Tag Archives: computing education

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

Post Syndicated from Gemma Coleman original https://www.raspberrypi.org/blog/making-the-most-of-hello-world-18-five-years/

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.

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170 research papers about teaching programming, summarised

Post Syndicated from Jane Waite original https://www.raspberrypi.org/blog/research-report-teaching-programming/

Computer programming is now part of the school curriculum in England and many other countries. Although not necessarily the primary focus of the computing curriculum, programming can be the area teachers find most challenging to teach. There is much evidence emerging from research on how to teach programming, particularly from projects with undergraduate learners. That’s why I recently wrote a report summarising over 170 programming pedagogy papers: Teaching programming in schools: A review of approaches and strategies.

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

I hope this blog post about how I approached writing the report whets your appetite to read it, and encourages you to read more research summaries in general.

My approach to summarising research papers

Summarising findings from more than 170 research papers into 34 pages was not a task for the faint-hearted. I could not have embarked on this task without previous experience of writing similar, smaller reviews; working on a host of research projects; and writing reports about research for many different audiences.

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

I love reading about computer science education. It evokes very strong emotions, making me by turns happy, curious, impressed, alarmed, and even cross. When I summarise the papers of other researchers, I am very careful when deciding what to include and what to leave out, in order to do the researchers’ work justice while not overselling it or misleading readers. Sometimes research papers can be hard to fathom, with lots of jargon and statistics. In other papers, the conclusions drawn have many limitations: the project the paper describes hasn’t produced robust enough evidence to give a clear, generalisable message. Academic integrity and not misrepresenting the work of others is paramount. And naturally, there are many more than 170 papers about teaching programming, but I had to stop somewhere. All this makes summarising research a tricky task that one has to undertake with great care.

a teenage boy does coding during a computer science lesson.

Another important aspect of summarising research is how to group papers. A long list saying “this paper said this”, “this paper said that” would not be easy to access and would not draw out overall themes. Often research studies span many topics. What might be a helpful grouping for one reader might not be interesting for another.

For this report, I grouped papers into three sections:

  1. Classroom strategies: Here I included well-researched classroom strategies that teachers can use to teach programming in schools
  2. Contexts and environments for learning programming: Here I outlined research related to opportunities for teaching programming, including different programming languages and the classroom context
  3. Supporting learners: Here I summarised research that helps teachers support learners, particularly learners who have difficulties with programming
Download the report

Why you as a teacher should read research summaries

Teachers, as very busy professionals, have little time to replan lessons, and programming lessons are challenging to start with. However, the potential long-term benefit may outweigh the short-term cost when it comes to reading research summaries: new insights from firmly grounded research can improve your teaching and enable more of your learners to be successful.

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

The process of translating research into practice is an area that I and the research team here are particularly interested in investigating. We are looking forward to working with teachers to explore this.

The Raspberry Pi Foundation regularly shares research summaries in the form of:

You can also check out other computing education podcasts e.g. CSEdPod.org, as well as computing education books (e.g. The Cambridge Handbook of Computing Education Research,  Computer Science Education: Perspectives on Teaching and Learning, and many others), and other researchers’ blogs about computing education (e.g. Amy Ko, article summaries on CSEdresearch.org).

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Bias in the machine: How can we address gender bias in AI?

Post Syndicated from Sue Sentance original https://www.raspberrypi.org/blog/gender-bias-in-ai-machine-learning-biased-data/

At the Raspberry Pi Foundation, we’ve been thinking about questions relating to artificial intelligence (AI) education and data science education for several months now, inviting experts to share their perspectives in a series of very well-attended seminars. At the same time, we’ve been running a programme of research trials to find out what interventions in school might successfully improve gender balance in computing. We’re learning a lot, and one primary lesson is that these topics are not discrete: there are relationships between them.

A woman explains something to a man at a computer.
young people looking at a computer together

We can’t talk about AI education — or computer science education more generally — without considering the context in which we deliver it, and the societal issues surrounding computing, AI, and data. For this International Women’s Day, I’m writing about the intersection of AI and gender, particularly with respect to gender bias in machine learning.

