Schlagwort: education

  • Get kids coding and learning electronics with Raspberry Pi Pico

    Get kids coding and learning electronics with Raspberry Pi Pico

    Reading Time: 7 minutes

    Since the release of the Raspberry Pi Pico microcontroller in 2021, we have seen people all over the world come up with creative Pico-based inventions.

    Raspberry Pi Pico with its inbuilt LED blinking.
    The Raspberry Pi Pico microcontroller.

    Now, thanks to our brand-new and free ‘Introduction to Raspberry Pi Pico’ learning path, young coders can easily join in and make their own cool Pico projects! This free learning path has six guided projects to help kids to independently develop their coding skills, and their skills in physical computing and electronics.

    A girl creates a physical computing project.
    Physical computing is a great way to help young people get creative with coding.

    In this post, I’ll tell you about Raspberry Pi Pico, what kids can make by following our free ‘Intro to Pico’ path, and what skills they will be learning.

    Meet Raspberry Pi Pico

    Raspberry Pi Pico is a physical computing device that is low-cost and easy to use. It’s much smaller than any Raspberry Pi computer, and it needs much less power. That’s because it’s not a full computer but instead a microcontroller. That means Pico is a device that you program by writing code on any computer, and then sending that code to Pico via a USB cable.

    Raspberry Pi Pico has GPIO pins (like Raspberry Pi computers do). These pins mean it can interact with different types of physical computing components, such as buttons, buzzers, and LEDs.

    In the ‘Intro to Raspberry Pi Pico’ path, we’ve designed new digital making projects specifically using Pico. By following the projects in the path, young people learn to make things with different electronic components. They’ll bring to life their own LED fireflies; they’ll make music with a sound machine and dial (a potentiometer); they’ll look after themselves and people around them by making a mood indicator and a heart rate visualiser. To find out more, visit the path, or scroll to the bottom of this post and click on ‘Details about the projects’.

    The specially designed structure of our learning paths helps kids become confident and independent coders and digital makers. Through this project path, we want to show young people what is possible with Raspberry Pi Pico and inspire them to continue their digital making journey beyond the six projects. Seeing tech creations from our amazing community is super special to us, and we would love to hear about what your young coders have made with Pico. Kids can share their projects in the path gallery, or you can tag us on social media if you post photos!   

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    Learning skills and independence with our project paths 

    While young people make all these Raspberry Pi Pico projects, they will learn the skills and independence to make and code their very own, unique creations with a Pico. We have designed our new project paths to help kids become independent digital makers. As they progress through a path, kids gain new skills, practise what they have learnt, and finally write and follow their own project brief. 

    Our learning paths help kids develop many of the skills that are important to all coders and digital makers, no matter how much experience they have: 

    • How to turn an idea on paper into a tech creation
    • How to debug a project
    • How to combine new information with what they already know about digital making 

    The learning paths also encourage kids to make projects about the things that matter to them.  

    Key questions answered

    Who is this path for?

    We have written the projects in this path with young people around the age of 9 to 13 in mind. 

    Programs for Raspberry Pi Pico are written in a text-based language called MicroPython. That means a young person who wants to start the ‘Intro to Pico’ path needs to be familiar with typing on a keyboard.

    A young person codes at a Raspberry Pi computer.

    If your kid has never coded in a text-based language before, they could complete our free ‘Introduction to Python‘ project path first, but this is not a prerequisite.

    What will young people learn?

    To help with the programming aspects of the projects, the instructions in the path tell young people about:  

    • Displaying output
    • Arithmetic expressions
    • Importing from a library
    • While loops
    • Nested if statements
    • Defining and calling functions
    • Events
    Raspberry Pi Pico attached with jumper wires to a purple LED.
    We still get excited by a flashing LED.

    One of the great things about this project path is that it helps young people explore physical computing and electronics. In the ‘Intro to Pico’ path, they’ll use:

    • Single-colour LEDs
    • Multi-colour LEDs (so-called RGB LEDs)
    • Buzzers
    • Switches (including switches the kids will make out of craft materials!)
    • Buttons
    • Potentiometers (dials)

    How much time is needed to complete the path?

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

    What software is needed for the projects?

    Young people need a web browser so they can follow the project instructions. The first two projects in the path provide detailed instructions for how to install the free software needed for the projects. 

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    The projects in the path show you how to program Raspberry Pi Pico using MicroPython in the Thonny software.

    What hardware is needed for these projects?

    The first step of each project lists what components are needed to create the project. You can purchase a kit from Pimoroni that includes all of the components used in the path:

    ‘Intro to Raspberry Pi Pico’ kit list (click here)

    • 1 × soldered Raspberry Pi Pico
    • 1 × USB cable
    • 1 × red LED
    • 1 × blue LED
    • 2 × yellow LEDs
    • 6 × single-colour LEDs (random)
    • 3 × RGB LEDs
    • 15 × 75 ohm resistors (max 220 ohm)
    • 2 × potentiometers
    • 8 × push buttons (optional, these can be made from crafting materials)
    • 15 × pin–socket jumper wires
    • 38 × socket–socket jumper wires
    • 4 × pin–pin jumper wires

    What can young people do next?

    Explore Python coding with us 

    If your young coders enjoy MicroPython, they’ll also love our Python learning paths: ‘Introduction to Python‘ and ‘More Python‘. Both are structured in the same way as our Pico path, and will help young people learn Python while creating their own visual designs.

    A girl points happily at a project on the Raspberry Pi Foundation's projects site.
    Details about the projects in ‘Intro to Raspberry Pi Pico’

    The ‘Intro to Raspberry Pi Pico’ path is structured according to our Digital Making Framework, with three Explore projects, two Design projects, and a final Invent project. You can also check out our learning graph to see the progression of skills and knowledge throughout the path.

