The UK Bebras Challenge, the nation’s largest computing competition, is back and open for entries from schools. This year’s challenge will be open for entries from 4–15 November. Last year, over 400,000 students from across the UK took part. Read on to learn how your school can get involved.
What is UK Bebras?
UK Bebras is a free-to-enter annual competition that is designed to spark interest in computational thinking among students aged 6 to 19 by providing engaging and thought-provoking activities. The 45-minute challenge is accessible to everyone, offering age-appropriate interactive questions for students at different levels, including a tailored version for students with severe sight impairments.
The questions are designed to give every student the opportunity to showcase their potential, whether they excel in maths or computing, or not. With self-marking questions and no programming required, it’s easy for schools to participate.
“Thank you for another fantastic Bebras event! My students have really enjoyed it. This is the first year that one of my leadership team actually did the Bebras to understand what we are preparing the children for — she was very impressed!” Reference 5487
“I really enjoyed doing the Bebras challenge yesterday. It was the most accessible it’s ever been for me as a braillist/screen reader user.” Reference 5372
What does a UK Bebras question look like?
The questions are inspired by classic computing problems but are presented in a fun, age-appropriate way. For instance, a puzzle for 6- to 8-year-olds might involve guiding a hungry tortoise along the most efficient path across a lawn, while 16- to 19-year-olds could be asked to sort members for quiz teams based on who knows who — a challenging problem relating to graph theory.
Here’s a question we ran in 2023 for the Castors group (ages 8 to 10). Can you solve it?
Planting carrots
A robotic rabbit is planting carrot seeds in these four earth mounds.
It can respond to these commands:
jump left to the next mound
jump right to the next mound
plant a carrot seed in the mound you are on
Here is a sequence of commands for the rabbit:
We don’t know which mound the rabbit started on, but we do know that, when it followed this sequence, it placed each of three carrot seeds on different mounds.
Question:
Which picture shows how the carrot seeds could have been planted by the robot following the sequence of commands?
Example puzzle answer
The correct answer is:
The image below shows the route the robot takes by following the instructions:
After executing the first two commands
the rabbit places the seed on the mound to the far right:
It then executes the commands
and lays the next seed:
Then it jumps to the left twice and lays the last seed
So the carrot seeds will be on the hills in the order:
Did you get it right?
How do I get my school involved?
Visit the UK Bebras website for more information and to register your school. Once you’ve registered, you’ll get access to the entire UK Bebras back catalogue of questions, allowing you to create custom quizzes for your students to tackle at any time throughout the year. These quizzes are self-marking, and you can download your students’ results to keep track of their progress. Schools have found these questions perfect for enrichment activities, end-of-term quizzes, lesson starters, and even full lessons to develop computational thinking skills.
“Computational thinking is really about thinking, and sometimes about computing.” – Aman Yadav, Michigan State University
Computational thinking is a vital skill if you want to use a computer to solve problems that matter to you. That’s why we consider computational thinking (CT) carefully when creating learning resources here at the Raspberry Pi Foundation. However, educators are increasingly realising that CT skills don’t just apply to writing computer programs, and that CT is a fundamental approach to problem-solving that can be extended into other subject areas. To discuss how CT can be integrated beyond the computing classroom and help introduce the fundamentals of computing to primary school learners, we invited Dr Aman Yadav from Michigan State University to deliver the penultimate presentation in our seminar series on computing education for primary-aged children.
In his presentation, Aman gave a concise tour of CT practices for teachers, and shared his findings from recent projects around how teachers perceive and integrate CT into their lessons.
Research in context
Aman began his talk by placing his team’s work within the wider context of computing education in the US. The computing education landscape Aman described is dominated by the National Science Foundation’s ambitious goal, set in 2008, to train 10,000 computer science teachers. This objective has led to various initiatives designed to support computer science education at the K–12 level. However, despite some progress, only 57% of US high schools offer foundational computer science courses, only 5.8% of students enrol in these courses, and just 31% of the enrolled students are female. As a result, Aman and his team have worked in close partnership with teachers to address questions that explore ways to more meaningfully integrate CT ideas and practices into formal education, such as:
What kinds of experiences do students need to learn computing concepts, to be confident to pursue computing?
