Tag Archives: research seminar

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.

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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

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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.

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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.

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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.

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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

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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:

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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.

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170 research papers about teaching programming, summarised

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

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

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

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

My approach to summarising research papers

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

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

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

a teenage boy does coding during a computer science lesson.

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

For this report, I grouped papers into three sections:

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

Why you as a teacher should read research summaries

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

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

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

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

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

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Linking AI education to meaningful projects

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

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

Tara Chklovski.
Tara Chklovski

The Technovation Challenge

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

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

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

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

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

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

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

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

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

AI Families: Children and parents using AI to solve problems

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

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

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

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

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

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

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

Many perspectives on AI education

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

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

Join us for our next seminar

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

I want to sign up for the seminar

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

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

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The AI4K12 project: Big ideas for AI education

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

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

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

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

The AI4K12 project

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

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

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

Five Big Ideas of AI4K12

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

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

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

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

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

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

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

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

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

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

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

What is AI thinking?

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

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

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

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

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

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

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

Resources for AI education

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

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

Join our next seminar

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

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

I want to join the next seminar

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

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How can AI-based analysis help educators support students?

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

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

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

Rose Luckin.
Rose Luckin, UCL

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

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

Education and AI systems

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

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

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

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

What can AI systems do in education? 

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

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

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

Getting ready for AI systems in education

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

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

Rose described the seven steps of the framework as:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Two boys at laptops in a classroom.

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

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

Join our next free seminars

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

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

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

I want to join the seminars

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

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!

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

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

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

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

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

Machine behaviour — a new field of study?

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

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

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

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

Ideas or artefacts?

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

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

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

Studying machine learning: a different focus

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

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

These differences imply that our teaching should adapt too.

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

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

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

The ProDaBi curriculum includes:

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

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

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

Our speakers described example activities from three of the modules:

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

Questioning how we should teach AI and ML at school

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

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

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

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

Join our next seminar

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

I want to sign up for the next seminar

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

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

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

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Perspectives on supporting young people in low-income areas to access and engage with computing

Post Syndicated from Hayley Leonard original https://www.raspberrypi.org/blog/young-people-low-income-areas-computing-uk-usa-guyana/

The Raspberry Pi Foundation’s mission is to make computing and digital making accessible to all. To support young people at risk of educational disadvantage because they don’t have access to computing devices outside of school, we’ve set up the Learn at Home campaign. But access is only one part of the story. To learn more about what support these young people need across organisations and countries, we set up a panel discussion at the Tapia Celebration of Diversity in Computing conference.

Two young African women work at desktop computers.

The three panelists provided a stimulating discussion of some key issues in supporting young people in low-income areas in the UK, USA, and Guyana to engage with computing, and we hope their insights are of use to educators, youth workers, and organisations around the world.

The panellists and their perspectives

Our panellists represent three different countries, and all have experience of teaching in schools and/or working with young people outside of the formal education system. Because of the differences between countries in terms of access to computing, having this spread of expertise and contexts allowed the panelists to compare lessons learned in different sectors and locations.

Lenlandlar Singh

Panelist Lenandlar Singh is a Senior Lecturer in the Department of Computer Science at the University of Guyana. In Guyana, there is a range of computing-related courses for high school students, and access to optional qualifications in computer science at A level (age 17–18).

Yolanda Payne.

Panelist Yolanda Payne is a Research Associate at the Constellations Center at Georgia Tech, USA. In the US, computing curricula differ across states, although there is some national leadership through associations, centres, and corporations.

Christina Watson.

Christina Watson is Assistant Director of Design at UK Youth*, UK. The UK has a mandatory computing curriculum for learners aged 5–18, although curricula vary across the four home nations (England, Scotland, Wales, Northern Ireland).

As the moderator, I posed the following three questions, which the panelists answered from their own perspectives and experiences:

  • What are the key challenges for young people to engage with computing in or out of school, and what have you done to overcome these challenges?
  • What do you see as the role of formal and non-formal learning opportunities in computing for these young people?
  • What have you learned that could help other people working with these young people and their communities in the future?

Similarities across contexts

One of the aspects of the discussion that really stood out was the number of similarities across the panellists’ different contexts. 

