these out of the computer, out of their schools, and into local braiding shops by producing 3D-printed mannequins featuring their cornrow designs. Through engaging with the braiding shop owners, the students learned that the owners had challenges to do with the pH level of hair products, and this led to the students producing pH testing kits for them. The practical applications benefitted the communities connected to the braiding shops and inspired more student interest in the project — thus, a circular, mutually beneficial process of engagement emerged.

A generative cycle of STEM education, in which students learn with activities based on cultural artefacts and then use their learning to give back to the community the artefacts came from.
A generative cycle of STEM education, in which students learn with activities based on cultural artefacts and then use their learning to give back to the community the artefacts came from. As shown by Ron in his talk.

Importantly, the STEM activities that Ron and his collaborators have developed cannot be separated from their cultural context. This way of teaching STEM is not about recruiting young people to become software developers or other tech professionals, but instead about giving them the skills to be creative contributors and problem solvers within communities so that they can help promote the circulation of value.

Rethinking diversity

I have long been enthusiastic about the potential of computing and digital making as a tool for many disciplines, and Ron’s talk made me consider what this might mean at a much deeper level than providing different routes into computing. There is a lot of discussion about how we need to increase diversity in the STEM field to make the field more equitable and able to positively contribute to society, but Ron’s presentation challenged me to think about the cultural assumptions that shape the nature of STEM, and how these influence who engages with the field. Increasing diversity and inclusion in computing and STEM is not just a case of making opportunities open to everyone, but about actually re-shaping the nature of the field so it can be equitable in its interactions with ecological systems, cultures, and human experiences.

Do watch the video of Ron’s presentation and the following Q&A for more on these concepts, examples of the computing activities and how to use them, and discussion of these fundamental ideas. You’ll find his presentation slides on our ‘previous seminars’ page.

You can find the resources Ron shared at csdt.org and generativejustice.org/projects.

Join us at our next online seminar

We are taking a break from our monthly research seminars in August! In the meantime, you can revisit our previous seminars about diversity and inclusion. On 7 September, we’ll be back to start our new seminar series focusing on AI, machine learning, and data science education, in partnership with The Alan Turing Institute. At these seminars, you’ll hear from a range of international speakers about current best practices in teaching young people the technical concepts and ethical considerations involved in these technologies. Do sign up and put the dates in your calendar!

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

Post Syndicated from Sue Sentance original https://www.raspberrypi.org/blog/ai-machine-learning-data-science-education-seminars/

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.

I want to sign up to join

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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Introducing the Raspberry Pi Computing Education Research Centre

Post Syndicated from Philip Colligan original https://www.raspberrypi.org/blog/raspberry-pi-computing-education-research-centre-university-of-cambridge/

I am delighted to announce the creation of the Raspberry Pi Computing Education Research Centre at the University of Cambridge.

University of Cambridge logo

With computers and digital technologies increasingly shaping all of our lives, it’s more important than ever that every young person, whatever their background or circumstances, has meaningful opportunities to learn about how computers work and how to create with them. That’s our mission at the Raspberry Pi Foundation.

Woman computing teacher and young female student at a laptop.
The Raspberry Pi Computing Education Research Centre will work with educators to translate its research into practice and effect positive change in learners’ lives.

Why research matters

Compared to subjects like mathematics, computing is a relatively new field and, while there are enduring principles and concepts, it’s a subject that’s changing all the time as the pace of innovation accelerates. If we’re honest, we just don’t know enough about what works in computing education, and there isn’t nearly enough investment in high-quality research.

Two teenagers sit at laptops in a computing classroom.
We need research to find the best ways of teaching young people how computers work and how to create with them.

That’s why research and evidence has always been a priority for the Raspberry Pi Foundation, from rigorously evaluating our own programmes and running structured experiments to test what works in areas like gender balance in computing, to providing a platform for the world’s best computing education researchers to share their findings through our seminar series. 

Through our research activities we hope to make a contribution to the field of computing education and, as an operating foundation working with tens of thousands of educators and millions of learners every year, we’re uniquely well-placed to translate that research into practice. You can read more about our research work here.

The Raspberry Pi Computing Education Research Centre 

The new Research Centre is a joint initiative between the University of Cambridge and the Raspberry Pi Foundation, and builds on our longstanding partnership with the Department of Computer Science and Technology. That partnership goes all the way back to 2008, to the creation of the Raspberry Pi Foundation and the invention of the Raspberry Pi computer. More recently, we have collaborated on Isaac Computer Science, an online platform that is already being used by more than 2500 teachers and 36,000 students of A level Computer Science in England, and that we will shortly expand to cover GCSE content.

Woman computing teacher and female students at a computer.
Computers and digital technologies shape our lives and society — how do we make sure young people have the skills to use them to solve problems?

Through the Raspberry Pi Computing Education Research Centre, we want to increase understanding of what works in teaching and learning computing, with a particular focus on young people who come from backgrounds that are traditionally underrepresented in the field of computing or who experience educational disadvantage.

The Research Centre will combine expertise from both institutions, undertaking rigorous original research and working directly with teachers and other educators to translate that research into practice and effect positive change in young peoples’ lives.

The scope will be computing education — the teaching and learning of computing, computer science, digital making, and wider digital skills — for school-aged young people in primary and secondary education, colleges, and non-formal settings.

We’re starting with three broad themes: 

  • Computing curricula, pedagogy, and assessment, including teacher professional development and the learning and teaching process
  • The role of non-formal learning in computing and digital making learning, including self-directed learning and extra-curricular programmes
  • Understanding and removing the barriers to computing education, including the factors that stand in the way of young people’s engagement and progression in computing education

While we’re based in the UK and expect to run a number of research projects here, we are eager to establish collaborations with universities and researchers in other countries, including the USA and India. 

Get involved

We’re really excited about this next chapter in our research work, and doubly excited to be working with the brilliant team at the Department of Computer Science and Technology. 

