Tag Archives: computing education

Are you technocentric? Shifting from technology to people

Post Syndicated from Jane Waite original https://www.raspberrypi.org/blog/technocentrism-shifting-from-technology-to-people-computing-education-pratim-sengupta-research-seminar/

When we teach children and young people about computing, do we consider how the subject has developed over time, how it relates to our students’ lives, and importantly, what our values are? Professor Pratim Sengupta shared some of the research he and his colleagues have been working on related to these questions in our June 2022 research seminar.

Pratim Sengupta.
Prof. Pratim Sengupta

Pratim revealed a complex landscape where we as educators can be easily trapped by what may seem like good intentions, thereby limiting learning and excluding some students. His presentation, entitled Computational heterogeneity in STEM education, introduced me to the concept of technocentrism and profoundly impacted my thinking about the essence of programming and how I research it. In this blog post, particularly for those unable to attend this stimulating seminar, I give my simplified view of the rich philosophy shared by Pratim, and my fledgling steps to admit to my technocentrism and overcome it.

Our seminars on teaching cross-disciplinary computing

Between May 2022 and November 2022, we are hosting a new series of free research seminars about teaching computing in different ways and in different contexts. This second seminar of the series was well attended with participants from the USA, Asia, Africa, and Europe, including teachers, researchers, and industry professionals, who contributed to a lively and thought-provoking discussion.

Two teachers and a group of learners are gathered around a laptop screen.

Pratim is a learning scientist based in Canada with a long and distinguished career. He has studied how to teach computational modelling in K-12 STEM classrooms and investigates the complexity of learning. Grounded in working with teachers and students, he brings together computing, science, education, and social justice. Based on his work at Northwestern University, Vanderbilt University, and now with the Mind, Matter and Media lab at the University of Calgary, Pratim has published hundreds of academic papers over some 20 years. Pratim and his team challenge how we focus on making technological artefacts — code for code’s sake — in computing education, and refocuses us on the human experience of coding and learning to code.

What is technocentrism?

Pratim started the seminar by giving us an overview of some of the key ideas that underpin the way that computing is usually taught in schools, including technocentrism (Figure 1).

Pratim Sengupta's summary of technocentrism: device-centred approaches for pedagogy and computational design; ignores teaching, social and institutional infrastructures, cultural histories; transparency or universality of code as symbolic power; recursive methods for education research, experience measured by being folded back onto devices; leads to symbolic violence, misrecognition of experience, muting and omission of voices, affect and moral dimensions of experience.
Figure 1: The features of technocentrism, a way of thinking about how we teach computing, particularly programming (Sengupta, 2022). Click to enlarge.

I have come to a simplified understanding of technocentrism. To me, it appears to be a way of looking at how we learn about computer science, where one might:

  • Focus on the finished product (e.g. a computer program), rather than thinking about the people who create, learn about, or use a program
  • Ignore the context and the environment, rather than paying attention to the history, the political situation, and the social context of the task at hand
  • View computing tasks as being implemented (enacted) by writing code, rather than seeing computing activities as rich and complex jumbles of meaning-making and communication that involve people using chatter, images, and lots of gestures
  • Anchor learning in concepts and skills, rather than placing the values and viewpoints of learners at the heart of teaching 

Examples of technocentrism and how to overcome it

Pratim recounted several research activities that he and his team have engaged with. These examples highlight instances of potential technocentrism and investigate how we might overcome it.

In the first example research activity, Pratim explained how in maths and physics lessons, middle school students were asked to develop models to solve time and distance problems. Rather than immediately coding a potential solution, the researcher and teacher supported the learners to spend much time developing a shared perspective to understand and express the problems first. Students grappled with different ways of representing the context, including graphs and diagrams (see Figure 2). Gradually and carefully, teachers shifted students to recognise what was important and what was not, to move them toward a meaningful language to describe and solve the problems.

Research results from Pratim Sengupta showing students' graph designs and how much time they spent on various activities during the graphing task.
Figure 2: Two graphs from students showing different representations of a context, and a researcher’s bar chart representing how students’ shared understanding emerged over time (Sengupta, 2022). Click to enlarge.

In a second example research activity, students were asked to build a machine that draws shapes using sensors, motors, and code. Rather than jumping straight to a solution, the students spent time with authentic users of their machines. Throughout the process, students worked with others, expressing the context through physical movement, clarifying their thoughts by drawing diagrams, and finding the sweet spot between coding, engineering design, and maths (see Figure 3).

Research results from Pratim Sengupta showing images documenting a physical computing design activity and how learners explained their design.
Figure 3:  Students used physical movements and user guides to be with others and publicly share and experience the task with authentic users (Sengupta, 2022). Click to enlarge.

In a third example research activity, racial segregation of US communities was discussed with pre-service teachers. The predominately white teachers found talking about the topic very difficult at the beginning of the activity. To overcome this hesitancy, teachers were first asked to work with a simulation that modelled the process of segregation through abstracted dots (or computational agents), a transitional other. Following this hypothetical representation, the context was then recontextualised through a map of real data points of the ethnicity of residents in an area of the US. This kind of map is called a Racial Dot Map based on US census data. When the teachers were able to interpret the link between the abstracted dot simulation and the real-world data they were able to talk about racism and segregation in a way they could not do before. The initial simulation and the recontextualisation were a pedagogical tool to reveal racism and provide a space where students felt comfortable discussing their values and beliefs that would otherwise have remained implicit.

