Tag Archives: computing education

How we’re learning to explain AI terms for young people and educators

Post Syndicated from Veronica Cucuiat original https://www.raspberrypi.org/blog/explaining-ai-terms-young-people-educators/

What do we talk about when we talk about artificial intelligence (AI)? It’s becoming a cliche to point out that, because the term “AI” is used to describe so many different things nowadays, it’s difficult to know straight away what anyone means when they say “AI”. However, it’s true that without a shared understanding of what AI and related terms mean, we can’t talk about them, or educate young people about the field.

A group of young people demonstrate a project at Coolest Projects.

So when we started designing materials for the Experience AI learning programme in partnership with leading AI unit Google DeepMind, we decided to create short explanations of key AI and machine learning (ML) terms. The explanations are doubly useful:

  1. They ensure that we give learners and teachers a consistent and clear understanding of the key terms across all our Experience AI resources. Within the Experience AI Lessons for Key Stage 3 (age 11–14), these key terms are also correlated to the target concepts and learning objectives presented in the learning graph. 
  2. They help us talk about AI and AI education in our team. Thanks to sharing an understanding of what terms such as “AI”, “ML”, “model”, or “training” actually mean and how to best talk about AI, our conversations are much more productive.

As an example, here is our explanation of the term “artificial intelligence” for learners aged 11–14:

Artificial intelligence (AI) is the design and study of systems that appear to mimic intelligent behaviour. Some AI applications are based on rules. More often now, AI applications are built using machine learning that is said to ‘learn’ from examples in the form of data. For example, some AI applications are built to answer questions or help diagnose illnesses. Other AI applications could be built for harmful purposes, such as spreading fake news. AI applications do not think. AI applications are built to carry out tasks in a way that appears to be intelligent.

You can find 32 explanations in the glossary that is part of the Experience AI Lessons. Here’s an insight into how we arrived at the explanations.

Reliable sources

In order to ensure the explanations are as precise as possible, we first identified reliable sources. These included among many others:

Explaining AI terms to Key Stage 3 learners: Some principles

Vocabulary is an important part of teaching and learning. When we use vocabulary correctly, we can support learners to develop their understanding. If we use it inconsistently, this can lead to alternate conceptions (misconceptions) that can interfere with learners’ understanding. You can read more about this in our Pedagogy Quick Read on alternate conceptions.

Some of our principles for writing explanations of AI terms were that the explanations need to: 

  • Be accurate
  • Be grounded in education research best practice
  • Be suitable for our target audience (Key Stage 3 learners, i.e. 11- to 14-year-olds)
  • Be free of terms that have alternative meanings in computer science, such as “algorithm”

We engaged in an iterative process of writing explanations, gathering feedback from our team and our Experience AI project partners at Google DeepMind, and adapting the explanations. Then we went through the feedback and adaptation cycle until we all agreed that the explanations met our principles.

A real banana and an image of a banana shown on the screen of a laptop are both labelled "Banana".
Image: Max Gruber / Better Images of AI / Ceci n’est pas une banane / CC-BY 4.0

An important part of what emerged as a result, aside from the explanations of AI terms themselves, was a blueprint for how not to talk about AI. One aspect of this is avoiding anthropomorphism, detailed by Ben Garside from our team here.

As part of designing the the Experience AI Lessons, creating the explanations helped us to:

  • Decide which technical details we needed to include when introducing AI concepts in the lessons
  • Figure out how to best present these technical details
  • Settle debates about where it would be appropriate, given our understanding and our learners’ age group, to abstract or leave out details

Using education research to explain AI terms

One of the ways education research informed the explanations was that we used semantic waves to structure each term’s explanation in three parts: 

  1. Top of the wave: The first one or two sentences are a high-level abstract explanation of the term, kept as short as possible, while introducing key words and concepts.
  2. Bottom of the wave: The middle part of the explanation unpacks the meaning of the term using a common example, in a context that’s familiar to a young audience. 
  3. Top of the wave: The final one or two sentences repack what was explained in the example in a more abstract way again to reconnect with the term. The end part should be a repeat of the top of the wave at the beginning of the explanation. It should also add further information to lead to another concept. 

Most explanations also contain ‘middle of the wave’ sentences, which add additional abstract content, bridging the ‘bottom of the wave’ concrete example to the ‘top of the wave’ abstract content.

Here’s the “artificial intelligence” explanation broken up into the parts of the semantic wave:

  • Artificial intelligence (AI) is the design and study of systems that appear to mimic intelligent behaviour. (top of the wave)
  • Some AI applications are based on rules. More often now, AI applications are built using machine learning that is said to ‘learn’ from examples in the form of data. (middle of the wave)
  • For example, some AI applications are built to answer questions or help diagnose illnesses. Other AI applications could be built for harmful purposes, such as spreading fake news (bottom of the wave)
  • AI applications do not think. (middle of the wave)
  • AI applications are built to carry out tasks in a way that appears to be intelligent. (top of the wave)
Our "artificial intelligence" explanation broken up into the parts of the semantic wave.
Our “artificial intelligence” explanation broken up into the parts of the semantic wave. Red = top of the wave; yellow = middle of the wave; green = bottom of the wave

Was it worth our time?

Some of the explanations went through 10 or more iterations before we agreed they were suitable for publication. After months of thinking about, writing, correcting, discussing, and justifying the explanations, it’s tempting to wonder whether I should have just prompted an AI chatbot to generate the explanations for me.

A window of three images. On the right is a photo of a big tree in a green field in a field of grass and a bright blue sky. The two on the left are simplifications created based on a decision tree algorithm. The work illustrates a popular type of machine learning model: the decision tree. Decision trees work by splitting the population into ever smaller segments. I try to give people an intuitive understanding of the algorithm. I also want to show that models are simplifications of reality, but can still be useful, or in this case visually pleasing. To create this I trained a model to predict pixel colour values, based on an original photograph of a tree.
Rens Dimmendaal & Johann Siemens / Better Images of AI / Decision Tree reversed / CC-BY 4.0

I tested this idea by getting a chatbot to generate an explanation of “artificial intelligence” using the prompt “Explain what artificial intelligence is, using vocabulary suitable for KS3 students, avoiding anthropomorphism”. The result included quite a few inconsistencies with our principles, as well as a couple of technical inaccuracies. Perhaps I could have tweaked the prompt for the chatbot in order to get a better result. However, relying on a chatbot’s output would mean missing out on some of the value of doing the work of writing the explanations in collaboration with my team and our partners.

The visible result of that work is the explanations themselves. The invisible result is the knowledge we all gained, and the coherence we reached as a team, both of which enabled us to create high-quality resources for Experience AI. We wouldn’t have gotten to know what resources we wanted to write without writing the explanations ourselves and improving them over and over. So yes, it was worth our time.

What do you think about the explanations?

The process of creating and iterating the AI explanations highlights how opaque the field of AI still is, and how little we yet know about how best to teach and learn about it. At the Raspberry Pi Foundation, we now know just a bit more about that and are excited to share the results with teachers and young people.

You can access the Experience AI Lessons and the glossary with all our explanations at experience-ai.org. The glossary of AI explanations is just in its first published version: we will continue to improve it as we find out more about how to best support young people to learn about this field.

Let us know what you think about the explanations and whether they’re useful in your teaching. Onwards with the exciting work of establishing how to successfully engage young people in learning about and creating with AI technologies.

The post How we’re learning to explain AI terms for young people and educators appeared first on Raspberry Pi Foundation.

Introducing the Hello World newsletter

Post Syndicated from Gemma Coleman original https://www.raspberrypi.org/blog/hello-world-newsletter/

Launched six years ago, Hello World magazine is the education magazine about computing and digital making. It’s made for educators by educators, and a community of teachers around the world reads and contributes to every issue. We’re now starting a monthly Hello World newsletter to bring you more great content for computing educators while you await each new magazine issue.

Cover of Hello World issue 21.
Cover of The Big Book of Computing Content.
Cover of The Big Book of Computing Pedagogy.

A monthly newsletter for Hello World readers

The Hello World community is an amazing group of people, and we love hearing your ideas about what could make Hello World even better at supporting your classroom practice. That’s why we host a fun and informative Hello World podcast to chat with educators around the globe about all things computing and digital making, and why we regularly share some of our favourite past magazine articles online to keep the conversation on important topics going.

Now we’re starting a monthly newsletter to offer you another way to get regular computing education ideas and insights you can use in your teaching. Every month, we’ll be curating a couple of interesting Hello World articles, plus news about the free education resources, research, community stories, and events from the Foundation. You can expect bite-size summaries of all items, plus links for you to explore more in your own time.

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Keep up with all of the education news from the Raspberry Pi Foundation and Hello World by signing up for the Hello World newsletter today.

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If you’re already signed up to the Raspberry Pi LEARN newsletter, then you don’t need to do anything: this newsletter replaces LEARN and you will be automatically subscribed.

We hope you’ll enjoy the first Hello World newsletter, which we will send out this Wednesday. As always, let us know what you think of it on Twitter or Facebook, or here in the comments.

PS Remember that if you work or volunteer as an educator in the UK, you can subscribe to receive free Hello World print copies to your home or workplace.

The post Introducing the Hello World newsletter appeared first on Raspberry Pi Foundation.

Hello World #21 out now: Focus on primary computing education

Post Syndicated from Gemma Coleman original https://www.raspberrypi.org/blog/hello-world-21-primary-computing-education/

How do we best prepare young children for a world filled with digital technology? This is the question the writers in our newest issue of Hello World respond to with inspiration and ideas for computing education in primary school.

Cover of Hello World issue 21.

It is vital that young children gain good digital literacy skills and understanding of computing concepts, which they can then build on as they grow up. Digital technology is here to stay, and as Sethi De Clercq points out in his article, we need to prepare our youngest learners for circumstances and jobs that don’t yet exist.

In a computing classroom, a boy looks down at a keyboard.
A group of young people work together at a computer.

Primary computing education: Inspiration and ideas

Issue 21 of Hello World covers a big range of topics in the theme of primary computing education, including:

  • Cross-curricular project ideas to keep young learners engaged
  • Perfecting typing skills in the primary school classroom
  • Using picture books to introduce programming concepts to children
  • Toolkits for new and experienced computing primary teachers, by Neil Rickus and Catherine Archer
  • Explorations of different approaches to improving diversity in computing and instilling a sense of belonging from the very start of a child’s educational journey, by Chris Lovell and Peter Marshman

The issue also has useful news and updates about our work: we share insights from our primary-specialist learning managers, tell you a bit about the research presented at our ongoing primary education seminar series, and include some relevant lesson plans from The Computing Curriculum.

A child at a laptop in a classroom in rural Kenya.

As always, you’ll find many other articles to support and inspire you in your computing teaching in this new issue. Topics include programming with dyslexia, exploring filter bubbles with your learners to teach them about data science, and using metaphors, similes, and analogies to help your learners understand abstract concepts.

I want to download Hello World 21

What do you think?

This issue of Hello World focusses on primary computing education because readers like you told us in the annual readers’ survey that they’d like more articles for primary teachers.

We love to hear your ideas about what we can do to continue making Hello World interesting and relevant for you. So please get in touch on Twitter with your thoughts and suggestions.

The post Hello World #21 out now: Focus on primary computing education appeared first on Raspberry Pi Foundation.

Preparing young children for a digital world | Hello World #21

Post Syndicated from Sway Grantham original https://www.raspberrypi.org/blog/preparing-young-children-digital-world-hello-world-21/

How do we teach our youngest learners digital and computing skills? Hello World‘s issue 21 will focus on this question and all things primary school computing education. We’re excited to share this new issue with you on Tuesday 30 May. Today we’re giving you a taste by sharing an article from it, written by our own Sway Grantham.