What kinds of knowledge do teachers need to have to facilitate these learning experiences?
What kinds of experiences do teachers need to develop these kinds of knowledge?
The CT4EDU project
At the primary education level, the CT4EDU project posed the question “What does computational thinking actually look like in elementary classrooms, especially in the context of maths and science classes?” This project involved collaboration with teachers, curriculum designers, and coaches to help them conceptualise and implement CT in their core instruction.
During professional development workshops using both plugged and unplugged tasks, the researchers supported educators to connect their day-to-day teaching practice to four foundational CT constructs:
Debugging
Abstraction
Decomposition
Patterns
An emerging aspect of the research team’s work has been the important relationship between vocabulary, belonging, and identity-building, with implications for equity. Actively incorporating CT vocabulary in lesson planning and classroom implementation helps students familiarise themselves with CT ideas: “If young people are using the language, they see themselves belonging in computing spaces”.
A main finding from the study is that teachers used CT ideas to explicitly engage students in metacognitive thinking processes, and to help them be aware of their thinking as they solve problems. Rather than teachers using CT solely to introduce their students to computing, they used CT as a way to support their students in whatever they were learning. This constituted a fundamental shift in the research team’s thinking and future work, which is detailed further in a conceptual article.
The Smithsonian Science for Computational Thinking project
The work conducted for the CT4EDU project guided the approach taken in the Smithsonian Science for Computational Thinking project. This project entailed the development of a curriculum for grades 3 and 5 that integrates CT into science lessons.
Part of the project included surveying teachers about the value they place on CT, both before and after participating in professional development workshops focused on CT. The researchers found that even before the workshops, teachers make connections between CT and the rest of the curriculum. After the workshops, an overwhelming majority agreed that CT has value (see image below). From this survey, it seems that CT ties things together for teachers in ways not possible or not achieved with other methods they’ve tried previously.
Despite teachers valuing the CT approach, asking them to integrate coding into their practices from the start remains a big ask (see image below). Many teachers lack knowledge or experience of coding, and they may not be curriculum designers, which means that we need to develop resources that allow teachers to integrate CT and coding in natural ways. Aman proposes that this requires a longitudinal approach, working with teachers over several years, using plugged and unplugged activities, and working closely with schools’ STEAM or specialist technology teachers where applicable to facilitate more computationally rich learning experiences in classrooms.
Integrated computational thinking
Aman’s team is also engaged in a research project to integrate CT at middle school level for students aged 11 to 14. This project focuses on the question “What does CT look like in the context of social studies, English language, and art classrooms?”
For this project, the team conducted three Delphi studies, and consequently created learning pathways for each subject, which teachers can use to bring CT into their classrooms. The pathways specify practices and sub-practices to engage students with CT, and are available on the project website. The image below exemplifies the CT integration pathways developed for the arts subject, where the relationship between art and data is explored from both directions: by using CT and data to understand and create art, and using art and artistic principles to represent and communicate data.
Computational thinking in the primary classroom
Aman’s work highlights the broad value of CT in education. However, to meaningfully integrate CT into the classroom, Aman suggests that we have to take a longitudinal view of the time and methods required to build teachers’ understanding and confidence with the fundamentals of CT, in a way that is aligned with their values and objectives. Aman argues that CT is really about thinking, and sometimes about computing, to support disciplinary learning in primary classrooms. Therefore, rather than focusing on integrating coding into the classroom, he proposes that we should instead talk about using CT practices as the building blocks that provide the foundation for incorporating computationally rich experiences in the classroom.
Our 2024 seminar series is on the theme of teaching programming, with or without AI. In this series, we explore the latest research on how teachers can best support school-age learners to develop their programming skills.
The UK Bebras Challenge is back and ready to accept entries from schools for its annual event, which runs from 6 to 17 November.
More than 3 million students from 59 countries took part in the Bebras Computational Thinking Challenge in 2022. In the UK alone, over 365,000 students participated. Read on to find out how you can get your school involved.
