Tag Archives: education

Computer science education for what purpose? Some perspectives

Post Syndicated from Janina Ander original https://www.raspberrypi.org/blog/computer-science-education-equity-change-purpose/

As we’re coming to the end of Black History Month in the USA this year, we’ve been amazed by the variety of work the computing education community is doing to address inequities in their classrooms. For our part, we have learned a huge amount about equitable STEM and computer science (CS) education from the community, and through our own research.

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

In this post, we want to highlight two particular pieces of work that have influenced our work over the last year, shared by Dr Tia C. Madkins (University of Texas at Austin), Dr Nicol R. Howard (University of Redlands), and Dr Jakita O. Thomas (Auburn University, blackcomputeHER.org) at our research seminars.

Moving beyond access and achievement, towards equity and justice

Tia C. Madkins and Nicol R. Howard described that educators in schools (and associated professionals) need to build an awareness of how the learning in their classrooms might be affected by:

  • Personal beliefs, ways of knowing or thinking, stereotypes, and the cultural lens of the educator and the learners
  • Power dynamics and intersectional identities

They say: “Instead of viewing learners as deficient individuals who we need to ‘fix’ in our classrooms, we use strengths-based approaches where we as educators learn to recognise, draw on, and build upon learners’ strengths and lived experiences.”

The researchers encourage educators to connect with learners’ cultural practices and lived experiences, and to foster and maintain relationships with learners’ families and communities, in order to work together to facilitate equitable, social justice–oriented CS learning

To hear from Tia, Nicol, and their collaborator Shomari Jones, watch their seminar. You can also read Tia and Nicol’s article in our seminar proceedings, where you’ll find a list of their recommended resources to explore this thinking further.

Valuing existing knowledge and lived experience as expertise

Jakita O. Thomas described findings from her research project based on a free enrichment programme exploring how Black middle-school girls develop computational algorithmic thinking skills in the context of game design.

The programme was intentionally designed to position Black girls as knowledge holders with valuable experiences, and to offer them opportunities to shape their identities as producers, innovators, and people who challenge deficit perspectives. These are perspectives that include implicit assumptions that privilege the values, beliefs, and practices of one group over another, especially where the groups are racially, ethnically, or culturally different.

Jakita emphasised that it’s very important for educators to ask the questions “STEM learning for what?”, “For whom?”, “How?”, and “To what ends?” when they consider how to bring STEM learning experiences to Black girls (or other young people with multiple marginal identities). Educators need an awareness that the economic reasons of STEM learning, which are commonly spotlighted, may not be sufficient to convince young people who are marginalised to engage in these subjects.

To hear more about this from Jakita directly, watch her seminar:

Empowering learners to be agents of change

One thing these researchers’ work makes clear is that the reasons for why learners choose to engage in CS education are many, and that gaining CS skills to prepare for the job market is only one of them.

In both seminars, the speakers emphasised how important it is for educators to contribute to their learners’ self-view as agents of change, not only by demonstrating how CS can be used to solve problems, but also by being open and direct about existing technological inequities. This teaches learners to use CS as a tool, and to also examine the social context in which CS is being applied, and the positive and negative consequences of these applications. Learning CS can empower young people to address challenges their communities face, and educators, learners, and families can work together through CS on social justice issues.

Putting the power of computing into the hands of young people is the core of our mission, and we have a research project underway right now that looks at equitable computing education in UK schools. Find out more about it here, and download our practical guide for teachers.

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Bringing digital skills to disadvantaged children across India

Post Syndicated from Divya Joseph original https://www.raspberrypi.org/blog/digital-skills-disadvantaged-children-india-digital-divde/

India’s rapidly digitising economy needs people with IT and programming skills, as well as skills such as creativity, unstructured problem solving, teamwork, and communication. Unfortunately, too many children in India currently do not have access to digital technologies, or to opportunities to learn these technical skills.

A girl and boy in India learning at a computer

Roadblocks to accessing digital skills

Before children and young people in India can even get a chance to learn digital skills, many of them have to overcome numerous roadblocks. India’s digital divide is entrenched due to a lack of access to electricity, to the internet, and to digital devices. In 2017–18, only 47% of Indian households received electricity for more than 12 hours a day. Moreover, only 24% of households have internet access, with the figure dropping as low as 15% in rural regions. 

In rural India, a group of children cluster around a computer.

During the coronavirus pandemic, when children in India had to plunge head-first into adapting to restrictions, 29 million students around the country did not have access to a digital device. In addition, only 38% of households in India are digitally literate. At the Raspberry Pi Foundation, we define digital literacy as the skills and knowledge required to be an effective, safe, and discerning user of various computer systems. Digital literacy in rural regions stands far lower at 25%.

We partner with organisations in India

We are conscious that we cannot solve these massive access issues. Regardless, we are committed to moving the needle for those young people that need access to digital skills and digital literacy the most.

We partner with organisations around the country that are committed to bringing access to coding and digital skills to the most disadvantaged and digitally excluded young people. Our partnership model includes:

  • Co-designing learning experiences 
  • Providing free, open-source learning resources 
  • Designing bespoke training programmes 
  • Supporting with technology solutions 

The Pratham–Code Club programme for digital skills

Pratham means ‘first’ in Hindi, and rightly so: Pratham Education Foundation, a non-profit established in 1994, has been at the forefront of addressing gaps in the education system in India. In 2018, we joined hands with Pratham Education Foundation to introduce coding to children in hard-to-reach, disadvantaged communities around the country. We co-designed a Pratham–Code Club programme to provide youth in underserved communities with training and access to devices and learning resources. The goal of the training was to build the youth’s programming confidence so that they could go on to teach children in their communities.

Two boys use a PraDigi computer at a desk.

To be effective, it was crucial that the programme be localised. We made adaptations to our learning resources and training content to make them more relevant to the context of the learners, and we worked with volunteer translators to translate the material into Hindi, Kannada, and Marathi.

We also provided the youth with training to use the PraDigi kit — an innovative, lightweight device, developed by Pratham Education Foundation and based on the Raspberry Pi computer — for teaching children to code.

Adapting the programme during the pandemic

In 2020, when we could no longer implement the programme the same way due to the pandemic and the ensuing disruptions, we made several adaptations: 

Firstly, instead of the three-hour in-person training we had previously conducted, we hosted multiple 30-minute online sessions over a week, using cloud-based platforms like Zoom. Secondly, we used familiar apps such as WhatsApp and Facebook Workplace to share the training content.

A screenshot from a training webinar about HTML coding.

Finally, since the Pratham staff in the communities could not bring the PraDigi kits to the remote locations during lockdowns, we adapted the training content for smartphones and tablets, using the online Scratch editor and a phone-friendly online code editor called Repl.it. 

Over the course of the pandemic, we trained 300 youth from Pratham’s communities in the basics of programming and digital skills. The impact was:

  • 300 youth trained
  • 432 hours of virtual sessions
  • 350 projects with Scratch and HTML
  • 62% of youth said they were now interested in jobs that included coding skills

We also surveyed the youth for what non-technical skills they had learned during the training:

  • 66% of youth reported that they had improved their problem-solving skills
  • 60% of youth reported that they improved their communication skills

Where we are taking the programme next

Using a train-the-trainer model, we are now scaling our programme with Pratham Education Foundation to train 3000 youth from underserved communities. Once they have completed the training, we will help these 3000 youth pave the way to programming and digital skills for 15,000 young learners around the country.

In rural India, a group of adults and children pose for the photographer.

We look forward to continuing our partnership with Pratham Education Foundation to make digital skills and coding education accessible to children all over India.

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Linking AI education to meaningful projects

Post Syndicated from Sue Sentance original https://www.raspberrypi.org/blog/ai-education-meaningful-projects-tara-chklovski/

Our seminars in this series on AI and data science education, co-hosted with The Alan Turing Institute, have been covering a range of different topics and perspectives. This month was no exception. We were delighted to be able to host Tara Chklovski, CEO of Technovation, whose presentation was called ‘Teaching youth to use AI to tackle the Sustainable Development Goals’.

Tara Chklovski.
Tara Chklovski

The Technovation Challenge

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

Tara Chklovski describes the Technovation Challenge in an online seminar.
Click to enlarge

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

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

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

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

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

Tara Chklovski describes the Technovation Challenge in an online seminar.
Click to enlarge

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

AI Families: Children and parents using AI to solve problems

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

A list of lessons in the AI Families programme from Technovation.

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

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

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

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

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

Many perspectives on AI education

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

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

Join us for our next seminar

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

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

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

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Coding for kids: Art, games, and animations with our new beginners’ Python path

Post Syndicated from Rebecca Franks original https://www.raspberrypi.org/blog/coding-for-kids-art-games-animations-beginners-python-programming/

Python is a programming language that’s popular with learners and educators in clubs and schools. It also is widely used by professional programmers, particularly in the data science field. Many educators and young people like how similar the Python syntax is to the English language.

Two girls code together at a computer.

That’s why Python is often the first text-based language that young people learn to program in. The familiar syntax can lower the barrier to taking the first steps away from a block-based programming environment, such as Scratch.

In 2021, Python ranked in first place in an industry-standard popularity index of a major software quality assessment company, confirming its favoured position in software engineering. Python is, for example, championed by Google and used in many of its applications.

Coding for kids in Python

Python’s popularity means there are many excellent resources for learning this language. These resources often focus on creating programs that produce text outputs. We wanted to do something different.

Two young people code at laptops.

Our new ‘Introduction to Python’ project path focuses on creating digital visuals using the Python p5 library. This library is like a set of tools that allows you to get creative by using Python code to draw shapes, edit images, and create frame-by-frame animations. That makes it the perfect choice for young learners: they can develop their knowledge and skills in Python programming while creating cool visuals that they’ll be proud of. 

What is in the ‘Introduction to Python’ path?

The ‘Introduction to Python’ project path is designed according to our Digital Making Framework, encouraging learners to become independent coders and digital makers by gently removing scaffolding as they progress along the projects in a path. Paths begin with three Explore projects, in which learners are guided through tasks that introduce them to new coding skills. Next, learners complete two Design projects. Here, they are encouraged to practise their skills and bring in their own interests to personalise their coding creations. Finally, learners complete one Invent project. This is where they put everything that they have learned together and create something unique that matters to them.

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Emoji, archery, rockets, art, and movement are all part of this Python path.

The structure of our Digital Making Framework means that learners experience the structured development process of a coding project and learn how to turn their ideas into reality. The Framework also supports with finding errors in their code (debugging), showing them that errors are a part of computer programming and just temporary setbacks that you can overcome. 

What coding skills and knowledge will young people learn?

The Explore projects are where the initial learning takes place. The key programming concepts covered in this path are:

  • Variables
  • Performing calculations with variables
  • Using functions
  • Using selection (if, elif and else)
  • Using repetition (for loops)
  • Using randomisation
  • Importing from libraries

Learners also explore aspects of digital visual media concepts:

  • Coordinates
  • RGB colours
  • Screen size
  • Layers
  • Frames and animation

Learners then develop these skills and knowledge by putting them into practice in the Design and Invent projects, where they add in their own ideas and creativity. 