Cover of Hello World issue 21.

How are you preparing young children for a world filled with digital technology? Technology use of our youngest learners is a hotly debated topic. From governments to parents and from learning outcomes to screen-time rules, everyone has an opinion on the ‘right’ approach. Meanwhile, many young children encounter digital technology as a part of their world at home. For example in the UK, 87 percent of 3- to 4-year-olds and 93 percent of 5- to 7-year-olds went online at home in 2023. Schools should be no different.

A girl doing digital making on a tablet

As educators, we have a responsibility to prepare learners for life in a digital world. We want them to understand its uses, to be aware of its risks, and to have access to the wide range of experiences unavailable without it. And we especially need to consider the children who do not encounter technology at home. Education should be a great equaliser, so we need to ensure all our youngest learners have access to the skills they need to realise their full potential.

Exploring technology and the world

A major aspect of early-years or kindergarten education is about learners sharing their world with each other and discovering that everyone has different experiences and does things in their own way. Using digital technology is no different.

Allowing learners to share their experiences of using digital technology both accepts the central role of technology in our lives today and also introduces them to its broader uses in helping people to learn, talk to others, have fun, and do work. At home, many young learners may use technology to do just one of these things. Expanding their use of technology can encourage them to explore a wider range of skills and to see technology differently.

A girl shows off a robot she has built.

In their classroom environment, these explorations can first take place as part of the roleplay area of a classroom, where learners can use toys to show how they have seen people use technology. It may seem counterintuitive that play-based use of non-digital toys can contribute to reducing the digital divide, but if you don’t know what technology can do, how can you go about learning to use it? There is also a range of digital roleplay apps (such as the Toca Boca apps) that allow learners to recreate their experiences of real-world situations, such as visiting the hospital, a hair salon, or an office. Such apps are great tools for extending roleplay areas beyond the resources you already have.

Another aspect of a child’s learning that technology can facilitate is their understanding of the world beyond their local community. Technology allows learners to explore the wider world and follow their interests in ways that are otherwise largely inaccessible. For example:

  • Using virtual reality apps, such as Expeditions Pro, which lets learners explore Antarctica or even the bottom of the ocean
  • Using augmented reality apps, such as Octagon Studio’s 4D+ cards, which make sea creatures and other animals pop out of learners’ screens
  • Doing a joint project with a class of children in another country, where learners blog or share ‘email’ with each other

Each of these opportunities gives children a richer understanding of the world while they use technology in meaningful ways.

Technology as a learning tool

Beyond helping children to better understand our world, technology offers opportunities to be expressive and imaginative. For example, alongside your classroom art activities, how about using an app like Draw & Tell, which helps learners draw pictures and then record themselves explaining what they are drawing? Or what about using filters on photographs to create artistic portraits of themselves or their favourite toys? Digital technology should be part of the range of tools learners can access for creative play and expression, particularly where it offers opportunities that analogue tools don’t.

Young learners at computers in a classroom.

Using technology is also invaluable for learners who struggle with communication and language skills. When speaking is something you find challenging, it can often be intimidating to talk to others who speak much more confidently. But speaking to a tablet? A tablet only speaks as well as you do. Apps to record sounds and listen back to them are a helpful way for young children to learn about how clear their speech is and practise speech exercises. ChatterPix Kids is a great tool for this. It lets learners take a photo of an object, e.g. their favourite soft toy, and record themselves talking about it. When they play back the recording, the app makes it look like the toy is saying their words. This is a very engaging way for young learners to practise communicating.

Technology is part of young people’s world

No matter how we feel about the role of technology in the lives of young people, it is a part of their world. We need to ensure we are giving all learners opportunities to develop digital skills and understand the role of technology, including how people can use it for social good.

A woman and child follow instructions to build a digital making project at South London Raspberry Jam.

This is not just about preparing them for their computing education (although that’s definitely a bonus!) or about online safety (although this is vital — see my articles in Hello World issue 15 and issue 19 for more about the topic). It’s about their right to be active citizens in the digital world.

So I ask again: how are you preparing young children for a digital world?

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The first experiences children have with learning about computing and digital technologies are formative. That’s why primary computing education should be of interest to all educators, no matter what the age of your learners is. This issue covers for example:

And there’s much more besides. So don’t miss out on this upcoming issue of Hello World — subscribe for free today to receive every PDF edition in your inbox on the day of publication.

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The post Preparing young children for a digital world | Hello World #21 appeared first on Raspberry Pi Foundation.

Introducing data science concepts and skills to primary school learners

Post Syndicated from Katharine Childs original https://www.raspberrypi.org/blog/data-science-data-literacy-primary-school-scotland/

Every day, most of us both consume and create data. For example, we interpret data from weather forecasts to predict our chances of a good weather for a special occasion, and we create data as our carbon footprint leaves a trail of energy consumption information behind us. Data is important in our lives, and countries around the world are expanding their school curricula to teach the knowledge and skills required to work with data, including at primary (K–5) level.

Kate Farrell
Kate Farrell
Prof. Judy Robertson

In our most recent research seminar, attendees heard about a research-based initiative called Data Education in Schools. The speakers, Kate Farrell and Professor Judy Robertson from the University of Edinburgh, Scotland, shared how this project aims to empower learners to develop data literacy skills and succeed in a data-driven world.

“Data literacy is the ability to ask questions, collect, analyse, interpret and communicate stories about data.”

– Kate Farrell & Prof. Judy Robertson

Being a data citizen

Scotland’s national curriculum does not explicitly mention data literacy, but the topic is embedded in many subjects such as Maths, English, Technologies, and Social Studies. Teachers in Scotland, particularly in primary schools, have the flexibility to deliver learning in an interdisciplinary way through project-based learning. Therefore, the team behind Data Education in Schools developed a set of cross-curricular data literacy projects. Educators and education policy makers in other countries who are looking to integrate computing topics with other subjects may also be interested in this approach.

Becoming a data citizen involves finding meaning in data, controlling your personal data trail, being a critical consumer of data, and taking action based on data.
Data citizens have skills they need to thrive in a world shaped by digital technology.

The Data Education in Schools projects are aimed not just at giving learners skills they may need for future jobs, but also at equipping them as data citizens in today’s world. A data citizen can think critically, interpret data, and share insights with others to effect change.

Kate and Judy shared an example of data citizenship from a project they had worked on with a primary school. The learners gathered data about how much plastic waste was being generated in their canteen. They created a data visualisation in the form of a giant graph of types of rubbish on the canteen floor and presented this to their local council.

A child arranges objects to visualise data.
Sorting food waste from lunch by type of material

As a result, the council made changes that reduced the amount of plastic used in the canteen. This shows how data citizens are able to communicate insights from data to influence decisions.

A cycle for data literacy projects

Across its projects, the Data Education in Schools initiative uses a problem-solving cycle called the PPDAC cycle. This cycle is a useful tool for creating educational resources and for teaching, as you can use it to structure resources, and to concentrate on areas to develop learner skills.

The PPDAC project cycle.
The PPDAC data problem-solving cycle

The five stages of the cycle are: 

  1. Problem: Identifying the problem or question to be answered
  2. Plan: Deciding what data to collect or use to answer the question
  3. Data: Collecting the data and storing it securely
  4. Analysis: Preparing, modelling, and visualising the data, e.g. in a graph or pictogram
  5. Conclusion: Reviewing what has been learned about the problem and communicating this with others 

Smaller data literacy projects may focus on one or two stages within the cycle so learners can develop specific skills or build on previous learning. A large project usually includes all five stages, and sometimes involves moving backwards — for example, to refine the problem — as well as forwards.

Data literacy for primary school learners

At primary school, the aim of data literacy projects is to give learners an intuitive grasp of what data looks like and how to make sense of graphs and tables. Our speakers gave some great examples of playful approaches to data. This can be helpful because younger learners may benefit from working with tangible objects, e.g. LEGO bricks, which can be sorted by their characteristics. Kate and Judy told us about one learner who collected data about their clothes and drew the results in the form of clothes on a washing line — a great example of how tangible objects also inspire young people’s creativity.

In a computing classroom, a girl laughs at what she sees on the screen.

As learners get older, they can begin to work with digital data, including data they collect themselves using physical computing devices such as BBC micro:bit microcontrollers or Raspberry Pi computers.

Free resources for primary (and secondary) schools

For many attendees, one of the highlights of the seminar was seeing the range of high-quality teaching resources for learners aged 3–18 that are part of the Data Education in Schools project. These include: 

  • Data 101 videos: A set of 11 videos to help primary and secondary teachers understand data literacy better.
  • Data literacy live lessons: Data-related activities presented through live video.
  • Lesson resources: Lots of projects to develop learners’ data literacy skills. These are mapped to the Scottish primary and secondary curriculum, but can be adapted for use in other countries too.

More resources are due to be published later in 2023, including a set of prompt cards to guide learners through the PPDAC cycle, a handbook for teachers to support the teaching of data literacy, and a set of virtual data-themed escape rooms.  

You may also be interested in the units of work on data literacy skills that are part of The Computing Curriculum, our complete set of classroom resources to teach computing to 5- to 16-year-olds.

Join our next seminar on primary computing education

At our next seminar we welcome Aim Unahalekhaka from Tufts University, USA, who will share research about a rubric to evaluate young learners’ ScratchJr projects. If you have a tablet with ScratchJr installed, make sure to have it available to try out some activities. The seminar will take place online on Tuesday 6 June at 17.00 UK time, sign up now to not miss out.

I want to sign up for the next seminar

To find out more about connecting research to practice for primary computing education, you can see a list of our upcoming monthly seminars on primary (K–5) teaching and learning and watch the recordings of previous seminars in this series.

The post Introducing data science concepts and skills to primary school learners appeared first on Raspberry Pi Foundation.

Integrating primary computing and literacy through multimodal storytelling

Post Syndicated from Veronica Cucuiat original https://www.raspberrypi.org/blog/primary-computing-programming-literacy-storytelling/

Broadening participation and finding new entry points for young people to engage with computing is part of how we pursue our mission here at the Raspberry Pi Foundation. It was also the focus of our March online seminar, led by our own Dr Bobby Whyte. In this third seminar of our series on computing education for primary-aged children, Bobby presented his work on ‘designing multimodal composition activities for integrated K-5 programming and storytelling’. In this research he explored the integration of computing and literacy education, and the implications and limitations for classroom practice.

Young learners at computers in a classroom.

Motivated by challenges Bobby experienced first-hand as a primary school teacher, his two studies on the topic contribute to the body of research aiming to make computing less narrow and difficult. In this work, Bobby integrated programming and storytelling as a way of making the computing curriculum more applicable, relevant, and contextualised.

Critically for computing educators and researchers in the area, Bobby explored how theories related to ‘programming as writing’ translate into practice, and what the implications of designing and delivering integrated lessons in classrooms are. While the two studies described here took place in the context of UK schooling, we can learn universal lessons from this work.

What is multimodal composition?

In the seminar Bobby made a distinction between applying computing to literacy (or vice versa) and true integration of programming and storytelling. To achieve true integration in the two studies he conducted, Bobby used the idea of ‘multimodal composition’ (MMC). A multimodal composition is defined as “a composition that employs a variety of modes, including sound, writing, image, and gesture/movement [… with] a communicative function”.