    Explore project 1: LED firefly



    The ‘LED firefly’ project introduces creators to Raspberry Pi Pico while they make their first project with a blinking LED. They program the LED with a blink pattern that is common to fireflies in the wild. To upgrade their projects, creators can place their LED firefly into a glass jar to create a twinkling effect.  

    Explore project 2: Party popper



    ‘Party popper’ introduces creators to the RGB LED and a buzzer. To form the popper, they craft a pull switch out of kitchen foil and cardboard. When the popper is activated, the RGB LED flashes in their chosen colour, and a ‘tada’ sound plays on the buzzer. 

    Explore project 3: Beating heart



    ‘Beating heart’ uses a potentiometer (dial) to control the pulsing speed of an LED. Creators craft their own hearts using red paper and origami before placing the pulsing LED inside. In this way, they create a model of a heart they can use to learn about medicine or to bring to life a favourite toy. 

    Design project 1: Mood indicator



    In the ‘Mood indicator’ project, kids use switches and an RGB LED to create a device that can communicate a need or a mood to another person. This Design project gives young creators lots of opportunities to use their new skills to create something personal to them.

    Design project 2: Sound machine

     

    ‘Sound machine’ is a project for kids to work with the different tones that a buzzer can make. They can use the buzzer to create sound effects, or to recreate their favourite songs. Once they have decided on their sounds, they can think about how a user of their project might choose to play them. 

    Invent project: Sensory gadget

     

    This project gives creators that chance to pick their favourite elements of the path to create something totally unique to them. They could make all sorts of sensory gadgets, from a Picosaber to a candle that can be blown out. Creators are encouraged to showcase their creations in the path gallery to give other young makers inspiration. 

    Website: LINK

  • A storytelling approach for engaging girls in the Computing classroom: Pilot study results

    A storytelling approach for engaging girls in the Computing classroom: Pilot study results

    Reading Time: 7 minutes

    We’ve been running the Gender Balance in Computing programme of research since 2019, as part of the National Centre for Computing Education (NCCE) and with various partners. It’s a £2.4 million research programme funded by the Department for Education in England that aims to identify ways to encourage more girls and young women to engage with Computing and choose to study it further. The programme is made up of four separate areas of research, in which we are running a number of interventions.

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

    The first independent evaluation report from the Behavioural Insights Team (BIT) on our series of interventions has now been published. It relates to an intervention within the research area ‘Teaching Approach’, evaluating our pilot study of teaching computing to Key Stage 1 children using a storytelling approach. The evaluators from BIT found that this pilot study produced evidence of promise for the storytelling approach. They recommend conducting a full-size trial to test how effective this approach is for engaging female pupils with Computing.

    Teaching computing through storytelling

    Like many Computing curricula around the world, the English National Curriculum emphasises the importance of teaching Computing through a range of content so that pupils can express themselves and develop their ideas using digital tools. Our ‘Teaching Approach’ project builds on research grounded in sociocultural learning theories that suggest teaching approaches that encourage collaboration and use a variety of contexts can make Computing a more inclusive subject for all learners. Within this project, we are running three different interventions, each with learners of different ages.

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

    Evidence indicates that gender stereotypes around Computing develop early (1). Therefore we designed a trial — the first of its kind in England — to explore a storytelling approach for teaching Computing with younger children (6- to 7-year-olds). A small body of research suggests that using storytelling as a learning context for Computing can be engaging for both boys and girls. Research results indicate that:

    • Teaching computing through storytelling and story-writing is effective for motivating 11- to 14-year-old girls to learn programming (2)
    • Children who write computer programs to tell stories see Computing as a subject that is equally as easy or difficult for both boys and girls (3)
    • In a non-formal learning space, primary-aged girls are more likely to choose a storybook beginner electronics activity rather than open-ended beginner electronics free play (4)

    The pilot study and the evaluation methods

    As combining evidence from research with older students and in non-formal education is experimental, we designed this storytelling trial as a small pilot study. Our aim was to generate early evidence as to how feasible a teaching approach that uses storytelling might be in the primary Computing classroom.

    We recruited 53 schools to take part in the pilot study, which ran from April to July 2021. Many schools were still facing challenges due to the ongoing coronavirus pandemic, and we are very grateful to the teachers and learners who have taken part for their contribution to this important research.

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

    To conduct the study, we created a free online training course, and a scheme of work, for schools to teach Computing concepts to 6- and 7-year olds using a storytelling approach. Over a sequence of the 12 lessons in the scheme of work, pupils used the ScratchJr programming environment to animate their own digital stories and learn about Computing concepts, such as sequence and repetition, linked to elements of stories, such as structure, rhyme, and speech.

    A school's tweet about taking part in our pilot study of a storytelling approach to teaching computing to learners aged 6 to 7.

    To enable the independent evaluation of the effectiveness of the storytelling approach by BIT, schools were allocated either to an intervention group, which used the training course and the storytelling scheme of work, or to a control group, which taught Computing in their usual way and was not made aware that the approach being trialled involved storytelling. For their evaluation, BIT gathered data from both groups to compare them:

    • They conducted surveys measuring learners’ attitudes toward computing and their intentions to study it in the future
    • They carried out observations of lessons, interviews with teachers, and discussions with learners
    • They ran a survey to gather feedback about the trial from teachers

    The gathered data was assessed against five categories: evidence of promise, fidelity, acceptability, feasibility, and readiness for trial.

    Main findings of the evaluation team

    After analysing the data collected from observations, interviews, learner discussions, pupil surveys, and teacher surveys, the key finding of the independent evaluators was that the storytelling teaching approach had evidence of promise, and that it is worthwhile scaling up our intervention for a larger trial with more schools.

    The evaluators’ teacher interviews confirmed the early development of gender stereotypes in the classroom. This highlights the importance of introducing Computing to young learners in a way that engages both boys and girls. 