Explore project 1: Hello world emoji

In the first Explore project of this path, learners create an interactive program that uses emoji characters as the visual element.

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This is the first step into Python and gets learners used to the syntax for printing text, using variables, and defining functions.

Explore project 2: Target practice

In this Explore project, learners create an archery game. They are introduced to the p5 library, which they use to draw an archery board and create the arrows.

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The new programming concept covered in this project is selection, where learners use if, elif and else to allocate points for the game.

Explore project 3: Rocket launch

The final Explore project gets learners to animate a rocket launching into space. They create an interactive animation where the user is asked to enter an amount of fuel for the rocket launch. The animation then shows if the fuel is enough to get the rocket into orbit.

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The new programming concept covered here is repetition. Learners use for loops to animate smoke coming from the exhaust of the rocket.

Design project 1: Make a face

The first Design project allows learners to unleash their creativity by drawing a face using the Python coding skills that they have built in the Explore projects. They have full control of the design for their face and can explore three examples for inspiration.

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Learners are also encouraged to share their drawings in the community library, where there are lots of fun projects to discover already. In this project, learners apply all of the coding skills and knowledge covered in the Explore projects, including selection, repetition, and variables.

Design project 2: Don’t collide!

In the second Design project, learners code a scrolling game called ‘Don’t collide’, where a character or vehicle moves down the screen while having to avoid obstacles.

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Learners can choose their own theme for the game, and decide what will move down the screen and what the obstacles will look like. In this project, they also get to practice everything they learned in the Explore projects. 

Invent project: Powerful patterns

This project is the ultimate chance for learners to put all of their skills and knowledge into practice and get creative. They design their own unique patterns and create frame-by-frame animations.

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The Invent project offers ingredients, which are short reminders of all the key skills that learners have gained while completing the previous projects in the path. The ingredients encourage them to be independent whilst also supporting them with code snippets to help them along.

Key questions answered

Who is the Introduction to Python path for?

We have written the projects in the path with young people around the age of 9 to 13 in mind. To code in a text-based language, a young person needs to be familiar with using a keyboard, due to the typing involved. A learner may have completed one of our Scratch paths prior to this one, but this isn’t essential. and we encourage beginner coders to take this path first if that is their choice.

A young person codes at a Raspberry Pi computer.

What software do learners need to code these projects?

A web browser. In every project, starter code is provided in a free web-based development environment called Trinket, where learners add their own code. The starter Trinkets include everything that learners need to use Python and access the p5 library.

If preferred, the projects also include instructions for using a desktop-based programming environment, such as Thonny.

How long will the path take to complete?

We’ve designed the path to be completed in around six one-hour sessions, with one hour per project. However, the project instructions encourage learners to upgrade their projects and go further if they wish. This means that young people might want to spend a little more time getting their projects exactly as they imagine them. 

What can young people do next after completing this path?

Taking part in Coolest Projects Global

At the end of the path, learners are encouraged to register a project they’re making with their new coding skills for Coolest Projects Global, our world-leading online technology showcase for young people.

Three young tech creators show off their tech project at Coolest Projects.

Taking part is free, all online, and beginners as well as more experienced young tech creators are welcome and invited. This is their unique opportunity to share their ingenuity in an online gallery for the world and the Coolest Projects community to celebrate.

Coding more Python projects with us

Coming very soon is our ‘More Python’ path. In this path, learners will move beyond the basics they learned in Introduction to Python. They will learn how to use lists, dictionaries, and files to create charts, models, and artwork. Keep your eye on our blog and social media for the release of ‘More Python’.

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Calling all Computing and ICT teachers in the UK and Ireland: Have your say

Post Syndicated from Sue Sentance original https://www.raspberrypi.org/blog/computing-ict-teacher-survey-uk-ireland-ukicts-call-for-responses/

Back in October, I wrote about a report that the Brookings Institution, a US think tank, had published about the provision of computer science in schools around the world. Brookings conducted a huge amount of research on computer science curricula in a range of countries, and the report gives a very varied picture. However, we believe that, to see a more complete picture, it’s also important to gather teachers’ own perspectives on their teaching.

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

Complete our survey for computing teachers

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

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

The survey is:

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

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

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

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

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

What computing is taught in the UK and Ireland?

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

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

Male teacher and male students at a computer

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

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

A computing teacher and students in the classroom.

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

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

What is the survey?

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

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

A computing teacher and a learner do physical computing in the primary school classroom.

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

The research team

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

In alphabetical order, on the team are:

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

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

How can I find out more?

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

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It’s back: The Hello World podcast for the computing education community

Post Syndicated from Janina Ander original https://www.raspberrypi.org/blog/hello-world-podcast-season-3-computing-education/

We set out last year to gather more stories, ideas, and inspiration from and for the computing education community in between Hello World magazine issues: we launched the Hello World podcast. On the podcast, we dive deeper into articles from Hello World, and we speak with people from all over the world who work as teachers, educators, and other computing education professionals.

Hello World logo.

Season 3 of the Hello World podcast starts on Monday

The Hello World podcast helps connect the global community of computing educators and Hello World readers, and lets them share their experiences. After two seasons and a short pause during the autumn, we are finally back with a brand-new Hello World podcast season. Regular listeners will also notice a new theme music!

Each episode, we explore computing, coding, and digital making education by delving into an exciting topic together with our guests: experts, practitioners, and other members of the Hello World community.

 In season 3, we’re exploring:

  • The role of makerspaces, both within schools and the wider community 
  • The relevance of imagination and storytelling to computing 
  • Computing in the context of science and ecology
  • How learners can promote and support computing as digital leaders
  • And much more…
A phone with headphones plugged in next to a cup of coffee on a table.

Meet our guests for episode 1 of the new season

In our first episode, which will be available from 7 February, your hosts Carrie Anne and James ask the question “What role do makerspaces play in the classroom?”. We talk to two fantastic guests, each with a wealth of experience in designing and developing makerspaces:

Nick Provenzano.
Nick Provenzano

Nick Provenzano, who is a Teacher and Makerspace Director at University Liggett School in Michigan. He is also an author, makerspace builder, international keynote speaker and Raspberry Pi Certified Educator.

Chris Hillidge
Chris Hillidge

Chris Hillidge, who established FabLab Warrington in 2016 and manages the STEM strategy for students aged 4 to 19 across The Challenge Academy Trust. Chris is a Specialist Leader of Education, consultant, and Raspberry Pi Certified Educator.

If you’ve not tried out the Hello World podcast yet, why not get started by diving into one of our most popular episodes?

You’ll find the upcoming season and past episodes on your favourite podcast platform, where you can also subscribe to never miss an episode. Alternatively, you can listen via your browser at helloworld.cc/podcast.

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The Roots project: Implementing culturally responsive computing teaching in schools in England

Post Syndicated from Sue Sentance original https://www.raspberrypi.org/blog/culturally-responsive-computing-teaching-schools-england-roots-research-project/

Since last year, we have been investigating culturally relevant pedagogy and culturally responsive teaching in computing education. This is an important part of our research to understand how to make computing accessible to all young people. We are now continuing our work in this area with a new project called Roots, bridging our research team here at the Foundation and the team at the Raspberry Pi Computing Education Research Centre, which we jointly created with the University of Cambridge in its Department of Computer Science and Technology.

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

What do we mean by culturally relevant pedagogy?

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

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

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

The story so far

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

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

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

The next phase of work: Roots

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

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

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

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

More about equity, diversity, and inclusion in computing education

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

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

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

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Creating better online multiple choice questions

Post Syndicated from Jane Waite original https://www.raspberrypi.org/blog/better-online-multiple-choice-questions-education-edtech/

In this blog post we explore good practices around creating online computing questions, specifically multiple choice questions (MCQs). Multiple choice questions are a popular way to help teachers and learners work out the next steps in learning, and to assess learning in examinations. As a case study, we look at some data related to learner responses to computing questions on the Oak National Academy platform.

Someone fills in a standardised test with multiple choice questions using a pencil.

The case study illustrates the many things MCQ authors have to think about while designing questions, and that there is much more research needed to understand how to get an MCQ “just right”.

Uses of multiple choice questions

Online auto-marked MCQs are now being integrated into classroom activities, set as homework, and used in self-led learning at home. Software products involving MCQs, such as Kahoot and Socratic, are easy to use for many, and have become popular in some learning contexts. MCQ may have become more prevalent due to increased online teaching and the availability of whole curricula through platforms such as the Oak National Academy.

A girl does school work at a laptop at home.

An international group of researchers from China, Spain, Singapore, and the UK recently looked into the reasons why MCQ-based testing might improve learning. Chunliang Yang and his co-authors concluded that there are three main ways that MCQ tests help learners learn:

  • They provide learners with additional exposure to learning content
  • They provide learners with content in the same format that they will be later assessed in 
  • They motivate learners, e.g. to prompt them to commit more effort to learn in general

What does the research say about creating multiple choice questions?

In recent research reviewing the use of MCQs, Andrew Butler from Washington University in St Louis looked at the effectiveness of MCQs in relation to learning, rather than assessment. Andrew gives the following advice for educators creating MCQs for learning:

  • Think about the thinking processes the learner will use when answering the question, and make sure the processes are productive for their learning
  • Don’t make the question super easy or too difficult, but make it challenging — the difficulty needs to be “just right”
  • Keep the phrasing of the question simple 
  • Ensure that all answers are plausible; providing three or four answers is usually a good idea
  • Be aware that if learners pick the wrong answer, this can reinforce the wrong thinking
  • Provide corrective feedback to learners who pick the wrong answer

What I find particularly interesting about Andrew’s advice is the need to make the difficulty of the MCQ “just right” for learners. But what does “just right” look like in practice? More research is needed to work this out.

The anatomy of a multiple choice question

When talking about MCQs, there are technical terms to describe question features, e.g.:

  • Incorrect answers are called distractors (or lures)
  • A distractor is defined as plausible if it’s an answer a layperson would see as a reasonable answer
  • Plausible distractors are called working distractors

Here at the Foundation, we created MCQs for the Oak National Academy when we adapted our Teach Computing Curriculum classroom materials into video lessons and accompanying home learning content to support learners and teachers during school closures. Data about what questions are attempted on the Oak platform, and what answer options are chosen, is stored securely by Oak National Academy. The Oak team kindly provided us with four months of anonymous data related to responses to the MCQs in the ‘GCSE Computer Science – Data representations’ unit.

Over this period of four months, learners on the platform made more than 29,000 question attempts on the thirty-five questions across the nine lessons that make up this data representation unit. Here is a breakdown of the questions by topic area:

Data about responses to a set of multiple choice questions on the Oak Academy platform.of a multiple choice question on the Oak Academy platform.
Responses to MCQs in the GCSE Computer Science data representation unit on Oak National Academy, data from February 2021 to end of May 2021 (click to enlarge)

As shown in the table, more questions relate to binary arithmetic than to any other topic area. This was a specific design decision, as it is well-known that learners need lots of practice of the processes involved in answering binary arithmetic questions.