Storytelling comes together with programming in a multimodal composition as learners create a program to tell a story where they:

  • Decide on content and representation (the characters, the setting, the backdrop)
  • Structure text they’ve written
  • Use technical aspects (i.e. motion blocks, tension) to achieve effects for narrative purposes
A screenshot showing a Scratch project.
Defining multimodal composition (MMC) for a visual programming context

Multimodality for programming and storytelling in the classroom

To investigate the use of MMC in the classroom, Bobby started by designing a curriculum unit of lessons. He mapped the unit’s MMC activities to specific storytelling and programming learning objectives. The MMC activities were designed using design-based research, an approach in which something is designed and tested iteratively in real-world contexts. In practice that means Bobby collaborated with teachers and students to analyse, evaluate, and adapt the unit’s activities.

A list of learning objectives that could be covered by a multimodal composition activity.
Mapping of the MMC activities to storytelling and programming learning objectives

The first of two studies to explore the design and implementation of MMC activities was conducted with 10 K-5 students (age 9 to 11) and showed promising results. All students approached the composition task multimodally, using multiple representations for specific purposes. In other words, they conveyed different parts of their stories using either text, sound, or images.

Bobby found that broadcast messages and loops were the least used blocks among the group. As a consequence, he modified the curriculum unit to include additional scaffolding and instructional support on how and why the students might embed these elements.

A list of modifications to the MMC curriculum unit based on testing in a classroom.
Bobby modified the classroom unit based on findings from his first study

In the second study, the MMC activities were evaluated in a classroom of 28 K-5 students led by one teacher over two weeks. Findings indicated that students appreciated the longer multi-session project. The teacher reported being satisfied with the project work the learners completed and the skills they practised. The teacher also further integrated and adapted the unit into their classroom practice after the research project had been completed.

How might you use these research findings?

Factors that impacted the integration of storytelling and programming included the teacher’s confidence to teach programming as well as the teacher’s ability to differentiate between students and what kind of support they needed depending on their previous programming experience.

In addition, there are considerations regarding the curriculum. The school where the second study took place considered the activities in the unit to be literacy-light, as the English literacy curriculum is ‘text-heavy’ and the addition of multimodal elements ‘wastes’ opportunities to produce stories that are more text-based.

Woman teacher and female student at a laptop.

Bobby’s research indicates that MMC provides useful opportunities for learners to simultaneously pursue storytelling and programming goals, and the curriculum unit designed in the research proved adaptable for the teacher to integrate into their classroom practice. However, Bobby cautioned that there’s a need to carefully consider both the benefits and trade-offs when designing cross-curricular integration projects in order to ensure a fair representation of both subjects.

Can you see an opportunity for integrating programming and storytelling in your classroom? Let us know your thoughts or questions in the comments below.

You can watch Bobby’s full presentation:

And you can read his research paper Designing for Integrated K-5 Computing and Literacy through Story-making Activities (open access version).

You may also be interested in our pilot study on using storytelling to teach computing in primary school, which we conducted as part of our Gender Balance in Computing programme.

Join our next seminar on primary computing education

At our next seminar, we welcome Kate Farrell and Professor Judy Robertson (University of Edinburgh). This session will introduce you to how data literacy can be taught in primary and early-years education across different curricular areas. It will take place online on Tuesday 9 May at 17.00 UK time, don’t miss out and sign up now.

I want to sign up to join the seminar

Yo find out more about connecting research to practice for primary computing education, you can find other our upcoming monthly seminars on primary (K–5) teaching and learning and watch the recordings of previous seminars in this series.

The post Integrating primary computing and literacy through multimodal storytelling appeared first on Raspberry Pi Foundation.

Experience AI: The excitement of AI in your classroom

Post Syndicated from Duncan Maidens original https://www.raspberrypi.org/blog/experience-ai-launch-lessons/

We are delighted to announce that we’ve launched Experience AI, our new learning programme to help educators to teach, inspire, and engage young people in the subject of artificial intelligence (AI) and machine learning (ML).

Experience AI is a new educational programme that offers cutting-edge secondary school resources on AI and machine learning for teachers and their students. Developed in partnership by the Raspberry Pi Foundation and DeepMind, the programme aims to support teachers in the exciting and fast-moving area of AI, and get young people passionate about the subject.

The importance of AI and machine learning education

Artificial intelligence and machine learning applications are already changing many aspects of our lives. From search engines, social media content recommenders, self-driving cars, and facial recognition software, to AI chatbots and image generation, these technologies are increasingly common in our everyday world.

Young people who understand how AI works will be better equipped to engage with the changes AI applications bring to the world, to make informed decisions about using and creating AI applications, and to choose what role AI should play in their futures. They will also gain critical thinking skills and awareness of how they might use AI to come up with new, creative solutions to problems they care about.

The AI applications people are building today are predicted to affect many career paths. In 2020, the World Economic Forum estimated that AI would replace some 85 million jobs by 2025 and create 97 million new ones. Many of these future jobs will require some knowledge of AI and ML, so it’s important that young people develop a strong understanding from an early age.

A group of young people investigate computer hardware together.
 Develop a strong understanding of the concepts of AI and machine learning with your learners.

Experience AI Lessons

Something we get asked a lot is: “How do I teach AI and machine learning with my class?”. To answer this question, we have developed a set of free lessons for secondary school students (age 11 to 14) that give you everything you need including lesson plans, slide decks, worksheets, and videos.

The lessons focus on relatable applications of AI and are carefully designed so that teachers in a wide range of subjects can use them. You can find out more about how we used research to shape the lessons and how we aim to avoid misconceptions about AI.

The lessons are also for you if you’re an educator or volunteer outside of a school setting, such as in a coding club.

The six lessons

  1. What is AI?: Learners explore the current context of artificial intelligence (AI) and how it is used in the world around them. Looking at the differences between rule-based and data-driven approaches to programming, they consider the benefits and challenges that AI could bring to society. 
  2. How computers learn: Learners focus on the role of data-driven models in AI systems. They are introduced to machine learning and find out about three common approaches to creating ML models. Finally the learners explore classification, a specific application of ML.
  3. Bias in, bias out: Learners create their own machine learning model to classify images of apples and tomatoes. They discover that a limited dataset is likely to lead to a flawed ML model. Then they explore how bias can appear in a dataset, resulting in biased predictions produced by a ML model.
  4. Decision trees: Learners take their first in-depth look at a specific type of machine learning model: decision trees. They see how different training datasets result in the creation of different ML models, experiencing first-hand what the term ‘data-driven’ means. 
  5. Solving problems with ML models: Learners are introduced to the AI project lifecycle and use it to create a machine learning model. They apply a human-focused approach to working on their project, train a ML model, and finally test their model to find out its accuracy.
  6. Model cards and careers: Learners finish the AI project lifecycle by creating a model card to explain their machine learning model. To finish off the unit, they explore a range of AI-related careers, hear from people working in AI research at DeepMind, and explore how they might apply AI and ML to their interests.

As part of this exciting first phase, we’re inviting teachers to participate in research to help us further develop the resources. All you need to do is sign up through our website, download the lessons, use them in your classroom, and give us your valuable feedback.

I want to access the Experience AI lessons
An educator points to an image on a student's computer screen.
 Ben Garside, one of our lead educators working on Experience AI, takes a group of students through one of the new lessons.

Support for teachers

We’ve designed the Experience AI lessons with teacher support in mind, and so that you can deliver them to your learners aged 11 to 14 no matter what your subject area is. Each of the lesson plans includes a section that explains new concepts, and the slide decks feature embedded videos in which DeepMind’s AI researchers describe and bring these concepts to life for your learners.

We will also be offering you a range of new teacher training opportunities later this year, including a free online CPD course — Introduction to AI and Machine Learning — and a series of AI-themed webinars.

Tell us your feedback

We will be inviting schools across the UK to test and improve the Experience AI lessons through feedback. We are really looking forward to working with you to shape the future of AI and machine learning education.

Visit the Experience AI website today to get started.

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How anthropomorphism hinders AI education

Post Syndicated from Ben Garside original https://www.raspberrypi.org/blog/ai-education-anthropomorphism/

In the 1950s, Alan Turing explored the central question of artificial intelligence (AI). He thought that the original question, “Can machines think?”, would not provide useful answers because the terms “machine” and “think” are hard to define. Instead, he proposed changing the question to something more provable: “Can a computer imitate intelligent behaviour well enough to convince someone they are talking to a human?” This is commonly referred to as the Turing test.

It’s been hard to miss the newest generation of AI chatbots that companies have released over the last year. News articles and stories about them seem to be everywhere at the moment. So you may have heard of machine learning (ML) chatbots such as ChatGPT and LaMDA. These chatbots are advanced enough to have caused renewed discussions about the Turing Test and whether the chatbots are sentient.

Chatbots are not sentient

Without any knowledge of how people create such chatbots, it’s easy to imagine how someone might develop an incorrect mental model around these chatbots being living entities. With some awareness of Sci-Fi stories, you might even start to imagine what they could look like or associate a gender with them.

A person in front of a cloudy sky, seen through a refractive glass grid. Parts of the image are overlaid with a diagram of a neural network.
Image: Alan Warburton / © BBC / Better Images of AI / Quantified Human / CC BY 4.0

The reality is that these new chatbots are applications based on a large language model (LLM) — a type of machine learning model that has been trained with huge quantities of text, written by people and taken from places such as books and the internet, e.g. social media posts. An LLM predicts the probable order of combinations of words, a bit like the autocomplete function on a smartphone. Based on these probabilities, it can produce text outputs. LLM chatbots run on servers with huge amounts of computing power that people have built in data centres around the world.

Our AI education resources for young people

AI applications are often described as “black boxes” or “closed boxes”: they may be relatively easy to use, but it’s not as easy to understand how they work. We believe that it’s fundamentally important to help everyone, especially young people, to understand the potential of AI technologies and to open these closed boxes to understand how they actually work.

As always, we want to demystify digital technology for young people, to empower them to be thoughtful creators of technology and to make informed choices about how they engage with technology — rather than just being passive consumers.

That’s the goal we have in mind as we’re working on lesson resources to help teachers and other educators introduce KS3 students (ages 11 to 14) to AI and ML. We will release these Experience AI lessons very soon.

Why we avoid describing AI as human-like

Our researchers at the Raspberry Pi Computing Education Research Centre have started investigating the topic of AI and ML, including thinking deeply about how AI and ML applications are described to educators and learners.

To support learners to form accurate mental models of AI and ML, we believe it is important to avoid using words that can lead to learners developing misconceptions around machines being human-like in their abilities. That’s why ‘anthropomorphism’ is a term that comes up regularly in our conversations about the Experience AI lessons we are developing.

To anthropomorphise: “to show or treat an animal, god, or object as if it is human in appearance, character, or behaviour”

https://dictionary.cambridge.org/dictionary/english/anthropomorphize

Anthropomorphising AI in teaching materials might lead to learners believing that there is sentience or intention within AI applications. That misconception would distract learners from the fact that it is people who design AI applications and decide how they are used. It also risks reducing learners’ desire to take an active role in understanding AI applications, and in the design of future applications.

Examples of how anthropomorphism is misleading

Avoiding anthropomorphism helps young people to open the closed box of AI applications. Take the example of a smart speaker. It’s easy to describe a smart speaker’s functionality in anthropomorphic terms such as “it listens” or “it understands”. However, we think it’s more accurate and empowering to explain smart speakers as systems developed by people to process sound and carry out specific tasks. Rather than telling young people that a smart speaker “listens” and “understands”, it’s more accurate to say that the speaker receives input, processes the data, and produces an output. This language helps to distinguish how the device actually works from the illusion of a persona the speaker’s voice might conjure for learners.