    “I’ve really noticed how there’s already differences in views of what’s a boy, what’s a girl, the boys are getting in front of me, like, ‘I want a boy car, I don’t want a girl car’. Then we’ve got the other side where we’ve got fairy tales and princesses and, ‘Oh, I’m a bunny. Do you want to play with me?’”

    Teacher (evaluation report, p. 22)

    Teachers told the evaluators that pupils enjoyed personalising their stories in ScratchJr, and that they themselves felt positive about the use of storytelling to teach computing. 

    “I think [the storytelling aspect] gives them something real to work through, so it’s not… abstract… I think through the storytelling, they’re able to make it as funny or whatever they want, and it’s also their own interest. [Female student], she dotes on animals, so she’s always having giraffes and all of that, so it’s something that they can make their own connections too… Yes, I did really like the storytelling.”

    Teacher (evaluation report, p. 26)

    Teacher feedback provided some evidence that the storytelling lessons had equally increased both male and female pupils’ interest, confidence, and skills.

    Young learners at computers in a classroom.

    The independent evaluation team advised caution when interpreting the quantitative data from the pupil surveys, due to the small sample size in this pilot study and the high attrition rates caused by coronavirus-related disruptions. We ourselves would like to add that the study raises questions about the reliability of quantitative survey data collected from very young children using Likert scales, BIT’s chosen survey format for this evaluation. Although the evaluators have made some positive steps in creating a new survey suitable for young children, this research instrument may need further testing; the survey results would need to be interpreted in this light, and more research in this area would be recommended.

    You can read the full evaluation report on the NCCE website.

    Future directions

    This intervention was based on one of the teaching approaches for which there was only early evidence of effectiveness, so it is a good outcome to have a larger trial recommended based on our pilot study. It’s often said that research ends up recommending more research, but in this case our small pilot project really does give robust evidence that we should trial the storytelling approach with more schools.

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

    The independent evaluators collected feedback from both teachers and pupils that confirms the storytelling intervention we designed is feasible in the classroom. The feedback also indicates where we can make small adjustments that will refine and develop the training and scheme of work for a larger-scale study (evaluation report, p. 35), and we will consider this feedback carefully. While some teachers suggested that the training be shortened, less experienced teachers highlighted the need to ensure the training introduces teachers to all of the content covered in the lessons. This feedback helps us to better understand how Computing is taught in primary schools, and how this is influenced by the wide variety of experience and subject knowledge that teachers have. Interestingly, in the control group, some of the teachers reported that they also introduced coding to their learners by having them create stories. We would like to conduct further research into how schools introduce young learners to programming, and we’ll be continuing to reflect on how best to offer flexible content for teacher training related to our research studies.

    We’re now looking at how to continue to investigate the effectiveness of the storytelling approach through a larger trial, alongside other projects in which we’re exploring female engagement in computing education through our recently established Raspberry Pi Computing Education Research Centre.

    More evaluations are on the way for our other studies in the Gender Balance in Computing programme, including:

    • Two other trials of teaching approaches
    • Interventions in non-formal education contexts
    • Trials of approaches to building a sense of belonging in Computing
    • Research into the impact of timetabling and options evenings

    If you would like to stay up-to-date with the research programme, you can sign up to the Gender Balance in Computing newsletter. We will also post our reflections on the projects on this blog when the evaluations are completed.

    1 Mulvey, K. L. and Irvin, M. J. (2018). Judgments and reasoning about exclusion from counter-stereotypic STEM career choices in early childhood. Early Child. Res. Q. 44, 220–230. https://doi.org/10.1016/j.ecresq.2018.03.016

    2 Kelleher, C., Pausch, R. and Kiesler, S. (2007). Storytelling alice motivates middle school girls to learn computer programming. In CHI ’07: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 1455–1464. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/1240624.1240844

    3 Zaidi, R., Freihofer, I. and Childress Townsend, G. (2017). Using Scratch and Female Role Models while Storytelling Improves Fifth-Grade Students’ Attitudes toward Computing. In SIGCSE ’17: Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education, 791–792. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3017680.3022451

    4 McLean, M., & Harlow, D. (2017). Designing inclusive STEM activities: A comparison of playful interactive experiences across gender. In IDC ’17: Proceedings of the 2017 Conference on Interaction Design and Children, 567–574. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3078072.3084326

    Website: LINK

  • A cybersecurity club for girls | Hello World #18

    A cybersecurity club for girls | Hello World #18

    Reading Time: 5 minutes

    In this article adapted from Hello World issue 18, teacher Babak Ebrahim explains how his school uses a cybersecurity club to increase interest in Computing among girls. Babak is a Computer Science and Mathematics teacher at Bishop Challoner Catholic College Secondary in Birmingham, UK. He is a CAS Community Leader, and works as a CS Champion for the National Centre for Computing Education in England.

    Babak Ebrahim.
    Cover of Hello World issue 18.

    Cybersecurity for girls

    It is impossible to walk into an upper-secondary computer science lesson and not notice the number of boys compared to girls. This is a common issue across the world; it is clear from reading community forums and news headlines that there is a big gap in female representation in computing. To combat this problem in my school, I started organising trips to local universities and arranging assembly talks for my Year 9 students (aged 13–14). Although this was helpful, it didn’t have as much impact as I expected on improving female representation.

    Girls do a cybersecurity activity at a school club.
    Girls engage in a cryptography activity at the club.

    This led me to alter our approach and target younger female students with an extracurricular club. As part of our lower-secondary curriculum, all pupils study encryption and cryptography, and we were keen to extend this interest beyond lesson time. I discovered the CyberFirst Girls Competition, aimed at Year 8 girls in England (aged 12–13) with the goal of influencing girls when choosing their GCSE subjects (qualifications pupils take aged 14–16). Each school can enter as many teams as they like, with a maximum of four girls in each team. I advertised the event by showing a video of the previous year’s attendees and the winning team. To our delight, 19 girls, in five teams, entered the competition.