Part of the graph of learning objectives for the Teach Computing Curriculum unit GCSE Computer Science data representation.
Part of the graph of learning objectives for the Teach Computing Curriculum unit GCSE Computer Science — Data representations (click to enlarge)

Let’s look at an example question from the binary arithmetic topic area, with one correct answer and two distractors. The learning objective being addressed with this question is ‘Perform addition in binary on two binary numbers’.

Screenshot of a multiple choice question on the Oak Academy platform.
One of the MCQs in the GCSE Computer Science data representation unit on the Oak National Academy, as displayed on the online platform

As shown in the table below, in four months, 1170 attempts were made to answer the example question. 65% of the attempts were correct responses, and 35% were not, with 21% of responses being distractor b, and 14% distractor c. These distractors appear to be working distractors, as they were chosen by more than 5% of learners, which has been suggested as a rule-of-thumb threshold that distractors have to clear to be classed as working.

Data about responses to a multiple choice question on the Oak Academy platform.
Example MCQ in the GCSE Computer Science data representation unit on the Oak National Academy, plus response data from February 2021 to end of May 2021 (click to enlarge)

However, because of the lack of research into MCQs, we cannot say for certain that this question is “just right” — it may be too hard. We need to do further research to find this out.

Creating multiple choice questions is not easy

The process of creating good MCQs is not an easy task, because question authors need to think about many things, including:

  • What learning objectives are to be addressed
  • What plausible distractors can be used
  • What level of difficulty is right for learners
  • What type of thinking the questions are encouraging, and how this is useful for learners

In order for MCQs to be useful for learners and teachers, much more research is needed in this area to show how to reliably produce MCQs that are “just right” and encourage productive thinking processes. We are very much looking forward to looking at this topic in our research work.

To find out more about the computing education research we are doing, you can browse our website, take part in our monthly seminars, and read our publications.

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How can AI-based analysis help educators support students?

Post Syndicated from Henna Gorsia original https://www.raspberrypi.org/blog/ai-sytems-in-education-learner-support-research-seminar/

We are hosting a series of free research seminars about how to teach artificial intelligence (AI) and data science to young people, in partnership with The Alan Turing Institute.

In the fifth seminar of this series, we heard from Rose Luckin, Professor of Learner Centred Design at the University College London (UCL) Knowledge Lab. Rose is Founder of EDUCATE Ventures Research Ltd., a London consultancy service working with start-ups, researchers, and educators to develop evidence-based educational technology.

Rose Luckin.
Rose Luckin, UCL

Based on her experience at EDUCATE, Rose spoke about how AI-based analysis could help educators gain a deeper understanding of their students, and how educators could work with AI systems to provide better learning resources to their students. This provided us with a different angle to the first four seminars in our current series, where we’ve been thinking about how young people learn to understand AI systems.

Rose Luckin's definition of AI: technology capable of actions and behaviours "requiring intelligence when done by humans".
Rose’s definition of artificial intelligence for this presentation.

Education and AI systems

AI systems have the potential to impact education in a number of different ways, which Rose distilled into three areas: 

  1. Using AI in education to tackle some of the big educational challenges
  2. Educating teachers about AI so that they can use it safely and effectively 
  3. Changing education so that we focus on human intelligence and prepare people for an AI world

It is clear that the three areas are interconnected, meaning developments in one area will affect the others. Rose’s focus during the seminar was the second area: educating people about AI.

Rose Luckin's definition of the three intersections of education and artificial intelligence, see text in list above.

What can AI systems do in education? 

Through giving examples of existing AI-based systems used for education, Rose described what in particular it is about AI systems that can be useful in an education setting. The first point she raised was that AI systems can adapt based on learning from data. Her main example was the AI-based platform ENSKILLS, which detects the user’s level of competency with spoken English through the user’s interactions with a virtual character, and gradually adapts the character to the user’s level. Other examples of adaptive AI systems for education include Carnegie Learning and Century Intelligent Learning.

We know that AI systems can respond to different forms of data. Rose introduced the example of OyaLabs to demonstrate how AI systems can gather and process real-time sensory data. This is an app that parents can use in a young child’s room to monitor the child’s interactions with others. The app analyses the data it gathers and produces advice for parents on how they can support their child’s language development.

AI system creators can also combine adaptivity and real-time sensory data processing  in their systems. One example Rosa gave of this was SimSensei from the University of Southern California. This is a simulated coach, which a student can interact with and which gathers real-time data about how the student is speaking, including their tone, speed of speech, and facial expressions. The system adapts its coaching advice based on these interactions and on what it learns from interactions with other students.

Getting ready for AI systems in education

For the remainder of her presentation, Rose focused on the framework she is involved in developing, as part of the EDUCATE service, to support organisations to prepare for implementing AI systems, including educators within these organisations. The aim of this ETHICAI framework is to enable organisations and educators to understand:

  • What AI systems are capable of doing
  • The strengths and weaknesses of AI systems
  • How data is used by AI systems to learn
The EDUCATE consultancy service's seven-part AI readiness framework, see test below for list.

Rose described the seven steps of the framework as:

  1. Educate, enthuse, excite – about building an AI mindset within your community 
  2. Tailor and Hone – the particular challenges you want to focus on
  3. Identify – identify (wisely), collate and …
  4. Collect – new data relevant to your focus
  5. Apply – AI techniques to the relevant data you have brought together
  6. Learn – understand what the data is telling you about your focus and return to step 5 until you are AI ready
  7. Iterate

She then went on to demonstrate how the framework is applied using the example of online teaching. Online teaching has been a key part of education throughout the coronavirus pandemic; AI systems could be used to analyse datasets generated during online teaching sessions, in order to make decisions for and recommendations to educators.

The first step of the ETHICAI framework is educate, enthuse, excite. In Rose’s example, this step consisted of choosing online teaching as a scenario, because it is very pertinent to a teacher’s practice. The second step is to tailor and hone in on particular challenges that are to be the focus, capitalising on what AI systems can do. In Rose’s example, the challenge is assessing the quality of online lessons in a way that would be useful to educators. The third step of the framework is to identify what data is required to perform this quality assessment.

Examples of data to be fed into an AI system for education, see text.

The fourth step is the collection of new data relevant to the focus of the project. The aim is to gain an increased understanding of what happens in online learning across thousands of schools. Walking through the online learning example, Rose suggested we might be able to collect the following types of data:

  • Log data
  • Audio data
  • Performance data
  • Video data, which includes eye-movement data
  • Historical data from tests and interviews
  • Behavioural data from surveying teachers and parents about how they felt about online learning

It is important to consider the ethical implications of gathering all this data about students, something that was a recurrent theme in both Rose’s presentation and the Q&A at the end.

Step five of the ETHICAI framework focuses on applying AI techniques to the relevant data to combine and process it. The figure below shows that in preparation, the various data sets need to be collated, cleaned, organised, and transformed.

Presentation slide showing that data for an AI system needs to be collated, cleaned, organised, and transformed.

From the correctly prepared data, interaction profiles can be produced in order to put characteristics from different lessons into groups/profiles. Rose described how cluster analysis using a combination of both AI and human intelligence could be used to sort lessons into groups based on common features.

The sixth step in Rose’s example focused on what may be learned from analysing collected data linked to the particular challenge of online teaching and learning. Rose said that applying an AI system to students’ behavioural data could, for example, give indications about students’ focus and confidence, and make or recommend interventions to educators accordingly.

Presentation slide showing example graphs of results produced by an AI system in education.

Where might we take applications of AI systems in education in the future?

Rose described that AI systems can possess some types of intelligence humans have or can develop: interdisciplinary academic intelligence, meta-knowing intelligence, and potentially social intelligence. However, there are types such as meta-contextual intelligence and perceived self-efficacy that AI systems are not able to demonstrate in the way humans can.

The seven types of human intelligence as defined by Rose Luckin: interdisciplinary academic knowledge, meta-knowing intelligence, social intelligence, metacognitive intelligence, meta-subjective intelligence, meta-contextual knowledge, perceived self-efficacy.

The use of AI systems in education can cause ethical issues. As an example, Rose pointed out the use of virtual glasses to identify when students need help, even if they do not realise it themselves. A system like this could help educators with assessing who in their class needs more help, and could link this back to student performance. However, using such a system like this has obvious ethical implications, and some of these were the focus of the Q&A that followed Rose’s presentation.

It’s clear that, in the education domain as in all other domains, both positive and negative outcomes of integrating AI are possible. In a recent paper written by Wayne Holmes (also from the UCL Knowledge Lab) and co-authors, ‘Ethics of AI in Education: Towards a Community Wide Framework’ [1], the authors suggest that the interpretation of data, consent and privacy, data management, surveillance, and power relations are all ethical issues that should be taken into consideration. Finding consensus for a practical ethical framework or set of principles, with all stakeholders, at the very start of an AI-related project is the only way to ensure ethics are built into the project and the AI system itself from the ground up.

Two boys at laptops in a classroom.

Ethical issues of AI systems more broadly, and how to involve young people in discussions of AI ethics, were the focus of our seminar with Dr Mhairi Aitken back in September. You can revisit the seminar recording, presentation slides, and summary blog post.

I really enjoyed both the focus and content of Rose’s talk: educators understanding how AI systems may be applied to education in order to help them make more informed decisions about how to best support their students. This is an important factor to consider in the context of the bigger picture of what young people should be learning about AI. The work that Rose and her colleagues are doing also makes an important contribution to translating research into practical models that teachers can use.

Join our next free seminars

You may still have time to sign up for our Tuesday 11 January seminar, today at 17:00–18:30 GMT, where we will welcome Dave Touretzky and Fred Martin, founders of the influential AI4K12 framework, which identifies the five big ideas of AI and how they can be integrated into education.

Next month, on 1 February at 17:00–18:30 GMT, Tara Chklovski (CEO of Technovation) will give a presentation called Teaching youth to use AI to tackle the Sustainable Development Goals at our seminar series.

If you want to join any of our seminars, click the button below to sign up and we will send you information on how to join. We look forward to seeing you there!

You’ll always find our schedule of upcoming seminars on this page. For previous seminars, you can visit our past seminars and recordings page.

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How do we develop AI education in schools? A panel discussion

Post Syndicated from Sue Sentance original https://www.raspberrypi.org/blog/ai-education-schools-panel-uk-policy/

AI is a broad and rapidly developing field of technology. Our goal is to make sure all young people have the skills, knowledge, and confidence to use and create AI systems. So what should AI education in schools look like?

To hear a range of insights into this, we organised a panel discussion as part of our seminar series on AI and data science education, which we co-host with The Alan Turing Institute. Here our panel chair Tabitha Goldstaub, Co-founder of CogX and Chair of the UK government’s AI Council, summarises the event. You can also watch the recording below.

As part of the Raspberry Pi Foundation’s monthly AI education seminar series, I was delighted to chair a special panel session to broaden the range of perspectives on the subject. The members of the panel were:

  • Chris Philp, UK Minister for Tech and the Digital Economy
  • Philip Colligan, CEO of the Raspberry Pi Foundation 
  • Danielle Belgrave, Research Scientist, DeepMind
  • Caitlin Glover, A level student, Sandon School, Chelmsford
  • Alice Ashby, student, University of Brighton

The session explored the UK government’s commitment in the recently published UK National AI Strategy stating that “the [UK] government will continue to ensure programmes that engage children with AI concepts are accessible and reach the widest demographic.” We discussed what it will take to make this a reality, and how we will ensure young people have a seat at the table.