If you’d like to find out more or get involved in supporting the new Computing Education Research Centre, please subscribe to our research newsletter or email [email protected].

You can also join our free monthly research seminars.

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The digital divide: interactions between socioeconomic disadvantage and computing education

Post Syndicated from Sue Sentance original https://www.raspberrypi.org/blog/digital-divide-socioeconomic-disadvantage-computing-education/

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:

I want to sign up to attend

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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Universal design for learning in computing | Hello World #15

Post Syndicated from Hayley Leonard original https://www.raspberrypi.org/blog/universal-design-for-learning-in-computing-hello-world-15/

In our brand-new issue of Hello World magazine, Hayley Leonard from our team gives a primer on how computing educators can apply the Universal Design for Learning framework in their lessons.

Cover of issue 15 of Hello World magazine

Universal Design for Learning (UDL) is a framework for considering how tools and resources can be used to reduce barriers and support all learners. Based on findings from neuroscience, it has been developed over the last 30 years by the Center for Applied Special Technology (CAST), a nonprofit education research and development organisation based in the US. UDL is currently used across the globe, with research showing it can be an efficient approach for designing flexible learning environments and accessible content.

A computing classroom populated by students with diverse genders and ethnicities

Engaging a wider range of learners is an important issue in computer science, which is often not chosen as an optional subject by girls and those from some minority ethnic groups. Researchers at the Creative Technology Research Lab in the US have been investigating how UDL principles can be applied to computer science, to improve learning and engagement for all students. They have adapted the UDL guidelines to a computer science education context and begun to explore how teachers use the framework in their own practice. The hope is that understanding and adapting how the subject is taught could help to increase the representation of all groups in computing.

The UDL guidelines help educators anticipate barriers to learning and plan activities to overcome them.

A scientific approach

The UDL framework is based on neuroscientific evidence which highlights how different areas or networks in the brain work together to process information during learning. Importantly, there is variation across individuals in how each of these networks functions and how they interact with each other. This means that a traditional approach to teaching, in which a main task is differentiated for certain students with special educational needs, may miss out on the variation in learning between all students across different tasks.

A stylised representation of the human brain
The UDL framework is based on neuroscientific evidence

The UDL guidelines highlight different opportunities to take learner differences into account when planning lessons. The framework is structured according to three main principles, which are directly related to three networks in the brain that play a central role in learning. It encourages educators to plan multiple, flexible methods of engagement in learning (affective networks), representation of the teaching materials (recognition networks), and opportunities for action and expression of what has been learnt (strategic networks).

The three principles of UDL are each expanded into guidelines and checkpoints that allow educators to identify the different methods of engagement, representation, and expression to be used in a particular lesson. Each principle is also broken down into activities that allow learners to access the learning goals, remain engaged and build on their learning, and begin to internalise the approaches to learning so that they are empowered for the future.

Examples of UDL guidelines for computer science education from the Creative Technology Research Lab

Multiple means of engagement Multiple means of representation Multiple means of
action and expression
Provide options for recruiting interests
* Give students choice (software, project, topic)
* Allow students to make projects relevant to culture and age		 Provide options for perception
* Model computing through physical representations as well as through interactive whiteboard/videos etc.
* Select coding apps and websites that allow adjustment of visual settings (e.g. font size/contrast) and that are compatible with screen readers		 Provide options for physical action
* Include CS unplugged activities that show physical relationships of abstract computing concepts
* Use assistive technology, including a larger or smaller mouse or touchscreen devices
Provide options for sustaining effort and persistence
* Utilise pair programming and group work with clearly defined roles
* Discuss the integral role of perseverance and problem-solving in computer science		 Provide options for language, mathematical expressions, and symbols
* Teach and review computing vocabulary (e.g. code, animations, algorithms)
* Provide reference sheets with images of blocks, or with common syntax when using text		 Provide options for expression and communication
* Provide sentence starters or checklists for communicating in order to collaborate, give feedback, and explain work
* Provide options that include starter code
Provide options for self-regulation
* Break up coding activities with opportunities for reflection, such as ‘turn and talk’ or written questions
* Model different strategies for dealing with frustration appropriately		 Provide options for comprehension
* Encourage students to ask questions as comprehension checkpoints
* Use relevant analogies and make cross-curricular connections explicit		 Provide options for executive function
* Embed prompts to stop and plan, test, or debug throughout a lesson or project
* Demonstrate debugging with think-alouds

Each principle of the UDL framework is associated with three areas of activity which may be considered when planning lessons or units of work. It will not be the case that each area of activity should be covered in every lesson, and some may prove more important in particular contexts than others. The full table and explanation can be found on the Creative Technology Research Lab website at ctrl.education.ufl.edu/projects/tactic.

Applying UDL to computer science education

While an advantage of UDL is that the principles can be applied across different subjects, it is important to think carefully about what activities to address these principles could look like in the case of computer science.

Maya Israel
Researcher Maya Israel will speak at our April seminar

Researchers at the Creative Technology Research Lab, led by Maya Israel, have identified key activities, some of which are presented in the table on the previous page. These guidelines will help educators anticipate potential barriers to learning and plan activities that can overcome them, or adapt activities from those in existing schemes of work, to help engage the widest possible range of students in the lesson.

UDL in the classroom

As well as suggesting approaches to applying UDL to computer science education, the research team at the Creative Technology Research Lab has also investigated how teachers are using UDL in practice. Israel and colleagues worked with four novice computer science teachers in US elementary schools to train them in the use of UDL and understand how they applied the framework in their teaching.

Smiling learners in a computing classroom

The research found that the teachers were most likely to include in their teaching multiple means of engagement, followed by multiple methods of representation. For example, they all offered choice in their students’ activities and provided materials in different formats (such as oral and visual presentations and demonstrations). They were less likely to provide multiple means of action and expression, and mainly addressed this principle through supporting students in planning work and checking their progress against their goals.

