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.

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.

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.

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.

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.

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.

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.

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.

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?

Subscribe to the Hello World digital edition for free

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:

• Instilling a sense of belonging in children
• Using picture books to introduce programming concepts to children
• Perfecting typing skills
• Cross-curricular project ideas to keep young learners engaged at the end of primary school

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.

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

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.

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.

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 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.

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

You can access the seminar slides here.

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.

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.

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

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.

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.

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.

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.

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 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.

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.

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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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.

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.

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:

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’d like to get involved with Experience AI and use our lessons with your class, you can start by visiting us at experience-ai.org.

The post How anthropomorphism hinders AI education appeared first on Raspberry Pi Foundation.

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. 

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). 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.

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).

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.

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.

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.

If you’d like to get involved with Experience AI as an educator and use our lessons with your class, you can start by visiting us at experience-ai.org.

The post AI education resources: What do we teach young people? appeared first on Raspberry Pi Foundation.

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 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.

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 identify 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.

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.

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.

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.”

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.

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.

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.

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 trials for links).

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.

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.

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 Foundation.

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.

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.

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.

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

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.

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.

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:

• Download our comprehensive classroom resources in The Computing Curriculum, which includes units of programming lessons for age 5 and upwards.
• Take one of our online courses about teaching computing. The course Teaching programming to 5- to 11-year-olds is especially relevant to this seminar.
• Read The Big Book of Computing Pedagogy, a special edition of our Hello World magazine.

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:

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!

The post Supporting beginner programmers in primary school using TIPP-SEE appeared first on Raspberry Pi Foundation.