    Club activities at school

    To make sure that this wasn’t a one-off event, we started an after-school cybersecurity club for girls. All Computing teachers encouraged their female students to attend. We had a number of female teachers who were teaching Maths and Computing as their second subjects, and I found it more effective when these teachers encouraged the girls to join. They would also help with running the club. We found it to be most popular with Year 7 students (aged 11–12), with 15 girls regularly attending. We often do cryptography tasks in the club, including activities from established competitions. For example, I recently challenged the club to complete tasks from the most recent Alan Turing Cryptography Competition. A huge benefit of completing these tasks in the club, rather than in the classroom, was that students could work more informally and were not under pressure to succeed. I found this year’s tasks quite challenging for younger students, and I was worried that this could put them off returning to the club. To avoid this, I first taught the students the skills that they would need for one of the challenges, followed by small tasks that I made myself over two or three sessions.

    Three teenage girls at a laptop

    For example, one task required students to use the Playfair cipher to break a long piece of code. In order to prepare students for decoding this text, I showed them how the cipher works, then created empty grids (5 x 5 tables) and modelled the technique with simple examples. The girls then worked in teams of two to encrypt a short quote. I gave each group a different quotation, and they weren’t allowed to let other groups know what it was. Once they applied the cipher, they handed the encrypted message to another group, whose job was to decrypt it. At this stage, some would identify that the other group had made mistakes using the techniques, and they would go through the text together to identify them. Once students were confident and competent in using this cipher, I presented them with the competition task, and they then applied the same process. Of course, some students would still make mistakes, but they would realise this and be able to work through them, rather than being overwhelmed by them. Another worthwhile activity in the club has been for older pupils, who are in their second year of attending, to mentor and support girls in the years below them, especially in preparation for participating in competitions.

    Trips afield

    Other club activities have included a trip to Bletchley Park. As a part of the package, students took part in a codebreaking workshop in which they used the Enigma machine to crack encrypted messages. This inspirational trip was a great experience for the girls, as they discovered the pivotal roles women had in breaking codes during the Second World War. If you’re not based in the UK, Bletchley Park also runs a virtual tour and workshops. You could also organise a day trip to a local university where students could attend different workshops run by female lecturers or university students; this could involve a mixture of maths, science, and computer science activities.

    Girls do a cybersecurity activity at a school club.
    Girls engage in a cryptography activity at the club.

    We are thrilled to learn that one of our teams won this year’s CyberFirst Girls Competition! More importantly, the knowledge gained by all the students who attend the club is most heartening, along with the enthusiasm that is clearly evident each week, and the fun that is had. Whether this will have any impact on the number of girls who take GCSE Computer Science remains to be seen, but it certainly gives the girls the opportunity to discover their potential, learn the importance of cybersecurity, and consider pursuing a career in a male-dominated profession. There are many factors that influence a child’s mind as to what they would like to study or do, and every little extra effort that we put into their learning journey will shape who they will become in the future.

    What next?

    Find out more about teaching cybersecurity

    Find out more about the factors influencing girls’ and young women’ engagement in Computing

    Website: LINK

  • AI literacy research: Children and families working together around smart devices

    AI literacy research: Children and families working together around smart devices

    Reading Time: 6 minutes

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

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

    Stefania Druga.
    Stefania Druga, University of Washington

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

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

    – Stefania Druga

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

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

    A slide from Stefania Druga's AI literacy seminar. Content is described in the blog text.
    A slide from Stefania’s AI literacy seminar. Click to enlarge.

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

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

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

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

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

    A slide from Stefania Druga's AI literacy seminar.
    A slide from Stefania’s AI literacy seminar. Click to enlarge.

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

    A slide from Stefania Druga's AI literacy seminar.
    A slide from Stefania’s AI literacy seminar. Click to enlarge.

    AI literacy: Programming ‘smart’ devices, algorithmic bias

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

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

    A parent and child work together at a Raspberry Pi computer.

    AI literacy: Kinaesthetic activities, sharing discussions

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

    A slide from Stefania Druga's AI literacy seminar, describing two research questions about how children and parents learn about AI together, and about how to design learning supports for family AI literacies.
    A slide from Stefania’s AI literacy seminar. Click to enlarge.

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

    A slide from Stefania Druga's AI literacy seminar. Content described in the blog text.
    A slide from Stefania’s AI literacy seminar. Click to enlarge.

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

    A slide from Stefania Druga's AI literacy seminar. Content described in the blog text.
    A slide from Stefania’s AI literacy seminar. Click to enlarge.

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

    Find out more

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

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

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

    Join our next free research seminar

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

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

    Website: LINK

  • Python coding for kids: Moving beyond the basics

    Python coding for kids: Moving beyond the basics

    Reading Time: 7 minutes

    We are excited to announce our second new Python learning path, ‘More Python’, which shows young coders how to add real data to their programs while creating projects from a chart of Olympic medals to an interactive world map. The six guided Python projects in this free learning path are designed to enable young people to independently create their own Python projects about the topics that matter to them.

    A girl points excitedly at a project on the Raspberry Pi Foundation's projects site.
    Two kids are at a laptop with one of our coding projects.

    In this post, we’ll show you how kids use the projects in the ‘More Python’ path, what they can make by following the path, and how the path structure helps them become confident and independent digital makers.

    Python coding for kids: Our learning paths

    Our ‘Introduction to Python’ learning path is the perfect place to start learning how to use Python, a text-based programming language. When we launched the Intro path in February, we explained why Python is such a popular, useful, and accessible programming language for young people.

    Because Python has so much to offer, we have created a second Python path for young people who have learned the basics in the first path. In this new set of six projects, learners will discover new concepts and see how to add different types of real data to their programs.