The first of these similarities was the lack of access to computing amongst young people from low-income families, particularly in more rural areas, across all three countries. These access issues concerned devices and digital infrastructure, but also the types of opportunities in and out of school that young people were able to engage with.

Two girls code at a desktop computer while a female mentor observes them.

Christina (UK) shared results from a survey conducted with Aik Saath, a youth organisation in the UK Youth network (see graphs below). The results highlighted that very few young people in low-income areas had access to their own device for online learning, and mostly their access was to a smartphone or tablet rather than a computer. She pointed out that youth organisations can struggle to provide access to computing not only due to lack of funding, but also because they don’t have secure spaces in which to store equipment.

Lenandlar (Guyana) and Christina (UK) also discussed the need to improve the digital skills and confidence of teachers and youth workers so they can support young people with their computing education. While Lenandlar spoke about recruitment and training of qualified computing teachers in Guyana, Christina suggested that it was less important for youth workers in the UK to become experts in the field and more important for them to feel empowered and confident in supporting young people to explore computing and understand different career paths. UK Youth found that partnering with organisations that provided technical expertise (such as us at the Raspberry Pi Foundation) allowed youth workers to focus on the broader support that the young people needed.

  • Educators sit at laptops to learn about coding and digital making.
  • An educator helps a young person with a computing problem.

Both Yolanda (US) and Lenandlar (Guyana) discussed the restrictive nature of the computing curriculum in schools, agreeing with Christina (UK) that outside of the classroom, there was more freedom for young people to explore different aspects of computing. All three agreed that introducing more fun and relevant activities into the curriculum made young people excited about computing and reduced stereotypes and misconceptions about the discipline and career. Yolanda explained that using modern, real-life examples and role models was a key part of connecting with young people and engaging them in computing.

What can teachers do to support young people and their families?

Yolanda (US) advocated strongly for listening to students and their communities to help understand what is meaningful and relevant to them. One example of this approach is to help young people and their families understand the economics of technology, and how computing can be used to support, develop, and sustain businesses and employment in their community. As society has become more reliant on computing and technology, this can translate into real economic impact.

A CoderDojo coding session for young people.

Both Yolanda (US) and Lenandlar (Guyana) emphasised the importance of providing opportunities for digital making, allowing students opportunities to become creators rather than just consumers of technology. They also highly recommended providing relevant contexts for computing and identifying links with different careers.

The panellists also discussed the importance of partnering with other education settings, with tech companies, and with non-profit organisations to provide access to equipment and opportunities for students in schools that have limited budgets and capacity for computing. These links can also highlight key role models and help to build strong relationships in the community between businesses and schools.

What is the role of non-formal settings in low-income areas?

All of the panellists agreed that non-formal settings provided opportunities for further exploration and skill development outside of a strict curriculum. Christina (UK) particularly highlighted that these settings helped support young people and families who feel left behind by the education system, allowing them to develop practical skills and knowledge that can help their whole family. She emphasised the strong relationships that can be developed in these settings and how these can provide relatable role models for young people in low-income areas.

A young girl uses a computer.

Tips and suggestions

After the presentation, the panelists responded to the audience’s questions with some practical tips and suggestions for engaging young people in low-income communities with computing:

How do you engage young people who are non-native English speakers with mainly English computing materials?

  • For curriculum materials, it’s possible to use Google Translate to allow students to access them. The software is not always totally accurate but goes some way to supporting these students. You can also try to use videos that have captioning and options for non-English subtitles.
  • We offer translated versions of our free online projects, thanks to a community of dedicated volunteer translators from around the world. Learners can choose from up to 30 languages (as shown in the picture below).
The Raspberry Pi Foundation's projects website, with the drop-down menu to choose a human language highlighted.
Young people can learn about computing in their first language by using the menu on our projects site.

How do you set up partnerships with other organisations?

  • Follow companies on social media and share how you are using their products or tools, and how you are aligned with their goals. This can form the basis of future partnerships.
  • When you are actively applying for partnerships, consider the following points:
    • What evidence do you have that you need support from the potential partner?
    • What support are you asking for? This may differ across potential partners, so make sure your pitch is relevant and tailored to a specific partner.
    • What evidence could you use to show the impact you are already having or previous successful projects or partnerships?