The quest for gender equality

Gender inequality is everywhere, and researchers, activists, and initiatives, and governments themselves, have struggled since the 1960s to tackle it. As women and girls around the world continue to suffer from discrimination, the United Nations has pledged, in its Sustainable Development Goals, to achieve gender equality and to empower all women and girls.

A woman explains something to a man at a computer.
Two women work together at a computer.

While progress has been made, new developments in technology may be threatening to undo this. As Susan Leahy, a machine learning researcher from the Insight Centre for Data Analytics, puts it:

Artificial intelligence is increasingly influencing the opinions and behaviour of people in everyday life. However, the over-representation of men in the design of these technologies could quietly undo decades of advances in gender equality.

Susan Leavy, 2018 [1]

Gender-biased data

In her 2019 award-winning book Invisible Women: Exploring Data Bias in a World Designed for Men [2], Caroline Ceriado Perez discusses the effects of gender-biased data. She describes, for example, how the designs of cities, workplaces, smartphones, and even crash test dummies are all based on data gathered from men. She also discusses that medical research has historically been conducted by men, on male bodies.

A woman explains something to a man at a whiteboard.

Looking at this problem from a different angle, researcher Mayra Buvinic and her colleagues highlight that in most countries of the world, there are no sources of data that capture the differences between male and female participation in civil society organisations, or in local advisory or decision making bodies [3]. A lack of data about girls and women will surely impact decision making negatively. 

Bias in machine learning

Machine learning (ML) is a type of artificial intelligence technology that relies on vast datasets for training. ML is currently being use in various systems for automated decision making. Bias in datasets for training ML models can be caused in several ways. For example, datasets can be biased because they are incomplete or skewed (as is the case in datasets which lack data about women). Another example is that datasets can be biased because of the use of incorrect labels by people who annotate the data. Annotating data is necessary for supervised learning, where machine learning models are trained to categorise data into categories decided upon by people (e.g. pineapples and mangoes).

A banana, a glass flask, and a potted plant on a white surface. Each object is surrounded by a white rectangular frame with a label identifying the object.
Max Gruber / Better Images of AI / Banana / Plant / Flask / CC-BY 4.0

In order for a machine learning model to categorise new data appropriately, it needs to be trained with data that is gathered from everyone, and is, in the case of supervised learning, annotated without bias. Failing to do this creates a biased ML model. Bias has been demonstrated in different types of AI systems that have been released as products. For example:

Facial recognition: AI researcher Joy Buolamwini discovered that existing AI facial recognition systems do not identify dark-skinned and female faces accurately. Her discovery, and her work to push for the first-ever piece of legislation in the USA to govern against bias in the algorithms that impact our lives, is narrated in the 2020 documentary Coded Bias. 

Natural language processing: Imagine an AI system that is tasked with filling in the missing word in “Man is to king as woman is to X” comes up with “queen”. But what if the system completes “Man is to software developer as woman is to X” with “secretary” or some other word that reflects stereotypical views of gender and careers? AI models called word embeddings learn by identifying patterns in huge collections of texts. In addition to the structural patterns of the text language, word embeddings learn human biases expressed in the texts. You can read more about this issue in this Brookings Institute report. 

Not noticing

There is much debate about the level of bias in systems using artificial intelligence, and some AI researchers worry that this will cause distrust in machine learning systems. Thus, some scientists are keen to emphasise the breadth of their training data across the genders. However, other researchers point out that despite all good intentions, gender disparities are so entrenched in society that we literally are not aware of all of them. White and male dominance in our society may be so unconsciously prevalent that we don’t notice all its effects.

Three women discuss something while looking at a laptop screen.

As sociologist Pierre Bourdieu famously asserted in 1977: “What is essential goes without saying because it comes without saying: the tradition is silent, not least about itself as a tradition.” [4]. This view holds that people’s experiences are deeply, or completely, shaped by social conventions, even those conventions that are biased. That means we cannot be sure we have accounted for all disparities when collecting data.

What is being done in the AI sector to address bias?