Eight photos of the same tree taken at different times of the year, displayed in a grid. The final photo is highly pixelated. Groups of white blocks run across the grid from left to right, gradually becoming aligned.
Image: David Man & Tristan Ferne / Better Images of AI / Trees / CC BY 4.0

Another example is the use of AI in computer vision. ML models can, for example, be trained to identify when there is a dog or a cat in an image. An accurate ML model, on the surface, displays human-like behaviour. However, the model operates very differently to how a human might identify animals in images. Where humans would point to features such as whiskers and ear shapes, ML models process pixels in images to make predictions based on probabilities.

Better ways to describe AI

The Experience AI lesson resources we are developing introduce students to AI applications and teach them about the ML models that are used to power them. We have put a lot of work into thinking about the language we use in the lessons and the impact it might have on the emerging mental models of the young people (and their teachers) who will be engaging with our resources.

It’s not easy to avoid anthropomorphism while talking about AI, especially considering the industry standard language in the area: artificial intelligence, machine learning, computer vision, to name but a few examples. At the Foundation, we are still training ourselves not to anthropomorphise AI, and we take a little bit of pleasure in picking each other up on the odd slip-up.

Here are some suggestions to help you describe AI better:

Avoid using Instead use
Avoid using phrases such as “AI learns” or “AI/ML does” Use phrases such as “AI applications are designed to…” or “AI developers build applications that…”
Avoid words that describe the behaviour of people (e.g. see, look, recognise, create, make) Use system type words (e.g. detect, input, pattern match, generate, produce)
Avoid using AI/ML as a countable noun, e.g. “new artificial intelligences emerged in 2022” Refer to ‘AI/ML’ as a scientific discipline, similarly to how you use the term “biology”

The purpose of our AI education resources

If we are correct in our approach, then whether or not the young people who engage in Experience AI grow up to become AI developers, we will have helped them to become discerning users of AI technologies and to be more likely to see such products for what they are: data-driven applications and not sentient machines.

If you want to use the Experience AI lessons to teach your learners, please sign up to be the first to hear when we launch these resources.

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AI education resources: What do we teach young people?

Post Syndicated from Jane Waite original https://www.raspberrypi.org/blog/ai-education-resources-what-to-teach-seame-framework/

People have many different reasons to think that children and teenagers need to learn about artificial intelligence (AI) technologies. Whether it’s that AI impacts young people’s lives today, or that understanding these technologies may open up careers in their future — there is broad agreement that school-level education about AI is important.

A young person writes Python code.

But how do you actually design lessons about AI, a technical area that is entirely new to young people? That was the question we needed to answer as we started Experience AI, our exciting collaboration with DeepMind, a leading AI company.

Our approach to developing AI education resources

As part of Experience AI, we are creating a free set of lesson resources to help teachers introduce AI and machine learning (ML) to KS3 students (ages 11 to 14). In England this area is not currently part of the national curriculum, but it’s starting to appear in all sorts of learning materials for young people. 

Two learners and a teacher in a physical computing lesson.

While developing the six Experience AI lessons, we took a research-informed approach. We built on insights from the series of research seminars on AI and data science education we had hosted in 2021 and 2022, and on research we ourselves have been conducting at the Raspberry Pi Computing Education Research Centre.

We reviewed over 500 existing resources that are used to teach AI and ML.

As part of this research, we reviewed over 500 existing resources that are used to teach AI and ML. We found that the vast majority of them were one-off activities, and many claimed to be appropriate for learners of any age. There were very few sets of lessons, or units of work, that were tailored to a specific age group. Activities often had vague learning objectives, or none at all. We rarely found associated assessment activities. These were all shortcomings we wanted to avoid in our set of lessons.

To analyse the content of AI education resources, we use a simple framework called SEAME. This framework is based on work I did in 2018 with Professor Paul Curzon at Queen Mary University of London, running professional development for educators on teaching machine learning.

The SEAME framework gives you a simple way to group learning objectives and resources related to teaching AI and ML, based on whether they focus on social and ethical aspects (SE), applications (A), models (M), or engines (E, i.e. how AI works).
Click to enlarge.

The SEAME framework gives you a simple way to group learning objectives and resources related to teaching AI and ML, based on whether they focus on social and ethical aspects (SE), applications (A), models (M), or engines (E, i.e. how AI works). We hope that it will be a useful tool for anyone who is interested in looking at resources to teach AI. 

What do AI education resources focus on?

The four levels of the SEAME framework do not indicate a hierarchy or sequence. Instead, they offer a way for teachers, resource developers, and researchers to talk about the focus of AI learning activities.

Social and ethical aspects (SE)

The SE level covers activities that relate to the impact of AI on everyday life, and to its implications for society. Learning objectives and their related resources categorised at this level introduce students to issues such as privacy or bias concerns, the impact of AI on employment, misinformation, and the potential benefits of AI applications.

A slide from a lesson about AI that describes an AI application related to timetables.
An example activity in the Experience AI lessons where learners think about the social and ethical issues of an AI application that predicts what subjects they might want to study. This activity is mostly focused on the social and ethical level of the SEAME framework, but also links to the applications and models levels.

Applications (A)

The A level refers to activities related to applications and systems that use AI or ML models. At this level, learners do not learn how to train models themselves, or how such models work. Learning objectives at this level include knowing a range of AI applications and starting to understand the difference between rule-based and data-driven approaches to developing applications.

Models (M)

The M level concerns the models underlying AI and ML applications. Learning objectives at this level include learners understanding the processes used to train and test models. For example, through resources focused on the M level, students could learn about the different learning paradigms of ML (i.e., supervised, unsupervised, or reinforcement learning).

A slide from a lesson about AI that describes an ML model to classify animals.
An example activity in the Experience AI lessons where students learn about classification. This activity is mostly focused on the models level of the SEAME framework, but also links to the social and ethical and the applications levels.

Engines (E)

The E level is related to the engines that make AI models work. This is the most hidden and complex level, and for school-aged learners may need to be taught using unplugged activities and visualisations. Learning objectives could include understanding the basic workings of systems such as data-driven decision trees and artificial neural networks.

Covering the four levels

Some learning activities may focus on a single level, but activities can also span more than one level. For example, an activity may start with learners trying out an existing ‘rock-paper-scissors’ application that uses an ML model to recognise hand shapes. This would cover the applications level. If learners then move on to train the model to improve its accuracy by adding more image data, they work at the models level.

A teacher helps a young person with a coding project.

Other activities cover several SEAME levels to address a specific concept. For example, an activity focussed on bias might start with an example of the societal impact of bias (SE level). Learners could then discuss the AI applications they use and reflect on how bias impacts them personally (A level). The activity could finish with learners exploring related data in a simple ML model and thinking about how representative the data is of all potential application users (M level).

The set of lessons on AI we are developing in collaboration with DeepMind covers all four levels of SEAME.

The set of Experience AI lessons we are developing in collaboration with DeepMind covers all four levels of SEAME. The lessons are based on carefully designed learning objectives and specifically targeted to KS3 students. Lesson materials include presentations, videos, student activities, and assessment questions.

We’re releasing the Experience AI lessons very soon — if you want to be the first to hear news about them, please sign up here.

I want to hear more about Experience AI

The SEAME framework as a tool for research on AI education

For researchers, we think the SEAME framework will, for example, be useful to analyse school curriculum material to see whether some age groups have more learning activities available at one level than another, and whether this changes over time. We may find that primary school learners work mostly at the SE and A levels, and secondary school learners move between the levels with increasing clarity as they develop their knowledge. It may also be the case that some learners or teachers prefer activities focused on one level rather than another. However, we can’t be sure: research is needed to investigate the teaching and learning of AI and ML across all year groups.

That’s why we’re excited to welcome Salomey Afua Addo to the Raspberry Pi Computing Education Research Centre. Salomey joined the Centre as a PhD student in January, and her research will focus on approaches to the teaching and learning of AI. We’re looking forward to seeing the results of her work.

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Launching Ada Computer Science, the new platform for learning about computer science

Post Syndicated from Duncan Maidens original https://www.raspberrypi.org/blog/ada-computer-science/

We are excited to launch Ada Computer Science, the new online learning platform for teachers, students, and anyone interested in learning about computer science.

Ada Computer Science logo on dark background.

With the rapid advances being made in AI systems and chatbots built on large language models, such as ChatGPT, it’s more important than ever that all young people understand the fundamentals of computer science. 

Our aim is to enable young people all over the world to learn about computer science through providing access to free, high-quality and engaging resources that can be used by both students and teachers.

A female computing educator with three female students at laptops in a classroom.

A partnership between the Raspberry Pi Foundation and the University of Cambridge, Ada Computer Science offers comprehensive resources covering everything from algorithms and data structures to computational thinking and cybersecurity. It also has nearly 1000 rigorously researched and automatically marked interactive questions to test your understanding. Ada Computer Science is improving all the time, with new content developed in response to user feedback and the latest research. Whatever your interest in computer science, Ada is the place for you.

A teenager learning computer science.

If you’re teaching or studying a computer science qualification at school, you can use Ada Computer Science for classwork, homework, and revision. Computer science teachers can select questions to set as assignments for their students and have the assignments marked directly. The assignment results help you and your students understand how well they have grasped the key concepts and highlights areas where they would benefit from further tuition. Students can learn with the help of written materials, concept illustrations, and videos, and they can test their knowledge and prepare for exams.

I want to sign up to Ada Computer Science

A comprehensive resource for computing education

Ada Computer Science builds on work we’ve done to support the English school system as part of the National Centre for Computing Education, funded by the Department for Education.

The topics on the website map to exam board specifications for England’s Computer Science GCSE and A level, and will map to other curricula in the future.

A teenager learning computer science.

In addition, we want to make it easy for educators and learners across the globe to use Ada Computer Science. That’s why each topic is aligned to our own comprehensive taxonomy of computing content for education, which is independent of the English curriculum, and organises the content into 11 strands, including programming, computing systems, data and information, artificial intelligence, creating media, and societal impacts of digital technology.

If you are interested in how we can specifically adapt Ada Computer Science for your region, exam specification, or specialist area, please contact us.

Why use Ada Computer Science at school?

Ada Computer Science enables teachers to:

  • Plan lessons around high-quality content
  • Set self-marking homework questions
  • Pinpoint areas to work on with students
  • Manage students’ progress in a personal markbook

Students get:

  • Free computer science resources, written by specialist teachers
  • A huge bank of interactive questions, designed to support learning
  • A powerful revision tool for exams
  • Access wherever and whenever you want

In addition:

  • The topics include real code examples in Python, Java, VB, and C#
  • The live code editor features interactive coding tasks in Python
  • Quizzes make it quick and easy to set work

Get started with Ada Computer Science today by visiting adacomputerscience.org.

The post Launching Ada Computer Science, the new platform for learning about computer science appeared first on Raspberry Pi Foundation.

How can computing education promote an equitable digital future? Ideas from research

Post Syndicated from Katharine Childs original https://www.raspberrypi.org/blog/computing-education-gender-equality-equitable-digital-future-iwd-23/

This year’s International Women’s Day (IWD) focuses on innovation and technology for gender equality. This cause aligns closely with our mission as a charity: to enable young people to realise their full potential through the power of computing and digital technologies. An important part of our mission is to shift the gender balance in computing education.

Learners in a computing classroom.

Gender inequality in the digital and computing sector

As the UN Women’s announcement for IWD 2023 says: “Growing inequalities are becoming increasingly evident in the context of digital skills and access to technologies, with women being left behind as the result of this digital gender divide. The need for inclusive and transformative technology and digital education is therefore crucial for a sustainable future.”

A woman works at a multi-screen computer setup on a desk.