“This is now an annual event for our Year 5 and 6 students, and one of the things I actually love about it is the results are not always what you might predict. There are children who have a clear aptitude for these puzzles who find this is their opportunity to shine!”
Bebras is a free, annual challenge that helps schools introduce computational thinking to their students. No programming is involved, and it’s completely free for schools to enter. All Bebras questions are self-marking.
We’re making Bebras accessible by offering age-appropriate challenges for different school levels and a challenge tailored for visually impaired students. Schools can enter students from age 6 to 18 and know they’ll get interesting and challenging (but not too challenging) activities.
The winners of the Oxford University Computing Challenge 2023, with Professor Peter Millican at the OUCC Prize Day in the Raspberry Pi Foundation office.
What is the thinking behind Bebras?
We want young people to get excited about computing. Through Bebras, they will learn about computational and logical thinking by answering questions and solving problems.
Bebras questions are based on classic computing problems and are presented in a friendly, age-appropriate way. For example, an algorithm-based puzzle for learners aged 6 to 8 is presented in terms of a hungry tortoise finding an efficient eating path across a lawn; for 16- to 18-year-olds, a difficult problem based on graph theory asks students to sort out quiz teams by linking quizzers who know each other.
“This has been a really positive experience. Thank you. Shared results with Head and Head of Key Stage 3. Really useful for me when assessing Key Stage 4 options.”
– Secondary teacher, North Yorkshire
Can you solve our example Bebras puzzle?
Here’s a Bebras question for the Castors category (ages 8 to 10) from 2021. You will find the answer at the end of this blog.
Cleaning
A robot picks up litter.
The robot moves to the closest piece of litter and picks it up.
It then moves to the next closest piece of litter and picks it up.
It carries on in this way until all the litter has been picked up.
Question: Which kind of litter will the robot pick up last?
How do I get my school involved in Bebras?
The Bebras challenge for UK schools takes place from 6 to 17 November. Register at bebras.uk/admin to get free access to the challenge.
By registering, you also get access to the Bebras back catalogue of questions, from which you can build your own quizzes to use in your school at any time during the year. All the quizzes are self-marking, and you can download your students’ results for your mark book. Schools have reported using these questions for end-of-term activities, lesson starters, and schemes of lessons about computational thinking.
Puzzle answer
The answer to the example puzzle is:
The image below shows the route the robot takes by following the instructions:
Today we share a guest blog from Chris Roffey, who manages the UK Bebras Challenge, a computational thinking challenge we run every year in partnership with the University of Oxford.
Bebras is a free annual challenge that helps schools introduce computational thinking to their learners through online, self-marking tasks. Taking part in Bebras, students solve accessible, interesting problems using their developing computational thinking skills. No programming is involved in taking part. The UK challenge is for school students aged 6 to 18 years old, with a special category for students with severe visual impairments.
Bebras means ‘beaver’
Preparing the UK Bebras Challenge for schools
While UK schools take part in Bebras throughout two weeks in November, for me the annual cycle starts much earlier. May is the time of the annual Bebras international workshop where the year’s new tasks get decided. In 2022, 60 countries were represented — some online, some in person. For nearly a week, computer scientists and computing teachers met to discuss and work on the new cycle’s task proposals submitted by participating countries a little earlier.
After the workshop, in collaboration with teams from other European countries, the UK Bebras team chose its task sets and then worked to localise, copy-edit, and test them to get them ready for schools participating in Bebras during November. From September, schools across the UK create accounts for their students, with over 360,000 students ultimately taking part in 2022. All in all, more than 3 million students from 59 countries took part in the 2022/2023 Bebras challenge cycle.
An invitation to the Oxford University Computing Challenge
In this cycle, the UK Bebras partnership between the Raspberry Pi Foundation and the University of Oxford has been extended to include the Oxford University Computing Challenge (OUCC). This is an invitation-based, online coding challenge for students aged 10 to 18, offered in the UK as well as Australia, Jamaica, and China. We invited the students with the top 10% best results in the UK Bebras challenge to take part in the OUCC — an exciting opportunity for them.
