An AI literacy framework

From her research, Shuchi has developed a framework for teaching about AI that is structured as four interlocking components, each representing a key area of understanding:

• Basic understanding of AI, which refers to foundational knowledge such as what AI is, types of AI systems, and the capabilities of AI technologies
• Ethics and human–AI relationship, which includes the role of humans in regard to AI, ethical considerations, and public perceptions of AI
• Computational thinking/literacy, which relates to how AI works, including building AI applications and training machine learning models
• Data literacy, which addresses the importance of data, including examining data features, data visualisation, and biases

This framework shows the multifaceted nature of AI literacy, which involves an understanding of both technical aspects and ethical and societal considerations. 

Shuchi emphasised the importance of learning about AI ethics, highlighting the topic of bias. There are many ways that bias can be embedded in applications of AI and machine learning, including through the data sets that are used and the design of machine learning models. Shuchi discussed supporting learners to engage with the topic through exploring bias in facial recognition software, sharing activities and resources to use in the classroom that can prompt meaningful discussion, such as this talk by Joy Buolamwini. She also highlighted the Kapor Foundation’s Responsible AI and Tech Justice: A Guide for K–12 Education, which contains questions that educators can use with learners to help them to carefully consider the ethical implications of AI for themselves and for society. 

Computational thinking and AI

In computer science education, computational thinking is generally associated with traditional rule-based programming — it has often been used to describe the problem-solving approaches and processes associated with writing computer programs following rule-based principles in a structured and logical way. However, with the emergence of machine learning, Shuchi described a need for computational thinking frameworks to be expanded to also encompass data-driven, probabilistic approaches, which are foundational for machine learning. This would support learners’ understanding and ability to work with the models that increasingly influence modern technology.

Example activities from research studies

Shuchi shared that a variety of pedagogies have been used in recent research projects on AI education, ranging from hands-on experiences, such as using APIs for classification, to discussions focusing on ethical aspects. You can find out more about these pedagogies in her award-winning paper Teaching AI to K-12 Learners: Lessons, Issues and Guidance. This plurality of approaches ensures that learners can engage with AI and machine learning in ways that are both accessible and meaningful to them.

Shuchi shared examples of activities from two research projects that she has led:

• CS Frontiers engaged high school students in a number of activities involving using NetsBlox and accessing real-world data sets. For example, in one activity, students participated in data science activities such as creating data visualisations to answer questions about climate change. 
• AI & Cybersecurity for Teens explored approaches to teaching AI and machine learning to 13- to 15-year-olds through the use of cybersecurity scenarios. The project aimed to provide learners with insights into how machine learning models are designed, how they work, and how human decisions influence their development. An example activity guided students through building a classification model to analyse social media accounts to determine whether they may be bot accounts or accounts run by a human.

Closing thoughts

At the end of her talk, Shuchi shared some final thoughts addressing teaching about AI to K–12 learners: 

• AI learning requires contextualisation: Think about the data sets, ethical issues, and examples of AI tools and systems you use to ensure that they are relatable to learners in your context.
• AI should not be a solution in search of a problem: Both teachers and learners need to be educated about AI before they start to use it in the classroom, so that they are informed consumers.

Join our next seminar

In our current seminar series, we are exploring teaching about AI and data science. Join us at our next seminar on Tuesday 11 March at 17:00–18:30 GMT to hear Lukas Höper and Carsten Schulte from Paderborn University discuss supporting middle school students to develop their data awareness. 

To sign up and take part in the seminar, click the button below — we will then send you information about joining. We hope to see you there.

I want to join the next seminarThe schedule of our upcoming seminars is online. You can catch up on past seminars on our previous seminars and recordings page.

The post Teaching about AI in K–12 education: Thoughts from the USA appeared first on Raspberry Pi Foundation.

In our monthly seminar in September, Arto and Juho presented their research on using AI tools to provide personalised learning experiences and automated feedback to help requests, as well as their findings on teaching students how to write effective prompts for generative AI systems. While their research focuses primarily on undergraduate students — given that they teach such students — many of their findings have potential relevance for primary and secondary (K-12) computing education. 

Generative AI consists of algorithms that can generate new content, such as text, code, and images, based on the input received. Ever since large language models (LLMs) such as ChatGPT and Copilot became widely available, there has been a great deal of attention on how to use this technology in computing education. 