Although the study included only four teachers, it highlighted the flexibility of the UDL approach in catering for different needs within variable teaching contexts. More research will be needed in future, with larger samples, to understand how successful the approach is in helping a wide range of students to achieve good learning outcomes.

Find out more about using UDL

There are numerous resources designed to help teachers learn more about the UDL framework and how to apply it to teaching computing. The CAST website (helloworld.cc/cast) includes an explainer video and the detailed UDL guidelines. The Creative Technology Research Lab website has computing-specific ideas and lesson plans using UDL (helloworld.cc/udl).

Maya Israel will be presenting her research at our computing education research seminar series, on 20 April 2021. Our seminars are free to attend and open to anyone from anywhere around the world. Find out more about the current seminar series, which focuses on diversity and inclusion, and sign up to attend for free.

Further reading

The post Universal design for learning in computing | Hello World #15 appeared first on Raspberry Pi.

What does equity-focused teaching mean in computer science education?

Post Syndicated from Sue Sentance original https://www.raspberrypi.org/blog/equity-focused-teaching-in-computer-science-education/

Today, I discuss the second research seminar in our series of six free online research seminars focused on diversity and inclusion in computing education, where we host researchers from the UK and USA together with the Royal Academy of Engineering. 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 this seminar, we were delighted to hear from Prof Tia Madkins (University of Texas at Austin), Dr Nicol R. Howard (University of Redlands), and Shomari Jones (Bellevue School District) (find their bios here), who talked to us about culturally responsive pedagogy and equity-focused teaching in K-12 Computer Science.

  • Tia Madkins
    Prof Tia Madkins
  • Nicol Howard
    Dr Nicol R. Howard
  • Shomari Jones
    Shomari Jones

Equity-focused computer science teaching

Tia began the seminar with an audience-engaging task: she asked all participants to share their own definition of equity in the seminar chat. Amongst their many suggestions were “giving everybody the same opportunity”, “equal opportunity to access high-quality education”, and “everyone has access to the same resources”. I found Shomari’s own definition of equity very powerful: 

“Equity is the fair treatment, access, opportunity, and advancement of all people, while at the same time striving to identify and eliminate barriers that have prevented the full participation of some groups. Improving equity involves increasing justice and fairness within the procedures and processes of institutions or systems, as well as the distribution of resources. Tackling equity requires an understanding of the root cause of outcome disparity within our society.”

Shomari Jones

This definition is drawn directly from the young people Shomari works with, and it goes beyond access and opportunity to the notion of increasing justice and fairness and addressing the causes of outcome disparity. Justice was a theme throughout the seminar, with all speakers referring to the way that their work looks at equity in computer science education through a justice-oriented lens.

Removing deficit thinking

Using a justice-oriented approach means that learners should be encouraged to use their computer science knowledge to make a difference in areas that are important to them. It means that just having access to a computer science education is not sufficient for equity.

Tia Madkins presents a slide: "A justice-oriented approach to computer science teaching empowers students to use CS knowledge for transformation, moves beyond access and achievement frames, and is an asset- or strengths-based approach centering students and families"

Tia spoke about the need to reject “deficit thinking” (i.e. focusing on what learners lack) and instead focus on learners’ strengths or assets and how they bring these to the school classroom. For researchers and teachers to do this, we need to be aware of our own mindset and perspective, to think about what we value about ethnic and racial identities, and to be willing to reflect and take feedback.

Activities to support computer science teaching

Nicol talked about some of the ways of designing computing lessons to be equity-focused. She highlighted the benefits of pair programming and other peer pedagogies, where students teach and learn from each other through feedback and sharing ideas/completed work. She suggested using a variety of different programs and environments, to ensure a range of different pathways to understanding. Teachers and schools can aim to base teaching around tools that are open and accessible and, where possible, available in many languages. If the software environment and tasks are accessible, they open the doors of opportunity to enable students to move on to more advanced materials. To demonstrate to learners that computer science is applicable across domains, the topic can also be introduced in the context of mathematics and other subjects.

Nicol Howard presents a slide: "Considerations for equity-focused computer science teaching include your beliefs (and your students' beliefs) and how they impact CS classrooms; tiered activities and pair programming; self-expressions versus CS preparation; equity-focused lens"

Learners can benefit from learning computer science regardless of whether they want to become a computer scientist. Computing offers them skills that they can use for self-expression or to be creative in other areas of their life. They can use their knowledge for a specific purpose and to become more autonomous, particularly if their teacher does not have any deficit thinking. In addition, culturally relevant teaching in the classroom demonstrates a teacher’s deliberate and explicit acknowledgment that they value all students in their classroom and expect students to excel.

Engaging family and community

Shomari talked about the importance of working with parents and families of ethnically diverse students in order to hear their voices and learn from their experiences.

Shomari Jones presents a slide: “Parents without backgrounds and insights into the changing landscape of technology struggle to negotiate what roles they can play, such as how to work together in computing activities or how to find learning opportunities for their children.”

He described how the absence of a background in technology of parents and carers can drastically impact the experiences of young people.

“Parents without backgrounds and insights into the changing landscape of technology struggle to negotiate what roles they can play, such as how to work together in computing activities or how to find learning opportunities for their children.”

Betsy DiSalvo, Cecili Reid, and Parisa Khanipour Roshan. 2014

Shomari drew on an example from the Pacific Northwest in the US, a region with many successful technology companies. In this location, young people from wealthy white and Asian communities can engage fully in informal learning of computer science and can have aspirations to enter technology-related fields, whereas amongst the Black and Latino communities, there are significant barriers to any form of engagement with technology. This already existent inequity has been enhanced by the coronavirus pandemic: once so much of education moved online, it became widely apparent that many families had never owned, or even used, a computer. Shomari highlighted the importance of working with pre-service teachers to support them in understanding the necessity of family and community engagement.