In contrast to Bebras, which doesn’t require participants to do any coding, the OUCC asks students to create code to solve computational thinking problems. This requires students to prepare and challenges them to develop their computational thinking skills further. The two younger age groups, 10- to 14-year-olds, solve problems using the Blockly programming language. The older two age groups can use one of the 11 programming languages that Bebras supports, including all the most common ones taught in UK schools.
Over 20,000 Bebras participants took up the invitation to the first round of the OUCC in the third week of January. Then in March, the top 20 participants from each of the four OUCC age groups took part in the final round. The finalists all did amazingly well. In the first round, many of them had solved all the available tasks correctly, even though the expectation is that participants only try to solve as many as they can within the round’s time limit. In the final round, a few of the finalists managed to repeat this feat with the even more advanced tasks — which is, in modern parlance, literally impossible!
Celebrating together
Many of the participants are about to take school exams, so the last stage of the annual cycle — the prize winners’ celebration day— takes place when the exam period has ended. This year we are holding this celebration on Friday 30 June at the Raspberry Pi Foundation’s headquarters in Cambridge. It will be a lovely way to finish the annual Bebras cycle and I am looking forward to it immensely.
This November, teachers across the UK helped 367,023 learners participate in the annual free UK Bebras Challenge of computational thinking.
‘Bebras’ is Lithuanian and means ‘beaver’.
We support this challenge in the UK, together with Oxford University, and Bebras Challenges run across the world, with more than 3 million learners from schools in 54 countries taking part in 2021. Bebras encourages a love of computational thinking, computer science, and problem solving, especially among learners who haven’t yet realised they have these skills.
More and more schools are taking part in the UK Bebras Challenge
Nearly every year since 2013, more UK schools have been participating in Bebras. We think this is because for teachers, registering and entering learners is easy, the online system does all the marking automatically, and teachers receive comprehensive results that can be helpful for assessment.
The computational thinking problems within Bebras are tailored for different age groups, use clear language, and are accessible to colour-blind learners. There is also a challenge for learners with visual impairments. Teachers who run Bebras in their schools seem to love it and regularly tell colleagues about it.
“Our pupils really enjoy [Bebras] and I find it so helpful to teach computational thinking with real-life strategies. We also find the data and information about our pupils’ performance extremely helpful.” — Teacher in London
Age-appropriate computational thinking problems
In the UK Bebras Challenge, the younger learners aged 6 to 10 usually take part in teams and have plenty of time to discuss how to solve the computational thinking problems they are presented with.
Older learners, aged 10 to 18, try to solve as many problems as they can in 40 minutes. The problems they are presented with start off easy and get increasingly difficult. The 10% of participants who solve the most problems are then invited to take part in the Oxford University Computing Challenge (OUCC), an annual programming challenge.
Year-round free resources for teachers
Although the OUCC is only open to some Bebras participants, all of the OUCC problems are archived and teachers registered with Bebras can use them to make auto-marking quizzes for all of their learners at any time of the year. Part of the goal of UK Bebras is to support teachers with free resources, and the UK Bebras online quizzes facility now has computational thinking tasks from the Bebras archive, plus auto-marking Blockly programming problems and text-based programming problems, which can be solved using commonly taught programming languages.
The UK Bebras Challenge is back and ready to accept entries from schools for its annual event from 7 to 18 November.
More than 3 million students from 54 countries took part in the Bebras Challenge in 2021. Read on to find out how you can get your school involved.
What is Bebras?
Bebras a free, annual challenge that helps schools introduce computational thinking to their students. No programming is involved, and it’s completely free for schools to take part. All Bebras questions are self-marking. Schools can enter students from age 6 to 18 and know they’ll get interesting and challenging (but not too challenging) activities.
“This has been a really positive experience. Thank you. Shared results with head and Head of KS3. Really useful for me when assessing KS4 options.” – Secondary teacher, North Yorkshire
We’re making Bebras accessible by offering age-appropriate challenges for different school levels, and a challenge tailored for visually impaired students.