Developers and researchers of AI systems have been trying to establish rules for how to avoid bias in AI models. An example rule set is given in an article in the Harvard Business Review, which describes the fact that speech recognition systems originally performed poorly for female speakers as opposed to male ones, because systems analysed and modelled speech for taller speakers with longer vocal cords and lower-pitched voices (typically men).

A women looks at a computer screen.

The article recommends four ways for people who work in machine learning to try to avoid gender bias:

  • Ensure diversity in the training data (in the example from the article, including as many female audio samples as male ones)
  • Ensure that a diverse group of people labels the training data
  • Measure the accuracy of a ML model separately for different demographic categories to check whether the model is biased against some demographic categories
  • Establish techniques to encourage ML models towards unbiased results

What can everybody else do?

The above points can help people in the AI industry, which is of course important — but what about the rest of us? It’s important to raise awareness of the issues around gender data bias and AI lest we find out too late that we are reintroducing gender inequalities we have fought so hard to remove. Awareness is a good start, and some other suggestions, drawn out from others’ work in this area are:

Improve the gender balance in the AI workforce

Having more women in AI and data science, particularly in both technical and leadership roles, will help to reduce gender bias. A 2020 report by the World Economic Forum (WEF) on gender parity found that women account for only 26% of data and AI positions in the workforce. The WEF suggests five ways in which the AI workforce gender balance could be addressed:

  1. Support STEM education
  2. Showcase female AI trailblazers
  3. Mentor women for leadership roles
  4. Create equal opportunities
  5. Ensure a gender-equal reward system
A woman works at a desktop computer.
Three women sit on a sofa and work on laptops.

Ensure the collection of and access to high-quality and up-to-date gender data

We need high-quality dataset on women and girls, with good coverage, including country coverage. Data needs to be comparable across countries in terms of concepts, definitions, and measures. Data should have both complexity and granularity, so it can be cross-tabulated and disaggregated, following the recommendations from the Data2x project on mapping gender data gaps.

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

Educate young people about AI

At the Raspberry Pi Foundation we believe that introducing some of the potential (positive and negative) impacts of AI systems to young people through their school education may help to build awareness and understanding at a young age. The jury is out on what exactly to teach in AI education, and how to teach it. But we think educating young people about new and future technologies can help them to see AI-related work opportunities as being open to all, and to develop critical and ethical thinking.

Three teenage girls at a laptop

In our AI education seminars we heard a number of perspectives on this topic, and you can revisit the videos, presentation slides, and blog posts. We’ve also been curating a list of resources that can help to further AI education — although there is a long way to go until we understand this area fully. 

We’d love to hear your thoughts on this topic.

References

[1] Leavy, S. (2018). Gender bias in artificial intelligence: The need for diversity and gender theory in machine learning. Proceedings of the 1st International Workshop on Gender Equality in Software Engineering, 14–16.

[2] Perez, C. C. (2019). Invisible Women: Exploring Data Bias in a World Designed for Men. Random House.

[3] Buvinic M., Levine R. (2016). Closing the gender data gap. Significance 13(2):34–37 

[4] Bourdieu, P. (1977). Outline of a Theory of Practice (No. 16). Cambridge University Press. (p.167)

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Computer science education for what purpose? Some perspectives

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

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

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

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

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.

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

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

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

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

Complete our survey for computing teachers

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

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

Complete the survey in English, Welsh, or Irish/Gaeilge
Complete the survey in Gaelic

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.

The post Calling all Computing and ICT teachers in the UK and Ireland: Have your say appeared first on Raspberry Pi.

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

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

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

  • 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.

Download the guidelines

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.

  • Woman teacher and female students at a computer
  • A male teachers and three female students at a computer
  • Two teenage boys do coding at a shared computer during a computer science lesson while their woman teacher observes them.

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.  

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Creating better online multiple choice questions

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

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

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

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

Uses of multiple choice questions

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

A girl does school work at a laptop at home.

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

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

What does the research say about creating multiple choice questions?