Building classroom communities

Building a classroom community starts by fostering and maintaining relationships with students, families, and their communities. Our speakers emphasised how important it is to understand the lives of learners and their situations. Through this understanding, learning experiences can be designed that connect with the learners’ lived experiences and cultural practices. In addition, by tapping into what matters most to learners, teachers can inspire them to be change agents in their communities. Tia gave the example of learning to code or learning to build an app, which provides learners with practical tools they can use for projects they care about, and with skills to create artefacts that challenge and document injustices they see happening in their communities.

Find out more

If you want to learn more about this topic, a great place to start is the recent paper Tia and Nicol have co-authored that lays out more detail on the work described in the seminar: Engaging Equity Pedagogies in Computer Science Learning Environments, by Tia C. Madkins, Nicol R. Howard and Natalie Freed, 2020.

You can access the presentation slides via our seminars page.

Join our next free seminar

In our next seminar on Tuesday 2 March at 17:00–18:30 BST / 12:00–13:30 EDT / 9:00–10:30 PDT / 18:00–19:30 CEST, we’ll welcome Jakita O. Thomas (Auburn University), who is going to talk to us about Designing STEM Learning Environments to Support Computational Algorithmic Thinking and Black Girls: A Possibility Model for Changing Hegemonic Narratives and Disrupting STEM Neoliberal Projects. To join this free online seminar, simply sign up with your name and email address.

I want to sign up for the next seminar

Once you’ve signed up, we’ll email you the seminar meeting link and instructions for joining. If you attended Peter’s and Billy’s seminar, the link remains the same.

The post What does equity-focused teaching mean in computer science education? appeared first on Raspberry Pi.

Computing education and underrepresentation: the data from England

Post Syndicated from Sue Sentance original https://www.raspberrypi.org/blog/computing-education-underrepresentation-data-england-schools/

In this blog post, I’ll discuss the first research seminar in our six-part series about diversity and inclusion. Let’s start by defining our terms. Diversity is 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.

It’s vital that we are inclusive in computing education, because we need to ensure that everyone can access and learn the empowering and enabling technical skills they need to support all aspects of their lives.

One male and two female teenagers at a computer

Between January and June of this year, we’re partnering with the Royal Academy of Engineering to host speakers from the UK and USA for a series of six research seminars focused on diversity and inclusion in computing education.

We kicked off the series with a seminar from Dr Peter Kemp and Dr Billy Wong focused on computing education in England’s schools post-14. Peter is a Lecturer in Computing Education at King’s College London, where he leads on initial teacher education in computing. His research areas are digital creativity and digital equity. Billy is an Associate Professor at the Institute of Education, University of Reading. His areas of research are educational identities and inequalities, especially in the context of higher education and STEM education.

  • Peter Kemp
    Dr Peter Kemp
  • Dr Billy Wong

Computing in England’s schools

Peter began the seminar with a comprehensive look at the history of curriculum change in Computing in England. This was very useful given our very international audience for these seminars, and I will summarise it below. (If you’d like more detail, you can look over the slides from the seminar. Note that these changes refer to England only, as education in the UK is devolved, and England, Northern Ireland, Scotland, and Wales each has a different education system.)

In 2014, England switched from mandatory ICT (Information and Communication Technology) to mandatory Computing (encompassing information technology, computer science, and digital literacy). This shift was complemented by a change in the qualifications for students aged 14–16 and 16–18, where the primary qualifications are GCSEs and A levels respectively:

  • At GCSE, there has been a transition from GCSE ICT to GCSE Computer Science over the last five years, with GCSE ICT being discontinued in 2017
  • At A level before 2014, ICT and Computing were on offer as two separate A levels; now there is only one, A level Computer Science

One of the issues is that in the English education system, there is a narrowing of the curriculum at age 14: students have to choose between Computer Science and other subjects such as Geography, History, Religious Studies, Drama, Music, etc. This means that those students that choose not to take a GCSE Computer Science (CS) may find that their digital education is thereby curtailed from then onwards. Peter’s and Billy’s view is that having a more specialist subject offer for age 14+ (Computer Science as opposed to ICT) means that fewer students take it, and they showed evidence of this from qualifications data. The number of students taking CS at GCSE has risen considerably since its introduction, but it’s not yet at the level of GCSE ICT uptake.

GCSE computer science and equity

Only 64% of schools in England offer GCSE Computer Science, meaning that just 81% of students have the opportunity to take the subject (some schools also add selection criteria). A higher percentage (90%) of selective grammar schools offer GCSE CS than do comprehensive schools (80%) or independent schools (39%). Peter suggested that this was making Computer Science a “little more elitist” as a subject.

Peter analysed data from England’s National Pupil Database (NPD) to thoroughly investigate the uptake of Computer Science post-14 with respect to the diversity of entrants.

He found that the gender gap for GCSE CS uptake is greater than it was for GCSE ICT. Now girls make up 22% of the cohort for GCSE CS (2020 data), whereas for the ICT qualification (2017 data), 43% of students were female.

Peter’s analysis showed that there is also a lower representation of black students and of students from socio-economically disadvantaged backgrounds in the cohort for GCSE CS. In contrast, students with Chinese ancestry are proportionally more highly represented in the cohort. 

Another part of Peter’s analysis related gender data to the Income Deprivation Affecting Children Index (IDACI), which is used as an indicator of the level of poverty in England’s local authority districts. In the graphs below, a higher IDACI decile means more deprivation in an area. Relating gender data of GCSE CS uptake against the IDACI shows that:

  • Girls from more deprived areas are more likely to take up GCSE CS than girls from less deprived areas are
  • The opposite is true for boys
Two bar charts relating gender data of GCSE uptake against the Income Deprivation Affecting Children Index. The graph plotting GCSE ICT data shows that students from areas with higher deprivation are slightly more likely to choose the GCSE, irrespective of gender. The graph plotting GCSE Computer Science data shows that girls from more deprived areas are more likely to take up GCSE CS than girls from less deprived areas, and the opposite is true for boys.