    Illustration of different graph types
    By following the ‘More Python’ path, young people learn the skills to independently create a data visualisation for a topic they are passionate about in the final project.

    Key questions answered

    Who is this path for?

    We have written the projects in this path with young people around the age of 10 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. Learners should have already completed the ‘Introduction to Python’ project path, as they will build on the learning from that path.

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

    How do young people learn with the projects? 

    Young people need access to a web browser to complete our project paths. Each project contains step-by-step instructions for learners to follow, and tick boxes to mark when they complete each step. On top of that, the projects have steps for learners to:

    • Reflect on what they have covered in the project
    • Share their projects with others
    • See suggestions to upgrade their projects

    Young people also have the option to sign up for an account with us so they can save their progress at any time and collect badges.

    A young person codes at a Raspberry Pi computer.

    While learners follow the project instructions in this project path, they write their code into Trinket, a free web-based coding platform accessible in a browser. Each project contains a link to a starter Trinket, which includes everything to get started writing Python code — no need to install any additional software.

    Screenshot of Python code in the online IDE Trinket.
    This is what Python code on Trinket looks like.

    If they prefer, however, young people also have the option of instead writing their code in a desktop-based programming environment, such as Thonny, as they work through the projects.

    What will young people learn?  

    To use data in their Python programs, the project instructions show learners how to:

    • Create and use lists
    • Create and use dictionaries
    • Read data from a data file

    The projects support learners as they explore new concepts of digital visual media and: 

    • Create charts using the Python library Pygal
    • Plot pins on a map
    • Create randomised artwork

    In each project, learners reflect and answer questions about their work, which is important for connecting the project’s content to their pre-existing knowledge.

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

    As they work through the projects, learners see different ways to present data and then decide how they want to present their data in the final project in the path. You’ll find out what the projects are on the path page, or at the bottom of this blog post.

    The project path helps learners become independent coders and digital makers, as each project contains slightly less support than the one before. You can read about how our project paths are designed to increase young people’s independence, and explore our other free learning paths for young coders

    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, at home, in school, or at a coding club. The project instructions encourage learners to add code 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.

    In a classroom, a teacher and a student look at a computer screen while the student types on the keyboard.

    What can young people do next?

    Use Unity to create a 3D world

    Unity is a free development environment for creating 3D virtual environments, including games, visual novels, and animations, all with the text-based programming language C#. Our ‘Introduction to Unity’ project path for keen coders shows how to make 3D worlds and games with collectibles, timers, and non-player characters.

    Take part in Coolest Projects Global

    At the end of the ‘More Python’ path, learners are encouraged to register a project they’ve made using their new coding skills for Coolest Projects Global, our free and world-leading online technology showcase for young tech creators. The project they register will become part of the online gallery, where members of the Coolest Projects community can celebrate each other’s creations.

    A young coder shows off her tech project for Coolest Projects to two other young tech creators.

    We welcome projects from all young people, whether they are beginners or experienced coders and digital makers. Coolest Projects Global is a unique opportunity for young people to share their ingenuity with the world and with other young people who love coding and creating with digital technology.

    Details about the projects in ‘More Python’

    The ‘More Python’ path is structured according to our Digital Making Framework, with three Explore project, two Design projects, and a final Invent project.

    Explore project 1: Charting champions

    Illustration of a fast-moving, smiling robot wearing a champion's rosette.

    In this Explore project, learners discover the power of lists in Python by creating an interactive chart of Olympic medals. They learn how to read data from a text file and then present that data as a bar chart.

    Explore project 2: Solar system

    Illustration of our solar system.

    In this Explore project, learners create a simulation of the solar system. They revisit the drawing and animation skills that they learned in the ‘Introduction to Python’ project path to produce animated planets orbiting the sun. The animation is based on real data taken from a data file to simulate the speed that the planets move at as they orbit. The simulation is also interactive, using dictionaries to display data about the planets that have been selected.

    Explore project 3: Codebreaker

    Illustration of a person thinking about codebreaking.

    The final Explore project gets learners to build on their knowledge of lists and dictionaries by creating a program that encodes and decodes a message using an Atbash cipher. The Atbash cipher was originally developed in the Hebrew language. It takes the alphabet and matches it to its reverse order to create a secret message. They also create a script that checks how many times certain letters have been used in an encoded message, so that they can discover patterns.

    Design project 1: Encoded art

    Illustration of a robot painting a portrait of another robot.

    The first Design project allows learners to create fun pieces of artwork by encoding the letters of their name into images, patterns, or drawings. Learners can choose the images that will be produced for each letter, and whether these appear at random or in a geometric pattern.
    Learners are encouraged to share their encoded artwork 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 working with dictionaries and lists.

    Design project 2: Mapping data

    Illustration of a map and a hand of someone marking it with a large pin.

    In the next Design project, learners access data from a data file and use it to create location pins on a world map. They have six datasets to choose from, so they can use one that interests them. They can also choose from a variety of maps and design their own pin to truly personalise their projects.

    Invent project: Persuasive data presentation

    Illustration of different graph types

    This project is designed to use all of the skills and knowledge covered in this path, and most of the skills from the ‘Introduction to Python’ path. Learners can choose from eight datasets to create data visualisations. They are also given instructions on how to access and prepare other datasets if they want to visualise data about a different topic.

    Once learners have chosen their dataset, they can decide how they want it to be displayed. This could be a chart, a map with pins, or a unique data visualisation. There are lots of example projects to provide inspiration for learners. One of our favourites is the ISS Expedition project, which places flags on the ISS depending on the expedition number you enter.

    Website: LINK

  • Exploring cross-disciplinary computing education in our new seminar series

    Exploring cross-disciplinary computing education in our new seminar series

    Reading Time: 5 minutes

    We are delighted to launch our next series of free online seminars, this time on the topic of cross-disciplinary computing, running monthly from May to November 2022. As always, our seminars are for all researchers, educators, and anyone else interested in research related to computing education.