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

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

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

The anatomy of a multiple choice question

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

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

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

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

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

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

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

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

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

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

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

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

Creating multiple choice questions is not easy

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

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

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

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

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The AI4K12 project: Big ideas for AI education

Post Syndicated from Sue Sentance original https://www.raspberrypi.org/blog/ai-education-ai4k12-big-ideas-ai-thinking/

What is AI thinking? What concepts should we introduce to young people related to AI, including machine learning (ML), and data science? Should we teach with a glass-box or an opaque-box approach? These are the questions we’ve been grappling with since we started our online research seminar series on AI education at the Raspberry Pi Foundation, co-hosted with The Alan Turing Institute.

  • Dave Touretzky.
    Dave Touretzky
  • Fred Martin.
    Fred G. Martin

Over the past few months, we’d already heard from researchers from the UK, Germany, and Finland. This month we virtually travelled to the USA, to hear from Prof. Dave Touretzky (Carnegie Mellon University) and Prof. Fred G. Martin (University of Massachusetts Lowell), who have pioneered the influential AI4K12 project together with their colleagues Deborah Seehorn and Christina Gardner-McLure.

The AI4K12 project

The AI4K12 project focuses on teaching AI in K-12 in the US. The AI4K12 team have aligned their vision for AI education to the CSTA standards for computer science education. These Standards, published in 2017, describe what should be taught in US schools across the discipline of computer science, but they say very little about AI. This was the stimulus for starting the AI4K12 initiative in 2018. A number of members of the AI4K12 working group are practitioners in the classroom who’ve made a huge contribution in taking this project from ideas into the classroom.

Dave Touretzky presents the five big ideas of the AI4K12 project at our online research seminar.
Dave gave us an overview of the AI4K12 project (click to enlarge)

The project has a number of goals. One is to develop a curated resource directory for K-12 teachers, and another to create a community of K-12 resource developers. On the AI4K12.org website, you can find links to many resources and sign up for their mailing list. I’ve been subscribed to this list for a while now, and fascinating discussions and resources have been shared. 

Five Big Ideas of AI4K12

If you’ve heard of AI4K12 before, it’s probably because of the Five Big Ideas the team has set out to encompass the AI field from the perspective of school-aged children. These ideas are: 

  1. Perception — the idea that computers perceive the world through sensing
  2. Representation and reasoning — the idea that agents maintain representations of the world and use them for reasoning
  3. Learning — the idea that computers can learn from data
  4. Natural interaction — the idea that intelligent agents require many types of knowledge to interact naturally with humans
  5. Societal impact — the idea that artificial intelligence can impact society in both positive and negative ways

Sometimes we hear concerns that resources being developed to teach AI concepts to young people are narrowly focused on machine learning, particularly supervised learning for classification. It’s clear from the AI4K12 Five Big Ideas that the team’s definition of the AI field encompasses much more than one area of ML. Despite being developed for a US audience, I believe the description laid out in these five ideas is immensely useful to all educators, researchers, and policymakers around the world who are interested in AI education.

Fred Martin presents one of the five big ideas of the AI4K12 project at our online research seminar.
Fred explained how ‘representation and reasoning’ is a big idea in the AI field (click to enlarge)

During the seminar, Dave and Fred shared some great practical examples. Fred explained how the big ideas translate into learning outcomes at each of the four age groups (ages 5–8, 9–11, 12–14, 15–18). You can find out more about their examples in their presentation slides or the seminar recording (see below). 

I was struck by how much the AI4K12 team has thought about progression — what you learn when, and in which sequence — which we do really need to understand well before we can start to teach AI in any formal way. For example, looking at how we might teach visual perception to young people, children might start when very young by using a tool such as Teachable Machine to understand that they can teach a computer to recognise what they want it to see, then move on to building an application using Scratch plugins or Calypso, and then to learning the different levels of visual structure and understanding the abstraction pipeline — the hierarchy of increasingly abstract things. Talking about visual perception, Fred used the example of self-driving cars and how they represent images.

A diagram of the levels of visual structure.
Fred used this slide to describe how young people might learn abstracted elements of visual structure

AI education with an age-appropriate, glass-box approach

Dave and Fred support teaching AI to children using a glass-box approach. By ‘glass-box approach’ we mean that we should give students information about how AI systems work, and show the inner workings, so to speak. The opposite would be a ‘opaque-box approach’, by which we mean showing students an AI system’s inputs and the outputs only to demonstrate what AI is capable of, without trying to teach any technical detail.