Peter covered much more data in the seminar, so do watch the video recording (below) if you want to learn more.

Peter’s analysis shows a lack of equity (i.e. equality of outcome in the form of proportional representation) in uptake of GCSE CS after age 14. It is also important to recognise, however, that England does mandate — not simply provide or offer — Computing for all pupils at both primary and secondary levels; making a subject mandatory is the only way to ensure that we do give access to all pupils.

What can we do about the lack of equity?

Billy presented some of the potential reasons for why some groups of young people are not fully represented in GCSE Computer Science:

  • There are many stereotypes surrounding the image of ‘the computer scientist’, and young people may not be able to identify with the perception they hold of ‘the computer scientist’
  • There is inequality in access to resources, as indicated by the research on science and STEM capital being carried out within the ASPIRES project

More research is needed to understand the subject choices young people make and their reasons for choosing as they do.

We also need to look at how the way we teach Computing to students aged 11 to 14 (and younger) affects whether they choose CS as a post-14 subject. Our next seminar revolves around equity-focused teaching practices, such as culturally relevant pedagogy or culturally responsive teaching, and how educators can use them in their CS learning environments. 

Meanwhile, our own research project at the Raspberry Pi Foundation, Gender Balance in Computing, investigates particular approaches in school and non-formal learning and how they can impact on gender balance in Computer Science. For an overview of recent research around barriers to gender balance in school computing, look back on the research seminar by Katharine Childs from our team.

Peter and Billy themselves have recently been successful in obtaining funding for a research project to explore female computing performance and subject choice in English schools, a project they will be starting soon!

If you missed the seminar, watch recording here. You can also find Peter and Billy’s presentation slides on our seminars page.

Next up in our seminar series

In our next research seminar on Tuesday 2 February at 17:00–18:30 BST / 12:00–13:30 EDT / 9:00–10:30 PDT / 18:00–19:30 CEST, we’ll welcome Prof Tia Madkins (University of Texas at Austin), Dr Nicol R. Howard (University of Redlands), and Shomari Jones (Bellevue School District), who are going to talk to us about culturally responsive pedagogy and equity-focused teaching in K-12 Computer Science. To join this free online seminar, simply sign up with your name and email address.

I want to sign up for the next seminar

Once you’ve signed up, we’ll email you the seminar meeting link and instructions for joining. If you attended Peter’s and Billy’s seminar, the link remains the same.

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Learning at home with the Raspberry Pi Foundation

Post Syndicated from Philip Colligan original https://www.raspberrypi.org/blog/learning-at-home-with-the-raspberry-pi-foundation/

As the UK — like many countries around the world — kicks off the new year with another national lockdown, meaning that millions of young people are unable to attend school, I want to share an update on how the Raspberry Pi Foundation is helping young people to learn at home.

Please help us spread the word to teachers, school leaders, governors, parents, and carers. Everything we are offering here is 100% free and the more people know about it, the more young people will benefit.

A girl and mother doing a homeschooling lesson at a laptop

Supporting teachers and pupils 

Schools and teachers all over the world have been doing a heroic job over the past ten months, managing the transition to emergency remote teaching during the first round of lockdowns, supporting the most vulnerable pupils, dealing with uncertainty, changing the way that schools worked to welcome pupils back safely, helping pupils catch up with lost learning, and much, much more.

Both in my role as Chief Executive of the Raspberry Pi Foundation and as chair of governors at a state school here in Cambridge, I’ve seen first-hand the immense pressure that schools and teachers are under. I’ve also seen them display the most amazing resilience, commitment, and innovation. I want to say a huge thank you to all teachers and school staff for everything you’ve done and continue to do to help young people through this crisis. 

Here’s some of the resources and tools that we’ve created to help you continue to deliver a world-class computing education: 

  • The Teach Computing Curriculum is a comprehensive set of lesson plans for KS1–4 (learners aged 5–16) as well as homework, progression mapping, and assessment materials.
  • Working with the fabulous Oak National Academy, we’ve produced 100 hours of video for 300 video lessons based on the Teach Computing Curriculum.
  • Isaac Computer Science is our online learning platform for advanced computer science (A level, learners aged 16–18) and includes comprehensive, interactive materials and videos. It also allows you to set your learners self-marking questions. 

All of these resources are mapped to the English computing curriculum and produced as part of the National Centre for Computing Education. They are available for everyone, anywhere in the world, for free. 

Making something fun with code

Parents and carers are the other heroes of remote learning during lockdown. I know from personal experience that juggling work and supporting home learning can be really tough, and we’re all trying to find meaningful, fun alternatives to letting our kids binge YouTube or Netflix (other video platforms and streaming services are available).

That’s why we’ve been working really hard to provide parents and carers with easy, accessible ways for you to help your young digital makers to get creative with technology:

A Coolest Projects participant

Getting computers into the hands of young people who need them 

One of the harsh lessons we learned last year was that far too many young people don’t have a computer for learning at home. There has always been a digital divide; the pandemic has just put it centre-stage. The good news is that the cost of solving this problem is now trivial compared to the cost of allowing it to persist.

That’s why the Raspberry Pi Foundation has teamed up with UK Youth and a network of grassroots youth and community organisations to get computers into the hands of disadvantaged young people across the UK.

A young person receives a Raspberry Pi kit to learn at home

For under £200 we can provide a vulnerable child with everything they need to learn at home, including a Raspberry Pi desktop computer, a monitor, a webcam, free educational software, and ongoing support from a local youth worker and the Foundation team. So far, we have managed to get 2000 Raspberry Pi computers into the hands of the most vulnerable young people in the UK. A drop in the ocean compared to the size of the problem, but a huge impact for every single young person and family.

This has only been possible thanks to the generous support of individuals, foundations, and businesses that have donated to support our work. If you’d like to get involved too, you can find out more here.