Two teenage girls do coding during a computer science lesson.

Why AI education for young people?

It was clear that the Minister felt it is very important for young people to understand AI. He said, “The government takes the view that AI is going to be one of the foundation stones of our future prosperity and our future growth. It’s an enabling technology that’s going to have almost universal applicability across our entire economy, and that is why it’s so important that the United Kingdom leads the world in this area. Young people are the country’s future, so nothing is complete without them being at the heart of it.”

A teacher watches two female learners code in Code Club session in the classroom.

Our panelist Caitlin Glover, an A level student at Sandon School, reiterated this from her perspective as a young person. She told us that her passion for AI started initially because she wanted to help neurodiverse young people like herself. Her idea was to start a company that would build AI-powered products to help neurodiverse students.

What careers will AI education lead to?

A theme of the Foundation’s seminar series so far has been how learning about AI early may impact young people’s career choices. Our panelist Alice Ashby, who studies Computer Science and AI at Brighton University, told us about her own process of deciding on her course of study. She pointed to the fact that terms such as machine learning, natural language processing, self-driving cars, chatbots, and many others are currently all under the umbrella of artificial intelligence, but they’re all very different. Alice thinks it’s hard for young people to know whether it’s the right decision to study something that’s still so ambiguous.

A young person codes at a Raspberry Pi computer.

When I asked Alice what gave her the courage to take a leap of faith with her university course, she said, “I didn’t know it was the right move for me, honestly. I took a gamble, I knew I wanted to be in computer science, but I wanted to spice it up.” The AI ecosystem is very lucky that people like Alice choose to enter the field even without being taught what precisely it comprises.

We also heard from Danielle Belgrave, a Research Scientist at DeepMind with a remarkable career in AI for healthcare. Danielle explained that she was lucky to have had a Mathematics teacher who encouraged her to work in statistics for healthcare. She said she wanted to ensure she could use her technical skills and her love for math to make an impact on society, and to really help make the world a better place. Danielle works with biologists, mathematicians, philosophers, and ethicists as well as with data scientists and AI researchers at DeepMind. One possibility she suggested for improving young people’s understanding of what roles are available was industry mentorship. Linking people who work in the field of AI with school students was an idea that Caitlin was eager to confirm as very useful for young people her age.

We need investment in AI education in school

The AI Council’s Roadmap stresses how important it is to not only teach the skills needed to foster a pool of people who are able to research and build AI, but also to ensure that every child leaves school with the necessary AI and data literacy to be able to become engaged, informed, and empowered users of the technology. During the panel, the Minister, Chris Philp, spoke about the fact that people don’t have to be technical experts to come up with brilliant ideas, and that we need more people to be able to think creatively and have the confidence to adopt AI, and that this starts in schools. 

A class of primary school students do coding at laptops.

Caitlin is a perfect example of a young person who has been inspired about AI while in school. But sadly, among young people and especially girls, she’s in the minority by choosing to take computer science, which meant she had the chance to hear about AI in the classroom. But even for young people who choose computer science in school, at the moment AI isn’t in the national Computing curriculum or part of GCSE computer science, so much of their learning currently takes place outside of the classroom. Caitlin added that she had had to go out of her way to find information about AI; the majority of her peers are not even aware of opportunities that may be out there. She suggested that we ensure AI is taught across all subjects, so that every learner sees how it can make their favourite subject even more magical and thinks “AI’s cool!”.

A primary school boy codes at a laptop with the help of an educator.

Philip Colligan, the CEO here at the Foundation, also described how AI could be integrated into existing subjects including maths, geography, biology, and citizenship classes. Danielle thoroughly agreed and made the very good point that teaching this way across the school would help prepare young people for the world of work in AI, where cross-disciplinary science is so important. She reminded us that AI is not one single discipline. Instead, many different skill sets are needed, including engineering new AI systems, integrating AI systems into products, researching problems to be addressed through AI, or investigating AI’s societal impacts and how humans interact with AI systems.

On hearing about this multitude of different skills, our discussion turned to the teachers who are responsible for imparting this knowledge, and to the challenges they face. 

The challenge of AI education for teachers

When we shifted the focus of the discussion to teachers, Philip said: “If we really want to equip every young person with the knowledge and skills to thrive in a world that shaped by these technologies, then we have to find ways to evolve the curriculum and support teachers to develop the skills and confidence to teach that curriculum.”

Teenage students and a teacher do coding during a computer science lesson.

I asked the Minister what he thought needed to happen to ensure we achieved data and AI literacy for all young people. He said, “We need to work across government, but also across business and society more widely as well.” He went on to explain how important it was that the Department for Education (DfE) gets the support to make the changes needed, and that he and the Office for AI were ready to help.

Philip explained that the Raspberry Pi Foundation is one of the organisations in the consortium running the National Centre for Computing Education (NCCE), which is funded by the DfE in England. Through the NCCE, the Foundation has already supported thousands of teachers to develop their subject knowledge and pedagogy around computer science.

A recent study recognises that the investment made by the DfE in England is the most comprehensive effort globally to implement the computing curriculum, so we are starting from a good base. But Philip made it clear that now we need to expand this investment to cover AI.

Young people engaging with AI out of school

Philip described how brilliant it is to witness young people who choose to get creative with new technologies. As an example, he shared that the Foundation is seeing more and more young people employ machine learning in the European Astro Pi Challenge, where participants run experiments using Raspberry Pi computers on board the International Space Station. 

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

Philip also explained that, in the Foundation’s non-formal CoderDojo club network and its Coolest Projects tech showcase events, young people build their dream AI products supported by volunteers and mentors. Among these have been autonomous recycling robots and AI anti-collision alarms for bicycles. Like Caitlin with her company idea, this shows that young people are ready and eager to engage and create with AI.

We closed out the panel by going back to a point raised by Mhairi Aitken, who presented at the Foundation’s research seminar in September. Mhairi, an Alan Turing Institute ethics fellow, argues that children don’t just need to learn about AI, but that they should actually shape the direction of AI. All our panelists agreed on this point, and we discussed what it would take for young people to have a seat at the table.

A Black boy uses a Raspberry Pi computer at school.

Alice advised that we start by looking at our existing systems for engaging young people, such as Youth Parliament, student unions, and school groups. She also suggested adding young people to the AI Council, which I’m going to look into right away! Caitlin agreed and added that it would be great to make these forums virtual, so that young people from all over the country could participate.

The panel session was full of insight and felt very positive. Although the challenge of ensuring we have a data- and AI-literate generation of young people is tough, it’s clear that if we include them in finding the solution, we are in for a bright future. 

What’s next for AI education at the Raspberry Pi Foundation?

In the coming months, our goal at the Foundation is to increase our understanding of the concepts underlying AI education and how to teach them in an age-appropriate way. To that end, we will start to conduct a series of small AI education research projects, which will involve gathering the perspectives of a variety of stakeholders, including young people. We’ll make more information available on our research pages soon.

In the meantime, you can sign up for our upcoming research seminars on AI and data science education, and peruse the collection of related resources we’ve put together.

The post How do we develop AI education in schools? A panel discussion appeared first on Raspberry Pi.

The machine learning effect: Magic boxes and computational thinking 2.0

Post Syndicated from Jane Waite original https://www.raspberrypi.org/blog/machine-learning-education-school-computational-thinking-2-0-research-seminar/

How does teaching children and young people about machine learning (ML) differ from teaching them about other aspects of computing? Professor Matti Tedre and Dr Henriikka Vartiainen from the University of Eastern Finland shared some answers at our latest research seminar.

Three smiling young learners in a computing classroom.
We need to determine how to teach young people about machine learning, and what teachers need to know to help their learners form correct mental models.

Their presentation, titled ‘ML education for K-12: emerging trajectories’, had a profound impact on my thinking about how we teach computational thinking and programming. For this blog post, I have simplified some of the complexity associated with machine learning for the benefit of readers who are new to the topic.

a 3D-rendered grey box.
Machine learning is not magic — what needs to change in computing education to make sure learners don’t see ML systems as magic boxes?

Our seminars on teaching AI, ML, and data science

We’re currently partnering with The Alan Turing Institute to host a series of free research seminars about how to teach artificial intelligence (AI) and data science to young people.

The seminar with Matti and Henriikka, the third one of the series, was very well attended. Over 100 participants from San Francisco to Rajasthan, including teachers, researchers, and industry professionals, contributed to a lively and thought-provoking discussion.

Representing a large interdisciplinary team of researchers, Matti and Henriikka have been working on how to teach AI and machine learning for more than three years, which in this new area of study is a long time. So far, the Finnish team has written over a dozen academic papers based on their pilot studies with kindergarten-, primary-, and secondary-aged learners.

Current teaching in schools: classical rule-driven programming

Matti and Henriikka started by giving an overview of classical programming and how it is currently taught in schools. Classical programming can be described as rule-driven. Example features of classical computer programs and programming languages are:

  • A classical language has a strict syntax, and a limited set of commands that can only be used in a predetermined way
  • A classical language is deterministic, meaning we can guarantee what will happen when each line of code is run
  • A classical program is executed in a strict, step-wise order following a known set of rules

When we teach this type of programming, we show learners how to use a deductive problem solving approach or workflow: defining the task, designing a possible solution, and implementing the solution by writing a stepwise program that is then run on a computer. We encourage learners to avoid using trial and error to write programs. Instead, as they develop and test a program, we ask them to trace it line by line in order to predict what will happen when each line is run (glass-box testing).

A list of features of rule-driven computer programming, also included in the text.
The features of classical (rule-driven) programming approaches as taught in computer science education (CSE) (Tedre & Vartiainen, 2021).

Classical programming underpins the current view of computational thinking (CT). Our speakers called this version of CT ‘CT 1.0’. So what’s the alternative Matti and Henriikka presented, and how does it affect what computational thinking is or may become?

Machine learning (data-driven) models and new computational thinking (CT 2.0) 

Rule-based programming languages are not being eradicated. Instead, software systems are being augmented through the addition of machine learning (data-driven) elements. Many of today’s successful software products, such as search engines, image classifiers, and speech recognition programs, combine rule-driven software and data-driven models. However, the workflows for these two approaches to solving problems through computing are very different.

A table comparing problem solving workflows using computational thinking 1.0 versus computational thinking 2.0, info also included in the text.
Problem solving is very different depending on whether a rule-driven computational thinking (CT 1.0) approach or a data-driven computational thinking (CT 2.0) approach is used (Tedre & Vartiainen,2021).

Significantly, while in rule-based programming (and CT 1.0), the focus is on solving problems by creating algorithms, in data-driven approaches, the problem solving workflow is all about the data. To highlight the profound impact this shift in focus has on teaching and learning computing, Matti introduced us to a new version of computational thinking for machine learning, CT 2.0, which is detailed in a forthcoming research paper.