AI4K12 advice for educators supporting K-12 students: 1. Use transparent AI demonstrations. 2. Help students build mental models. 3. Encourage students to build AI applications.
AI4K12 teacher guidelines for AI education

Our speakers are keen for learners to understand, at an age-appropriate level, what is going on “inside” an AI system, not just what the system can do. They believe it’s important for young people to build mental models of how AI systems work, and that when the young people get older, they should be able to use their increasing knowledge and skills to develop their own AI applications. This aligns with the views of some of our previous seminar speakers, including Finnish researchers Matti Tedre and Henriikka Vartiainen, who presented at our seminar series in November. 

What is AI thinking?

Dave addressed the question of what AI thinking looks like in school. His approach was to start with computational thinking (he used the example of the Barefoot project’s description of computational thinking as a starting point) and describe AI thinking as an extension that includes the following skills:

  • Perception 
  • Reasoning
  • Representation
  • Machine learning
  • Language understanding
  • Autonomous robots

Dave described AI thinking as furthering the ideas of abstraction and algorithmic thinking commonly associated with computational thinking, stating that in the case of AI, computation actually is thinking. My own view is that to fully define AI thinking, we need to dig a bit deeper into, for example, what is involved in developing an understanding of perception and representation.

An image demonstrating that AI systems for object recognition may not distinguish between a real banana on a desk and the photo of a banana on a laptop screen.
Image: Max Gruber / Better Images of AI / Ceci n’est pas une banane / CC-BY 4.0

Thinking back to Matti Tedre and Henriikka Vartainen’s description of CT 2.0, which focuses only on the ‘Learning’ aspect of the AI4K12 Five Big Ideas, and on the distinct ways of thinking underlying data-driven programming and traditional programming, we can see some differences between how the two groups of researchers describe the thinking skills young people need in order to understand and develop AI systems. Tedre and Vartainen are working on a more finely granular description of ML thinking, which has the potential to impact the way we teach ML in school.

There is also another description of AI thinking. Back in 2020, Juan David Rodríguez García presented his system LearningML at one of our seminars. Juan David drew on a paper by Brummelen, Shen, and Patton, who extended Brennan and Resnick’s CT framework of concepts, practices, and perspectives, to include concepts such as classification, prediction, and generation, together with practices such as training, validating, and testing.

What I take from this is that there is much still to research and discuss in this area! It’s a real privilege to be able to hear from experts in the field and compare and contrast different standpoints and views.

Resources for AI education

The AI4K12 project has already made a massive contribution to the field of AI education, and we were delighted to hear that Dave, Fred, and their colleagues have just been awarded the AAAI/EAAI Outstanding Educator Award for 2022 for AI4K12.org. An amazing achievement! Particularly useful about this website is that it links to many resources, and that the Five Big Ideas give a framework for these resources.

Through our seminars series, we are developing our own list of AI education resources shared by seminar speakers or attendees, or developed by us. Please do take a look.

Join our next seminar

Through these seminars, we’re learning a lot about AI education and what it might look like in school, and we’re having great discussions during the Q&A section.

On Tues 1 February at 17:00–18:30 GMT, we’ll hear from Tara Chklovski, who will talk about AI education in the context of the Sustainable Development Goals. To participate, click the button below to sign up, and we will send you information about joining. I really hope you’ll be there for this seminar!

I want to join the next seminar

The schedule of our upcoming seminars is online. You can also (re)visit past seminars and recordings on the blog.

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How can AI-based analysis help educators support students?

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

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

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

Rose Luckin.
Rose Luckin, UCL

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

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

Education and AI systems

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

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

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

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

What can AI systems do in education? 

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

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

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

Getting ready for AI systems in education

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

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

Rose described the seven steps of the framework as:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Two boys at laptops in a classroom.

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

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

Join our next free seminars

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

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

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

I want to join the seminars

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

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

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

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

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

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

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

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

Two teenage girls do coding during a computer science lesson.