    An educator helps two learners set up a Raspberry Pi computer.

    Crossing disciplinary boundaries

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

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

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

    Mark Guzdial on computing in history and mathematics

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

    Mark Guzdial.

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

    Pratim Sengupta on countering technocentrism

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

    Pratim Sengupta.

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

    Yasmin Kafai on curriculum design with e-textiles

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

    Yasmin Kafai.

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

    Genevieve Smith-Nunes on exploring data ethics

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

    Genevieve Smith-Nunes.

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

    Sign up now to attend the seminars

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

    As usual, the seminars will take place online on a Tuesday at 17:00 to 18:30 local UK time. Later on in the series, we will also host a talk by our own researchers and developers at the Raspberry Pi Foundation about our non-formal learning research. Watch this space for details about the October and November seminars, which we are still finalising.

    Website: LINK

  • Making the most of Hello World magazine | Hello World #18

    Making the most of Hello World magazine | Hello World #18

    Reading Time: 9 minutes

    Hello World magazine, our free magazine written by computing educators for computing educators, has been running for 5 years now. In the newest issue, Alan O’Donohoe shares his top tips for educators to make the most out of Hello World.

    Issues of Hello World magazine arranged to form a number five.

    Alan has over 20 years’ experience teaching and leading technology, ICT, and computing in schools in England. He runs exa.foundation, delivering professional development to engage digital makers, supporting computing teaching, and promoting the appropriate use of technology.

    Alan’s top tips

    Years before there was a national curriculum for computing, Hello World magazines, or England’s National Centre for Computing Education (NCCE), I had ambitious plans to overhaul our school’s ICT curriculum with the introduction of computer science. Since the subject team I led consisted mostly of non-specialist teachers, it was clear I needed to be the one steering the change. To do this successfully, I realised I’d need to look for examples and case studies outside of our school, to explore exactly what strategies, resources and programming languages other teachers were using. However, I drew a blank. I couldn’t find any local schools teaching computer science. It was both daunting and disheartening not knowing anyone else I could refer to for advice and experience.

    An educator holds up a copy of Hello World magazine in front of their face.
    “Hello World helps me keep up with the current trends in our thriving computing community.” – Matt Moore

    Thankfully, ten years later, the situation has significantly improved. Even with increased research and resources, though, there can still be the sense of feeling alone. With scarce prospects to meet other computing teachers, there’s fewer people to be inspired by, to bounce ideas off, to celebrate achievement, or share the challenges of teaching computing with. Some teachers habitually engage with online discussion forums and social media platforms to plug this gap, but these have their own drawbacks. 

    It’s great news then that there’s another resource that teachers can turn to. You all know by now that Hello World magazine offers another helping hand for computing teachers searching for richer experiences for their students and opportunities to hone their professional practice. In this Insider’s Guide, I offer practical suggestions for how you can use Hello World to its full potential.  

    Put an article into practice  

    Teachers have often told me that strategies like PRIMM and pair programming have had a positive impact on their teaching, after first reading about them in Hello World. Over the five years of its publication, there’s likely to have been an article or research piece that particularly struck a chord with you — so why not try putting the learnings from that article into practice?

    An educator holds up a copy of Hello World magazine in front of their face.
    “Hello World gives me loads of ideas that I’m excited to try out in my own classroom.” – Steve Rich

    You may choose to go this route on your own, but you could persuade colleagues to join you. Not only is there safety in numbers, but the shared rewards and motivation that come from teamwork. Start by choosing an article. This could be an approach that made an impression on you, or something related to a particular theme or topic that you and your colleagues have been seeking to address. You could then test out some of the author’s suggestions in the article; if they represent something very different from your usual approach, then why not try them first with a teaching group that is more open to trying new things? For reflection and analysis, consider conducting some pupil voice interviews with your classes to see what their opinions are of the activity, or spend some time reflecting on the activity with your colleagues. Finally, you could make contact with the author to compare your experiences, seek further support, or ask questions. 

    Strike up a conversation

    Authors generally welcome correspondence from readers, even those that don’t agree with their opinions! While it’s difficult to predict exactly what the outcome may be, it could lead to a productive professional correspondence. Here are some suggestions: 

    • Establish the best way to contact the author. Some have contact details or clues about where to find them in their articles. If not, you might try connecting with them on LinkedIn, or social media. Don’t be disappointed if they don’t respond promptly; I’ve often received replies many months after sending. 
    • Open your message with an introduction to yourself moving onto some positive praise, describing your appreciation for the article and points that resonated deeply with you.
    • If you have already tried some of the author’s suggestions, you could share your experiences and pupil outcomes, where appropriate, with them.
    An educator holds up a copy of Hello World magazine in front of their face.
    “One of the things I love about Hello World is the huge number of interesting articles that represent a wide range of voices and experiences in computing education.” – Catherine Elliott
    • Try to maintain a constructive tone. Even if you disagree with the piece, the author will be more receptive to a supportive tone than criticism. If the article topic is a ‘work in progress’, the author may welcome your suggestions.
    • Enquire as to whether the author has changed their practice since writing the article or if their thinking has developed.
    • You might take the opportunity to direct questions at the author asking for further examples, clarity or advice.  
    • If the author has given you an idea for an article, project, or research on a similar theme, they’re likely to be interested in hearing more. Describe your proposal in a single sentence summary and see if they’d be interested in reading an early draft or collaborating with you.