Because of the focus on data rather than algorithms, developing a machine learning model is not at all like developing a classical rule-driven program. In classical programming, programs can be traced, and we can predict what will happen when they run. But in data-driven development, there is no flow of rules, and no absolutely right or wrong answer.

A table comparing conceptual differences between computational thinking 1.0 versus computational thinking 2.0, info also included in the text.
There are major differences between rule-driven computational thinking (CT 1.0) and data-driven computational thinking (CT 2.0), which impact what computing education needs to take into account (Tedre & Vartiainen,2021).

Machine learning models are created iteratively using training data and must be cross-validated with test data. A tiny change in the data provided can make a model useless. We rarely know exactly why the output of an ML model is as it is, and we cannot explain each individual decision that the model might have made. When evaluating a machine learning system, we can only say how well it works based on statistical confidence and efficiency. 

Machine learning education must cover ethical and societal implications 

The ethical and societal implications of computer science have always been important for students to understand. But machine learning models open up a whole new set of topics for teachers and students to consider, because of these models’ reliance on large datasets, the difficulty of explaining their decisions, and their usefulness for automating very complex processes. This includes privacy, surveillance, diversity, bias, job losses, misinformation, accountability, democracy, and veracity, to name but a few.

I see the shift in problem solving approach as a chance to strengthen the teaching of computing in general, because it opens up opportunities to teach about systems, uncertainty, data, and society.

Jane Waite

Teaching machine learning: the challenges of magic boxes and new mental models

For teaching classical rule-driven programming, much time and effort has been put into researching learners’ understanding of what a program will do when it is run. This kind of understanding is called a learner’s mental model or notional machine. An approach teachers often use to help students develop a useful mental model of a program is to hide the detail of how the program works and only gradually reveal its complexity. This approach is described with the metaphor of hiding the detail of elements of the program in a box. 

Data-driven models in machine learning systems are highly complex and make little sense to humans. Therefore, they may appear like magic boxes to students. This view needs to be banished. Machine learning is not magic. We have just not figured out yet how to explain the detail of data-driven models in a way that allows learners to form useful mental models.

An example of a representation of a machine learning model in TensorFlow, an online machine learning tool (Tedre & Vartiainen,2021).

Some existing ML tools aim to help learners form mental models of ML, for example through visual representations of how a neural network works (see Figure 2). But these explanations are still very complex. Clearly, we need to find new ways to help learners of all ages form useful mental models of machine learning, so that teachers can explain to them how machine learning systems work and banish the view that machine learning is magic.

Some tools and teaching approaches for ML education

Matti and Henriikka’s team piloted different tools and pedagogical approaches with different age groups of learners. In terms of tools, since large amounts of data are needed for machine learning projects, our presenters suggested that tools that enable lots of data to be easily collected are ideal for teaching activities. Media-rich education tools provide an opportunity to capture still images, movements, sounds, or sense other inputs and then use these as data in machine learning teaching activities. For example, to create a machine learning–based rock-paper-scissors game, students can take photographs of their hands to train a machine learning model using Google Teachable Machine.

Photos of hands are used to train a machine learning model as part of a project to create a rock-paper-scissors game.
Photos of hands are used to train a Teachable Machine machine learning model as part of a project to create a rock-paper-scissors game (Tedre & Vartiainen, 2021).

Similar to tools that teach classic programming to novice students (e.g. Scratch), some of the new classroom tools for teaching machine learning have a drag-and-drop interface (e.g. Cognimates). Using such tools means that in lessons, there can be less focus on one of the more complex aspects of learning to program, learning programming language syntax. However, not all machine learning education products include drag-and-drop interaction, some instead have their own complex languages (e.g. Wolfram Programming Lab), which are less attractive to teachers and learners. In their pilot studies, the Finnish team found that drag-and-drop machine learning tools appeared to work well with students of all ages.

The different pedagogical approaches the Finnish research team used in their pilot studies included an exploratory approach with preschool children, who investigated machine learning recognition of happy or sad faces; and a project-based approach with older students, who co-created machine learning apps with web-based tools such as Teachable Machine and Learn Machine Learning (built by the research team), supported by machine learning experts.

Example of a middle school (age 8 to 11) student’s pen and paper design for a machine learning app that recognises different instruments and chords.
Example of a middle school (age 8 to 11) student’s design for a machine learning app that recognises different instruments and chords (Tedre & Vartiainen, 2021).

What impact these pedagogies have on students’ long-term mental models about machine learning has yet to be researched. If you want to find out more about the classroom pilot studies, the academic paper is a very accessible read.

My take-aways: new opportunities, new research questions

We all learned a tremendous amount from Matti and Henriikka and their perspectives on this important topic. Our seminar participants asked them many questions about the pedagogies and practicalities of teaching machine learning in class, and raised concerns about squeezing more into an already packed computing curriculum.

For me, the most significant take-away from the seminar was the need to shift focus from algorithms to data and from CT 1.0 to CT 2.0. Learning how to best teach classical rule-driven programming has been a long journey that we have not yet completed. We are forming an understanding of what concepts learners need to be taught, the progression of learning, key mental models, pedagogical options, and assessment approaches. For teaching data-driven development, we need to do the same.  

The question of how we make sure teachers have the necessary understanding is key.

Jane Waite

I see the shift in problem solving approach as a chance to strengthen the teaching of computing in general, because it opens up opportunities to teach about systems, uncertainty, data, and society. I think it will help us raise awareness about design, context, creativity, and student agency. But I worry about how we will introduce this shift. In my view, there is a considerable risk that we will be sucked into open-ended, project-based learning, with busy and fun but shallow learning experiences that result in restricted conceptual development for students.

I also worry about how we can best help teachers build up the knowledge and experience to support their students. In the Q&A after the seminar, I asked Matti and Henriikka about the role of their team’s machine learning experts in their pilot studies. It seemed to me that without them, the pilot lessons would not have worked, as the participating teachers and students would not have had the vocabulary to talk about the process and would not have known what was doable given the available time, tools, and student knowledge.

The question of how we make sure teachers have the necessary understanding is key. Many existing professional development resources for teachers wanting to learn about ML seem to imply that teachers will all need a PhD in statistics and neural network optimisation to engage with machine learning education. This is misleading. But teachers do need to understand the machine learning concepts that their students need to learn about, and I think we don’t yet know exactly what these concepts are. 

In summary, clearly more research is needed. There are fundamental questions still to be answered about what, when, and how we teach data-driven approaches to software systems development and how this impacts what we teach about classical, rule-based programming. But to me, that is exciting, and I am very much looking forward to the journey ahead.

Join our next free seminar

To find out what others recommend about teaching AI and ML, catch up on last month’s seminar with Professor Carsten Schulte and colleagues on centring data instead of code in the teaching of AI.

We have another four seminars in our monthly series on AI, machine learning, and data science education. Find out more about them on this page, and catch up on past seminar blogs and recordings here.

At our next seminar on Tuesday 7 December at 17:00–18:30 GMT, we will welcome Professor Rose Luckin from University College London. She will be presenting on what it is about AI that makes it useful for teachers and learners.

We look forward to meeting you there!

PS You can build your understanding of machine learning by joining our latest free online course, where you’ll learn foundational concepts and train your own ML model!

The post The machine learning effect: Magic boxes and computational thinking 2.0 appeared first on Raspberry Pi.

Introducing Code Club World: a new way for young people to learn to code at home

Post Syndicated from Laura Kirsop original https://www.raspberrypi.org/blog/code-club-world-free-online-platform-young-people-children-learn-to-code-at-home/

Today we are introducing you to Code Club World — a free online platform where young people aged 9 to 13 can learn to make stuff with code.

Images from Code Club World, a free online platform for children who want to learn to code

In Code Club World, young people can:

  • Start out by creating their personal robot avatar
  • Make music, design a t-shirt, and teach their robot avatar to dance!
  • Learn to code on islands with structured activities
  • Discover block-based and text-based coding in Scratch and Python
  • Earn badges for their progress 
  • Share their coding creations with family, friends, and the Code Club World community

Learning to code at home with Code Club World: meaningful, fun, flexible

When we spoke to parents and children about learning at home during the pandemic, it became clear to us that they were looking for educational tools that the children can enjoy and master independently, and that are as fun and social as the computer games and other apps the children love.

A girl has fun learning to code at home, sitting with a laptop on a sofa, with a dog sleeping next to her and her father writing code too.
Code Club World is educational, and as fun as the games and apps young people love.

What’s more, a free tool for learning to code at home is particularly important for young people who are unable to attend coding clubs in person. We believe every child should have access to a high-quality coding and digital making education. And with this in mind, we set out to create Code Club World, an online environment as rich and engaging as a face-to-face extracurricular learning experience, where all young people can learn to code.

The Code Club World activities are mapped to our research-informed Digital Making Framework — a coding and digital making curriculum for non-formal settings. That means when children are in the Code Club World environment, they are learning to code and use digital making to independently create their ideas and address challenges that matter to them.

Islands in the Code Club World online platform for children who want to learn to code for free.
Welcome to Code Club World — so many islands to explore!

By providing a structured pathway through the coding activities, a reward system of badges to engage and motivate learners, and a broad range of projects covering different topics, Code Club World supports learners at every stage, while making the activities meaningful, fun, and flexible.

A girl has fun learning to code at home on a tablet sitting on a sofa.
Code Club World’s home island works as well on mobile phones and tablets as on computers.

We’ve also designed Code Club World to be mobile-friendly, so if a young person uses a phone or tablet to visit the platform, they can still code cool things they will be proud of.

Created with the community

Since we started developing Code Club World, we have been working with a community of more than 1000 parents, educators, and children who are giving us valuable input to shape the direction of the platform. We’ve had some fantastic feedback from them:

“I’ve not coded before, but found this really fun! … I LOVED making the dance. It was so much fun and made me laugh!”

Learner, aged 11

“I love the concept of having islands to explore in making the journey through learning coding, it is fabulous and eye-catching.”

Parent

The platform is still in beta status — this means we’d love you to share it with young people in your family, school, or community so they can give their feedback and help make Code Club World even better.

Together, we will ensure every child has an equal opportunity to learn to code and make things that change their world.

The post Introducing Code Club World: a new way for young people to learn to code at home appeared first on Raspberry Pi.

Cat Lamin on building a global educator family | Hello World #17

Post Syndicated from Gemma Coleman original https://www.raspberrypi.org/blog/global-staffroom-mental-health-hello-world-17/

Cat Lamin.

In Hello World issue 17, Raspberry Pi Certified Educator Cat Lamin talks about how building connections and sharing the burden can help make us better educators, even in times of great stress:

“I felt like I needed to play my part”

In March 2020, the world suddenly changed. For educators, we jumped from face-to-face teaching to a stark new landscape, with no idea of how the future would look. As generous teachers pushed out free resources, I felt like I needed to play my part. Suddenly, an idea struck me. In September 2017, I had decided to be brave and submit a talk to PyConUK to discuss my mental health. Afterwards, several people in the audience shared their own stories with me or let me know that it helped them just to hear that someone else struggled too. I realised that in times of pressure, we need a chance to talk and we had lost these outlets. In school, we would pop to the staffroom or a friend’s classroom for a quick vent, but that wasn’t an option anymore. People were feeling isolated, scared, stressed and didn’t have anyone to turn to.