Why AI education for young people?

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

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

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

What careers will AI education lead to?

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

A young person codes at a Raspberry Pi computer.

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

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

We need investment in AI education in school

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

A class of primary school students do coding at laptops.

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

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

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

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

The challenge of AI education for teachers

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

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

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

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

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

Young people engaging with AI out of school

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

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

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

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

A Black boy uses a Raspberry Pi computer at school.

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

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

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

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

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

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

The machine learning effect: Magic boxes and computational thinking 2.0

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

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

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

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

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

Our seminars on teaching AI, ML, and data science

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

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

  • 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 Figure 2). But these explanations are still very complex. Clearly, we need to find new ways to help learners of all ages form useful mental models of machine learning, so that teachers can explain to them how machine learning systems work and banish the view that machine learning is magic.

Some tools and teaching approaches for ML education

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

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

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

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

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

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

My take-aways: new opportunities, new research questions

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

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

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

Jane Waite

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

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

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

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

Join our next free seminar

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

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

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

I want to sign up for the next seminar

We look forward to meeting you there!

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

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

Computer science education is a global challenge

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

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

Woman teacher and female students at a computer

Why a global challenge?

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

Student using a Raspberry Pi computer

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

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

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

Capacity building: IT infrastructure and beyond

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

A young woman codes in a computing classroom.

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

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

Comparing countries — what policies are in place?

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

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

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

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

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

Computing education policy in England

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

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

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

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

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

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

Lessons learned, lessons missed

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

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

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

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

Sue Sentance

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

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

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

Moving forward together

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

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

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

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

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

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

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

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

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

Monica McGill, founder & CEO of CSEDResearch.org

Cover of The Big Book of Computing Pedagogy.

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

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

Grounded in research and practice

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

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

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

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

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

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

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

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

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

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

Download The Big Book of Computing Pedagogy now

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

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

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Take part in the UK Bebras Challenge 2021 for schools!

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

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

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

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

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

– A UK-based teacher

Ten key facts about Bebras

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

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

I want to register my school for Bebras

Remember to sign up by 4 November!

The benefits of Bebras

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

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

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

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

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

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

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

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

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

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

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

Machine behaviour — a new field of study?

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

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

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

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

Ideas or artefacts?

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

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

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

Studying machine learning: a different focus

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

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

These differences imply that our teaching should adapt too.

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

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

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

The ProDaBi curriculum includes:

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

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

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

Our speakers described example activities from three of the modules:

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

Questioning how we should teach AI and ML at school

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

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

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

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

Join our next seminar

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

I want to sign up for the next seminar

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

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

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

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New free resources for young people to become independent digital makers

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

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

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

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

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

Supporting learners to become independent tech creators

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

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

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

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

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

The new content structure

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

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

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

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

More features for an augmented learning experience

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

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

Access the new free learning content now

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

Let me see the new free learning paths

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

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

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

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

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

Between September 2021 and March 2022, we’re partnering with The Alan Turing Institute to host speakers from the UK, Finland, Germany, and the USA presenting a series of free research seminars about AI and data science education for young people. These rapidly developing technologies have a huge and growing impact on our lives, so it’s important for young people to understand them both from a technical and a societal perspective, and for educators to learn how to best support them to gain this understanding.

Mhairi Aitken.

In our first seminar we were beyond delighted to hear from Dr Mhairi Aitken, Ethics Fellow at The Alan Turing Institute. Mhairi is a sociologist whose research examines social and ethical dimensions of digital innovation, particularly relating to uses of data and AI. You can catch up on her full presentation and the Q&A with her in the video below.

Why we need AI ethics

The increased use of AI in society and industry is bringing some amazing benefits. In healthcare for example, AI can facilitate early diagnosis of life-threatening conditions and provide more accurate surgery through robotics. AI technology is also already being used in housing, financial services, social services, retail, and marketing. Concerns have been raised about the ethical implications of some aspects of these technologies, and Mhairi gave examples of a number of controversies to introduce us to the topic.

“Ethics considers not what we can do but rather what we should do — and what we should not do.”