Arto and Juho described generative AI as one of the fastest-moving topics they had ever worked on, and explained that they were trying to see past the hype and find meaningful uses of LLMs in their computing courses. They presented three studies in which they used generative AI tools with students in ways that aimed to improve the learning experience. 

Using generative AI tools to create personalised programming exercises

An important strand of computing education research investigates how to engage students by personalising programming problems based on their interests. The first study in Arto and Juho’s research  took place within an online programming course for adult students. It involved developing a tool that used GPT-4 (the latest version of ChatGPT available at that time) to generate exercises with personalised aspects. Students could select a theme (e.g. sports, music, video games), a topic (e.g. a specific word or name), and a difficulty level for each exercise.

Arto, Juho, and their students evaluated the personalised exercises that were generated. Arto and Juho used a rubric to evaluate the quality of the exercises and found that they were clear and had the themes and topics that had been requested. Students’ feedback indicated that they found the personalised exercises engaging and useful, and preferred these over randomly generated exercises. 

Arto and Juho also evaluated the personalisation and found that exercises were often only shallowly personalised, however. In shallow personalisations, the personalised content was added in only one sentence, whereas in deep personalisations, the personalised content was present throughout the whole problem statement. It should be noted that in the examples taken from the seminar below, the terms ‘shallow’ and ‘deep’ were not being used to make a judgement on the worthiness of the topic itself, but were rather describing whether the personalisation was somewhat tokenistic or more meaningful within the exercise. 

The findings suggest that this personalised approach may be particularly effective on large university courses, where instructors might struggle to give one-on-one attention to every student. The findings further suggest that generative AI tools can be used to personalise educational content and help ensure that students remain engaged. 

How might all this translate to K-12 settings? Learners in primary and secondary schools often have a wide range of prior knowledge, lived experiences, and abilities. Personalised programming tasks could help diverse groups of learners engage with computing, and give educators a deeper understanding of the themes and topics that are interesting for learners. 

Responding to help requests using large language models

Another key aspect of Alto and Juho’s work is exploring how LLMs can be used to generate responses to students’ requests for help. They conducted a study using an online platform containing programming exercises for students. Every time a student struggled with a particular exercise, they could submit a help request, which went into a queue for a teacher to review, comment on, and return to the student. 

The study aimed to investigate whether an LLM could effectively respond to these help requests and reduce the teachers’ workloads. An important principle was that the LLM should guide the student towards the correct answer rather than provide it. 

The study used GPT-3.5, which was the newest version at the time. The results found that the LLM was able to analyse and detect logical and syntactical errors in code, but concerningly, the responses from the LLM also addressed some non-existent problems! This is an example of hallucination, where the LLM outputs something false that does not reflect the real data that was inputted into it. 

The finding that LLMs often generated both helpful and unhelpful problem-solving strategies suggests that this is not a technology to rely on in the classroom just yet. Arto and Juho intend to track the effectiveness of LLMs as newer versions are released, and explained that GPT-4 seems to detect errors more accurately, but there is no systematic analysis of this yet. 

In primary and secondary computing classes, young learners often face similar challenges to those encountered by university students — for example, the struggle to write error-free code and debug programs. LLMs seemingly have a lot of potential to support young learners in overcoming such challenges, while also being valuable educational tools for teachers without strong computing backgrounds. Instant feedback is critical for young learners who are still developing their computational thinking skills — LLMs can provide such feedback, and could be especially useful for teachers who may lack the resources to give individualised attention to every learner. Again though, further research into LLM-based feedback systems is needed before they can be implemented en-masse in classroom settings in the future. 

Teaching students how to prompt large language models

Finally, Arto and Juho presented a study where they introduced the idea of ‘Prompt Problems’: programming exercises where students learn how to write effective prompts for AI code generators using a tool called Promptly. In a Prompt Problem exercise, students are presented with a visual representation of a problem that illustrates how input values will be transformed to an output. Their task is to devise a prompt (input) that will guide an LLM to generate the code (output) required to solve the problem. Prompt-generated code is evaluated automatically by the Promptly tool, helping students to refine the prompt until it produces code that solves the problem.

Feedback from students suggested that using Prompt Problems was a good way for them to gain experience of using new programming concepts and develop their computational thinking skills. However, students were frustrated that bugs in the code had to be fixed by amending the prompt — it was not possible to edit the code directly. 