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Block-based programming: does it help students learn?

Post Syndicated from Sue Sentance original https://www.raspberrypi.org/blog/block-based-programming-does-it-help-students-learn-research-seminar/

At the Raspberry Pi Foundation, we are continually inspired by young learners in our community: they embrace digital making and computing to build creative projects, supported by our resources, clubs, and volunteers. While creating their projects, they are learning the core programming skills that underlie digital making.

Over the years, many tools and environments have been developed to make programming more accessible to young people. Scratch is one example of a block-based programming environment for young learners, and it’s been shown to make programming more accessible to them; on our projects site we offer many step-by-step Scratch project resources.

Mark Scratch
A Scratch code-along, led by one of our educators on our weekly Digital Making at Home live stream

But does block-based programming actually help learning? Does it increase motivation and support students? Where is the hard evidence? In our latest research seminar, we were delighted to hear from Dr David Weintrop, an Assistant Professor at the University of Maryland who has done research in this area for several years and published widely on the differences between block-based and text-based programming environments.

David Weintrop

A variety of block-based programming environments

The first useful insight David shared was that we should avoid thinking about block-based programming as synonymous with the well-known Scratch environment. There are several other environments, with different affordances, that David referred to in his talk, such as Snap, Pencil Code, Blockly, and more.

Logos of block-based programming environments

Some of these, for example Pencil Code, offer a dual-modality (or hybrid) environment, where learners can write the same program in a text-based and a block-based programming environment side by side. Dual-modality environments provide this side-by-side approach based on the assumption that being able to match a text-based program to its block-based equivalent supports the development of understanding of program syntax in a text-based language.

Screenshot of the Pencil Code dual-modality programming environment

As a tool for transitioning to text-based programming

Another aspect of the research around block-based programming focuses on its usefulness as a transition to a text-based language. David described a 15-week study he conducted in high schools in the USA to investigate differences in student learning caused by use of block-based, text-based, and hybrid (a mixture of both using a dual-modality platform) programming tools.

Details of the study design: classroom-based, 3 conditions, 2 phases, quasi-experimental mixed method study

The 90 students in the study (14 to 16 years old) were divided into three groups, each with a different intervention but taught by the same teacher. In the first phase of the study (5 weeks), the groups were set the same tasks with the same learning objectives, but they used either block-based programming, text-based programming, or the hybrid environment.

After 5 weeks, students were given a test to assess learning outcomes, and they were asked questions about their attitudes to programming (specifically their perception of computing and their confidence). In the second phase (10 weeks), all the students were taught Java (a common language taught in the USA for end-of-school assessment), and then the test and attitudinal questions were repeated.

The results showed that at the 5-week point, the students who had used block-based programming scored higher in their learning outcome assessment, but at the final assessment after 15 weeks, all groups’ scores were roughly equivalent.  

A graph of assessment scores of the three groups in the study. The final scores are not significantly different.

In terms of students’ perception of computing and confidence, the responses of the Blocks group were very positive at the 5-week point, while at the 15-week point, the responses were less positive. The responses from the Text group showed a gradual increase in positivity between the 5- and 15-week points. The Hybrid group’s responses weren’t as negative as those of the Text group at the 5-week point, and their positivity didn’t decrease like the Blocks group’s did.

Taking both methods of assessment into account, the Hybrid group showed the best results in the study. The gains associated with the block-based introduction to programming did not translate to those students being further ahead when learning Java, but starting with block-based programming also did not hamper students’ transition to text-based programming.

David completed his talk by recommending dual-modality environments (such as Pencil Code) for teaching programming, as used by the Hybrid group in his study. 

More research is needed

The seminar audience raised many questions about David’s study, for example whether the actual teaching (pedagogy) may have differed for the three groups, and whether the results are not just due to the specific tools or environments that were used. This is definitely an area for further research. 

It seems that students may benefit from different tools at different times, which is why a dual-modality environment can be very useful. Of course, competence in programming takes a long time to develop, so there is room on the research agenda for longitudinal studies that monitor students’ progress over many months and even years. Such studies could take into account both the teaching approach and the programming environment in order to determine what factors impact a deep understanding of programming concepts, and students’ desire to carry on with their programming journey. 

Next up in our series

If you missed the seminar, you can find David’s presentation slides and a recording of his talk on our seminars page.

Our next free online seminar takes place on Tuesday 5 January at 17:00–18:00 BST / 12:00–13:00 EDT / 9:00–10:00 PDT / 18:00–19:00 CEST. We’ll welcome Peter Kemp and Billy Wong, who are going to share insights from their research on computing education for underrepresented groups. To join this free online seminar, simply sign up with your name and email address.

I want to sign up for the next seminar

Once you’ve signed up, we’ll email you the seminar meeting link and instructions for joining. If you attended David’s seminar, the link remains the same.

The January seminar will be the first one in our series focusing on diversity and inclusion in computing education, which we’re co-hosting with the Royal Academy for Engineering. We hope to see you there!

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Diversity and inclusion in computing education — new research seminars

Post Syndicated from Sue Sentance original https://www.raspberrypi.org/blog/diversity-inclusion-computing-education-research-seminars/

At the Raspberry Pi Foundation, we host a free online research seminar once a month to explore a wide variety of topics in the area of digital and computing education. This year, we’ve hosted eleven seminars — you can (re)discover slides and recordings on our website.

A classroom of young learners and a teacher at laptops

Now we’re getting ready for new seminars in 2021! In the coming months, our seminars are going to focus on diversity and inclusion in computing education. This topic is extremely important, as we want to make sure that computing is accessible to all, that we understand how to actively remove barriers to participation for learners, and that we understand how to teach computing in an inclusive way. 

We are delighted to announce that these seminars focusing on diversity and inclusion will be co-hosted by the Royal Academy of Engineering. The Royal Academy of Engineering is harnessing the power of engineering to build a sustainable society and an inclusive economy that works for everyone.