Pratim Sengupta explains a research activity with predominantly white pre-service teachers who learned to discuss racism and segregation through a transitional othering activity using maps and graphing census data.
Figure 4: To facilitate discussion of racial segregation, a simulation was used that bridges abstracted dots and real people, giving pre-service teachers a space to reflect on discrimination  (Sengupta, 2022). Click to enlarge.

My takeaways

Pratim shared four implications of this research for computing pedagogy (see Figure 5).

Pratim Sengupta presents the pedagogical implications of shifting from technocentrism to perspectival heterogeneity in education: code as utterances and intertext; heterogeneity and tranformation of representational genres, code lives in translation; teachers' voice needs to be centred in system and activity design and classroom work, researchers must listen; uncertainty and ambiguity play central roles, recognition takes time.
Figure 5: Pratim’s four implications for pedagogy. Click to enlarge

As a researcher of pedagogy, these points provide takeaways that I can relate to my own research practice:

  • Code is a voice within an experience rather than symbols at a point in time. For example, when I listen to students predicting what a snippet of code will do, I think of the active nature of each carefully chosen command and how for each student, the code corresponds with them differently.
  • Code lives as a translation bridging many dimensions, such as data representation, algorithms, syntax, and user views. This statement resonates deeply with my liking of Carsten Schultes’s block model [1] but extends to include the people involved.
  • We should listen carefully and attentively to teachers, rather than making assumptions about what happens in classrooms. Teachers create new ideas. This takeaway is very important and reminds me about the trust and relationships built between teachers and researchers and how important it is to listen.
  • Uncertainty and ambiguity exist in learning, and this can take time to recognise. This final point makes me smile. As a developer, teacher, and researcher, I have found dealing with ambiguity hard at various points in my career. Still, over time, I think I am getting better at seeing it and celebrating it. 

Listening to Pratim share his research on the teaching and learning of computing and the pitfalls of technocentrism has made me think deeply about how I view computer science as a subject and do research about it. I have shared some of my reflections in this blog, and I plan to incorporate the underlying theory and ideas in my ongoing research projects.

If you would like to find out more about Pratim’s work, please look over his slides, watch his presentation, read the upcoming chapter in our seminar proceedings, or respond to this blog by leaving a comment so we can discuss!

Join our next seminar

We have another four seminars in our current series on cross-disciplinary computing. 

At our next seminar on 12 July 2022 at 17:00–18:30 BST / 12:00–13:30 EDT / 9:00–10:30 PDT / 18:00–19:30 CEST, we will welcome Prof. Yasmin Kafai and Elaine Griggs, who are going to present research on introductory equity-oriented computer science with electronic textiles for high school students.

Sign up for the free seminar now

We look forward to meeting you there.

[1] You can learn more in the Hello World article where our Chief Learning Officer Sue Sentance talks about the block model.

The post Are you technocentric? Shifting from technology to people appeared first on Raspberry Pi.

A pair programming approach for engaging girls in the Computing classroom: Study results

Post Syndicated from Katharine Childs original https://www.raspberrypi.org/blog/gender-balance-in-computing-pair-programming-approach-engaging-girls/

Today we share the second report in our series of findings from the Gender Balance in Computing research programme, which we’ve been running as part of the National Centre for Computing Education and with various partners. In this £2.4 million research programme, funded by the Department for Education in England, we aim to identify ways to encourage more female learners to engage with Computing and choose to study it further.

A teacher encourages a learner in the computing classroom.

Previously, we shared the evaluation report about our pilot study of using a storytelling approach with very young computing learners. This new report, again coming from the Behavioural Insights Team (BIT) which acts as the programme’s independent evaluator, describes our study of another teaching approach.

Existing research suggests that computing is not always taught in a way that is engaging for girls in particular [1], and that we can improve this. With the intervention at hand, we wanted to explore the effects of using a pair programming teaching approach with primary school learners aged 8 to 11. We have critically and carefully examined the findings, which show mixed outcomes regarding the effectiveness of the approach, and we believe that the research provides insights that increase our shared understanding of how to teach computing effectively to young learners. 

Computing education through a collaborative lens

Many people think that writing computer programs is a task carried out by people working individually. A 2017 study of 8- and 9-year-olds [2] confirms this: when asked to draw a picture of a computer scientist doing work, 90% of the children drew a picture of one person working alone. This stereotype is present in teaching and learning about computing and computer science; many computer programming lessons take place in a way that promotes solitary working, with individual students sitting in front of separate computers, working on their own code and debugging their own errors.

A girl codes at a laptop while a woman looks on during a Code Club session.

Professional software development rarely happens like this. For example, at the Raspberry Pi Foundation, our software engineers work collaboratively on design and often pair up to solve problems. Computing education research also has identified the importance of looking at computer programming through a collaborative lens. This viewpoint allows us to see computing as a subject with scope for collaborative group work in which students create useful applications together and are part of a community where programming has a shared social context [3]. 

Researching collaborative learning in the primary computing classroom 

One teaching approach in computing that promotes collaborative learning is pair programming (a practice also used in industry). This is a structured way of working on programming tasks  where learners are paired up and take turns acting as the driver or the navigator. The driver controls the keyboard and mouse and types the code. The navigator reads the instructions, supports the driver by watching out for errors in the code, and thinks strategically about next steps and solutions to problems. Learners swap roles every 5 to 10 minutes, to ensure that both partners can contribute equally and actively to the collaborative learning.

Two female learners code at a computer together.

As one part of the Gender Balance in Computing programme, we designed a project to explore the effect of pair programming on girls’ attitudes towards computing. This project builds on research from the USA which suggests that solving problems collaboratively increases girls’ persistence when they encounter difficulties in programming tasks [4].