Mhairi Aitken

One such controversy in England took place during the coronavirus pandemic, when an AI system was used to make decisions about school grades awarded to students. The system’s algorithm drew on grades awarded in previous years to other students of a school to upgrade or downgrade grades given by teachers; this was seen as deeply unfair and raised public consciousness of the real-life impact that AI decision-making systems can have.

An AI system was used in England last year to make decisions about school grades awarded to students — this was seen as deeply unfair.

Another high-profile controversy was caused by biased machine learning-based facial recognition systems and explored in Shalini Kantayya’s documentary Coded Bias. Such facial recognition systems have been shown to be much better at recognising a white male face than a black female one, demonstrating the inequitable impact of the technology.

What should AI be used for?

There is a clear need to consider both the positive and negative impacts of AI in society. Mhairi stressed that using AI effectively and ethically is not just about mitigating negative impacts but also about maximising benefits. She told us that bringing ethics into the discussion means that we start to move on from what AI applications can do to what they should and should not do. To outline how ethics can be applied to AI, Mhairi first outlined four key ethical principles:

  • Beneficence (do good)
  • Nonmaleficence (do no harm)
  • Autonomy
  • Justice

Mhairi shared a number of concrete questions that ethics raise about new technologies including AI: 

  • How do we ensure the benefits of new technologies are experienced equitably across society?
  • Do AI systems lead to discriminatory practices and outcomes?
  • Do new forms of data collection and monitoring threaten individuals’ privacy?
  • Do new forms of monitoring lead to a Big Brother society?
  • To what extent are individuals in control of the ways they interact with AI technologies or how these technologies impact their lives?
  • How can we protect against unjust outcomes, ensuring AI technologies do not exacerbate existing inequalities or reinforce prejudices?
  • How do we ensure diverse perspectives and interests are reflected in the design, development, and deployment of AI systems? 

Who gets to inform AI systems? The kangaroo metaphor

To mitigate negative impacts and maximise benefits of an AI system in practice, it’s crucial to consider the context in which the system is developed and used. Mhairi illustrated this point using the story of an autonomous vehicle, a self-driving car, developed in Sweden in 2017. It had been thoroughly safety-tested in the country, including tests of its ability to recognise wild animals that may cross its path, for example elk and moose. However, when the car was used in Australia, it was not able to recognise kangaroos that hopped into the road! Because the system had not been tested with kangaroos during its development, it did not know what they were. As a result, the self-driving car’s safety and reliability significantly decreased when it was taken out of the context in which it had been developed, jeopardising people and kangaroos.

A parent kangaroo with a young kangaroo in its pouch stands on grass.
Mitigating negative impacts and maximising benefits of AI systems requires actively involving the perspectives of groups that may be affected by the system — ‘kangoroos’ in Mhairi’s metaphor.

Mhairi used the kangaroo example as a metaphor to illustrate ethical issues around AI: the creators of an AI system make certain assumptions about what an AI system needs to know and how it needs to operate; these assumptions always reflect the positions, perspectives, and biases of the people and organisations that develop and train the system. Therefore, AI creators need to include metaphorical ‘kangaroos’ in the design and development of an AI system to ensure that their perspectives inform the system. Mhairi highlighted children as an important group of ‘kangaroos’. 

AI in children’s lives

AI may have far-reaching consequences in children’s lives, where it’s being used for decision-making around access to resources and support. Mhairi explained the impact that AI systems are already having on young people’s lives through these systems’ deployment in children’s education, in apps that children use, and in children’s lives as consumers.

A young child sits at a table using a tablet.
AI systems are already having an impact on children’s lives.

Children can be taught not only that AI impacts their lives, but also that it can get things wrong and that it reflects human interests and biases. However, Mhairi was keen to emphasise that we need to find out what children know and want to know before we make assumptions about what they should be taught. Moreover, engaging children in discussions about AI is not only about them learning about AI, it’s also about ethical practice: what can people making decisions about AI learn from children by listening to their views and perspectives?

AI research that listens to children

UNICEF, the United Nations Children’s Fund, has expressed concerns about the impact of new AI technologies used on children and young people. They have developed the UNICEF Requirements for Child-Centred AI.