Royal Academy of Engineering logo

We’re very excited to be partnering with the Academy because of our shared interest in ensuring that computing and engineering are inclusive and accessible to all.

Our upcoming seminars

The seminars take place on the first Tuesday of the month at 17:00–18:30 GMT / 12:00–13:30 EST / 9:00–10:30 PST / 18:00–19:30 CET.

  • 5 January 2021: Peter Kemp (King’s College London) and Billy Wong (University of Reading) will be looking at computing education in England, particularly GCSE computer science, and how it is accessed by groups typically underrepresented in computing.
  • 2 February 2021: Professor Tia Madkins (University of Texas at Austin), Nicol R. Howard (University of Redlands), and Shomari Jones (Bellevue School District) will be talking about equity-focused teaching in K–12 computer science. Find out more.
  • 2 March 2021: Dr Jakita O. Thomas (Auburn University, Alabama) will be talking about her research on supporting computational algorithmic thinking in the context of intersectional computing.
  • April 2021: event to be confirmed
  • 4 May 2021: Dr Cecily Morrison (Microsoft Research) will be speaking about her work on physical programming for people with visual impairments.

Join the seminars

We’d love to welcome you to these seminars so we can learn and discuss together. To get access, simply sign up with your name and email address.

I want to sign up for the seminar series

Once you’ve signed up, we’ll email you the seminar meeting link and instructions for joining. If you attended our seminars in the past, the link remains the same.

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PRIMM: encouraging talk in programming lessons

Post Syndicated from Oliver Quinlan original https://www.raspberrypi.org/blog/primm-talk-in-programming-lessons-research-seminar/

Whenever you learn a new subject or skill, at some point you need to pick up the particular language that goes with that domain. And the only way to really feel comfortable with this language is to practice using it. It’s exactly the same when learning programming.

A girl doing Scratch coding in a Code Club classroom

In our latest research seminar, we focused on how we educators and our students can talk about programming. The seminar presentation was given by our Chief Learning Officer, Dr Sue Sentance. She shared the work she and her collaborators have done to develop a research-based approach to teaching programming called PRIMM, and to work with teachers to investigate the effects of PRIMM on students.

Sue Sentance

As well as providing a structure for programming lessons, Sue’s research on PRIMM helps us think about ways in which learners can investigate programs, start to understand how they work, and then gradually develop the language to talk about them themselves.

Productive talk for education

Sue began by taking us through the rich history of educational research into language and dialogue. This work has been heavily developed in science and mathematics education, as well as language and literacy.

In particular the work of Neil Mercer and colleagues has shown that students need guidance to develop and practice using language to reason, and that developing high-quality language improves understanding. The role of the teacher in this language development is vital.

Sue’s work draws on these insights to consider how language can be used to develop understanding in programming.

Why is programming challenging for beginners?

Sue identified shortcomings of some teaching approaches that are common in the computing classroom but may not be suitable for all beginners.

  • ‘Copy code’ activities for learners take a long time, lead to dreaded syntax errors, and don’t necessarily build more understanding.
  • When teachers model the process of writing a program, this can be very helpful, but for beginners there may still be a huge jump from being able to follow the modeling to being able to write a program from scratch themselves.

PRIMM was designed by Sue and her collaborators as a language-first approach where students begin not by writing code, but by reading it.

What is PRIMM?

PRIMM stands for ‘Predict, Run, Investigate, Modify, Make’. In this approach, rather than copying code or writing programs from scratch, beginners instead start by focussing on reading working code.

In the Predict stage, the teacher provides learners with example code to read, discuss, and make output predictions about. Next, they run the code to see how the output compares to what they predicted. In the Investigate stage, the teacher sets activities for the learners to trace, annotate, explain, and talk about the code line by line, in order to help them understand what it does in detail.

In the seminar, Sue took us through a mini example of the stages of PRIMM where we predicted the output of Python Turtle code. You can follow along on the recording of the seminar to get the experience of what it feels like to work through this approach.

The impact of PRIMM on learning

The PRIMM approach is informed by research, and it is also the subject of research by Sue and her collaborators. They’ve conducted two studies to measure the effectiveness of PRIMM: an initial pilot, and a larger mixed-methods study with 13 teachers and 493 students with a control group.

The larger study used a pre and post test, and found that the group who experienced a PRIMM approach performed better on the tests than the control group. The researchers also collected a wealth of qualitative feedback from teachers. The feedback suggested that the approach can help students to develop a language to express their understanding of programming, and that there was much more productive peer conversation in the PRIMM lessons (sometimes this meant less talk, but at a more advanced level).

The PRIMM structure also gave some teachers a greater capacity to talk about the process of teaching programming. It facilitated the discussion of teaching ideas and learning approaches for the teachers, as well as developing language approaches that students used to learn programming concepts.

The research results suggest that learners taught using PRIMM appear to be developing the language skills to talk coherently about their programming. The effectiveness of PRIMM is also evidenced by the number of teachers who have taken up the approach, building in their own activities and in some cases remixing the PRIMM terminology to develop their own take on a language-first approach to teaching programming.

Future research will investigate in detail how PRIMM encourages productive talk in the classroom, and will link the approach to other work on semantic waves. (For more on semantic waves in computing education, see this seminar by Jane Waite and this symposium talk by Paul Curzon.)

Resources for educators who want to try PRIMM

If you would like to try out PRIMM with your learners, use our free support materials:

Join our next seminar

If you missed the seminar, you can find the presentation slides alongside the recording of Sue’s talk on our seminars page.

In our next seminar on Tuesday 1 December at 17:00–18:30 GMT / 12:00–13:30 EsT / 9:00–10:30 PT / 18:00–19:30 CEST. Dr David Weintrop from the University of Maryland will be presenting on the role of block-based programming in computer science education. To join, simply sign up with your name and email address.