In the Pair Programming project, we worked with teachers of Year 4 (ages 8–9) and Year 6 (ages 10–11) in schools in England. From January to March 2020, we ran a pilot study with 10 schools and used the resulting teacher feedback to finalise the training and teaching materials for a full randomised controlled trial. Due to the coronavirus pandemic, we trained teachers in the pair programming approach using an online course instead of face-to-face training.

A tweet from a school about taking part in the pair programming intervention of the Gender Balance in Computing research programme.
A tweet from a school about taking part in the pair programming study.

The randomised controlled trial ran from September to December 2021 with 97 schools. Schools were randomly allocated to either the intervention group and used the pair programming training and the scheme of work we designed, or to the control group and taught Computing in their usual way, not aware that we were investigating the effects of pair programming. Due to the coronavirus pandemic, our training of teachers in the pair programming approach had to take place via an online course instead of face to face.

Teachers in both groups delivered 12 weeks of Computing lessons, in which learners used Scratch programming to draw shapes and create animations. The lessons covered computing concepts from Key Stage 2 (ages 7–11), such as using sequences, selection, and repetition in programs, as well as digital literacy skills such as using technology respectfully.

What can we learn about pair programming from the study? 

The findings about this particular intervention were limited by the amount of data the independent evaluators at BIT were able to collect amongst learners and teachers given the ongoing pandemic. BIT’s evaluation was primarily based on quantitative data collected from learners at the start and the end of the intervention. To collect the data, they used a validated instrument called the Student Computer Science Attitude Survey (SCSAS), which asks learners about their attitudes towards Computing. The evaluators compared the datasets gathered from the intervention group (who took part in pair programming lessons) and the control group (who took part in Computing lessons taught with a ‘business as usual’ model).

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

The evaluators’ data analysis found no statistically significant evidence that the pair programming approach positively affected girls’ attitudes towards computing or their intention to study computing in the future. The lack of statistically significant results, called a null result in research projects, can appear disappointing at first. But our work involves careful reflection and critical thinking about all outcomes of our research, and the result of this project is no exception. These are factors that may have contributed towards the result: 

  • The independent evaluators suggested that the intervention may lead to different findings if it were implemented again without the disruptions caused by the pandemic. One of their recommendations was to revert to our original planned model of providing face-to-face training to teachers delivering the pair programming approach, and we believe this would embed a deeper understanding of the approach. 
  • Our research built upon a prior study [4] that suggested a connection between pair programming and increased confidence about problem-solving in girls of a similar age. That study took place in a non-formal setting in an all-girls group, whereas our research was situated in formal education in mixed gender groups. It may be that these differences are significant. 
  • It may be that there is no causal link between using the pair programming approach and an increase in girls’ attitudes towards computing, or that the link may only become apparent over a longer time-scale, or that the pair programming approach needs to be combined with other strategies to achieve a positive effect. 

The evaluators also gathered qualitative data by running teacher and learner interviews, and we were pleased that this data provided some rich insights into the benefits of using a pair programming approach in the primary classroom, and gave some promising indications of possible benefits for female learners in particular. 

  1. Teachers spoke positively about the use of paired activities, and felt that having the defined roles of driver and navigator helped both partners to contribute equally to the programming tasks. Learners said that they enjoyed working in pairs, even though there could be some moments of frustration. Some of the teachers were even planning to integrate pair programming into future lessons. This suggests that the approach was effective both in engaging and motivating learners, as well as in facilitating the planned learning outcomes of the lessons,  and that it can be used more widely in primary computing teaching.

“I don’t know why I’ve never thought to do computing like that, actually, because it’s a really good vehicle for the fact that there are two roles, clearly defined. There’s all your conversation, and knowledge comes through that, and then they’re both equally having a turn.” — Primary school teacher (report, p. 38)

“I like working with both [both as a partner and by yourself] because when you do pair programming, you’re collaborating with your partner, making links, and you have to tell them what to do. But if you have a really good idea and then they put the wrong thing in the wrong place, it’s quite annoying.” — Female learner (report, p. 40)

  1. Both teachers and learners felt that having the support of a partner boosted learners’ confidence, which echoes previous research in the field [5, 6]. In computing, boys more accurately assess their capabilities, whereas girls tend to underestimate their performance [7]. When learners feel a positive emotion such as confidence towards a subject, combined with a belief that they can succeed in tasks related to that subject, this shows self-efficacy [8]. Our findings suggest that, through the use of the pair programming approach, both boys and girls improved their sense of self-efficacy towards Computing, which is corroborated by quotes from learners themselves. This is interesting because a sense of self-efficacy in Computing is linked to the decisions to pursue further study in the subject [9]. More research could build on this observation. 

“I do think that having that equal time to have a go at both, thinking of the girls I’ve got, will have helped my girls, because they lack a bit of confidence. They were learning very quickly that, ‘Actually, yes, we are sure. We can do this.’” — Primary teacher (report, p. 44)

“It might be easier to do pair programming [compared to ‘normal’ lessons] because if you’re stuck, your partner can be helpful.” — Female learner (report, p. 43)

Find out more about pair programming 

  • Download our Big Book of Computing Pedagogy a free PDF and read about pair programming on pages 58 and 59.
  • Watch this short video that shows pair programming being used in a primary classroom. 
  • Read the evaluation report of the pair programming intervention, where you’ll also find more quotes from teachers and learners.
  • Try the free training course on pair programming we designed and used for this project. It also includes links to the lesson plans that teachers worked with. 

Collaboration in our research

We will continue to publish evaluation reports and our reflections on the other projects in the Gender Balance in Computing programme. If you would like to stay up-to-date with the programme, you can sign up to the newsletter.