Unicef Requirements for Child-Centred AI: Support childrenʼs development and well-being. Ensure inclusion of and for children. Prioritise fairness and non-discrimination for children. Protect childrenʼs data and privacy. Ensure safety for children. Provide transparency, explainability, and accountability for children. Empower governments and businesses with knowledge of AI and childrenʼs rights. Prepare children for present and future developments in AI. Create an enabling environment for child-centred AI. Engage in digital cooperation.
UNICEF’s requirements for child-centred AI, as presented by Mhairi. Click to enlarge.

Together with UNICEF, Mhairi and her colleagues working on the Ethics Theme in the Public Policy Programme at The Alan Turing Institute are engaged in new research to pilot UNICEF’s Child-Centred Requirements for AI, and to examine how these impact public sector uses of AI. A key aspect of this research is to hear from children themselves and to develop approaches to engage children to inform future ethical practices relating to AI in the public sector. The researchers hope to find out how we can best engage children and ensure that their voices are at the heart of the discussion about AI and ethics.

We all learned a tremendous amount from Mhairi and her work on this important topic. After her presentation, we had a lively discussion where many of the participants relayed the conversations they had had about AI ethics and shared their own concerns and experiences and many links to resources. The Q&A with Mhairi is included in the video recording.

What we love about our research seminars is that everyone attending can share their thoughts, and as a result we learn so much from attendees as well as from our speakers!

It’s impossible to cover more than a tiny fraction of the seminar here, so I do urge you to take the time to watch the seminar recording. You can also catch up on our previous seminars through our blogs and videos.

Join our next seminar

We have six more seminars in our free series on AI, machine learning, and data science education, taking place every first Tuesday of the month. At our next seminar on Tuesday 5 October at 17:00–18:30 BST / 12:00–13:30 EDT / 9:00–10:30 PDT / 18:00–19:30 CEST, we will welcome Professor Carsten Schulte, Yannik Fleischer, and Lukas Höper from the University of Paderborn, Germany, who will be presenting on the topic of teaching AI and machine learning (ML) from a data-centric perspective (find out more here). Their talk will raise the questions of whether and how AI and ML should be taught differently from other themes in the computer science curriculum at school.

Sign up now and we’ll send you the link to join on the day of the seminar — don’t forget to put the date in your diary.

I want to sign up for the next free seminar

I look forward to meeting you there!

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

The post What’s a kangaroo?! AI ethics lessons for and from the younger generation appeared first on Raspberry Pi.

Free computer science courseware and hardware for American educators

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

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

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

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

Learn how to teach the essentials of programming

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

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

Discover how to teach physical computing

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

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

Get one of 1,000 free hardware kits

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

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

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

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

Thank you to Infosys Foundation USA

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

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Delivering a culturally relevant computing curriculum: new guide for teachers

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

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

Download the guide as a PDF

Why we need to make computing culturally relevant

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

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

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

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

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

Some shared definitions

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

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

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

Curriculum, teaching approaches, and learning materials

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

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

Each aspect plays an important role in culturally relevant pedagogy:

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

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

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

What was being part of the working group like?

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

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

Computer science teacher Joe Arday.

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

Continuing the work

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

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

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

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

Working group

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

Lynda Chinaka, University of Roehampton, UK

Mike Deutsch, Kids Code Jeunesse, Canada

Dr Yota Dimitriadi, University of Reading, UK

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

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

Professor Joanna Goode, University of Oregon, USA

Alain Ndabala, St George Catholic College, Hampshire, UK

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

Rohini Shah, Queens Park Community School, London, UK

Neelu Vasishth, Hampton Court House, Surrey, UK

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Exploring how culture and computing intersect

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

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

Ron Eglash.
Dr Ron Eglash, University of Michigan

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

Where do our ideas about computing and STEM come from?

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

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

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

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

What ideas pattern our cultures?

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

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

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

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

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

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

Computing activities based on circulation of value

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

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

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

Returning creative contributions

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

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

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

Rethinking diversity

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

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

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

Join us at our next online seminar

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

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