According to the UN, women currently hold only 2 in every 10 science, engineering, and information and communication technology jobs globally. Women are a minority of university-level students in science, technology, engineering, and mathematics (STEM) courses, at only 35%, and in information and communication technology courses, at just 3%. This is especially concerning since the WEF predicts that by 2050, 75% of jobs will relate to STEM.

We see this situation reflected in England: computer science is the secondary school subject with the largest gender gap at A level, with girls accounting for only 15% of students. That’s why over the past three years, we have run a research programme to trial ways to encourage more young women to study Computer Science. The programme, Gender Balance in Computing, has produced useful insights for designing equitable computing education around the world.

Who belongs in computing?

The UN says that “across countries, girls are systematically steered away from science and math careers. Teachers and parents, intentionally or otherwise, perpetuate biases around areas of education and work best ‘suited’ for women and men.” There is strong evidence to suggest that the representation of women and girls in computing can be improved by introducing them to computing role models such as female computing students or women in tech careers.

A learner and educator at a desktop computer.

Presenting role models was central to the Belonging trial in our Gender Balance in Computing programme. One arm of this trial used resources developed by WISE called My Skills My Life to explore the effect of introducing role models into computing lessons for primary school learners. The trial provided opportunities for learners to speak to women who work in technology. It also offered a quiz to help learners identify their strengths and characteristics and to match them with role models who were similar to them, which research shows is more effective for increasing learners’ confidence.

A young woman codes in a computing classroom.
A woman teacher helps a young person with a coding project.
A girl does physical computing in a classroom.

Teachers who used the resources reported learners’ increased understanding of the types and range of technology jobs, and a widening of learners’ career aspirations. 

“Learning about computing makes me feel good because it helps me think more about what I want to be.” — Primary school learner in the Belonging trial

“When [the resources were] showing all of the females in the jobs, nobody went ‘Oh, I didn’t know that a female could do that’, but I think they were amazed by the role of jobs and the fact it was all females doing it.“ — Primary school teacher in the Belonging trial

Learning together to give everyone a voice

When teachers and students enter a computing classroom, they bring with them diverse social identities that affect the dynamics of the classroom. Although these dynamics are often unspoken, they can become apparent in which students answer questions or succeed visibly in activities. Without intervention, a dominant group of confident speakers can emerge, and students who are not in this dominant group may lose confidence in their abilities. When teachers set collaborative learning activities that use defined roles or structured discussions, this gives a wider range of students the opportunity to speak up and participate.

In a computing classroom, a smiling girl raises her hand.

Pair programming is one such activity that has been used in research studies to improve learner attitudes and confidence towards computing. In pair programming, one learner is the ‘driver’.  They control the keyboard and mouse to write the code. The other learner is the ‘navigator’. They read out the instructions and monitor the code for errors. Learners swap roles regularly, so that both can participate equitably. The Pair Programming trial we conducted as part of Gender Balance in Computing explored the use of this teaching approach with students aged 8 to 11. Feedback from the teachers showed that learners found working in structured pairs engaging. 

“Even those who are maybe a little bit more reluctant… those who put their hands up today and said they still prefer to work independently, they are still all engaging quite clearly in that with their pair and doing it really, really well. However much they say they prefer working independently, I think they clearly showed how much they enjoy it, engage with it. And you know they’re achieving with it — so we should be doing this.” – Primary school teacher in the Pair Programming trial

Another collaborative teaching approach is peer instruction. In lessons that use peer instruction, students work in small groups to discuss the answer to carefully constructed multiple choice questions. A whole-class discussion then follows. In the Peer Instruction trial with learners aged 12 to 13 in our Gender Balance in Computing programme, we found that this approach was welcomed by the learners, and that it changed which learners offered answers and ideas. 

“I prefer talking in a group because then you get the other side of other people’s thoughts.” – Secondary school learner (female) in the Peer Instruction trial

“[…] you can have a bit of time to think for yourself then you can bounce ideas off other people.” – Secondary school learner (male) in the Peer Instruction trial

“I was very pleased that a lot of the girls were doing a lot of the talking.” – Secondary school teacher in the Peer Instruction trial

We need to do more, and sooner

Our Gender Balance in Computing research programme showed that no single intervention we trialled significantly increased girls’ engagement in computing or their intention to study it further. Combining several of the approaches we tested may be more impactful. If you’re part of an educational setting where you’d like to adopt multiple approaches at the same time, you can freely access the materials associated with the research programme (see our blog posts about the trails for links).

In a computing classroom, a girl looks at a computer screen.

The research programme also showed that age matters: across Gender Balance in Computing, we observed a big difference in intent to study Computing between primary school and secondary school learners (data from ages 8–11 and 12–13). Fewer secondary school learners reported intent to study the subject further, and while this difference was apparent for both girls and boys, it was more marked for girls.

This finding from England is mirrored by a study the UN Women’s Gender Snapshot 2022 refers to: “A 2020 study of Filipina girls demonstrated that loss of interest in STEM subjects started as early as age 10, when girls began perceiving STEM careers as male-dominated and believing that girls are naturally less adept in STEM subjects. The relative lack of female STEM role models reinforced such perceptions.” That’s why it’s necessary that all primary school learners — no matter what their gender is — have a successful start in the computing classroom, that they encounter role models they can relate to, and that they are supported to engage in computing and creating with technology by their parents, teachers, and communities.

An educator teaches students to create with technology.

The Foundation’s vision is that every young person develops the knowledge, skills, and confidence to use digital technologies effectively, and to be able to critically evaluate these technologies and confidently engage with technological change. While making changes inside the computing classroom will be beneficial for gender equality, this is just one aspect of building an equitable digital future. We all need to contribute to creating a world where innovation and technology support gender equity.

What do you think is needed?

In all our work, we make sure gender equity is at the forefront, whether that’s in programmes we run for young people, in resources we create for schools, or in partnerships we have, such as with Pratham Education Foundation in India or Team4Tech and Kenya Connect in Wamunyu, Kenya. Computing education is a global challenge, and we are proud to be part of a community that is committed to making it equitable.

Kenyan educators work on a physical computing project.

This IWD, we invite you to share your thoughts on what equitable computing education means to you, and what you think is needed to achieve it, whether that’s in your school or club, in your local community, or in your country. 

The post How can computing education promote an equitable digital future? Ideas from research appeared first on Raspberry Pi.

Supporting beginner programmers in primary school using TIPP&SEE

Post Syndicated from Bobby Whyte original https://www.raspberrypi.org/blog/teaching-programming-in-primary-school-tippsee/

Every young learner needs a successful start to their learning journey in the primary computing classroom. One aspect of this for teachers is to introduce programming to their learners in a structured way. As computing education is introduced in more schools, the need for research-informed strategies and approaches to support beginner programmers is growing. Over recent years, researchers have proposed various strategies to guide teachers and students, such as the block model, PRIMM, and, in the case of this month’s seminar, TIPP&SEE.

A young person smiles while using a laptop.
We need to give all learners a successful start in the primary computing classroom.

We are committed to make computing and creating with digital technologies accessible to all young people, including through our work with educators and researchers. In our current online research seminar series, we focus on computing education for primary-aged children (K–5, ages 5 to 11). In the series’ second seminar, we were delighted to welcome Dr Jean Salac, researcher in the Code & Cognition Lab at the University of Washington.

Dr Jean Salac
Dr Jean Salac

Jean’s work sits across computing education and human-computer interaction, with an emphasis on justice-focused computing for youth. She talked to the seminar attendees about her work on developing strategies to support primary school students learning to program in Scratch. Specifically, Jean described an approach called TIPP&SEE and how teachers can use it to guide their learners through programming activities.

What is TIPP&SEE?

TIPP&SEE is a metacognitive approach for programming in Scratch. The purpose of metacognitive strategies is to help students become more aware of their own learning processes.

The TIPP&SEE learning strategy is a sequence of steps named Title, Instructions, Purpose, Play, Sprites, Events, Explore.
The stages of the TIPP&SEE approach

TIPP&SEE scaffolds students as they learn from example Scratch projects: TIPP (Title, Instructions, Purpose, Play) is a scaffold to read and run a Scratch project, while SEE (Sprites, Events, Explore) is a scaffold to examine projects more deeply and begin to adapt them. 

Using, modifying and creating

TIPP&SEE is inspired by the work of Irene Lee and colleagues who proposed a progressive three-stage approach called Use-Modify-Create. Following that approach, learners move from reading pre-existing programs (“not mine”) to adapting and creating their own programs (“mine”) and gradually increase ownership of their learning.

A diagram of the Use-Create-Modify learning strategy for programming, which involves moving from exploring existing programs to writing your own.
TIPP&SEE builds on the Use-Modify-Create progression.

Proponents of scaffolded approaches like Use-Modify-Create argue that engaging learners in cycles of using existing programs (e.g. worked examples) before they move to adapting and creating new programs encourages ownership and agency in learning. TIPP&SEE builds on this model by providing additional scaffolding measures to support learners.

Impact of TIPP&SEE

Jean presented some promising results from her research on the use of TIPP&SEE in classrooms. In one study, fourth-grade learners (age 9 to 10) were randomly assigned to one of two groups: (i) Use-Modify-Create only (the control group) or (ii) Use-Modify-Create with TIPP&SEE. Jean found that, compared to learners in the control group, learners in the TIPP&SEE group:

  • Were more thorough, and completed more tasks
  • Wrote longer scripts during open-ended tasks
  • Used more learned blocks during open-ended tasks
A graph showing that learners using TIPP&SEE outperformed learners using only Use-Modify-Create in a research study.
The TIPP&SEE group performed better than the control group in assessments

In another study, Jean compared how learners in the TIPP&SEE and control groups performed on several cognitive tests. She found that, in the TIPP&SEE group, students with learning difficulties performed as well as students without learning difficulties. In other words, in the TIPP&SEE group the performance gap was much narrower than in the control group. In our seminar, Jean argued that this indicates the TIPP&SEE scaffolding provides much-needed support to diverse groups of students.

Using TIPP&SEE in the classroom

TIPP&SEE is a multi-step strategy where learners start by looking at the surface elements of a program, and then move on to examining the underlying code. In the TIPP phase, learners first read the title and instructions of a Scratch project, identify its purpose, and then play the project to see what it does.

The TIPP&SEE learning strategy is a sequence of steps named Title, Instructions, Purpose, Play, Sprites, Events, Explore.

In the second phase, SEE, learners look inside the Scratch project to click on sprites and predict what each script is doing. They then make changes to the Scratch code and see how the project’s output changes. By changing parameters, learners can observe which part of the output changes as a result and then reason how each block functions. This practice is called deliberate tinkering because it encourages learners to observe changes while executing programs multiple times with different parameters.

The TIPP&SEE learning strategy is a sequence of steps named Title, Instructions, Purpose, Play, Sprites, Events, Explore.

You can read more of Jean’s research on TIPP&SEE on her website. There’s also a video on how TIPP&SEE can be used, and free lesson resources based on TIPP&SEE are available in Elementary Computing for ALL and Scratch Encore.

Learning about learning in computing education

Jean’s talk highlighted the need for computing to be inclusive and to give equitable access to all learners. The field of computing education is still in its infancy, though our understanding of how young people learn about computing is growing. We ourselves work to deepen our understanding of how young people learn through computing and digital making experiences.

In our own research, we have been investigating similar teaching approaches for programming, including the use of the PRIMM approach in the UK, so we were very interested to learn about different approaches and country contexts. We are grateful to Dr Jean Salac for sharing her work with researchers and teachers alike. Watch the recording of Jean’s seminar to hear more:

Free support for teaching programming and more to primary school learners

If you are looking for more free resources to help you structure your computing lessons:

Join our next seminar

In the next seminar of our online series on primary computing, I will be presenting my research on integrated computing and literacy activities. Sign up now to join us for this session on Tues 7 March:

I want to join the next seminar

As always, the seminars will take place online on the first Tuesday of the month at 17:00–18:30 UK time. Hope to see you there!