Two learners at a desktop computer doing coding.

The insights gained from this trial will feed forwards into our future work. Through the process of working with schools on this project, we have increased our understanding of the process of research in educational settings in many ways. We are very grateful for the input from teachers who took part in the first stage of the trial, with whom we developed an effective co-production model for developing resources, a model we will use in future research projects. Teachers who took part in the second stage of the project told us that the resources we provided were of good quality, which demonstrates the success of this co-production approach to developing resources. 

In our new Raspberry Pi Computing Education Research Centre, created with the University of Cambridge Department of Computer Science and Technology, we will collaborate closely with teachers and schools when implementing and evaluating research projects. You are invited to the free in-person launch event of the Centre on 20 July in Cambridge, UK, where we hope to meet many teachers, researchers, and other education practitioners to strengthen a collaborative community around computing education research.

References

[1] Goode, J., Estrella, R., & Margolis, J. (2018). Lost in Translation: Gender and High School Computer Science. In Women and Information Technology. https://doi.org/10.7551/mitpress/7272.003.0005

[2] Alexandria K. Hansen, Hilary A. Dwyer, Ashley Iveland, Mia Talesfore, Lacy Wright, Danielle B. Harlow, and Diana Franklin. 2017. Assessing Children’s Understanding of the Work of Computer Scientists: The Draw-a-Computer-Scientist Test. In Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education (SIGCSE ’17). Association for Computing Machinery, New York, NY, USA, 279–284. https://doi.org/10.1145/3017680.3017769

[3] Yasmin B. Kafai and Quinn Burke. 2013. The social turn in K-12 programming: moving from computational thinking to computational participation. In Proceeding of the 44th ACM technical symposium on Computer science education (SIGCSE ’13). Association for Computing Machinery, New York, NY, USA, 603–608. https://doi.org/10.1145/2445196.2445373

[4] Linda Werner & Jill Denning (2009) Pair Programming in Middle School, Journal of Research on Technology in Education, 42:1, 29-49. https://doi.org/10.1080/15391523.2009.10782540

[5] Charlie McDowell, Linda Werner, Heather E. Bullock, and Julian Fernald. 2006. Pair programming improves student retention, confidence, and program quality. Commun. ACM 49, 8 (August 2006), 90–95. https://doi.org/10.1145/1145287.1145293

[6] Denner, J., Werner, L., Campe, S., & Ortiz, E. (2014). Pair programming: Under what conditions is it advantageous for middle school students? Journal of Research on Technology in Education, 46(3), 277–296. https://doi.org/10.1080/15391523.2014.888272

[7] Maria Kallia and Sue Sentance. 2018. Are boys more confident than girls? the role of calibration and students’ self-efficacy in programming tasks and computer science. In Proceedings of the 13th Workshop in Primary and Secondary Computing Education (WiPSCE ’18). Association for Computing Machinery, New York, NY, USA, Article 16, 1–4. https://doi.org/10.1145/3265757.3265773

[8] Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215. https://doi.org/10.1037/0033-295X.84.2.191

[9] Allison Mishkin. 2019. Applying Self-Determination Theory towards Motivating Young Women in Computer Science. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education (SIGCSE ’19). Association for Computing Machinery, New York, NY, USA, 1025–1031. https://doi.org/10.1145/3287324.3287389

The post A pair programming approach for engaging girls in the Computing classroom: Study results appeared first on Raspberry Pi.

We’ll see you at CSTA 2022 Annual Conference

Post Syndicated from James Robinson original https://www.raspberrypi.org/blog/csta-2022/

Connecting face to face with educators around the world is a key part of our mission at the Raspberry Pi Foundation, and it’s something that we’ve sorely missed doing over the last two years. We’re therefore thrilled to be joining over 1000 computing educators in the USA at the Computer Science Teachers Association (CSTA) Annual Conference in Chicago in July.

Gemma Coleman
Kevin Johnson
James Robinson

You will find us at booth 521 in the expo hall throughout the conference, as well as running four sessions. Gemma, Kevin, James, Sue, and Jane are team members representing Hello World magazine, the Raspberry Pi Computing Education Research Centre, and our other free programmes and education initiatives. We thank the team at CSTA for involving us in what we know will be an amazing conference.

Sue Sentance
Jane Waite.
Jane Waite

Talk to us about computer science pedagogy

Developing and sharing effective computing pedagogy is our theme for CSTA 2022. We’ll be talking to you about our 12 pedagogy principles, laid out in The Big Book of Computing Pedagogy, available to download for free.

Cover of The Big Book of Computing Pedagogy.

An exciting piece of news is that everyone attending CSTA 2022 will find a free print copy of the Big Book in their conference goodie bag!

We’re really looking forward to sharing and discussing the book and all our work with US educators, and to seeing some familiar faces. We’re also hoping to interview lots of old and new friends about your approaches to teaching computing and computer science for future Hello World podcast episodes.

Your sessions with us

Our team will also be running a number of sessions where you can join us to learn, discuss, and prepare lesson plans.

Semantic Waves and Wavy Lessons: Connecting Theory to Practical Activities and Back Again

Thursday 14 July, 9am–12pm: Pre-conference workshop (booking required) with James Robinson and Jane Waite

If you enjoy explaining concepts using unplugged activities, analogy, or storytelling, then this practical pre-conference session is for you. In the session, we’ll introduce the idea of semantic waves, a learning theory that supports learners in building knowledge of new concepts through careful consideration of vocabulary and contexts. Across the world, this approach has been successfully used to teach topics ranging from ballet to chemistry — and now computing.