What is the idea behind Bebras?
We want young people to get excited about computing. Through Bebras, they will learn about computational and logical thinking by answering questions and solving puzzles.
Bebras questions are based on classic computing problems and presented in friendly, age-appropriate contexts. For example, an algorithm-based puzzle for learners aged 6 to 8 is presented in terms of a hungry tortoise find an efficient eating path across a lawn; for 16- to 18-year-olds, a difficult question based on graph theory asks students to sort out some quiz teams by linking quizzers who know each other.
Can you solve the example puzzle?
Here’s a question from the 2021 challenge for the Junior category (ages 10 to 12). You’ll find the correct answer at the bottom of this blog post.
Science Fair
Bebras High School is having a science fair.
All the events in the fair need to follow a specific order, and only one event can be held at a time.
The diagram below shows all the events that must be included in the flow of the science fair.
The arrows between events indicate that the event the arrow is drawn from has to occur before the event the arrow points to. For example, ‘Social Interaction’ can only happen after both ‘Opening Speeches’ and ‘Project Presentations’ have finished.
Question: What is the correct order of events for the science fair?
How do I get my school involved?
The Bebras challenge for UK schools takes place from 7 to 18 November. Register at bebras.uk/admin to get full access to the challenge.
By registering, you also get access to the back catalogue of questions, from which you can build your own quizzes to use in your school at any time during the year. All the quizzes are self-marking, and you can download your students’ results for your mark book. Schools have reported using the back catalogue of questions for end-of-term activities, lesson starters, and schemes of lessons about computational thinking.
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.
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.
Some learners may think machine learning (ML) is like a magic box, but ML is not magic. Research is needed to find out what mental models are most useful for learning about ML.
Our seminars on teaching AI, ML, and data science
We’re currently partnering with The Alan Turing Institute to host a series of free research seminars about how to teach artificial intelligence (AI) and data science to young people.
The seminar with Matti and Henriikka, the third one of the series, was very well attended. Over 100 participants from San Francisco to Rajasthan, including teachers, researchers, and industry professionals, contributed to a lively and thought-provoking discussion.
Matti Tedre
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).
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.
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.
There are major differences between rule-driven computational thinking (CT 1.0) and data-driven computational thinking (CT 2.0), which impact what computing education needs to take into account (Tedre & Vartiainen, 2021).
Machine learning models are created iteratively using training data and must be cross-validated with test data. A tiny change in the data provided can make a model useless. We rarely know exactly why the output of an ML model is as it is, and we cannot explain each individual decision that the model might have made. When evaluating a machine learning system, we can only say how well it works based on statistical confidence and efficiency.
Machine learning education must cover ethical and societal implications
The ethical and societal implications of computer science have always been important for students to understand. But machine learning models open up a whole new set of topics for teachers and students to consider, because of these models’ reliance on large datasets, the difficulty of explaining their decisions, and their usefulness for automating very complex processes. This includes privacy, surveillance, diversity, bias, job losses, misinformation, accountability, democracy, and veracity, to name but a few.
I see the shift in problem solving approach as a chance to strengthen the teaching of computing in general, because it opens up opportunities to teach about systems, uncertainty, data, and society.
Jane Waite
Teaching machine learning: the challenges of magic boxes and new mental models
For teaching classical rule-driven programming, much time and effort has been put into researching learners’ understanding of what a program will do when it is run. This kind of understanding is called a learner’s mental model or notional machine. An approach teachers often use to help students develop a useful mental model of a program is to hide the detail of how the program works and only gradually reveal its complexity. This approach is described with the metaphor of hiding the detail of elements of the program in a box.
Data-driven models in machine learning systems are highly complex and make little sense to humans. Therefore, they may appear like magic boxes to students. This view needs to be banished. Machine learning is not magic. We have just not figured out yet how to explain the detail of data-driven models in a way that allows learners to form useful mental models.
An example of a representation of a machine learning model in TensorFlow, an online machine learning tool (Tedre & Vartiainen, 2021).