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Teach your learners with The Computing Curriculum

Post Syndicated from Sway Grantham original https://www.raspberrypi.org/blog/computing-curriculum-lesson-plans/

Computing combines a very broad mixture of concepts and skills. We work to support any school to teach students about the whole of computing and how to create with digital technologies. A key part of this support is The Computing Curriculum.

Two girls code at a desktop computer while a female mentor observes them.
We help schools around the world teach their learners computing.

The Computing Curriculum: Free and comprehensive

The Computing Curriculum is our complete bank of free lesson plans and other resources that offer you everything you need to teach computing lessons to all school-aged learners. It helps you cover the full breadth of computing, including computing systems, programming, creating media, data and information, and societal impacts of digital technology.

Young learners at computers in a classroom.
A girl in a university computing classroom.

The 500 hours of free, downloadable resources within The Computing Curriculum include all the materials you need in your classroom: from lesson plans and slide decks to activity sheets, homework, and assessments. To our knowledge, this is the most comprehensive set of free teaching and learning materials for computing and digital skills in the world.

Two learners and a teacher in a physical computing lesson.
We continuously update The Computing Curriculum to reflect the latest research about this young subject.

Our Curriculum’s resources are based on clear progression and content frameworks we’ve designed, and we continuously update them based on the latest research and feedback from practising teachers. Doing this is particularly important for computing education resources, because computing is a young subject where thoughts and understanding about the best teaching approaches are still evolving.

Computing lesson plans that save time and engage your learners

With The Computing Curriculum, we support educators of all levels of experience. Whether you specialise in computing, or you are a newcomer to the subject, the Curriculum will save you time and help you deliver engaging lessons.

In our 2022 survey of teachers who have used The Computing Curriculum resources:

  • 91% said the Curriculum was effective or very effective at saving teachers time
  • 89% said it was effective or very effective at developing teachers’ subject knowledge
  • 81% said it was effective or very effective at engaging students

The resources are organised as themed units, and they support your computing lesson planning, preparation, and delivery because they are comprehensive as well as adaptable. You are free to use the resources as they are, or adjust them to your context, access to hardware, and learners’ needs and experience level.

A Kenyan child smiles at a computer.
The Computing Curriculum will help you plan and deliver engaging lessons.

One aspect of The Computing Curriculum that will facilitate your teaching is the progression framework on which the resources are based. In creating the resources, we have considered the learning objectives throughout each unit and year group, and throughout the entire schooling period. This progression is detailed in curriculum maps and learning graphs, and you’ll be able to use these documents to plan your lessons and to check your learners’ understanding.

Start teaching with The Computing Curriculum

You can download and use the resources for the year groups you teach computing right now. And please tell us of your experiences using The Computing Curriculum in your classroom, so that we can make the resources even better for educators around the world.

I want to download resources from The Computing Curriculum

If you are interested in curriculum resources tailored for your region, please contact us via this form. You can find out how we adapted resources from The Computing Curriculum for learners living in a refugee camp in Kenya if you’d like to learn about our approach to tailoring resources.

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Combining computing and maths to teach primary learners about variables

Post Syndicated from Katharine Childs original https://www.raspberrypi.org/blog/variables-primary-school-computing-maths-education-seminar/

In our first seminar of 2023, we were delighted to welcome Dr Katie Rich and Carla Strickland. They spoke to us about teaching the programming construct of variables in Grade 3 and 4 (age 8 to 10).

Dr Katie Rich
Dr Katie Rich
Carla Strickland
Carla Strickland

We are hearing from a diverse range of speakers in our current series of monthly online research seminars focused on primary (K-5) computing education. Many of them work closely with educators to translate research findings into classroom practice to make sure that all our younger learners have positive first experiences of learning computing. An important goal of their research is to impact the development of pedagogy, resources, and professional development to support educators to deliver computing concepts with confidence.

Variables in computing and mathematics

Dr Katie Rich (American Institutes of Research) and Carla Strickland (UChicago STEM Education) are both part of a team that worked on a research project called Everyday Computing, which aims to integrate computational thinking into primary mathematics lessons. A key part of the Everyday Computing project was to develop coherent learning resources across a number of school years. During the seminar, Katie and Carla presented on a study in the project that revolved around teaching variables in Grade 3 and 4 (age 8 to 10) by linking this computing concept to mathematical concepts such as area, perimeter, and fractions.

Young person using Scratch.

Variables are used in both mathematics and computing, but in significantly different ways. In mathematics, a variable, often represented by a single letter such as x or y, corresponds to a quantity that stays the same for a given problem. However, in computing, a variable is an identifier used to label data that may change as a computer program is executed. A variable is one of the programming constructs that can be used to generalise programs to make them work for a range of inputs. Katie highlighted that the research team was keen to explore the synergies and tensions that arise when curriculum subjects share terms, as is the case for ‘variable’. 

Defining a learning trajectory

At the start of the project, in order to be able to develop coherent learning resources across school years, the team reviewed research papers related to teaching the programming construct of variables. In the papers, they found a variety of learning goals that related to facts (what learners need to know) and skills (what learners need to be able to do). They grouped these learning goals and arranged the groups into ‘levels of thinking’, which were then mapped onto a learning trajectory to show progression pathways for learning.

Four of the five levels of thinking identified in the study: Data storer, data user, variable user, variable creator.
Four of the five levels of thinking identified in the study: Data Storer, Data User, Variable User, Variable Creator. Click to enlarge.

Learning materials about variables

Carla then shared three practical examples of learning resources their research team created that integrated the programming construct of variables into a maths curriculum. The three activities, described below, form part of a series of lessons called Action Fractions. You can read more about the series of lessons in this research paper.

Robot Boxes is an unplugged activity that is positioned at the Data User level of thinking. It relates to creating instructions for a fictional robot. Learners have to pay attention to different data the robot needs in order to draw a box, such as the length and width, and also to the value that the robot calculates as area of the box. The lesson uses boxes on paper as concrete representations of variables to which learners can physically add values.

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Ambling Animals is set at the ‘Data Storer’ and ‘Variable Interpreter’ levels of thinking. It includes a Scratch project to help students to locate and compare fractions on number lines. During this lesson, find a variable that holds the value of the animal that represents the larger of two fractions.

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Adding Fractions draws on facts and skills from the ‘Variable Interpreter’ and ‘Variable Implementer’ levels of thinking and also includes a Scratch project. The Scratch project visualises adding fractions with the same denominator on a number line. The lesson starts to explain why variables are so important in computer programs by demonstrating how using a variable can make code more efficient. 

Takeaways: Cross-curricular teaching, collaborative research

Teaching about the programming construct of variables can be challenging, as it requires young learners to understand abstract ideas. The research Katie and Carla presented shows how integrating these concepts into a mathematics curriculum is one way to highlight tangible uses of variables in everyday problems. The levels of thinking in the learning trajectory provide a structure helping teachers to support learners to develop their understanding and skills; the same levels of thinking could be used to introduce variables in other contexts and curricula.

A learner does physical computing in the primary school classroom.

Many primary teachers use cross-curricular learning to increase children’s engagement and highlight real-world examples. The seminar showed how important it is for teachers to pay attention to terms used across subjects, such as the word ‘variable’, and to explicitly explain a term’s different meanings. Katie and Carla shared a practical example of this when they suggested that computing teachers need to do more to stress the difference between equations such as xy = 45 in maths and assignment statements such as length = 45 in computing.

The Everyday Computing project resources were created by a team of researchers and educators who worked together to translate research findings into curriculum materials. This type of collaboration can be really valuable in driving a research agenda to directly improve learning outcomes for young people in classrooms. 

How can this research influence your classroom practice or other activities as an educator? Let us know your thoughts in the comments. We’ll be continuing to reflect on this question throughout the seminar series.

You can watch Katie’s and Carla’s full presentation here:

Join our seminar series on primary computing education

Our monthly seminar series on primary (K–5) teaching and learning is of interest to a global audience of educators, including those who want to understand the prior learning experiences of older learners.

I want to sign up to join a seminar

We continue on Tuesday 7 February at 17.00 UK time, when we will hear from Dr Jean Salac, University of Washington. Jean will present her work in identifying inequities in elementary computing instruction and in developing a learning strategy, TIPP&SEE, to address these inequities. Sign up now, and we will send you a joining link for the session.

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Gender Balance in Computing — the big picture

Post Syndicated from Sue Sentance original https://www.raspberrypi.org/blog/gender-balance-in-computing-big-picture/

Improving gender balance in computing is part of our work to ensure equitable learning opportunities for all young people. Our Gender Balance in Computing (GBIC) research programme has been the largest effort to date to explore ways to encourage more girls and young women to engage with Computing.

A girl in a university computing classroom.

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

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

  • Teaching Approach:
  • Belonging: Supporting learners to feel that they “belong” in computer science
  • Non-formal Learning: Establishing the connections between in-school and out-of-school computing
  • Relevance: Making computing relatable to everyday life
  • Subject Choice: How computer science is presented to young people as a subject choice 

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

Gender balance in computing is not a new problem

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

A computing classroom filled with learners.

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

Computer Science qualification (England): In 2018: In 2021: In 2022:
GCSE (age 16) 20.41% 20.77% 21.37%
A level (age 18) 11.74% 14.71% 15.17%
Percentage of girls among the students achieving Computer Science qualifications in England’s secondary schools

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

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

In total we conducted the following studies:

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

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

The impact of the pandemic

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

A learner and a teacher in a computing classroom.

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

The overall results of Gender Balance in Computing

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

Woman teacher and female students at a computer

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

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

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

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

2. Age matters

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

Bar graph showing that in the Gender Balance in Computing research programme, learners intent to continue studying computing was lower in secondary school than primary school, and that this difference is more pronounced for girls.
Compared to primary school age girls, girls aged 12 to 13 show dramatically reduced intent to continue studying computing.

What’s next?

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

In a computing classroom, a girl looks at a computer screen.

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

Stay in touch

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

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Combining research and practice to evaluate and improve computing education in non-formal settings

Post Syndicated from Bonnie Sheppard original https://www.raspberrypi.org/blog/research-practice-evaluate-improve-computing-education-non-formal-settings-seminar/

In the final seminar in our series on cross-disciplinary computing, Dr Tracy Gardner and Rebecca Franks, who work here at the Foundation, described the framework underpinning the Foundation’s non-formal learning pathways. They also shared insights from our recently published literature review about the impact that non-formal computing education has on learners.

Dr Tracy Gardner.
Dr Tracy Gardner
Rebecca Franks
Rebecca Franks

Tracy and Rebecca both have extensive experience in teaching computing, and they are passionate about inspiring young learners and broadening access to computing education. In their work here, they create resources and content for learners in coding clubs and young people at home.

How non-formal learning creates opportunities for computing education

UNESCO defines non-formal learning as “institutionalised, intentional, and planned… an addition, alternative, and/or complement to formal education within the process of life-long learning of individuals”. In terms of computing education, this kind of learning happens in after-school programmes or children’s homes as they engage with materials that have been carefully designed by education providers.

Two children code on laptops while an adult supports them.
Three young people code at laptops in a CoderDojo.