I want to sign up to the next seminar

Once you’ve signed up, we’ll email you the seminar meeting link and instructions for joining. If you attended this past seminar, the link remains the same.

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Why a great teacher can make all the difference

Post Syndicated from Dan Fisher original https://www.raspberrypi.org/blog/a-great-teacher-can-make-all-the-difference/

When we think back to our school days, we can all recall that one teacher who inspired us, believed in us, and made all the difference to how we approached a particular subject. It was someone we maybe took for granted at the time and so we only realised (much) later how amazing they were. 

I hope this post makes you think of a teacher or mentor who has made a key difference in your life!

Here computer science student Jonathan Alderson and our team’s Ben Garside talk to me about how Ben supported and inspired Jonathan in his computer science classroom.

Ben Garside and Jonathan Alderson holding physical and virtual chess games
The teacher: Ben Garside. The student: Jonathan Alderson.

Hi Jonathan! How did you get into computing?

Jonathan: My first memories of using a computer were playing 3D Pinball, Club Penguin, and old Disney games, so nothing productive there…or so I thought! I was always good at IT and Maths at school, and Computing seemed to be a cross between the two, so I thought it would be good.

Jonathan and Ben, can you remember your time working together? It’s been a while now! 

Jonathan: I met Mr Garside at the start of sixth form. Our school didn’t have a computer science course, so a few of us would walk between schools twice a week. Mr Garside really made me feel welcome in a place where I didn’t know anyone.

When learning computer science, it’s difficult to understand the importance of new concepts like recursion, classes, or linked lists when the examples are so small. Mr Garside’s teaching made me see the relevance of them and how they could fit into other projects; it’s easy to go a long time without using concepts because you don’t necessarily need them, even when it would make your life a lot easier.

Mr Garside really made me feel welcome in a place where I didn’t know anyone. […] Mr Garside’s teaching made me see the relevance of [new computer science concepts] and how they could fit into other projects.

Jonathan Alderson

Ben: It was a real pleasure to teach Jonathan. He stands out as being one of the most inquisitive students that I have taught. If something wasn’t clear to him, he’d certainly let me know and ask relevant questions so that he could fully understand. Jonathan was also constantly working on his own programming projects outside of lessons. During his A level, I remember him taking it upon himself to write a program that played chess. Each week he would demonstrate the progress he had made to the class. It was a perfect example of decomposition as he tackled the project in small sections and had a clear plan as to what he wanted to achieve. By the end of his project, not only did he have a program that played chess, but it was capable of playing against real online users including making the mouse clicks on the screen!

Moving from procedural to object-oriented programming (OOP) can be a sticking point for a lot of learners, and I remember Jonathan finding this difficult at first. I think what helped Jonathan in particular was getting him to understand that this wasn’t as new a concept as he first thought. OOP was just a different paradigm where he could still apply all of the coding structures that he was already confident in using.

That sounds like a very cool project. What other projects did you make, Jonathan? And how did Ben help you?

Jonathan: My final-year project, [a video game] called Vector Venture, ended up becoming quite a mammoth task! I didn’t really have a clue about organising large projects, what an IDE was, or you could split files apart. Mr Garside helped me spend enough time on the final report and get things finished. He was very supportive of me releasing the game and got me a chance to speak at the Python North East group, which was a great opportunity.

Ben: Vector Venture was a very ambitious project that Jonathan undertook, but I think by then he had learned a lot about how to tackle a project of that size from previous projects such as the chess program. The key to his success was that whilst he was learning, he was picking projects to undertake that he had a genuine interest in and enjoyed developing. I would also tell my A level students to pick as a project something that they will enjoy developing. Jonathan clearly enjoyed developing games, but I also had students who picked projects to develop programs that would solve problems. For example, one of my students developed a system that would take online bookings for food orders and manage table allocation for a local restaurant.

I would tell my A level students to pick as a project something that they will enjoy developing.

Ben Garside

I think that point about having fun while learning something challenging like programming is really important to highlight. So what are you doing now, Jonathan?

Jonathan: I have just completed my undergraduate degree at the University of Leeds (UoL) with a place on the Dean’s List and am staying to complete a Masters in High Performance Graphics. 

During my time at UoL, I’ve had three summer placements creating medical applications and new systems for the university. This helped me understand the social benefits of computer science; it was great to work on something that is now benefitting so many people. My dissertation was on music visualisation, mapping instrument attributes of a currently playing song to control parameters inside sharers on the GPU to produce reactive visualisations. I’ve just completed an OpenGL project to create procedural underwater scenes, with realistic lighting, reflections, and fish simulations. I’m now really looking forward to completing my Game Engine project for my masters and graduating.

Teachers are often brilliant at taking something complicated and presenting it in a clearer way. Are those moments of clarity part of what motivates you to teach, Ben? 

Ben: There are lots of things that excite me about teaching computer science. Before I worked for the Raspberry Pi Foundation, there was a phrase I heard Carrie Anne Philbin say when I attended a Picademy: we are teaching young people to be digital makers, logical thinkers, and problem solvers, not just to be consumers of technology. I felt this really summed up how great it is to teach our subject. Teaching computer science means that we’re educating young people about the world around them and how technology plays its part in their lives. By doing this, we are empowering them to solve problems and to make educated choices about how they use technology.

Teaching computer science means that we’re educating young people about the world around them and how technology plays its part in their lives.

Ben Garside

As for my previous in-school experiences, I loved those lightbulb moments when something suddenly made sense to a student and a loud “Yesssss!” would break the silence of a quietly focused classroom. I loved teaching something that regularly sparked their imaginations; give them a single lesson on programming, and they would start to ask questions like: “Now I’ve made it do that…does this mean I could make it do this next?“. It wasn’t uncommon for students to want to do more outside of the classroom that wasn’t a homework activity. That, for me, was the ultimate win! 

How about you?

Who was the teacher who helped shape your future when you were at school? Tell us about them in the comments below.

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