    Start a reading group

    Take inspiration from book clubs, but rather than discuss works of fiction, instead invite members of your professional groups or curriculum teams to discuss content from issues of Hello World. This could become a regular feature of your meetings where attendees can be invited to contribute their own opinions. To achieve this, firstly identify a group that you’re a part of where this is most likely to be received well. This may be with your colleagues, or fellow computing teachers you’ve met at conferences or training days. To begin, you might prescribe one specific single article or broaden it to include a whole issue. It makes sense to select an article likely to be popular with your group, or one that addresses a current or future area of concern.

    An educator holds up a copy of Hello World magazine in front of their face.
    “I love Hello World! I encourage my teaching students to sign up, and give out copies when I can. I refer to articles in my lectures.” – Fiona Baxter

    To familiarise attendees with the content, share a link to the issue for them to read in advance of the meeting. If you’re reviewing a whole issue, suggest pages likely to be most relevant. If you’re reviewing a single article, make it clear whether you are referring to the page numbers as printed or those in the PDF. You could make it easier by removing all other pages from the PDF and sending it as an attachment. Remember that you can download back issues of Hello World as PDFs, which you can then edit or print. 

    Encourage your attendees to share the aspects of the article that appealed to them, or areas they could not agree with the author or struggled to see working in their particular setting. Invite any points of issue for further discussion and explanation — somebody in the group might volunteer to strike up a conversation with the author by passing on the feedback from the group. Alternatively, you could invite the author of the piece to join your meeting via video conference to address questions and promote discussion of the themes. This could lead to developing a productive friendship or professional association with the author.  

    Propose an article

    “I wish!” is a typical response I hear when I suggest to a teacher that they should seriously consider writing an article for Hello World. I often get the responses, “I don’t have enough time”, “Nobody would read anything I write”, or, “I don’t do anything worth writing about”. The most common concern I hear, though, is, “But I’m not a writer!”. So you’re not the only one thinking that! 

    “We strongly encourage first-time writers. My job is to edit your work and worry about grammar and punctuation — so don’t worry if this isn’t your strength! Remember that as an educator, you’re writing all the time. Lesson plans, end-of-term reports, assessment feedback…you’re more of a writer than you think! If you’re not sure where to start, you could write a lesson plan, or contribute to our ‘Me and my Classroom’ feature.”

    — Gemma Coleman, Editor of Hello World

    Help and support is available from the editorial team. I for one have found this to be extremely beneficial, especially as I really don’t rate my own writing skills! Don’t forget, you’re writing about your own practice, something that you’ve done in your career — so you’ll be an expert on you. Each article starts with a proposal, the editor replies with some suggestions, then a draft follows and some more refinements. I ask friends and colleagues to review parts of what I’ve written to help me and I even ask non-teaching members of my family for their opinions. 

    Writing an article for Hello World can really help boost your own professional development and career prospects. Writing about your own practice requires humility, analytical thinking and self reflection. To ensure you have time to write an article, make it fit in with something of interest to you. This could be an objective from your own performance management or appraisal. This reduces the need for additional work and adds a level of credibility.

    An educator reads a copy of Hello World magazine on public transport.
    “Professionally, writing for Hello World provides recognition that you know what you’re talking about and that you share your knowledge in a number of different ways.” – Neil Rickus

    If that isn’t enough to persuade you, for contributors based outside of the UK (who usually aren’t eligible for free print copies), Hello World will send you a complimentary print copy of the magazine that you feature in to say thank you. Picture the next Hello World issue arriving featuring an article written by you. How does this make you feel? Be honest — your heart flutters as you tear off the wrapper to go straight to your article. You’ll be impressed to see how much smarter it looks in print than the draft you did in Microsoft Word. You’ll then want to show others, because you’ll be proud of your work. It generates a tremendous sense of pride and achievement in seeing your own work published in a professional capacity. 

    Hello World offers busy teachers a fantastic, free and accessible resource of shared knowledge, experience and inspiring ideas. When we feel most exhausted and lacking inspiration, we should treasure those mindful moments where we can sit down with a cup of tea and make the most of this wonderful publication created especially for us.

    Celebrate 5 years of Hello World with us

    We marked Hello World’s fifth anniversary with a recent Twitter Spaces event with Alan and Catherine Elliot as our guests. You can catch up with the event recording on the Hello World podcast. And the newest Hello World issue, with a focus on cybersecurity, is available as a free PDF download — dive it today.

    Cover of Hello World issue 18.

    How have you been using Hello World in your practice in the past five years? What do you hope to see in the magazine in the next five? Let us know on Twitter by tagging @HelloWorld_Edu.

    Website: LINK

  • Computer science education for what purpose? Some perspectives

    Computer science education for what purpose? Some perspectives

    Reading Time: 4 minutes

    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.

    Tia Madkins
    Prof Tia C. Madkins
    Nicol Howard
    Dr Nicol R. Howard
    Dr Jakita O. Thomas

    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.

    Website: LINK

  • Bringing digital skills to disadvantaged children across India

    Bringing digital skills to disadvantaged children across India

    Reading Time: 4 minutes

    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.

    In a classroom, a group of people watch the speaker at the front give a presentation.

    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 Replit. 

    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.

    Website: LINK

  • Linking AI education to meaningful projects

    Linking AI education to meaningful projects

    Reading Time: 6 minutes

    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.

    Website: LINK

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

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

    Reading Time: 7 minutes

    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.

    ""
    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.

    ""

    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.

    ""

    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.

    ""

    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.

    ""

    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.

    ""

    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.

    ""

    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’.

    Website: LINK

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

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

    Reading Time: 6 minutes

    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.

    Website: LINK

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

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

    Reading Time: 3 minutes

    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.

    Website: LINK

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

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

    Reading Time: 5 minutes

    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.

    • Educators and learners in a school in Australia
    • Three teenage boys do coding at a shared computer during a computer science lesson.

    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.

    • Two learners do physical computing in the primary school classroom.
    • Two learners do physical computing in the primary school classroom.

    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. 