Make use of our free training resources and guides

For anyone wishing to learn computing knowledge and skills, and the skills you need to teach young people in and out of school about these topics, we provide a wide range of free online training courses to cover all your needs. Educators in England can also access the free CPD that we and our consortium partners offer through the National Centre for Computing Education.

To help you support your learners in and out of school to engage with computing in ways that are meaningful and relevant for them, we recently published a guide on culturally relevant teaching.

We also support a worldwide network of volunteers to run CoderDojos, which are coding clubs for young people in local community spaces. Head over to the CoderDojo website to discover more about the free materials and help we’ve got for you.

We would like to thank our panellists Lenandlar Singh, Yolanda Payne, and Christina Watson for sharing their time and expertise, and the Tapia conference organisers for providing a great platform to discuss issues of diversity, equality, and inclusion in computing.

*UK Youth is a leading charity working across the UK with an open network of over 8000 youth organisations. The charity has influence as a sector-supporting infrastructure body, a direct delivery partner, and a campaigner for social change.

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

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

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

Mhairi Aitken.

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

Why we need AI ethics

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

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

Mhairi Aitken

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

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

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

What should AI be used for?

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

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

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

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

Who gets to inform AI systems? The kangaroo metaphor

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

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

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

AI in children’s lives

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

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

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

AI research that listens to children

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

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

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

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

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

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

Join our next seminar

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

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

I want to sign up for the next free seminar

I look forward to meeting you there!

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

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

Delivering a culturally relevant computing curriculum: new guide for teachers

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

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

Download the guide as a PDF

Why we need to make computing culturally relevant

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

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

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

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

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

Some shared definitions

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

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

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

Curriculum, teaching approaches, and learning materials

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

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

Each aspect plays an important role in culturally relevant pedagogy:

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

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

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

What was being part of the working group like?

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

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

Computer science teacher Joe Arday.

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

Continuing the work

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

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

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

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

Working group

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

Lynda Chinaka, University of Roehampton, UK

Mike Deutsch, Kids Code Jeunesse, Canada

Dr Yota Dimitriadi, University of Reading, UK

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

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

Professor Joanna Goode, University of Oregon, USA

Alain Ndabala, St George Catholic College, Hampshire, UK

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

Rohini Shah, Queens Park Community School, London, UK

Neelu Vasishth, Hampton Court House, Surrey, UK

The post Delivering a culturally relevant computing curriculum: new guide for teachers appeared first on Raspberry Pi.

Exploring how culture and computing intersect

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

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

Ron Eglash.
Dr Ron Eglash, University of Michigan

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

Where do our ideas about computing and STEM come from?

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

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

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

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

What ideas pattern our cultures?

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

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

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

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

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

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

Computing activities based on circulation of value

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

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

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

Returning creative contributions

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

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

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

Rethinking diversity

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

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

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

Join us at our next online seminar

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

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Educating young people in AI, machine learning, and data science: new seminar series

Post Syndicated from Sue Sentance original https://www.raspberrypi.org/blog/ai-machine-learning-data-science-education-seminars/

A recent Forbes article reported that over the last four years, the use of artificial intelligence (AI) tools in many business sectors has grown by 270%. AI has a history dating back to Alan Turing’s work in the 1940s, and we can define AI as the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.

A woman explains a graph on a computer screen to two men.
Recent advances in computing technology have accelerated the rate at which AI and data science tools are coming to be used.

Four key areas of AI are machine learning, robotics, computer vision, and natural language processing. Other advances in computing technology mean we can now store and efficiently analyse colossal amounts of data (big data); consequently, data science was formed as an interdisciplinary field combining mathematics, statistics, and computer science. Data science is often presented as intertwined with machine learning, as data scientists commonly use machine learning techniques in their analysis.