I realised that in times of pressure, we need a chance to talk, and we had lost these outlets.

Cat Lamin

Thus, the first Global Google Educator Group Staffroom: Mental Health Matters was launched on 14 March 2020, which coincided with the US government announcing school closures and UK teachers still waiting anxiously to hear when doors would close. The aim of Staffroom was to give teachers a safe space to talk about how they’re feeling under the overwhelming weight of school closures. To say it was a success would be an understatement, with teachers joining the calls from Australia, Malaysia, the USA, Colombia, Mexico, Brazil, Europe and more!

Pily Perfil.

Staffroom for me is a place and time to connect with other teachers from around the world. I remember seeing the calendar invites by mail and I kept thinking I should join but was afraid to do it. The first time I did it, I listened first and it made me realize that my struggles during pandemic online teaching were the same struggles as everywhere else.” – Pily Hernandez, Monterrey, Mexico

Which William are you today?

In those early days, we just gave teachers a chance to talk. The format of our meetings was simple: what’s your name, where are you from, and then an ice breaker question like ‘What colour do you feel like?’ or ‘What song represents your current mood?’ It wasn’t long before we hit upon a winning formula by making our own ‘Which image are you today?’ picture scale (see the ‘Which William’ image below!). Using the picture scales allowed people to really express how they felt. Often someone who had been happily chatting would explain that they were actually struggling to keep their head above water because a silly image allowed them to be honest.

A grid of photos of the same toddler expressing different emotions.
Which William are you today?

One of the most important messages from Staffroom was that many people involved with technology in schools were feeling alone. After years of suggesting teachers use technology, suddenly they were blamed for schools not being properly prepared. They were struggling with not necessarily knowing what to suggest to teachers with technology difficulties, as they were grappling with their own personal lockdown situations. Hearing that other people, all around the world, were experiencing something similar was hugely eye-opening and took a great amount of weight off their shoulders.

Abid Patel.

“As someone who thrived from having in person connections and networking opportunities, lockdown hit me hard. Staffroom really helped to keep those connections going and has developed into such a lovely safe space to talk and connect with others.” – Abid Patel, London, UK

We varied the tone of the sessions depending on the needs of the attendees. In the first few months, we shared our lockdown situations and our different experiences across the world. We could share advice and tips, as well as best practice for delivering content and things that had gone terribly wrong since switching to remote teaching. Or we’d discuss food in different countries around the world (did you know that in Australia, fish and chips is made from shark?) or joke about whether Vegemite was actually an edible product (it’s ok, I tried it live on camera during one Staffroom). Other days, we would discuss how difficult we were finding teaching, isolation or life in general during a pandemic.

An honest environment

One of the things that people continuously mentioned was that Staffroom was a safe place where they felt they could share, be listened to, and be understood. We made it clear that no one had to speak unless they wanted to. I made a point of always being completely honest about my own mental health. As a person who had suffered from depression and anxiety in the past, it was no surprise to me when I was diagnosed with both near the end of 2020, and I was fortunate enough to get virtual therapy. I shared my story with the group, which allowed attendees to feel more comfortable being open and talking about their own struggles, in some cases leading to their own diagnosis and getting much-needed support.

Frederick Ballew.

Staffroom has been the best ‘out of my comfort zone’ leap that I have ever taken. I have met educators from all over the world and learned that there are more things that unite us than divide us in this world of education.” – Frederick Ballew, Minnesota, USA

People would join Staffroom to share new jobs, engagements, even cross-country moves, but equally they would join after losing a loved one or hearing of a pupil sick in hospital. Staffroom became a safe haven for teachers, coaches, IT directors, and pretty much anyone involved in technology within education. It is a place where we could bond over shared experience, share a joke, ask questions, get ideas, and even plan our futures.

Do not underestimate the power of connections, and of sharing your story.

Cat Lamin

Alongside Staffroom, I also built a website to allow teachers to share their mental health stories and to feel a little less alone (mentalhealthineducation.com). I continue to host regular Staffrooms, although less frequently. 18 months ago, we needed a chance to talk three times a week, but now we meet two or three times a month instead. You can find current Staffroom dates at www.globalgeg.org/events. If you take one thing away from this article, however, it is this: do not underestimate the power of connections, and of sharing your story.

Cat Lamin is a Raspberry Pi Certified Educator, CAS Master Teacher, and Google Certified Innovator who works as a freelance trainer and coach, supporting schools with digital strategy and enabling educators to use technology more effectively. For running this regular mental health staffroom, she was awarded a Mental Health Champion Award from Edufuturist.

Share your thoughts about Hello World with me!

Your insights are invaluable to help us make Hello World as useful as it can be for computing educators around the globe. Hello World is a magazine for educators from educators — so if you are interested in having a 20-minute chat with me about what you like about the magazine, and what you would like to change, then please sign up here. I look forward to speaking with you.

Download Hello World for free

The brand-new issue of our free Hello World magazine for computing educators focuses on all things health and well-being.

Cover of issue 17 of Hello World.

It is full of inspiring stories and practical ideas for teaching your learners about computing in this context, and supporting them to use digital technologies in beneficial ways.

Download the new issue of Hello World for free today:

To never miss a new issue, you can subscribe to Hello World for free. Also check out the first-ever special edition of Hello World, The Big Book of Pedagogy. It focuses on approaches to teaching computing in the classroom, and you can download the special edition for free.

Wherever you are in the world, you can listen to our Hello World podcast too! Each episode, we explore a new topic with some of the computing educators who’ve written for the magazine.

The post Cat Lamin on building a global educator family | Hello World #17 appeared first on Raspberry Pi.

Computer science education is a global challenge

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

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

Woman teacher and female students at a computer

Why a global challenge?

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

Student using a Raspberry Pi computer

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

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

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

Capacity building: IT infrastructure and beyond

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

A young woman codes in a computing classroom.

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

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

Comparing countries — what policies are in place?

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

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

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

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

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

Computing education policy in England

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

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

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

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

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

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

Lessons learned, lessons missed

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

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

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

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

Sue Sentance

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

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

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

Moving forward together

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

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


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

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

The post Computer science education is a global challenge appeared first on Raspberry Pi.

Engaging Black students in computing at UK schools — interview with Joe Arday

Post Syndicated from Janina Ander original https://www.raspberrypi.org/blog/engaging-black-students-in-computing-uk-schools-joe-arday/

Joe Arday.

On the occasion of Black History Month UK, we speak to Joe Arday, Computer Science teacher at Woodbridge High School in Essex, UK, about his experiences in computing education, his thoughts about underrepresentation of Black students in the subject, and his ideas about what needs to be done to engage more Black students.

To start us off, can you share some of your thoughts about Black History Month as an occasion?

For me personally it’s an opportunity to celebrate our culture, but my view is it shouldn’t be a month — it should be celebrated every day. I am of Ghanaian descent, so Black History Month is an opportunity to share my culture in my school and my community. Black History Month is also an opportunity to educate yourself about what happened to the generations before you. For example, my parents lived through the Brixton riots. I was born in 1984, and I got to secondary school before I heard about the Brixton riots from a teacher. But my mother made sure that, during Black History Month, we went to a lot of extracurricular activities to learn about our culture.

For me it’s about embracing the culture I come from, being proud to be Black, and sharing that culture with the next generation, including my two kids, who are of mixed heritage. They need to know where they come from, and know their two cultures.

Tell us a bit about your own history: how did you come to computing education?

So I was a tech professional in the finance sector, and I was made redundant when the 2008 recession hit. I did a couple of consulting jobs, but I thought to myself, “I love tech, but in five years from now, do I really want to be going from job to job? There must be something else I can do.”

At that time there was a huge drive to recruit more teachers to teach what was called ICT back then and is now Computing. As a result, I started my career as a teacher in 2010. As a former software consultant, I had useful skills for teaching ICT. When Computing was introduced instead, I was fortunate to be at a school that could bring in external CPD (continued professional development) providers to teach us about programming and build our understanding and skills to deliver the new curriculum. I also did a lot of self-study and spoke to lots of teachers at other schools about how to teach the subject.

What barriers or support did you encounter in your teaching career? Did you have role models when you went into teaching?

Not really — I had to seek them out. In my environment, there are very few Black teachers, and I was often the only Black Computer Science teacher. A parent once said to me, “I hope you’re not planning to leave, because my son needs a role model in Computer Science.” And I understood exactly what she meant by that, but I’m not even a role model, I’m just someone who’s contributing to society the best way I can. I just want to pave the way for the next generation, including my children.

My current school is supporting me to lead all the STEM engagement for students, and in that role, some of the things I do are running a STEM club that focuses a lot on computing, and running new programmes to encourage girls into tech roles. I’ve also become a CAS Master Teacher and been part of a careers panel at Queen Mary University London about the tech sector, for hundreds of school students from across London. And I was selected by the National Centre for Computing Education as one of their facilitators in the Computer Science Accelerator CPD programme.

But there’s been a lack of leadership opportunities for me in schools. I’ve applied for middle-leadership roles and have been told my face doesn’t fit in an interview in a previous school. And I’m just as skilled and experienced as other candidates: I’ve been acting Head of Department, acting Head of Year — what more do I need to do? But I’ve not had access to middle-leadership roles. I’ve been told I’m an average teacher, but then I’ve been put onto dealing with “difficult” students if they’re Black, because a few of my previous schools have told me that I was “good at dealing with behaviour”. So that tells you about the role I was pigeonholed into.

It is very important for Black students to have role models, and to have a curriculum that reflects them.

Joe Arday

I’ve never worked for a Black Headteacher, and the proportion of Black teachers in senior leadership positions is very low, only 1%. So I am considering moving into a different area of computing education, such as edtech or academia, because in schools I don’t have the opportunities to progress because of my ethnicity.

Do you think this lack of leadership opportunities is an experience other Black teachers share?

I think it is, that’s why the number of Black teachers is so low. And as a Black student of Computer Science considering a teaching role, I would look around my school and think, if I go into teaching, where are the opportunities going to come from?

Black students are underrepresented in computing. Could you share your thoughts about why that’s the case?

There’s a lack of role models across the board: in schools, but also in tech leadership roles, CEOs and company directors. And the interest of Black students isn’t fostered early on, in Year 8, Year 9 (ages 12–14). If they don’t have a teacher who is able to take them to career fairs or to tech companies, they’re not going to get exposure, they’re not going to think, “Oh, I can see myself doing that.” So unless they have a lot of interest already, they’re not going to pick Computer Science when it comes to choosing their GCSEs, because it doesn’t look like it’s for them.

But we need diverse people in computing and STEM, especially girls. As the father of a boy and a girl of mixed heritage, that’s very important to me. Some schools I’ve worked in, they pushed computer science into the background, and it’s such a shame. They don’t have the money or the time for their teachers to do the CPD to teach it properly. And if attitudes at the top are negative, that’s going to filter down. But even if students don’t go into the tech industry, they still need digital skills to go into any number of sectors. Every young person needs them.