    • Polly Card.
      Polly Card
    • Saman Rizvi.
      Saman Rizvi
    • Katie Vanderpere-Brown.
      Katie Vanderpere-Brown

    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.  

    Website: LINK

  • Creating better online multiple choice questions

    Creating better online multiple choice questions

    Reading Time: 5 minutes

    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.

    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.

    Website: LINK

  • How can AI-based analysis help educators support students?

    How can AI-based analysis help educators support students?

    Reading Time: 8 minutes

    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.

    Website: LINK

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

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

    Reading Time: 8 minutes

    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.

    Website: LINK

  • Immerse Yourself in Arduino EDUvision Season 4

    Immerse Yourself in Arduino EDUvision Season 4

    Reading Time: 3 minutes
    Arduino EDUvision Season 4 Podcast

    Hopefully you’ve been with us during the roller-coaster ride of Arduino EDUvision season 4, which just came to a close. We’ve had a wonderful time, and the response from the community has been outstanding.

    Arduino EDUvision began life as a way to compensate for the lack of in-person events during 2020. And now we’ve already live streamed 40 episodes, with thousands of viewers tuning in each week as we interview guests from across education, tech, science and STEM.

    EDUvision Season 4 Podcast

    This latest season, which wrapped on 11th November, also broke out into an accompanying podcast. 

    Over the months, and with so many episodes under the EDUvision umbrella, we’ve had some amazing guests. The conversations you see in the episodes are only the tip of the interview iceberg. The new podcast gives Arduino fans the opportunity to listen to the full conversations that Melissa and Roxana have with the guests.

    The subjects go so much deeper, and there’s so much more to learn from these amazing, entertaining thought leaders who share their time with us all.

    You can listen to the Arduino EDUvision podcast anywhere you like. Here are a few links so you can catch up on the exciting edtech, STEM and science subjects we’ve delved into this season.

    EDUvision Live Show

    The good news is that all the EDUvision live streams remain online. So you can still watch them at your convenience. It’s been a really exciting season thanks to the diverse and fascinating guests who’ve shared their insights, work and projects with us.

    Educational technology expert Damien Kee joined us to celebrate International Programmers’ Day in the first episode. Stick around until the end, when he took the opportunity to show off his amazing DIY R2-D2. 

    There was an outpouring of excitement from the Arduino community when Locomation’s Çetin Meriçli showed us what the future has in store for self-driving trucks and cars.

    And Dr. Erica Colón from YouTube’s Nitty Gritty Science rounded out the season. She dazzled us with an amazing array of science projects you can do at home.

    Take a look at the whole season right here. And we want to hear all your thoughts on the subjects you loved, and what you want to see more of in Arduino EDUvision. 

    The famous and fabulous Hip Hop Scientist visited us for Halloween. We had a great discussion about bridging the gap between music and science by bringing it into everyday pop culture.

    Make sure you’re subscribed to the podcast and our YouTube channel. That way you won’t miss out on the upcoming holiday special!

    You can follow any responses to this entry through the RSS 2.0 feed.
    You can leave a response, or trackback from your own site.

    Website: LINK

  • The machine learning effect: Magic boxes and computational thinking 2.0

    The machine learning effect: Magic boxes and computational thinking 2.0

    Reading Time: 10 minutes

    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.

    A young girl and boy do a Scratch coding activity together at a desktop computer.

    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.
    Some learners may think machine learning (ML) is like a magic box, but ML is not magic. Research is needed to find out what mental models are most useful for learning about ML.

    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.

    • Matti Tedre.
      Matti Tedre
    • Henriikka Vartiainen.
      Henriikka Vartiainen

    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 above). 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!

    Website: LINK

  • Arduino Certification Explained

    Arduino Certification Explained

    Reading Time: 2 minutes

    Arduino Certification Explained

    Arduino Team — November 12th, 2021

    Arduino Certification

    Did you know that Arduino Education offers official certification?

    It’s aimed at educators who use (or want to use) Arduino kits in their middle school or high school classrooms. Arduino Certification is an online exam that tests your knowledge of electronics and programming. Students, makers, professionals and everyone in between can take the exam, too.

    Why take the exam?

    • Adding official Arduino certification to your resumé demonstrates your knowledge of electronics, programming, and coding.
    • You can boost your confidence in Arduino-related electronics, programming, and physical computing.
    • Become part of a wider professional network and thriving edtech community.
    • Add highly sought after and in-demand technical skills to your portfolio. Great for job applications!

    Even better, you don’t need any prior coding or electronics experience, and it’s really easy to get started.

    What does Arduino Certification cover?

    When you take the Arduino Certification, you’ll be learning about the fundamental concepts of electronics and programming.

    Key learning outcomes include:

    • Key electricity concepts, such as resistance and voltage, and how to measure and calculate them.
    • How electronics are represented visually, and reading and analyzing electronic circuits.
    • The functionality of the Arduino development environment (called Arduino IDE), serial communication, libraries, and errors.
    • The constitution and capabilities of an Arduino board and how it functions.
    • How various electronic components, such as LEDs, sensors, and motors work, and how to use them in a circuit.
    • The building blocks of the Arduino programming language, such as functions, arguments, variables, and loops.
    • How to program various electronic components.
    • Reading, analyzing, and troubleshooting Arduino code.

    Want to see how it works? Take a look at the exam demo.

    How to take the Arduino Certification exam

    There are two ways to earn the official Arduino Certification.

    You can either purchase the exam on its own (you will have one attempt to pass it). Or, you can purchase the exam along with the Arduino Starter Kit as the Certification bundle.

    Find out more about Arduino Certification.

    You can follow any responses to this entry through the RSS 2.0 feed.
    You can leave a response, or trackback from your own site.

    Website: LINK

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

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

    Reading Time: 3 minutes

    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.

    Website: LINK

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

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

    Reading Time: 7 minutes
    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.

    Website: LINK