Venn diagram showing the overlaps between computer science, AI, machine learning, statistics, and data science.
Computer science, AI, statistics, machine learning, and data science are overlapping fields. (Diagram from our forthcoming free online course about machine learning for educators)

AI impacts everyone, so we need to teach young people about it

AI and data science have recently received huge amounts of attention in the media, as machine learning systems are now used to make decisions in areas such as healthcare, finance, and employment. These AI technologies cause many ethical issues, for example as explored in the film Coded Bias. This film describes the fallout of researcher Joy Buolamwini’s discovery that facial recognition systems do not identify dark-skinned faces accurately, and her journey to push for the first-ever piece of legislation in the USA to govern against bias in the algorithms that impact our lives. Many other ethical issues concerning AI exist and, as highlighted by UNESCO’s examples of AI’s ethical dilemmas, they impact each and every one of us.

Three female teenagers and a teacher use a computer together.
We need to make sure that young people understand AI technologies and how they impact society and individuals.

So how do such advances in technology impact the education of young people? In the UK, a recent Royal Society report on machine learning recommended that schools should “ensure that key concepts in machine learning are taught to those who will be users, developers, and citizens” — in other words, every child. The AI Roadmap published by the UK AI Council in 2020 declared that “a comprehensive programme aimed at all teachers and with a clear deadline for completion would enable every teacher confidently to get to grips with AI concepts in ways that are relevant to their own teaching.” As of yet, very few countries have incorporated any study of AI and data science in their school curricula or computing programmes of study.

A teacher and a student work on a coding task at a laptop.
Our seminar speakers will share findings on how teachers can help their learners get to grips with AI concepts.

Partnering with The Alan Turing Institute for a new seminar series

Here at the Raspberry Pi Foundation, AI, machine learning, and data science are important topics both in our learning resources for young people and educators, and in our programme of research. So we are delighted to announce that starting this autumn we are hosting six free, online seminars on the topic of AI, machine learning, and data science education, in partnership with The Alan Turing Institute.

A woman teacher presents to an audience in a classroom.
Everyone with an interest in computing education research is welcome at our seminars, from researchers to educators and students!

The Alan Turing Institute is the UK’s national institute for data science and artificial intelligence and does pioneering work in data science research and education. The Institute conducts many different strands of research in this area and has a special interest group focused on data science education. As such, our partnership around the seminar series enables us to explore our mutual interest in the needs of young people relating to these technologies.

This promises to be an outstanding series drawing from international experts who will share examples of pedagogic best practice […].

Dr Matt Forshaw, The Alan Turing Institute

Dr Matt Forshaw, National Skills Lead at The Alan Turing Institute and Senior Lecturer in Data Science at Newcastle University, says: “We are delighted to partner with the Raspberry Pi Foundation to bring you this seminar series on AI, machine learning, and data science. This promises to be an outstanding series drawing from international experts who will share examples of pedagogic best practice and cover critical topics in education, highlighting ethical, fair, and safe use of these emerging technologies.”

Our free seminar series about AI, machine learning, and data science

At our computing education research seminars, we hear from a range of experts in the field and build an international community of researchers, practitioners, and educators interested in this important area. Our new free series of seminars runs from September 2021 to February 2022, with some excellent and inspirational speakers:

  • Tues 7 September: Dr Mhairi Aitken from The Alan Turing Institute will share a talk about AI ethics, setting out key ethical principles and how they apply to AI before discussing the ways in which these relate to children and young people.
  • Tues 5 October: Professor Carsten Schulte, Yannik Fleischer, and Lukas Höper from Paderborn University in Germany will use a series of examples from their ProDaBi programme to explore whether and how AI and machine learning should be taught differently from other topics in the computer science curriculum at school. The speakers will suggest that these topics require a paradigm shift for some teachers, and that this shift has to do with the changed role of algorithms and data, and of the societal context.
  • Tues 3 November: Professor Matti Tedre and Dr Henriikka Vartiainen from the University of Eastern Finland will focus on machine learning in the school curriculum. Their talk will map the emerging trajectories in educational practice, theory, and technology related to teaching machine learning in K-12 education.
  • Tues 7 December: Professor Rose Luckin from University College London will be looking at the breadth of issues impacting the teaching and learning of AI.
  • Tues 11 January: We’re delighted that Dr Dave Touretzky and Dr Fred Martin (Carnegie Mellon University and University of Massachusetts Lowell, respectively) from the AI4K12 Initiative in the USA will present some of the key insights into AI that the researchers hope children will acquire, and how they see K-12 AI education evolving over the next few years.
  • Tues 1 February: Speaker to be confirmed

How you can join our online seminars

All seminars start at 17:00 UK time (18:00 Central European Time, 12 noon Eastern Time, 9:00 Pacific Time) and take place in an online format, with a presentation, breakout discussion groups, and a whole-group Q&A.