It is very important for Black students to have role models, and to have a curriculum that reflects them. Students need to see themselves in their lessons and not feel ignored by what is being taught. I was very fortunate to be selected for the working group for the Raspberry Pi Foundation’s culturally relevant teaching guidelines, and I’m currently running some CPD for teachers around this. I bet in the future Ofsted will look at how diverse the curriculum of schools is.

What do you think tech organisations can do in order to engage more Black students in computing?

I think tech organisations need to work with schools and offer work experience placements. When I was a student, 20 years ago, I went on a placement, and that set me on the right path. Nowadays, many students don’t do work experience, they are school leavers before they do an internship. So why do so many schools and organisations not help 14- or 15-year-olds spend a week or two doing a placement and learning some real-life skills?

A mentor explains Scratch code using a projector in a coding club session.

And I think it’s very important for teachers to be able to keep up to date with the latest technologies so they can support their students with what they need to know when they start their own careers, and can be convincing doing it. I encourage my GCSE Computer Science students to learn about things like cloud computing and cybersecurity, about the newest types of technologies that are being used in the tech sector now. That way they’re preparing themselves. And if I was a Headteacher, I would help my students gain professional certifications that they can use when they apply for jobs.

What is a key thing that people in computing education can do to engage more Black students?

Teachers could run a STEM or computing club with a Black History Month theme to get Black students interested — and it doesn’t have to stop at Black History Month. And you can make computing cross-curricular, so there could be a project with all teachers, where each one runs a lesson that involves a bit of coding, so that all students can see that computing really is for everyone.

What would you say to teachers to encourage them to take up Computer Science as a subject?

Because of my role working for the NCCE, I always encourage teachers to join the NCCE’s Computer Science Accelerator programme and to retrain to teach Computer Science. It’s a beautiful subject, all you need to do is give it a chance.

Thank you, Joe, for sharing your thoughts with us!

Joe was part of the group of teachers we worked with to create our practical guide on culturally relevant teaching in the computing classroom. You can download it as a free PDF now to help you think about how to reflect all your students in your lessons.

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

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

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

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

Monica McGill, founder & CEO of CSEDResearch.org

Cover of The Big Book of Computing Pedagogy.

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

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

Grounded in research and practice

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

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

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

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

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

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

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

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

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

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

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

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

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Take part in the UK Bebras Challenge 2021 for schools!

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

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

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

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

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

– A UK-based teacher

Ten key facts about Bebras

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

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

Remember to sign up by 4 November!

The benefits of Bebras

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

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

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

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

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

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

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Learn the fundamentals of AI and machine learning with our free online course

Post Syndicated from Michael Conterio original https://www.raspberrypi.org/blog/fundamentals-ai-machine-learning-free-online-course/

Join our free online course Introduction to Machine Learning and AI to discover the fundamentals of machine learning and learn to train your own machine learning models using free online tools.

Drawing of a machine learning robot helping a human identify spam at a computer.

Although artificial intelligence (AI) was once the province of science fiction, these days you’re very likely to hear the term in relation to new technologies, whether that’s facial recognition, medical diagnostic tools, or self-driving cars, which use AI systems to make decisions or predictions.

By the end of this free online course, you will have an appreciation for what goes into machine learning and artificial intelligence systems — and why you should think carefully about what comes out.

Machine learning — a brief overview

You’ll also often hear about AI systems that use machine learning (ML). Very simply, we can say that programs created using ML are ‘trained’ on large collections of data to ‘learn’ to produce more accurate outputs over time. One rather funny application you might have heard of is the ‘muffin or chihuahua?’ image recognition task.

Drawing of a machine learning ars rover trying to decide whether it is seeing an alien or a rock.

More precisely, we would say that a ML algorithm builds a model, based on large collections of data (the training data), without being explicitly programmed to do so. The model is ‘finished’ when it makes predictions or decisions with an acceptable level of accuracy. (For example, it rarely mistakes a muffin for a chihuahua in a photo.) It is then considered to be able to make predictions or decisions using new data in the real world.

It’s important to understand AI and ML — especially for educators

But how does all this actually work? If you don’t know, it’s hard to judge what the impacts of these technologies might be, and how we can be sure they benefit everyone — an important discussion that needs to involve people from across all of society. Not knowing can also be a barrier to using AI, whether that’s for a hobby, as part of your job, or to help your community solve a problem.

some things that machine learning and AI systems can be built into: streetlamps, waste collecting vehicles, cars, traffic lights.

For teachers and educators it’s particularly important to have a good foundational knowledge of AI and ML, as they need to teach their learners what the young people need to know about these technologies and how they impact their lives. (We’ve also got a free seminar series about teaching these topics.)

To help you understand the fundamentals of AI and ML, we’ve put together a free online course: Introduction to Machine Learning and AI. Over four weeks in two hours per week, you’ll learn how machine learning can be used to solve problems, without going too deeply into the mathematical details. You’ll also get to grips with the different ways that machines ‘learn’, and you will try out online tools such as Machine Learning for Kids and Teachable Machine to design and train your own machine learning programs.

What types of problems and tasks are AI systems used for?

As well as finding out how these AI systems work, you’ll look at the different types of tasks that they can help us address. One of these is classification — working out which group (or groups) something fits in, such as distinguishing between positive and negative product reviews, identifying an animal (or a muffin) in an image, or spotting potential medical problems in patient data.

You’ll also learn about other types of tasks ML programs are used for, such as regression (predicting a numerical value from a continuous range) and knowledge organisation (spotting links between different pieces of data or clusters of similar data). Towards the end of the course you’ll dive into one of the hottest topics in AI today: neural networks, which are ML models whose design is inspired by networks of brain cells (neurons).

drawing of a small machine learning neural network.

Before an ML program can be trained, you need to collect data to train it with. During the course you’ll see how tools from statistics and data science are important for ML — but also how ethical issues can arise both when data is collected and when the outputs of an ML program are used.

By the end of the course, you will have an appreciation for what goes into machine learning and artificial intelligence systems — and why you should think carefully about what comes out.

Sign up to the course today, for free

The Introduction to Machine Learning and AI course is open for you to sign up to now. Sign-ups will pause after 12 December. Once you sign up, you’ll have access for six weeks. During this time you’ll be able to interact with your fellow learners, and before 25 October, you’ll also benefit from the support of our expert facilitators. So what are you waiting for?

Share your views as part of our research

As part of our research on computing education, we would like to find out about educators’ views on machine learning. Before you start the course, we will ask you to complete a short survey. As a thank you for helping us with our research, you will be offered the chance to take part in a prize draw for a £50 book token!

Learn more about AI, its impacts, and teaching learners about them

To develop your computing knowledge and skills, you might also want to:

If you are a teacher in England, you can develop your teaching skills through the National Centre for Computing Education, which will give you free upgrades for our courses (including Introduction to Machine Learning and AI) so you’ll receive certificates and unlimited access.

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Inspiring learners about computing through health and well-being projects | Hello World #17

Post Syndicated from Gemma Coleman original https://www.raspberrypi.org/blog/inspiring-learners-computing-health-well-being-projects-hello-world-17/

Your brand-new issue of the free Hello World magazine for computing educators focuses on all things health and well-being, featuring useful tools for educators, great ideas for schools, and inspiring projects, ideas, and resources from teachers around the world!

Cover of issue 17 of Hello World.

One such project was created by the students of James Abela, Head of Computing at Garden International School in Kuala Lumpur, Raspberry Pi Certified Educator, founder of the South East Asian Computer Science Teachers Association, and author of The Gamified Classroom:

Protecting children from breathing hazardous air

In 2018, Indonesia burned approximately 529,000 hectares of land. That’s an area more than three times the size of Greater London, or almost the size of Brunei. With so much forest being burned, the whole region felt the effects of the pollution. Schools frequently had to ban outdoor play and PE lessons, and on some days schools were closed completely. Many schools in the region had an on-site CO2 detector to know when pollution was bad, but by the time the message could get out, children had already been breathing in the polluted air for several minutes.

A forest fire.
The air pollution from a forest fire gets dispersed by winds and can spread way beyond the area of the fire.

My Year 12 students (aged 16–17) followed the news and weather forecasts intently, and we all started to see how the winds from Singapore and Sumatra were sending pollution to us in Kuala Lumpur. We also realised that if we had measurements from around the city, we might have some visibility as to when pollution was likely to affect our school.

Making room for student-led projects

I’ve always encouraged my students to do their own projects, because it gives programming tasks meaning and creates something that they can be genuinely proud of. The other benefit is that it is something to talk about in university essays and interviews, especially as they often need to do extensive research to solve the problems central to their projects.

This project was […] a genuine passion project in every sense of the word.

James Abela

This project was much more than this: it was a genuine passion project in every sense of the word. Three of my students approached me with the idea of tracking CO2 to give schools a better idea of when there was pollution and which way it was going. They had had some experience of using Raspberry Pi computers, and knew that it was possible to use them to make weather stations, and that the latest versions had wireless LAN capability that they could use. I agreed to support them during allocated programming time, and to help them reach out to other schools.

Circuit design of the CO2 sensor using just Raspberry Pi, designed on circuito.io

I was able to offer students support with this project because I flip quite a lot of the theory in my class. Flipped learning is a teaching approach in which some direct instruction, for example reading articles or watching specific videos, is done at home. This enables more class time to be used to answer questions, work through higher-order tasks, or do group work, and it creates more supervised coding time.

I was able to offer students support with this project because I flip quite a lot of the theory in my class.

James Abela

I initially started doing this because when I set coding challenges for homework, I often had students who confessed they spent all night trying to solve it, only for me to glance at the code and notice a missing colon or indentation issue. I began flipping the less difficult theory for students to do as homework, to create more programming time in class where we could resolve issues more quickly. This then evolved into a system where students could work much more at their own pace and eventually led to a point at which older students could, in effect, learn through their own projects, such as the pollution monitor.

Building the pollution monitor

The students started by looking at existing weather station projects — for example, there is an excellent tutorial on the Raspberry Pi Foundation’s projects site. Students discovered that wind data is relatively easy to get over a larger area, but the key component would be something to measure CO2. […]

Check out issue 17 of Hello World to read the rest of James’s article and find out all the details about the hardware and software his students used for this passion project. He says:

This project really helped these students to decide whether they enjoyed the hardware side of computing, and solving real-world issues really encouraged them to see computing as a practical subject. This is a message that has really resonated with other students, and we’ve since doubled the number of students taking A level computer science.

James Abela

Download the new Hello World for free!

Issue 17 of Hello World is bursting with inspiring ideas for teaching your learners about computing in the context of health and well-being. And you’ll find lots more great content in its 100 pages!

James’s article is also a wonderful example of an educator empowering their students to build a tech project they care about. You’ll discover more insights and practical tips on making computing relevant to all your learners in the following articles of the new Hello World issue:

  • Inspiring Young People With Contexts They Care About
  • Computing for all: Designing a Culturally Relevant Curriculum
  • Going Back to Basics: Part 2 — a follow-on from issue 16 about how to take beginner digital makers through their first physical computing projects

Download the new issue of Hello World for free today:

If you’re an educator based in the UK, you can subscribe to receive each new issue in print completely free! And wherever you are in the world, don’t forget to listen to the Hello World podcast, where each episode we dive into a new topic from the magazine with some of the computing educators who’ve written for us.