Some existing ML tools aim to help learners form mental models of ML, for example through visual representations of how a neural network works (see above). But these explanations are still very complex. Clearly, we need to find new ways to help learners of all ages form useful mental models of machine learning, so that teachers can explain to them how machine learning systems work and banish the view that machine learning is magic.
Some tools and teaching approaches for ML education
Matti and Henriikka’s team piloted different tools and pedagogical approaches with different age groups of learners. In terms of tools, since large amounts of data are needed for machine learning projects, our presenters suggested that tools that enable lots of data to be easily collected are ideal for teaching activities. Media-rich education tools provide an opportunity to capture still images, movements, sounds, or sense other inputs and then use these as data in machine learning teaching activities. For example, to create a machine learning–based rock-paper-scissors game, students can take photographs of their hands to train a machine learning model using Google Teachable Machine.
Photos of hands are used to train a 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 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.
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.
In the free Bebras Challenge, your students get to practise their computational thinking skills while solving a set of accessible, puzzling, and engaging tasks over 40 minutes. It’s tailored for age groups from 6 to 18.
“I just want to say how much the children are enjoying this competition. It is the first year we have entered, and I have students aged 8 to 11 participating in my Computing lessons, with some of our older students also taking on the challenges. It is really helping to challenge their thinking, and they are showing great determination to try and complete each task!”
– A UK-based teacher
Ten key facts about Bebras
It’s free!
The challenge takes place in school, and it’s a great whole-school activity
It’s open to learners aged 6 to 18, with activities for different age groups
The challenge is made up of a set of short tasks, and completing it takes 40 minutes
The closing date for registering your school is 4 November
Your learners need to complete the challenge between 8 and 19 November 2021
All the marking is done for you (hurrah!)
You’ll receive the results and answers the week after the challenge ends, so you can go through them with your learners and help them learn more
The tasks are logical thinking puzzles, so taking part does not require any computing knowledge
There are practice questions you can use to help your learners prepare for the challenge, and throughout the year to help them practice their computational thinking
Do you want to support your learners to take on the Bebras Challenge? Then register your school today!
Remember to sign up by 4 November!
The benefits of Bebras
Bebras is an international challenge that started in Lithuania in 2004 and has grown into a worldwide event. The UK became involved in Bebras for the first time in 2013, and the number of participating students has increased from 21,000 in the first year to more than half a million over the last two years! Internationally, nearly 2.5 million learners took part in 2020 despite the disruptions to schools.
On the left, a bracelet design from an activity for ages 10–12. On the right, a password checker from an activity for ages 14–16.
Bebras, brought to you in the UK by us and Oxford University, is a great way to give your learners of all age groups a taste of the principles behind computing by engaging them in fun problem-solving activities. The challenge results highlight computing principles, so Bebras can be educational for you as a teacher too.
Throughout the year, questions from previous years of the challenge are available to registered teachers on the bebras.uk website, where you can create self-marking quizzes to help you deliver the computational thinking part of the curriculum for your classes.
The Bebras Challenge is a great way for your students to practise their computational thinking skills while solving exciting, accessible, and puzzling questions. Usually this 40-minute challenge would take place in the classroom. However, this year for the first time, your students can participate from home too!
If your students haven’t entered before, now is a great opportunity for them to get involved: they don’t need any prior knowledge.
Do you have any students who are up for tackling the Bebras Challenge? Then register your school today!
What you need to know about the Bebras Challenge
It’s a great whole-school activity open to students aged 6 to 18, in different age group categories.
It’s completely free!
The closing date for registering your school is 30 October.
Let your students complete the challenge between 2 and 13 November 2020.
The challenge is made of a set of short tasks, and completing it takes 40 minutes.
The challenge tasks focus on logical thinking and do not require any prior knowledge of computer science.
There are practice questions to help your students prepare for the challenge.
This year, students can take part at home (please note they must still be entered through their school).
All the marking is done for you! The results will be sent to you the week after the challenge ends, along with the answers, so that you can go through them with your students.
“Thank you for another super challenge. It’s one of the highlights of my year as a teacher. Really, really appreciate the high-quality materials, website, challenge, and communication. Thank you again!”