I want to sign up to join

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

In the meantime, you can explore some of our educational resources related to machine learning and data science:

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Introducing the Raspberry Pi Computing Education Research Centre

Post Syndicated from Philip Colligan original https://www.raspberrypi.org/blog/raspberry-pi-computing-education-research-centre-university-of-cambridge/

I am delighted to announce the creation of the Raspberry Pi Computing Education Research Centre at the University of Cambridge.

University of Cambridge logo

With computers and digital technologies increasingly shaping all of our lives, it’s more important than ever that every young person, whatever their background or circumstances, has meaningful opportunities to learn about how computers work and how to create with them. That’s our mission at the Raspberry Pi Foundation.

Woman computing teacher and young female student at a laptop.
The Raspberry Pi Computing Education Research Centre will work with educators to translate its research into practice and effect positive change in learners’ lives.

Why research matters

Compared to subjects like mathematics, computing is a relatively new field and, while there are enduring principles and concepts, it’s a subject that’s changing all the time as the pace of innovation accelerates. If we’re honest, we just don’t know enough about what works in computing education, and there isn’t nearly enough investment in high-quality research.

Two teenagers sit at laptops in a computing classroom.
We need research to find the best ways of teaching young people how computers work and how to create with them.

That’s why research and evidence has always been a priority for the Raspberry Pi Foundation, from rigorously evaluating our own programmes and running structured experiments to test what works in areas like gender balance in computing, to providing a platform for the world’s best computing education researchers to share their findings through our seminar series. 

Through our research activities we hope to make a contribution to the field of computing education and, as an operating foundation working with tens of thousands of educators and millions of learners every year, we’re uniquely well-placed to translate that research into practice. You can read more about our research work here.

The Raspberry Pi Computing Education Research Centre 

The new Research Centre is a joint initiative between the University of Cambridge and the Raspberry Pi Foundation, and builds on our longstanding partnership with the Department of Computer Science and Technology. That partnership goes all the way back to 2008, to the creation of the Raspberry Pi Foundation and the invention of the Raspberry Pi computer. More recently, we have collaborated on Isaac Computer Science, an online platform that is already being used by more than 2500 teachers and 36,000 students of A level Computer Science in England, and that we will shortly expand to cover GCSE content.

Woman computing teacher and female students at a computer.
Computers and digital technologies shape our lives and society — how do we make sure young people have the skills to use them to solve problems?

Through the Raspberry Pi Computing Education Research Centre, we want to increase understanding of what works in teaching and learning computing, with a particular focus on young people who come from backgrounds that are traditionally underrepresented in the field of computing or who experience educational disadvantage.

The Research Centre will combine expertise from both institutions, undertaking rigorous original research and working directly with teachers and other educators to translate that research into practice and effect positive change in young peoples’ lives.

The scope will be computing education — the teaching and learning of computing, computer science, digital making, and wider digital skills — for school-aged young people in primary and secondary education, colleges, and non-formal settings.

We’re starting with three broad themes: 

  • Computing curricula, pedagogy, and assessment, including teacher professional development and the learning and teaching process
  • The role of non-formal learning in computing and digital making learning, including self-directed learning and extra-curricular programmes
  • Understanding and removing the barriers to computing education, including the factors that stand in the way of young people’s engagement and progression in computing education

While we’re based in the UK and expect to run a number of research projects here, we are eager to establish collaborations with universities and researchers in other countries, including the USA and India. 

Get involved

We’re really excited about this next chapter in our research work, and doubly excited to be working with the brilliant team at the Department of Computer Science and Technology. 

If you’d like to find out more or get involved in supporting the new Computing Education Research Centre, please subscribe to our research newsletter or email [email protected].

You can also join our free monthly research seminars.

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