Three computer science educators discuss something at a screen.

You’ll learn how this theory can be applied to deliver powerful explanations that connect abstract ideas and concrete experiences. By taking part in the session, you’ll gain a solid understanding of semantic wave theory, see it in practice in some freely available lesson plans, and apply it to your own planning.

Write for a Global Computing Community with Hello World Magazine

Friday 15 July, 1–2pm: Workshop with Gemma Coleman

Do you enjoy sharing your teaching ideas, successes, and challenges with others? Do you want to connect with a global community of over 30,000 computing educators? Have you always wanted to be a published author? Then come along to this workshop session.

Issues of Hello World magazine arranged to form a number five.
Hello World has been going strong for five years — find out how you can become one of its authors.

Every single computing or CS teacher out there has at least one lesson to share, idea to voice, or story to tell. In the session, you’ll discuss what makes a good article with Gemma Coleman, Hello World’s Editor, and you’ll learn top tips for how to communicate your ideas in writing. Gemma will also guide you through writing a plan for your very own article. Even if you’re not sure whether you want to write an article, doing this is a powerful way to reflect on your teaching practice.

Developing a Toolkit for Teaching Computer Science in School

Saturday 16 July, 4–5pm: Keynote talk by Sue Sentance

To teach any subject requires good teaching skills, knowledge about the subject being taught, and specific knowledge that a teacher gains about how to teach a particular topic, to their particular students, in a particular context. Teaching computer science is no different, and it’s a challenge for teachers to develop a go-to set of pedagogical strategies for such a new subject, especially for elements of the subject matter that they are just getting to grips with themselves.

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.

In this keynote talk, our Chief Learning Officer Sue Sentance will focus on some of the 12 pedagogy principles that we developed to support the teaching of computer science. We created this set of principles together with other teachers and researchers to help us and everyone in computing and computer science education reflect on how we teach our learners. Sue will share how we arrived at the principles, and she’ll use classroom examples to illustrate how you can apply them in practice.

Exploring the Hello World Big Book of Computing Pedagogy

Sunday 17 July, 9–10am: Workshop with Sue Sentance

The set of 12 pedagogy principles we’ve developed for teaching computing are presented in our Hello World Big Book of Computing Pedagogy. The book includes summaries, teachers’ perspectives, and lesson plans for each of the 12 principles.

A tweet praising The Big Book of Computing Pedagogy.

All CSTA attendees will get their own print copy of the Big Book, and in this practical session, we will use the book to explore together how you can use the 12 principles in the planning and delivery of your lessons. The session will be very hands-on, so bring along something you know you want or need to teach.

See you at CSTA in July

CSTA is now just a month away, and we can’t wait to meet old friends, make new connections, and learn from each other! Come find us at booth 521 or at our sessions to meet the team, discover Hello World magazine and the Hello World podcast, and find out more about our educational work. We hope to see you soon.

The post We’ll see you at CSTA 2022 Annual Conference appeared first on Raspberry Pi.

I belong in computer science

Post Syndicated from Janina Ander original https://www.raspberrypi.org/blog/i-belong-in-computer-science-isaac-computer-science/

At the Raspberry Pi Foundation, we believe everyone belongs in computer science, and that it is a much more varied field than is commonly assumed. One of the ways we want to promote inclusivity and highlight the variety of skills and interests needed in computer science is through our ‘I belong’ campaign. We do this because the tech sector lacks diversity. Similarly, in schools, there is underrepresentation of students in computing along the axes of gender, ethnicity, and economic situation. (See how researchers describe data from England, and data from the USA.)

Woman teacher and female students at a computer

The ‘I belong’ campaign is part of our work on Isaac Computer Science, our free online learning platform for GCSE and A level students (ages 14 to 18) and their teachers, funded by the Department for Education. The campaign celebrates young computer scientists and how they came to love the subject, what their career journey has been so far, and what their thoughts are about inclusivity and belonging in their chosen field.

These people are role models who demonstrate that everyone belongs in computer science, and that everyone can bring their interests and skills to bear in the field. In this way, we want to show young people that they can do much more with computing than they might think, and to inspire them to consider how computing could be part of their own life and career path.

Meet Salome

Salome is studying Computer Science with Digital Technology Solutions at the University of Leeds and doing a degree apprenticeship with PricewaterhouseCoopers (PwC).

Salome smiling. The text says I belong in computer science.

“I was quite lucky, as growing up I saw a lot about women in STEM which inspired me to take this path. I think to improve the online community, we need to keep challenging stereotypes and getting more and more people to join, thereby improving the diversity. This way, a larger number of people can have role models and identify themselves with someone currently there.”

“Another thing is the assumption that computer science is just coding and not a wide and diverse field. I still have to explain to my friends what computer science involves and can become, and then they will say, ‘Wow, that’s really interesting, I didn’t know that.’”

Read Salome’s full interview

Meet Devyani

Devyani is a third-year degree apprentice at Cisco. 

Devyani smiling. The text says I belong in computer science.

“It was at A level where I developed my programming skills, and it was more practical rather than theoretical. I managed to complete a programming project where I utilised PHP, JavaScript, and phpMyAdmin (which is a database). It was after this that I started looking around and applying for degree apprenticeships. I thought that university wasn’t for me, because I wanted a more practical and hands-on approach, as I learn better that way.”

“At the moment, I’m currently doing a product owner role, which is where I hope to graduate into. It’s a mix between both a business role and a technical role. I have to stay up to speed with the current technologies we are using and developing for our clients and customers, but also I have to understand business needs and ensure that the team is able to develop and deliver on time to meet those needs.”