At the Raspberry Pi Foundation, we support two global networks of free, volunteer-led coding clubs where regular non-formal learning takes place: Code Club, teacher- and volunteer-led coding clubs for 9- to 13-year-olds taking place in schools in more than160 countries; and CoderDojo, volunteer-led programming clubs for young people aged 7–17 taking place in community venues and offices in 100 countries. Through free learning resources and other support, we enable volunteers to run their club sessions, offering versatile opportunities and creative, inclusive spaces for young people to learn about computing outside of the school curriculum. Volunteers who run Code Clubs or CoderDojos report that participating in the club sessions positively impacts participants’ programming skills and confidence.

Rebecca and Tracy are part of the team here that writes the learning resources young people in Code Clubs and CoderDojos (and beyond) use to learn to code and create technology. 

Helping learners make things that matter to them

Rebecca started the seminar by describing how the team reviewed existing computing pedagogy research into non-formal learning, as well as large amounts of website visitor data and feedback from volunteers, to establish a new framework for designing and creating coding resources in the form of learning paths.

What the Raspberry Pi Foundation takes into account when creating non-formal learning resources: what young people are making, young people's interests, research, user data, our own experiences as educators, the Foundation's other educational offers, ideas of purpose-driven computing.
What the Raspberry Pi Foundation takes into account when creating non-formal learning resources. Click to enlarge.

As Rebecca explained, non-formal learning paths should be designed to bridge the so-called ‘Turing tar-pit’: the gap between what learners want to do, and what they have the knowledge and resources to achieve.

The Raspberry Pi Foundation's non-formal learning resources bridge the so-called Turing tar pit, in which learners get stuck when they feel everything is possible to create, but nothing is easy.

To prevent learners from getting frustrated and ultimately losing interest in computing, learning paths need to:

  • Be beginner-friendly
  • Include scaffolding
  • Support learner’s design skills
  • Relate to things that matter to learners

When Rebecca and Tracy’s team create new learning paths, they first focus on the things that learners want to make. Then they work backwards to bridge the gap between learners’ big ideas and the knowledge and skills needed to create them. To do this, they use the 3…2…1…Make! framework they’ve developed.

An illustration of the 3-2-1 structure of the new Raspberry Pi Foundation coding project paths.
An illustration of the 3…2…1…Make! structure of the new Raspberry Pi Foundation non-formal learning paths.

Learning paths designed according to the framework are made up of three different types of project in a 3-2-1 structure:

  • Three Explore projects to introduce creators to a set of skills and provide step-by-step instructions to help them develop initial confidence
  • Two Design projects to allow creators to practise the skills they learned in the previous Explore projects, and to express themselves creatively while they grow in independence
  • One Invent project where creators use their skills to meet a project brief for a particular audience

You can learn more about the framework in this blog post and this guide for adults who run sessions with young people based on the learning paths. And you can explore the learning paths yourself too.

Rebecca and Tracy’s team have created several new learning pathways based on the 3…2…1…Make! framework and received much positive feedback on them. They are now looking to develop more tools and libraries to support learners, to increase the accessibility of the paths, and also to conduct research into the impact of the framework. 

New literature review of non-formal computing education showcases its positive impact

In the second half of the seminar, Tracy shared what the research literature says about the impact of non-formal learning. She and researchers at the Foundation particularly wanted to find out what the research says about computing education for K–12 in non-formal settings. They systematically reviewed 421 papers, identifying 88 papers from the last seven years that related to empirical research on non-formal computing education for young learners. Based on these 88 papers, they summarised the state of the field in a literature review.

So far, most studies of non-formal computing education have looked at knowledge and skill development in computing, as well as affective factors such as interest and perception. The cognitive impact of non-formal education has been generally positive. The papers Tracy and the research reviewed suggested that regular learning opportunities, such as weekly Code Clubs, were beneficial for learners’ knowledge development, and that active teaching of problem solving skills can lead to learners’ independence.

In the literature review the Raspberry Pi Foundation team conducted, most research studies were university-organised on projects to broaden participation and interest development in immersive multi-day settings.

Non-formal computing education also seems to be beneficial in terms of affective factors (although it is unclear yet whether the benefits remain long-term, since most existing research studies conducted have been short-term ones). For example, out-of-school programmes can lead to more positive perception and increased awareness of computing for learners, and also boost learners’ confidence and self-efficacy if they have had little prior experience of computing. The social aspects of participating in coding clubs should not be underestimated, as learners can develop a sense of belonging and support as they work with their peers and mentors.

The affordances of non-formal computing activities that complement formal education: access and awareness, cultural relevance and equity, practice and personalisation, fun and engagement, community and identity, immediate impact.

The literature review showed that non-formal computing complements formal in-school education in many ways. Not only can Code Clubs and CoderDojos be accessible and equitable spaces for all young people, because the people who run them can tailor learning to the individuals. Coding clubs such as these succeed in making computing fun and engaging by enabling a community to form and allowing learners to make things that are meaningful to them.

What existing studies in non-formal computing aren’t telling us

Another thing the literature review made obvious is that there are big gaps in the existing understanding of non-formal computing education that need to be researched in more detail. For example, most of the studies the papers in the literature review described took place with female students in middle schools in the US.

That means the existing research tells us little about non-formal learning:

  • In other geographic locations
  • In other educational settings, such as primary schools or after-school programmes
  • For a wider spectrum of learners

We would also love to see studies that hone in on:

  • The long-term impact of non-formal learning
  • Which specific factors contribute to positive outcomes
  • Non-formal learning about aspects of computing beyond programming

3…2…1…research!

We’re excited to continue collaborating within the Foundation so that our researchers and our team creating non-formal learning content can investigate the impact of the 3…2…1…Make! framework.

At Coolest Projects, a group of people explore a coding project.
The aim of the 3…2…1…Make! framework is to enable young people to create things and solve problems that matter to them using technology.

This collaboration connects two of our long-term strategic goals: to engage millions of young people in learning about computing and how to create with digital technologies outside of school, and to deepen our understanding of how young people learn about computing and how to create with digital technologies, and to use that knowledge to increase the impact of our work and advance the field of computing education. Based on our research, we will iterate and improve the framework, in order to enable even more young people to realise their full potential through the power of computing and digital technologies. 

Join our seminar series on primary computing education

From January, you can join our new monthly seminar series on primary (K–5) teaching and learning. In this series, we’ll hear insights into how our youngest learners develop their computing knowledge, so whether you’re a volunteer in a coding club, a teacher, a researcher, or simply interested in the topic, we’d love to see you at one of these monthly online sessions.

The first seminar, on Tuesday 10 January at 5pm UK time, will feature researchers and educators Dr Katie Rich and Carla Strickland. They will share findings on how to teach children about variables, one of the most difficult aspects of computing for young learners. Sign up now, and we will send you notifications and joining links for each seminar session.

I want to sign up to attend

We look forward to seeing you soon, and to discussing with you how we can apply research results to better support all our learners.

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Reflecting on what we teach in computing education and how we teach it

Post Syndicated from James Robinson original https://www.raspberrypi.org/blog/reflecting-on-computing-education-hello-world-special-editions/

Reflecting is important within any line of work, and computing education is no different. Reflective practice is always valuable, whether you support learners in a non-formal setting, such as a Code Club or CoderDojo, or in a more formal environment, such as a school or college. When you reflect, you might for example evaluate a session or lesson and make changes for next time, or consider whether to reorder activities and learning across a longer time period, or even think broadly about what you teach and how you teach it.

Two special editions of Hello World: The big book of computing content, and the big book of computing pedagogy.

This is where our two special editions of Hello World come in: The Big Book of Computing Content and The Big Book of Computing Pedagogy. Both available as free downloads, they help you reflect on what you teach within Computing and how you teach it.

A computing teacher and a learner do physical computing in the primary school classroom.
A teacher explains something to a learner in a computer science class.

What you teach: The Big Book of Computing Content

Computing is a broad and interdisciplinary subject, and different curricula and courses around the world focus on different aspects of it. For all of us, therefore, computing is framed by the curricula with which we are working and the terms which we’re using to talk about the subject. Over the past years at the Foundation, we have been developing a Computing taxonomy to help describe the different aspects of the subject. The Big Book of Computing Content is based on this taxonomy. The aim of this special edition of Hello World is to illustrate the breadth of Computing, and to model language that describes the different concepts, knowledge, and skills that comprise it.

Cover of The Big Book of Computing Content.
The Big Book of Computing Content explores what we mean by Computing and aims to provide a common language to describe the subject. This book complements our Hello World special edition on pedagogy, introducing research alongside practical articles from teachers.

We have organised this Big Book according to our taxonomy’s 11 content strands and also included progressive learning outcomes for each strand at different stages of learning. These outcomes are not prescriptive; instead they illustrate the wide applications of the subject by highlighting the kinds of knowledge and understanding that learners could develop in each area of Computing.

Computing classroom with woman teacher and young students at laptops doing Scratch coding.
Male teacher and male students at a computer

We hope that The Big Book of Computing Content encourages educators to reflect on all aspects of Computing and how they interconnect, as well as on the language we use to describe Computing. Whether the Big Book helps you to discover new aspects to Computing, to think about the subject differently, or simply to see the differences in how we as educators talk about our subject, the time you spend reflecting is important and valuable.

How you teach: The Big Book of Computing Pedagogy

One part of our work as educators is understanding the breadth of Computing and the specific ideas within it. The other part is reflecting on how we teach the subject: the specific methods, strategies, and practices we can use with our learners. The Big Book of Computing Pedagogy describes a range of teaching approaches framed around our 12 pedagogical principles for teaching Computing. Each research-informed principle either reflects how general-purpose pedagogy applies within Computing or explores pedagogies specific to Computing itself. This Big Book consists of research summaries as well as practical articles from educators which illustrate how you can apply the different pedagogies.

Cover of The Big Book of Computing Pedagogy.
Hello World’s special edition on pedagogy lays out approaches to teaching computing in the classroom. It bridges the gap between research and practice, giving you accessible chunks of research, followed by stories from educators.

Rather than prescribing a set of principles that educators must follow, the aim of The Big Book of Computing Pedagogy is to help you develop your understanding of a range of pedagogical approaches which you can select, apply, and adapt to suit your context.

Reflect to develop your knowledge and agency

Ultimately we want to support all Computing and Computer Science educators to build their understanding of subject matter (that is, content) and pedagogy, or what is called pedagogical content knowledge (PCK, a term popularised by Lee Shulman). Combining your PCK with your grasp of the context of your learners, curricula, and setting will enable you to choose suitable practices for your content and context.

Three computer science educators discuss something at a screen.

We hope that you find the two Big Books to be valuable reference tools to help you and your peers reflect on what it is you mean when you talk about Computing, and on how you teach the concepts, knowledge, and skills within it. Both books are available as free PDF downloads.

We would love to hear examples of how you have used The Big Book of Computing Pedagogy or The Big Book of Computing Content to inform your own teaching practice or to discuss practice with colleagues. Tell us in the comments.

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Using relevant contexts to engage girls in the Computing classroom: Study results

Post Syndicated from Katharine Childs original https://www.raspberrypi.org/blog/gender-balance-in-computing-relevance/

Today we are sharing an evaluation report on another study that’s part of our Gender Balance in Computing research programme. In this study, we investigated the impact of using relevant contexts in classroom programming activities for 12- to 13-year-olds on girls’ and boys’ attitudes towards Computing.

Two female learners code at a computer together.

We have been working on Gender Balance in Computing since 2018, together with partner organisations Behavioural Insights Team, Apps for Good, and WISE, to conduct research studies exploring ways to encourage more girls and young women to engage with Computing in school. The research programme has been funded by the Department for Education, and we deliver it as part of the National Centre for Computing Education. The report we share today is about the penultimate study in the programme.