– A UK-based teacher
Support your students to develop their computational thinking skills with Bebras materials
Bebras is an international challenge that started in Lithuania in 2004 and has grown into an international event. The UK became involved in Bebras for the first time in 2013, and the number of participating students has increased from 21,000 in the first year to more than 260,000 last year! Internationally, nearly 3 million learners took part in 2019.
Bebras is a great way to engage your students of all ages in problem-solving and give them a taste of what computing is all about. In the challenge results, computing principles are highlighted, so Bebras can be educational for you as a teacher too.
The annual Bebras Challenge is only one part of the equation: questions from previous years are available as a resource that you can use to create self-marking quizzes for your classes. You can use these materials throughout the year to help you to deliver the computational thinking part of your curriculum!
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.
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.
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.
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.
Computational thinking (CT) comprises a set of skills that are fundamental to computing and being taught in more and more schools across the world. There has been much debate about the details of what CT is and how it should be approached in education, particularly for younger students.
In our research seminar this week, we were joined by María Zapata Cáceres from the Universidad Rey Juan Carlos in Madrid. María shared research she and her colleagues have done around CT. Specifically, she presented work on how we can understand what CT skills young children are developing. Building on existing work on assessing CT, she and her colleagues have developed a reliable test for CT skills that can be used with children as young as 5.
Why do we need to test computational thinking?
Until we can assess something, María argues, we don’t know what children have or haven’t learned or what they are capable of. While testing is often associated with the final stages in learning, in order to teach something well, educators need to understand where their students’ skills are to know what they are aiming for them to learn. With CT being taught in increasing numbers of schools and in many different ways, María argues that it is imperative to be able to test learners on it.
How was the test developed?
One of the key challenges for assessing learning is knowing whether the activities or questions you present to learners are actually testing what you intend them to. To make sure this is the case, assessments go through a process of validation: they are tried out with large groups to ensure that the results they give are valid. María’s and her colleagues’ CT test for beginners is based on a CT test developed by researcher Marcos Román González. That test had been validated, but since it is aimed at 10- to 16-year-olds, María and her colleagues needed to adapt it for younger children and then validate the adapted rest.
Developing the first version
The new test for beginners consists of 25 questions, each of which has four possible responses, which are to be answered within 40 minutes. The questions are of two types: one that involves using instructions to draw on a canvas, and one that involves moving characters through mazes. Since the test is for younger children, María and her colleagues designed it so it involves as little text as possible to reduce the need for reading; instead the test includes self-explanatory symbols.
Developing a second version based on feedback
To refine the test, the researchers consulted with a group of 45 experts about the difficulty of the questions and the test’s length of the test. The general feedback was very positive.
Drawing on the experts’ feedback, María and her colleagues made some very specific improvements to the test to make it more appropriate for younger children:
The improve test mandates that an verbal explanation be given to children at the start, to make sure they clearly understand how to take the test and don’t have to rely on reading the instructions.
In some areas, the researchers added written explanations where experts had identified that questions contained ambiguity that could cause the children to misinterpret them.
A key improvement was to adapt the grids in the original test to include pathways between each box of the maze. It was found that children could misinterpret the maze, for example as allowing diagonal moves between squares; the added pathways are visual cues that it clear that this is not possible.
Validating the test
After these improvements, the test was validated with 299 primary school students aged 5-12. To assess the differences the improvements might make, the students were given different version of the test. María and her colleagues found that the younger students benefited from the improvements, and the improvements made the test more reliable for testing students’ computational thinking: students made fewer errors due to ambiguity and misinterpretation.
Statistical analysis of the test results showed that the improved version of the test is reliable and can be used with confidence to assess the skills of younger children.
What can you use this test for?
Firstly, the test is a tool for educators who want to assess the skills young people have and develop over time. Secondly, the test is also valuable for researchers. It can be used to perform projects that evaluate the outcomes of different approaches to teaching computational thinking, as well as projects investigating the effectiveness of specific learning resources, because the test can be given to children before and again after they engage with the resources.