Read Devyani’s full interview

Meet Omar

Omar is a Mexican palaeontologist who uses computer science to study dinosaur bones.

Omar. The text says I belong in computer science.

“I try to bring aspects that are very well developed in computer science and apply them in palaeontology. For instance, when digitising the vertebrae, I use a lot of information theory. I also use a lot of data science and integrity to make sure that what we have captured is comparable with what other people have found.”

“What drove me to computers was the fact you are always learning. That’s what keeps me interested in science: that I can keep growing, learn from others, and I can teach people. That’s the other thing that makes me feel like I belong, which is when I am able to communicate the things I know to someone else and I can see the face of the other person when they start to grasp a theory.”

Read Omar’s full interview

Meet Tasnima

Tasnima is a computer science graduate from Queen Mary University of London, and is currently working as a software engineer at Credit Suisse.

Tasnima smiling. The text says I belong in computer science.

“During the pandemic, one of the good things to come out of it is that I could work from home, and that means working with people all over the world, bringing together every race, religion, gender, etc. Even though we are all very different, the one thing we all have in common is that we’re passionate about technology and computer science. Another thing is being able to work in technology in the real world. It has allowed me to work in an environment that is highly collaborative. I always feel like you’re involved from the get-go.”

“I think we need to also break the image that computer science is all about coding. I’ve had friends that have stayed away from any tech jobs because they think that they don’t want to code, but there’s so many other roles within technology and jobs that actually require no coding whatsoever.”

Read Tasnima’s whole interview

Meet Aleena

Aleena is a software engineer who works at a health tech startup in London and is also studying for a master’s degree in AI ethics at the University of Cambridge.

Aleena smiling. The text says I belong in computer science.

“I do quite a lot of different things as an engineer. It’s not just coding, which is part of it but it is a relatively small percentage, compared to a lot of other things. […] There’s a lot of collaborative time and I would say a quarter or third of the week is me by myself writing code. The other time is spent collaborating and working with other people and making sure that we’re all aligned on what we are working on.”

“I think it’s actually a very diverse field of tech to work in, once you actually end up in the industry. When studying STEM subjects at a college or university level it is often not very diverse. The industry is the opposite. A lot of people come from self-taught or bootcamp backgrounds, there’s a lot of ways to get into tech and software engineering, and I really like that aspect of it. Computer science isn’t the only way to go about it.”

Read Aleena’s full interview

Meet Alice

Alice is a final-year undergraduate student of Computer Science with Artificial Intelligence at the University of Brighton. She is also the winner of the Global Challenges COVID-19 Research Scholarship offered by Santander Universities.

Alice wearing a mask over her face and mouth. The text says I belong in computer science.

“[W]e need to advertise computer science as more than just a room full of computers, and to advertise computer sciences as highly collaborative. It’s very creative. If you’re on a team of developers, there’s a lot of communication involved.”

“There’s something about computer science that I think is so special: the fact that it is a skill anybody can learn, regardless of who they are. With the right idea, anybody can build anything.”

Read Alice’s full interview

Share these stories to inspire

Help us spread the message that everyone belongs in computer science: share this blog with schools, teachers, STEM clubs, parents, and young people you want to inspire.

You can learn computer science with us

Whether you’re studying or teaching computer science GCSE or A levels in the UK (or thinking about doing so!), or you’re a teacher or student in another part of the world, Isaac Computer Science is here to help you achieve your computer science goals. Our high-quality learning platform is free to use and open to all. As a student, you can register to keep track of your progress. As a teacher, you can sign up to guide your students’ learning.

Two teenage boys do coding at a shared computer during a computer science lesson while their woman teacher observes them.

And for younger learners, we have lots of fun project guides to try out coding and creating with digital technologies.

Three teenage girls at a laptop

The post I belong in computer science appeared first on Raspberry Pi.

Join us at the launch event of the Raspberry Pi Computing Education Research Centre

Post Syndicated from Sue Sentance original https://www.raspberrypi.org/blog/raspberry-pi-computing-education-research-centre-launch-event-invitation/

Last summer, the Raspberry Pi Foundation and the University of Cambridge Department of Computer Science and Technology created a new research centre focusing on computing education research for young people in both formal and non-formal education. The Raspberry Pi Computing Education Research Centre is an exciting venture through which we aim to deliver a step-change for the field.

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

Computing education research that focuses specifically on young people is relatively new, particularly in contrast to established research disciplines such as those focused on mathematics or science education. However, computing is now a mandatory part of the curriculum in several countries, and being taken up in education globally, so we need to rigorously investigate the learning and teaching of this subject, and do so in conjunction with schools and teachers.

You’re invited to our in-person launch event

To celebrate the official launch of the Raspberry Pi Computing Education Research Centre, we will be holding an in-person event in Cambridge, UK on Weds 20 July from 15.00. This event is free and open to all: if you are interested in computing education research, we invite you to register for a ticket to attend. By coming together in person, we want to help strengthen a collaborative community of researchers, teachers, and other education practitioners.

Register to attend the event

The launch event is your opportunity to meet and mingle with members of the Centre’s research team and listen to a series of short talks. We are delighted that Prof. Mark Guzdial (University of Michigan), who many readers will be familiar with, will be travelling from the US to join us in cutting the ribbon. Mark has worked in computer science education for decades and won many awards for his research, so I can’t think of anybody better to be our guest speaker. Our other speakers are Prof. Alastair Beresford from the Department of Computer Science and Technology, and Carrie Anne Philbin MBE, our Director of Educator Support at the Foundation.