Components of a Computing curriculum

A typical Computing curriculum is built around content: a list of concepts, knowledge, and skills that will be covered during the course. For some learners, that list will be enough to motivate and engage them in Computing. But other learners require more to engage with the subject, such as context about how they can use the computing skills they learn in the real world. Crucially, this difference between learners is often gendered. Research has shown that many boys become absorbed by the content in Computing courses, whereas for many girls the context for using computing skills is more important, and this context needs to relate to a variety of relevant scenarios where computing can solve problems.

In a computing classroom, a girl laughs at what she sees on the screen.

Developing teaching materials to highlight the relevance of Computing

In the Relevance study, we worked together with colleagues from Apps for Good to create teaching materials that present Computing in contexts that were relevant to pupils’ own interests. To do this, we drew on a research concept called identification. This states that when learners become interested in a topic because it relates to part of their own identity, that makes the subject more personally meaningful to them, which in turn means that they are more likely to continue studying it. In the materials we created, we drew on learners’ identities based on the communities that they belonged to (see image below). The materials asked them to identify the connections they had to their own communities, and to then use this as the context to design and create a mobile phone app.

A slide from a Computing lesson inviting learners to identify the communities they are part of based on their family, beliefs, school, interests, etc.
The intervention materials asked learners to think about the communities they belong to.

“I feel a sense of achievement in Computing when making your ideas a reality makes you proud of your creation, which is rewarding.” (Female learner, Relevance study evaluation report p. 57)

The Relevance research study

Between January 2022 and April 2022, more than 95 secondary schools were part of our study investigating the effect that learning with these resources might have on the attitudes of Year 8 pupils (aged 12–13) towards Computing. We are very grateful to all the schools, pupils, and teachers who took part in this study.

To enable evaluation of the study as a randomised controlled trial, the schools were randomly divided into two groups: a ‘control’ group that taught standard Computing lessons, and a ‘treatment’ group that delivered the intervention materials we had developed. The impact of the intervention was independently evaluated by the Behavioural Insights Team using data collected from pupils via surveys at the start and end of the intervention. The evaluators also collected data while conducting lesson observations, pupil group discussions, teacher interviews, and teacher surveys to understand how the intervention was delivered.

The girls who took part in the intervention chose an interesting range of contexts for their apps, including: 

  • Solving problems in the school community, such as homework timetabling and public transport
  • Interest-based communities, such as melody-making and interior design 
  • Issues in wider communities, such as sea life population and mental health

“I feel like it’s an important subject, and I feel like sea life is at risk right now, and I want to help people realise that.” (Female learner, Relevance study evaluation report p. 60)

“I feel like computing can create apps to do with solving mental health problems, which I think are very important and personally need a lot of improvement on the way we can cope with mental health.” (Female learner, Relevance study evaluation report p. 60)

What we learned from the Relevance study

The start of this blog refers to the core components of a Computing curriculum: concepts, knowledge, and skills. One way of building a curriculum is to list these components and develop a scheme of work which covers them all. However, in a recent computing education paper, researchers present an alternative way: developing curricula around the possible endpoints of learners. For computing, one endpoint could be the economic opportunities of a programming career, but equally, another could be using digital technologies for creative expression. The researchers argue that when learners have the opportunity to use computing as a tool related to personally meaningful contexts, a more diverse group of learners can become engaged in the subject.

A group of young people in a computer science classroom pose for a group photo.

Girls who took part in our Relevance study expressed the importance of creativity. “I think last term we had instructions and you follow them, whereas now it’s like your own ideas and your own creativity and whatever you make,” said one female learner (report, p. 56). The series of lessons where learners designed a prototype of their app was particularly popular among girls because this activity included creative expression. Girls who see themselves as creative align their interests with subjects that allow them to express this part of their identity.

A slide from a Computing lesson inviting learners to design a mobile phone app on paper.
With the intervention materials, learners developed a paper prototype of their app before going on to create code for it.

Based on learner responses to a ‘yes/no’ question about whether they were likely to choose GCSE Computer Science, the evaluators of the study found no statistically significant differences between the students who were part of the treatment and control groups. However, when learners were asked instead to select from a list which subjects they were likely to choose at GCSE, there was a statistically significant difference in the results: girls from schools in the treatment group were more likely to choose GCSE Computer Science as one of their options than girls in the control group. This finding suggests that it would be beneficial to gender balance in Computing if educators who design Computing curricula consider multiple endpoints for learners and include personally meaningful contexts to create learning experiences that are relevant to diverse groups of learners.

Find out more about making computing relevant for your learners

This is the penultimate report to be published about the studies that are part of the Gender Balance in Computing programme. If you would like to stay up-to-date with the programme, you can sign up to our newsletter. Our final report is about a study that explored the role that options booklets and evenings play in students’ subject choice.

The post Using relevant contexts to engage girls in the Computing classroom: Study results appeared first on Raspberry Pi.

Spotlight on primary computing education in our 2023 seminar series

Post Syndicated from Bonnie Sheppard original https://www.raspberrypi.org/blog/primary-computing-education-research-seminar-series-2023/

We are excited to announce our next free online seminars, running monthly from January 2023 and focusing on primary school (K–5) teaching and learning of computing.

Two children code on laptops while an adult supports them.

Our seminars, having covered various topics in computing education over the last three years, will now offer you a close look at current questions and research in primary computing education. Through this series we want to connect research and teaching practice, and further primary computing education across the globe.

Are these seminars for me?

Our upcoming seminars are for everyone interested in computing education, not just for primary school teachers — you are all cordially invited to join us. Previous seminars have been attended by a valuable mix of teachers, volunteers, tech industry professionals, and researchers, all keen to explore how computing education research can be put into practice.

Learner using Scratch on a laptop.

Whether you teach in a classroom, or support learners in a coding club, you will find out how our youngest learners develop their computing knowledge. You’ll also explore with us what this means for your learning context in practical terms.

What you can expect from the online seminars

Each seminar starts with a presenter explaining, in easy-to-understand terms, some recent research they have done. The presentation is followed by a discussion in smaller groups. We then regroup for a Q&A session with the presenter.

Attendees of our previous seminars have said:

“The seminar will be useful in my practice when our coding club starts.”

“I love this initiative, your choice of speakers has been fantastic. You are creating a very valuable CPD resource for Computer Science teachers and educators all over the world. Thank you. 🙏”

“Just wanted to say a huge thank you for organising this. It was brilliant to hear the presentation but also the input from other educators in the breakout room. I currently teach in a department of one, which can be quite lonely, so to join other educators was brilliant and a real encouragement.” 

Learn from specialists to benefit your own learners

Computer science has been taught in universities for many years, and only more recently has the subject been introduced in schools. That means there isn’t a lot of research about computing education for school-aged learners yet, and even less research about how young children of primary school age learn about computing. 

Young learners at computers in a classroom.

That’s why we are excited to invite you to learn with us as we hear from international primary computing research teams who share their knowledge in our online seminars:

  • Tuesday 10 January 2023: Kicking off our series are Dr Katie Rich and Carla Strickland from Chicago with a seminar on how they developed new instructional materials for teaching variables in primary school. They will specifically focus on how they combined research with classroom realities, and share experiences of using their new materials in class. 
  • Tuesday 7 February 2023: Dr Jean Salac from the University of Washington is particularly interested in identifying and addressing inequities in the computing classroom, and will speak about a new learning strategy that has been found to improve students’ understanding of computing concepts and to increase equal access to computing.
  • Tuesday 7 March 2023: Our own Dr Bobby Whyte from the Raspberry Pi Foundation will share practical examples of how primary computing can be integrated into literacy education. He will specifically look at storytelling elements within computing education and discuss the benefits of combining competency areas.
  • May 2023: Information coming soon
  • Tuesday 6 June 2023: In a collaborative seminar, Aim Unahalekhaka from Tufts University in Massachusetts will first present her research into how children learn coding through ScratchJr. Participants are encouraged to bring a tablet or device with ScratchJr to then look at practical project evaluations and teaching strategies that can help young learners create purposefully.
  • Tuesday 12 September 2023: Joining us from the University of Passau in Germany, Luisa Greifenstein will speak about how to give children appropriate feedback that encourages positive attitudes towards computing education. In particular, she will be looking at the effects of different feedback strategies and present a new Scratch tool that offers automated feedback.
  • October 2023: Information coming soon
  • Tuesday 7 November 2023: We are delighted to be joined by Dr Aman Yadav from Michigan State University who will focus on computational thinking and its value for primary schooling. In his seminar, he will not only discuss the unique opportunities for computational thinking in primary school but also discuss findings from a recent project that focused on teachers’ perspectives. 

Sign up now to attend the seminars

All our seminars start at 17:00 UK time (18:00 CET / 12:00 noon ET / 9:00 PT) and take place in an online format. Sign up now to receive a calendar invitation and the link to join on the day of each seminar.

I want to sign up to attend

We look forward to seeing you soon, and to discussing with you how we can apply research results to better support all our learners.

The post Spotlight on primary computing education in our 2023 seminar series appeared first on Raspberry Pi.

Out now: Hello World’s special edition on Computing content

Post Syndicated from Gemma Coleman original https://www.raspberrypi.org/blog/hello-world-special-edition-computing-content/

Hello World, our free magazine for computing and digital making educators, has just published its second special edition: The Big Book of Computing Content.

Cover of The Big Book of Computing Content.

A special edition on the content we teach in the Computing classroom

While Hello World‘s first special edition, The Big Book of Computing Pedagogy, focused on how we can teach Computing, this new book is about what we mean by Computing. It aims to demonstrate the breadth of knowledge and skills contained within this constantly evolving subject.

A tweet about Hello World's special edition The Big Book of Computing Content.
A tweet about Hello World's special edition The Big Book of Computing Content.

We have structured the new special edition around our taxonomy for formal Computing education, to which we map all our formal education resources. Originally we developed the taxonomy when we started work in the consortium setting up and delivering England’s National Centre for Computing Education, and specifically when we designed the 500 hours of classroom materials in the Teach Computing Curriculum.

The Raspberry Pi Foundation's computing content taxonomy, made of 11 strands: effective use of tools, safety and security, design and development, impact of technology, computing systems, networks, creating media, algorithms and data structures, programming, data and information, artificial intelligence.
The 11 strands of Computing content in our taxonomy.

Our Computing taxonomy comprises eleven strands and aims to categorise Computing conceptual knowledge and skills to both demonstrate the breadth of Computing as a discipline, and to provide a common language to describe the different areas of study and competencies.

A tweet about Hello World's special edition The Big Book of Computing Content.
A tweet about Hello World's special edition The Big Book of Computing Content.

The Big Book of Computing Content complements our first Hello World special edition and follows the same principle of introducing readers to up-to-date research, followed by our favourite stories from past Hello World issues by educators who put that content into practice. For each of the eleven strands in our taxonomy, we also present a table of learning outcomes, which provides examples of knowledge and skills that learners from ages 5 to 19 could develop at each stage of their formal computing education. 

Your thoughts on The Big Book of Computing Content

Hello World’s first special edition was very popular around the world, with educators setting up Big Book of Computing Pedagogy reading groups, leaders using the book to support pre-service teachers, and even of an upcoming translation into Thai.

We’ve already started to hear similar stories about The Big Book of Computing Content from Hello World readers, including CSEdResearch dedicating their Computer Science Education Discussion Group to all things Big Book of Computing Content in its first week of publication.

A tweet about Hello World's special edition The Big Book of Computing Content.

We’d love to hear from more educators about how you are using this new special edition, and how it complements your reading of the first Big Book.

I want to download The Big Book of Computing Content

You can also 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 voluntary work in education, you can subscribe for free print issues.

PS Have you listened to our Hello World podcast yet? Listen and subscribe wherever you get your podcasts.

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