All posts by Oliver Quinlan

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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PRIMM: encouraging talk in programming lessons

Post Syndicated from Oliver Quinlan original https://www.raspberrypi.org/blog/primm-talk-in-programming-lessons-research-seminar/

Whenever you learn a new subject or skill, at some point you need to pick up the particular language that goes with that domain. And the only way to really feel comfortable with this language is to practice using it. It’s exactly the same when learning programming.

A girl doing Scratch coding in a Code Club classroom

In our latest research seminar, we focused on how we educators and our students can talk about programming. The seminar presentation was given by our Chief Learning Officer, Dr Sue Sentance. She shared the work she and her collaborators have done to develop a research-based approach to teaching programming called PRIMM, and to work with teachers to investigate the effects of PRIMM on students.

Sue Sentance

As well as providing a structure for programming lessons, Sue’s research on PRIMM helps us think about ways in which learners can investigate programs, start to understand how they work, and then gradually develop the language to talk about them themselves.

Productive talk for education

Sue began by taking us through the rich history of educational research into language and dialogue. This work has been heavily developed in science and mathematics education, as well as language and literacy.

In particular the work of Neil Mercer and colleagues has shown that students need guidance to develop and practice using language to reason, and that developing high-quality language improves understanding. The role of the teacher in this language development is vital.

Sue’s work draws on these insights to consider how language can be used to develop understanding in programming.

Why is programming challenging for beginners?

Sue identified shortcomings of some teaching approaches that are common in the computing classroom but may not be suitable for all beginners.

  • ‘Copy code’ activities for learners take a long time, lead to dreaded syntax errors, and don’t necessarily build more understanding.
  • When teachers model the process of writing a program, this can be very helpful, but for beginners there may still be a huge jump from being able to follow the modeling to being able to write a program from scratch themselves.

PRIMM was designed by Sue and her collaborators as a language-first approach where students begin not by writing code, but by reading it.

What is PRIMM?

PRIMM stands for ‘Predict, Run, Investigate, Modify, Make’. In this approach, rather than copying code or writing programs from scratch, beginners instead start by focussing on reading working code.

In the Predict stage, the teacher provides learners with example code to read, discuss, and make output predictions about. Next, they run the code to see how the output compares to what they predicted. In the Investigate stage, the teacher sets activities for the learners to trace, annotate, explain, and talk about the code line by line, in order to help them understand what it does in detail.

In the seminar, Sue took us through a mini example of the stages of PRIMM where we predicted the output of Python Turtle code. You can follow along on the recording of the seminar to get the experience of what it feels like to work through this approach.

The impact of PRIMM on learning

The PRIMM approach is informed by research, and it is also the subject of research by Sue and her collaborators. They’ve conducted two studies to measure the effectiveness of PRIMM: an initial pilot, and a larger mixed-methods study with 13 teachers and 493 students with a control group.

The larger study used a pre and post test, and found that the group who experienced a PRIMM approach performed better on the tests than the control group. The researchers also collected a wealth of qualitative feedback from teachers. The feedback suggested that the approach can help students to develop a language to express their understanding of programming, and that there was much more productive peer conversation in the PRIMM lessons (sometimes this meant less talk, but at a more advanced level).

The PRIMM structure also gave some teachers a greater capacity to talk about the process of teaching programming. It facilitated the discussion of teaching ideas and learning approaches for the teachers, as well as developing language approaches that students used to learn programming concepts.

The research results suggest that learners taught using PRIMM appear to be developing the language skills to talk coherently about their programming. The effectiveness of PRIMM is also evidenced by the number of teachers who have taken up the approach, building in their own activities and in some cases remixing the PRIMM terminology to develop their own take on a language-first approach to teaching programming.

Future research will investigate in detail how PRIMM encourages productive talk in the classroom, and will link the approach to other work on semantic waves. (For more on semantic waves in computing education, see this seminar by Jane Waite and this symposium talk by Paul Curzon.)

Resources for educators who want to try PRIMM

If you would like to try out PRIMM with your learners, use our free support materials:

Join our next seminar

If you missed the seminar, you can find the presentation slides alongside the recording of Sue’s talk on our seminars page.

In our next seminar on Tuesday 1 December at 17:00–18:30 GMT / 12:00–13:30 EsT / 9:00–10:30 PT / 18:00–19:30 CEST. Dr David Weintrop from the University of Maryland will be presenting on the role of block-based programming in computer science education. To join, simply sign up with your name and email address.

I want to sign up to the next seminar

Once you’ve signed up, we’ll email you the seminar meeting link and instructions for joining. If you attended this past seminar, the link remains the same.

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Embedding computational thinking skills in our learning resources

Post Syndicated from Oliver Quinlan original https://www.raspberrypi.org/blog/computational-thinking-skills-in-our-free-learning-resources/

Learning computing is fun, creative, and exploratory. It also involves understanding some powerful ideas about how computers work and gaining key skills for solving problems using computers. These ideas and skills are collected under the umbrella term ‘computational thinking’.

When we create our online learning projects for young people, we think as much about how to get across these powerful computational thinking concepts as we do about making the projects fun and engaging. To help us do this, we have put together a computational thinking framework, which you can read right now.

What is computational thinking? A brief summary

Computational thinking is a set of ideas and skills that people can use to design systems that can be run on a computer. In our view, computational thinking comprises:

  • Decomposition
  • Algorithms
  • Patterns and generalisations
  • Abstraction
  • Evaluation
  • Data

All of these aspects are underpinned by logical thinking, the foundation of computational thinking.

What does computational thinking look like in practice?

In principle, the processes a computer performs can also be carried out by people. (To demonstrate this, computing educators have created a lot of ‘unplugged’ activities in which learners enact processes like computers do.) However, when we implement processes so that they can be run on a computer, we benefit from the huge processing power that computers can marshall to do certain types of activities.

A group of young people and educators smiling while engaging with a computer

Computers need instructions that are designed in very particular ways. Computational thinking includes the set of skills we use to design instructions computers can carry out. This skill set represents the ways we can logically approach problem solving; as computers can only solve problems using logical processes, to write programs that run on a computer, we need to use logical thinking approaches. For example, writing a computer program often requires the task the program revolves around to be broken down into smaller tasks that a computer can work through sequentially or in parallel. This approach, called decomposition, can also help people to think more clearly about computing problems: breaking down a problem into its constituent parts helps us understand the problem better.

Male teacher and male students at a computer

Understanding computational thinking supports people to take advantage of the way computers work to solve problems. Computers can run processes repeatedly and at amazing speeds. They can perform repetitive tasks that take a long time, or they can monitor states until conditions are met before performing a task. While computers sometimes appear to make decisions, they can only select from a range of pre-defined options. Designing systems that involve repetition and selection is another way of using computational thinking in practice.

Our computational thinking framework

Our team has been thinking about our approach to computational thinking for some time, and we have just published the framework we have developed to help us with this. It sets out the key areas of computational thinking, and then breaks these down into themes and learning objectives, which we build into our online projects and learning resources.

Read our computational thinking framework

To develop this computational thinking framework, we worked with a group of academics and educators to make sure it is robust and useful for teaching and learning. The framework was also influenced by work from organisations such as Computing At School (CAS) in the UK, and the Computer Science Teachers’ Association (CSTA) in the USA.

We’ve been using the computational thinking framework to help us make sure we are building opportunities to learn about computational thinking into our learning resources. This framework is a first iteration, which we will review and revise based on experience and feedback.

We’re always keen to hear feedback from you in the community about how we shape our learning resources, so do let us know what you think about them and the framework in the comments.

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