Mark Guzdial.
Prof. Mark Guzdial
Headshot of Alastair Beresford.
Prof. Alastair Beresford
Headshot of Carrie Anne Philbin.
Carrie Anne Philbin MBE

The event will take place at the Department of Computer Science and Technology in Cambridge. It will start at 15.00 with a reception where you’ll have the chance to talk to researchers and see the work we’ve been doing. We will then hear from our speakers, before wrapping up at 17.30. You can find more details about the event location on the ticket registration page.

Our research at the Centre

The aim of the Raspberry Pi Computing Education Research Centre is to increase our understanding of teaching and learning computing, computer science, and associated subjects, with a particular focus on young people who are from backgrounds that are traditionally under-represented in the field of computing or who experience educational disadvantage.

Young learners at computers in a classroom.

We have been establishing the Centre over the last nine months. In October, I was appointed Director, and in December, we were awarded funding by Google for a one-year research project on culturally relevant computing teaching, following on from a project at the Raspberry Pi Foundation. The Centre’s research team is uniquely positioned, straddling both the University and the Foundation. Our two organisations complement each other very well: the University is one of the highest-ranking universities in the world and renowned for its leading-edge academic research, and the Raspberry Pi Foundation works with schools, educators, and learners globally to pursue its mission to put the power of computing into the hands of young people.

In our research at the Centre, we will make sure that:

  1. We collaborate closely with teachers and schools when implementing and evaluating research projects
  2. We publish research results in a number of different formats, as promptly as we can and without a paywall
  3. We translate research findings into practice across the Foundation’s extensive programmes and with our partners

We are excited to work with a large community of teachers and researchers, and we look forward to meeting you at the launch event.

Stay up to date

At the end of June, we’ll be launching a new website for the Centre at computingeducationresearch.org. This will be the place for you to find out more about our projects and events, and to sign up to our newsletter. For announcements on social media, follow the Raspberry Pi Foundation on Twitter or Linkedin.

The post Join us at the launch event of the Raspberry Pi Computing Education Research Centre appeared first on Raspberry Pi.

A teaspoon of computing in every subject: Broadening participation in computer science

Post Syndicated from Sue Sentance original https://www.raspberrypi.org/blog/guzdial-teaspoon-computing-tsp-language-broadening-participation-school/

From May to November 2022, our seminars focus on the theme of cross-disciplinary computing. Through this seminar series, we want to explore the intersections and interactions of computing with all aspects of learning and life, and think about how they can help us teach young people. We were delighted to welcome Prof. Mark Guzdial (University of Michigan) as our first speaker.

Mark Guzdial.
Professor Mark Guzdial, University of Michigan

Mark has worked in computer science (CS) education for decades and won many awards for his research, including the prestigious ACM SIGCSE Outstanding Contribution to Computing Education award in 2019. He has written literally hundreds of papers about CS education, and he authors an extremely popular computing education research blog that keeps us all up to date with what is going on in the field.

Young learners at computers in a classroom.

In his talk, Mark focused on his recent work around developing task-specific programming (TSP) languages, with which teachers can add a teaspoon (also abbreviated TSP) of programming to a wide variety of subject areas in schools. Mark’s overarching thesis is that if we want everyone to have some exposure to CS, then we need to integrate it into a range of subjects across the school curriculum. And he explained that this idea of “adding a teaspoon” embraces some core principles; for TSP languages to be successful, they need to:

  • Meet the teachers’ needs
  • Be relevant to the context or lesson in which it appears
  • Be technically easy to get to grips with

Mark neatly summarised this as ‘being both usable and useful’. 

Historical views on why we should all learn computer science

We can learn a lot from going back in time and reflecting on the history of computing. Mark started his talk by sharing the views of some of the eminent computer scientists of the early days of the subject. C. P. Snow maintained, way back in 1961, that all students should study CS, because it was too important to be left to a small handful of people.

A quote by computer scientist C. S. Snow from 1961: A handful of people, having no relation to the will of society, having no communication with the rest of society, will be taking decisions in secret which are going to affect our lives in the deepest, sense.

Alan Perlis, also in 1961, argued that everyone at university should study one course in CS rather than a topic such as calculus. His reason was that CS is about process, and thus gives students tools that they can use to change the world around them. I’d never heard of this work from the 1960s before, and it suggests incredible foresight. Perhaps we don’t need to even have the debate of whether computer science is for everyone — it seems it always was!

What’s the problem with the current situation?

In many of our seminars over the last two years, we have heard about the need to broaden participation in computing in school. Although in England, computing is mandatory for ages 5 to 16 (in theory, in practice it’s offered to all children from age 5 to 14), other countries don’t have any computing for younger children. And once computing becomes optional, numbers drop, wherever you are.

""
Not enough students are experiencing computer science in school.

Mark shared with us that in US high schools, only 4.7% of students are enrolled in a CS course. However, students are studying other subjects, which brought him to the conclusion that CS should be introduced where the students already are. For example, Mark described that, at the Advanced Placement (AP) level in the US, many more students choose to take history than CS (399,000 vs 114,000) and the History AP cohort has more even gender balance, and a higher proportion of Black and Hispanic students. 

The teaspoon approach to broadening participation

A solution to low uptake of CS being proposed by Mark and his colleagues is to add a little computing to other subjects, and in his talk he gave us some examples from history and mathematics, both subjects taken by a high proportion of US students. His focus is on high school, meaning learners aged 14 and upwards (upper secondary in Europe, or key stage 4 and 5 in England). To introduce a teaspoon of CS to other subjects, Mark’s research group builds tools using a participatory design approach; his group collaborates with teachers in schools to identify the needs of the teachers and students and design and iterate TSP languages in conjunction with them.