The post Inspiring learners about computing through health and well-being projects | Hello World #17 appeared first on Raspberry Pi.

Engaging Black students in computing at school — interview with Lynda Chinaka

Post Syndicated from Janina Ander original https://www.raspberrypi.org/blog/engaging-black-students-in-computing-school-lynda-chinaka/

Lynda Chinaka.

On the occasion of Black History Month UK, we speak to Lynda Chinaka, Senior Lecturer in Computing in Education at the University of Roehampton, about her experiences in computing education, her thoughts about underrepresentation of Black students in the subject, and her ideas about what needs to be done to engage more Black students.

Lynda, to start us off, can you share your thoughts about Black History Month?

Black history is a really important topic, obviously, and I think that, when Black History Month was first introduced, it was very powerful — and it continues to be in certain places. But I think that, for where we are as a society, it’s time to move past talking about Black history for only one month of the year, albeit an important, focused celebration. And certainly that would include integrating Black history and Black figures across subjects in school. That would be a very useful way to celebrate the contributions that Black people have made, and continue to make, to society. Children need to be taught a history in which they are included and valued. Good history is always a matter of different perspectives. Too often in schools, children experience a single perspective.  

Please tell me a bit about your own history: how did you come to computing education as a field? What were the support or barriers you encountered?

In terms of my journey, I’ve always been passionate about Computing — formerly ICT. I’ve been a Computing subject lead in schools, moving on into senior management. Beyond my career in schools, I have worked as an ICT consultant and as a Teacher Leader for a London authority. During that time, my interest in Computing/ICT led me to undertake an MA in Computing in Education at King’s College London. This led me to become a teacher trainer in my current role. In some sense, I’m carrying on the work I did with the local authorities, but in a university setting. At the University of Roehampton, I teach computing to BA Primary Education and PGCE students. Training teachers is something that I’m very much interested in. It’s about engaging student teachers, supporting them in developing their understanding of Computing in the primary phases. Students learn about the principles of computing, related learning theories, and how children think and learn. Perhaps more importantly, I am keen to instil a love of the subject and broaden their notions about computing.

A teacher attending Picademy laughs as she works through an activity

In terms of the support I’ve received, I’ve worked in certain schools where Computing was really valued by the Headteacher, which enabled me to promote my vision for the subject. Supportive colleagues made a difference in their willingness take on new initiatives that I presented. I have been fortunate to work in local authorities that have been forward-thinking; one school became a test bed for Computing. So in that sense, schools have supported me. But as a Black person, a Black woman in particular, I would say that I faced barriers throughout my career. And those barriers have been there since childhood. In the Black community, people experience all sorts of things, and prejudice and barriers have been at play in my career.

Prejudice sometimes is very overt. An example I think I can share because it prevented me from getting a job: I went for an interview in a school. It was a very good interview, the Headteacher told me, “It was fantastic, you’re amazing, you’re excellent,” the problem was that there weren’t “enough Black pupils”, so she “didn’t see the need…”. And this is a discussion that was shared with me. Now in 2021 a Headteacher wouldn’t say that, but let’s just wind the clock back 15 years. These are the kinds of experiences that you go through as a Black teacher.

So what happens is, you tend to build up a certain resilience. People’s perceptions and low expectations of me have been a hindrance. This can be debilitating. You get tired of having to go through the same thing, of having to overcome negativity. Yes, I would say this has limited my progress. Obviously, I am speaking about my particular experiences as a Black woman, but I would say that these experiences are shared by many people like me.

An educator teaches students to create with technology.

But it’s my determination and the investment I’ve made that has resulted in me staying in the field. And a source of support for me is always Black colleagues, they understand the issues that are inherent within the profession. 

Black students are underrepresented in Computing as a subject. Drawing on your own work and experiences, could you share your thoughts about why that’s the case?

There need to be more Black teachers, because children need to see themselves represented in schools. As a Black teacher, I know that I have made a difference to Black children in my class who had a Black role model in front of them. When we talk about the poor performance of Black pupils, we need to be careful not to blame them for the failures of the education system. Policy makers, Headteachers, teachers, and practitioners need to be a lot more self-aware and examine the impact of racism in education. People need to examine their own policies and practice, especially people in positions of power.

A lot of collective work needs to be done.

Lynda Chinaka

Some local authorities do better than others, and some Headteachers I’ve worked with have been keen to build a diverse staff team. Black people are not well-represented at all in education. Headteachers need to be more proactive about their staff teams and recruitment. And they need to encourage Black teachers to go for jobs in senior management.

An educator helps a young person with a computing problem.

In all settings I taught in, no matter how many students of colour there were, these students would experience something in my classroom that their white counterparts had experienced all their lives: they would leave their home and come to school and be taught by someone who looks like them and perhaps speaks the same language as them. It’s enormously affirming for children to have that experience. And it’s important for all children actually, white children as well. Seeing a Black person teaching in the classroom, in a position of power or influence — it changes their mindset, and that ultimately changes perspectives.

So in terms of that route into Computing, Black students need to see themselves represented.

Why do you think it’s important to teach young people about Computing?

It’s absolutely vital to teach children about Computing. As adults, they are going to participate in a future that we know very little about, so I think it’s important that they’re taught computer science approaches, problem solving and computational thinking. So children need to be taught to be creators and not simply passive users of technology.

A Coolest Projects participant

One of the things some of my university students say is, “Oh my goodness, I can’t teach Computing, all the children know much more than me.”, but actually, that’s a little bit of a myth, I think. Children are better at using technologies than solving computing problems. They need to learn a range of computational approaches for solving problems. Computing is a life skill; it is the future. We saw during the pandemic the effects of digital inequity on pupils.

What do you think needs to change in computing education, the tech sector, or elsewhere in order to engage more Black students in Computing?

In education, we need to look at the curriculum and how to decolonise it to really engage young people. This also includes looking out for bias and prejudice in the things that are taught. Even when you’re thinking about specific computer science topics. So for example, the traditional example for algorithm design is making a cup of tea. But tea is a universal drink, it originates in China, and as a result of colonialism made its way to India and Kenya. So we drink tea universally, but the method (algorithm) for making tea doesn’t necessarily always include a china tea pot or a tea bag. There are lots of ways to introduce it, thinking about how it’s prepared in different cultures, say Kenya or the Punjab, and using that as a basis for developing children’s algorithmic thinking. This is culturally relevant. It’s about bringing the interests and experiences children have into the classroom.

Young women in a computing lesson.

For children to be engaged in Computing, there needs to be a payoff for them. For example, I’ve seen young people developing their own African emojis. They need to see a point to it! They don’t necessarily have to become computer scientists or software engineers, but Computing should be an avenue that opens for them because they can see it as something to further their own aims, their own causes. Young people are very socially and politically aware. For example, Black communities are very aware of the way that climate change affects the Global South and could use data science to highlight this. Many will have extended family living in these regions that are affected now.

So you don’t compromise on the quality of your teaching, and it require teachers to be more reflective. There is no quick fix. For example, you can’t just insert African masks into a lesson without exploring their meaning in real depth within the culture they originate from.

So in your Computing or Computer Science lessons, you need to include topics young people are interested in: climate change, discrimination, algorithms and algorithmic bias in software, surveillance and facial recognition. Social justice topics are close to their hearts. You can get them interested in AI and data science by talking about the off-the-shelf datasets that Big Tech uses, and about what impact these have in terms of surveillance and on minority communities specifically. 

Can you talk a bit about the different terms used to describe this kind of approach to education, ‘culturally relevant teaching’ and ‘decolonising the curriculum’?

‘Culturally relevant’ is easier to sit with. ‘Decolonising the curriculum’ provokes a reaction, but it’s really about teaching children about histories and perspectives on curricula that affect us all. We need to move towards a curriculum that is fit for purpose where children are taught different perspectives and truth that they recognise. Even if you’re in a school without any Black children at all, the curriculum still needs to be decolonised so that children can actually understand and benefit from the many ways that topics, events, subjects may be taught.

A woman teacher helps a young person with a coding project.

When we think about learning in terms of being culturally relevant and responsive, this is about harnessing children’s heritage, experiences, and viewpoints to engage learners such that the curriculum is meaningful and includes them. The goal here is to promote long-term and consistent engagement with Computing.

What is being missed by current initiatives to increase diversity and Black students’ engagement?

Diversity initiatives are a good step, but we need to give it time. 

The selection process for subjects at GCSE can sometimes affect the uptake of computing. Then there are individual attitudes and experiences of pupils. It has been documented that Black and Asian students have often been in the minority and experience marginalisation, particularly noted in the case of female students in GCSE Computer Science.

ITE (Initial Teacher Education) providers need to consider their partnerships with schools and support schools to be more inclusive. We need more Black teachers, as I said. We also need to democratise pathways for young people getting into computing and STEM careers. Applying to university is one way — there should be others.

Schools could also develop partnerships with organisations that have their roots in the Black community. Research has also highlighted discriminatory practices in careers advice, and in the application and interview processes of Russell Group universities. These need to be addressed.

A students in a computer science lecture.

There are too few Black academics at universities. This can have an impact on student choice and decisions about whether to attend an institution or not. Institutions may seem unwelcoming or unsympathetic. Higher education institutions need to eliminate bias through feedback and measuring course take-up. 

Outside the field of education, tech companies could offer summer schemes, short programmes to stimulate interest amongst young Black people. Really, the people in leadership positions, all the people with the power, need to be proactive.

A lot of collective work needs to be done. It’s a whole pipeline, and everybody needs to play a part.

What in your mind is a key thing right now that people in computing education who want to engage more Black students should do?

You can present children with Black pioneers in computing and tech. They can show Black children how to achieve their goals in life through computing. For example, create podcasts or make lists with various organisations that use data science to further specific causes.

It’s not a one-off, one teacher thing, it’s a project for the whole school.

Lynda Chinaka

Also, it’s not a one-off, one teacher thing, it’s project for the whole school. You need to build it into a whole curriculum map, do all the things you do to build a new curriculum map: get every teacher to contribute, so they take it on, own it, research it, make those links to the national curriculum so it’s relevant. Looking at it in isolation it’s a problem, but it’s a whole school approach that starts as a working group. And it’s senior management that sets the tone, and they really need to be proactive, but you can start by starting a working group. It won’t be implemented overnight. A bit like introducing a school uniform. Do it slowly, have a pilot year group. Get parents in, have a coffee evening, get school governors on board. It’s a whole staff team effort.

People need to recognise the size of the problem and not be discouraged by the fact that things haven’t happened overnight. But people who are in a position of influence need to start by having those conversations, because that’s the only way that change can happen, quite frankly.

Lynda, thank you for sharing your insights with us!

Lynda was one of the advisors in the group we worked with to create our recently published, practical guide on culturally relevant teaching. You can download it as a free PDF now. We hope it will help you kickstart conversations in your setting.

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