Assessment is one of the many tools educators use to shape their teaching and promote the learning of their students, and tools like this CT test developed by María and her colleagues allow us to better understand what children are learning.
Our final seminar of this series takes place Tuesday 28 July before we take a break for the summer. In the session, we will explore gender balance in computing, led by Katharine Childs, who works on the Gender Balance in Computing research project at the Raspberry Pi Foundation. You can find out more and sign up to attend for free on our Computing Education Research Seminars page.
‘Bebras’ means ‘beaver’ in Lithuanian; Prof. Valentina Dagiene named the competition after this hard-working, intelligent, and lively animal.
The Raspberry Pi Foundation has teamed up with Oxford University to support the Bebras Challenge, which every November invites students to use computational thinking to solve classical computer science problems re-worked into accessible and interesting questions.
Bebras is:
Open to students aged 6 to 18 (and it’s quite good fun for adults too)
Why should I get involved in the Bebras Challenge?
Bebras is an international challenge that started in Lithuania in 2004. Participating in Bebras is a great way to engage students of all ages in the fun of problem solving, and to give them an insight into computing and what it’s all about. Computing principles are highlighted in the answers, so Bebras can be quite educational for teachers too.
The UK became involved in Bebras for the first time in 2013, and the numbers of participating students have increased from 21,000 in the first year to 202,000 last year. Internationally, more than 2.78 million learners took part in 2018.
Bebras runs from 4 to 15 November this year
The challenge takes 40 minutes to complete
Use the practice questions on the website to get your students used to what they’ll encounter in challenge
All the marking is done for you
The results are sent to you the week after the challenge ends, along with an answer booklet, so that you can go through the answers with your learners
The highest-achieving students in each age group are invited to Oxford University to take part in the second round over a weekend in January
Support computational thinking at your school throughout the year with Bebras
The annual challenge is only one part of the equation: questions from previous years are available as a resource with which teachers can create self-marking quizzes to use with their classes! This means you can support the computational thinking part of the school curriculum throughout the whole year.
You can also use the Bebras App to try 100 computational thinking problems, and download sets of Bebras Cards for primary schools.
Follow @bebrasuk to stay up to date with what’s on offer for you.
Um dir ein optimales Erlebnis zu bieten, verwenden wir Technologien wie Cookies, um Geräteinformationen zu speichern und/oder darauf zuzugreifen. Wenn du diesen Technologien zustimmst, können wir Daten wie das Surfverhalten oder eindeutige IDs auf dieser Website verarbeiten. Wenn du deine Einwillligung nicht erteilst oder zurückziehst, können bestimmte Merkmale und Funktionen beeinträchtigt werden.
Funktional
Immer aktiv
Die technische Speicherung oder der Zugang ist unbedingt erforderlich für den rechtmäßigen Zweck, die Nutzung eines bestimmten Dienstes zu ermöglichen, der vom Teilnehmer oder Nutzer ausdrücklich gewünscht wird, oder für den alleinigen Zweck, die Übertragung einer Nachricht über ein elektronisches Kommunikationsnetz durchzuführen.
Vorlieben
Die technische Speicherung oder der Zugriff ist für den rechtmäßigen Zweck der Speicherung von Präferenzen erforderlich, die nicht vom Abonnenten oder Benutzer angefordert wurden.
Statistiken
Die technische Speicherung oder der Zugriff, der ausschließlich zu statistischen Zwecken erfolgt.Die technische Speicherung oder der Zugriff, der ausschließlich zu anonymen statistischen Zwecken verwendet wird. Ohne eine Vorladung, die freiwillige Zustimmung deines Internetdienstanbieters oder zusätzliche Aufzeichnungen von Dritten können die zu diesem Zweck gespeicherten oder abgerufenen Informationen allein in der Regel nicht dazu verwendet werden, dich zu identifizieren.
Marketing
Die technische Speicherung oder der Zugriff ist erforderlich, um Nutzerprofile zu erstellen, um Werbung zu versenden oder um den Nutzer auf einer Website oder über mehrere Websites hinweg zu ähnlichen Marketingzwecken zu verfolgen.