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

Mark demonstrated a number of TSP language prototypes his group has been building for use in particular contexts. The prototypes seem like simple apps, but can be classified as languages because they specify a process for a computational agent to execute. These small languages are designed to be used at a specific point in the lesson and should be learnable in ten minutes. For example, students can use a small ‘app’ specific to their topic, look at a script that generates a visualisation, and change some variables to find out how they impact the output. Students may also be able to access some program code, edit it, and see the impact of their edits. In this way, they discover through practical examples the way computer programs work, and how they can use CS principles to help build an understanding of the subject area they are currently studying. If the language is never used again, the learning cost was low enough that it was worth the value of adding computation to the one lesson.

We have recorded the seminar and will be sharing the video very soon, so bookmark this page.

Try TSP languages yourself

You can try out the TSP language prototypes Mark shared yourself, which will give you a good idea of how much a teaspoon is!

DV4L: For history students, the team and participating teachers have created a prototype called DV4L, which visualises historical data. The default example script shows population growth in Africa. Students can change some of the variables in the script to explore data related to other countries and other historical periods.

Pixel Equations: Mathematics and engineering students can use the Pixel Equations tool to learn about the way that pictures are made up of individual pixels. This can be introduced into lessons using a variety of contexts. One example lesson activity looks at images in the contexts of maps. This prototype is available in English and Spanish. 

Counting Sheets: Another example given by Mark was Counting Sheets, an interactive tool to support the exploration of counting problems, such as how many possible patterns can come from flipping three coins. 

Have a go yourself. What subjects could you imagine adding a teaspoon of computing to?

Join our next free research seminar

We’d love you to join us for the next seminar in our series on cross-disciplinary computing. On 7 June, we will hear from Pratim Sengupta, of the University of Calgary, Canada. He has conducted studies in science classrooms and non-formal learning environments, focusing on providing open and engaging experiences for anyone to explore code. Pratim will share his thoughts on the ways that more of us can become involved with code when we open up its richness and depth to a wider audience. He will also introduce us to his ideas about countering technocentrism, a key focus of his new book.

I want to sign up for the next seminar

And finally… save another date!

We will shortly be sharing details about the official in-person launch event of the Raspberry Pi Computing Education Research Centre at the University of Cambridge on 20 July 2022. And guess who is going to be coming to Cambridge, UK, from Michigan to officially cut the ribbon for us? That’s right, Mark Guzdial. More information coming soon on how you can sign up to join us for free at this launch event.

The post A teaspoon of computing in every subject: Broadening participation in computer science appeared first on Raspberry Pi.

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

Post Syndicated from Katharine Childs original https://www.raspberrypi.org/blog/gender-balance-in-computing-storytelling-approach-engaging-girls/

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

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

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

Teaching computing through storytelling

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

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

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

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

The pilot study and the evaluation methods

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

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

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

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

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

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

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

Main findings of the evaluation team

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

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

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

Teacher (evaluation report, p. 22)

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

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

Teacher (evaluation report, p. 26)

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

Young learners at computers in a classroom.

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

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

Future directions

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

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

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

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

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

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

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

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

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

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

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

The post A storytelling approach for engaging girls in the Computing classroom: Pilot study results appeared first on Raspberry Pi.

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

Post Syndicated from Sue Sentance original https://www.raspberrypi.org/blog/ai-literacy-children-families-working-together-ai-education-research/

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

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

Stefania Druga.
Stefania Druga, University of Washington

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

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

– Stefania Druga

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

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

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

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

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

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

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

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

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

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

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

AI literacy: Programming ‘smart’ devices, algorithmic bias

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

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

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

AI literacy: Kinaesthetic activities, sharing discussions

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

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

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

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

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

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

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

Find out more

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

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

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

Join our next free research seminar

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

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

I want to sign up for the next seminar

The post AI literacy research: Children and families working together around smart devices appeared first on Raspberry Pi.

Exploring cross-disciplinary computing education in our new seminar series

Post Syndicated from Sue Sentance original https://www.raspberrypi.org/blog/cross-disciplinary-computing-education-research-seminars/

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

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

Crossing disciplinary boundaries

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

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

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

Mark Guzdial on computing in history and mathematics

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

Mark Guzdial.

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

Pratim Sengupta on countering technocentrism

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

Pratim Sengupta.

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

Yasmin Kafai on curriculum design with e-textiles

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

Yasmin Kafai.

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

Genevieve Smith-Nunes on exploring data ethics

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

Genevieve Smith-Nunes.

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

Sign up now to attend the seminars

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

I want sign up to attend

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

The post Exploring cross-disciplinary computing education in our new seminar series appeared first on Raspberry Pi.

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.

The post Making the most of Hello World magazine | Hello World #18 appeared first on Raspberry Pi.

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).

The post 170 research papers about teaching programming, summarised appeared first on Raspberry Pi.

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)

The post Bias in the machine: How can we address gender bias in AI? appeared first on Raspberry Pi.

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.

The post Computer science education for what purpose? Some perspectives appeared first on Raspberry Pi.

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.  

The post The Roots project: Implementing culturally responsive computing teaching in schools in England appeared first on Raspberry Pi.

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.

The post Creating better online multiple choice questions appeared first on Raspberry Pi.

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.

The post The AI4K12 project: Big ideas for AI education appeared first on Raspberry Pi.

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.

The post How can AI-based analysis help educators support students? appeared first on Raspberry Pi.

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.