How these findings relate to K-12 computing education is still to be explored, but they indicate that Prompt Problems with text-based programming languages could be valuable exercises for older pupils with a solid grasp of foundational programming concepts. 

Balancing the use of AI tools with fostering a sense of community

At the end of the presentation, Arto and Juho summarised their work and hypothesised that as society develops more and more AI tools, computing classrooms may lose some of their community aspects. They posed a very important question for all attendees to consider: “How can we foster an active community of learners in the generative AI era?” 

In our breakout groups and the subsequent whole-group discussion, we began to think about the role of community. Some points raised highlighted the importance of working together to accurately identify and define problems, and sharing ideas about which prompts would work best to accurately solve the problems. 

As AI technology continues to evolve, its role in education will likely expand. There was general agreement in the question and answer session that keeping a sense of community at the heart of computing classrooms will be important. 

Further resources

The Raspberry Pi Computing Education Research Centre and Faculty of Education at the University of Cambridge have recently published a teacher guide on the use of generative AI tools in education. The guide provides practical guidance for educators who are considering using generative AI tools in their teaching. 

Join our next seminar

In our current seminar series, we are exploring how to teach programming with and without AI technology. Join us at our next seminar on Tuesday, 12 November at 17:00–18:30 GMT to hear Nicholas Gardella (University of Virginia) discuss the effects of using tools like GitHub Copilot on the motivation, workload, emotion, and self-efficacy of novice programmers. To sign up and take part in the seminar, click the button below — we’ll then send you information about joining. We hope to see you there.

The schedule of our upcoming seminars is online. You can catch up on past seminars on our previous seminars and recordings page.

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We used a set of ten areas of opportunity to scaffold and prompt teachers to look for ways that Computing resources could be adapted, including making changes to the content or the context of lessons, and using pedagogical techniques such as collaboration and open-ended tasks. 

Today’s blog lays out our findings about how teachers can bring students’ identities into the classroom as an entry point for culturally responsive Computing teaching.

Collaborating with teachers

A group of twelve primary teachers, from schools spread across England, volunteered to participate in the study. The primary objective was for our research team to collaborate with these teachers to adapt two units of work about creating digital images and vector graphics so that they better aligned with the cultural contexts of their students. The research team facilitated an in-person, one-day workshop where the teachers could discuss their experiences and work in small groups to adapt materials that they then taught in their classrooms during the following term.

A shared focus on identity

As the workshop progressed, an interesting pattern emerged. Despite the diversity of schools and student populations represented by the teachers, each group independently decided to focus on the theme of identity in their adaptations. This was not a directive from the researchers, but rather a spontaneous alignment of priorities among the teachers.

The focus on identity manifested in various ways. For some teachers, it involved adding diverse role models so that students could see themselves represented in computing, while for others, it meant incorporating discussions about students’ own experiences into the lessons. However, the most compelling commonality across all groups was the decision to have students create a digital picture that represented something important about themselves. This digital picture could take many forms — an emoji, a digital collage, an avatar to add to a game, or even creating fantastical animals. The goal of these activities was to provide students with a platform to express aspects of their identity that were significant to them whilst also practising the skills to manipulate vector graphics or digital images.

Funds of identity theory

After the teachers had returned to their classrooms and taught the adapted lessons to their students, we analysed the digital pictures created by the students using funds of identity theory. This theory explains how our personal experiences and backgrounds shape who we are and what makes us unique and individual, and argues that our identities are not static but are continuously shaped and reshaped through interactions with the world around us. 

In the context of our study, this theory argues that students bring their funds of identity into their Computing classrooms, including their cultural heritage, family traditions, languages, values, and personal interests. Through the image editing and vector graphics activities, students were able to create what the funds of identity theory refers to as identity artefacts. This allowed them to explore and highlight the various elements that hold importance in their lives, illuminating different facets of their identities. 

Students’ funds of identity

The use of the funds of identity theory provided a robust framework for understanding the digital artefacts created by the students. We analysed the teachers’ descriptions of the artefacts, paying close attention to how students represented their identities in their creations.

1. Personal interests and values 

One significant aspect of the analysis centered around the personal interests and values reflected in the artefacts. Some students chose to draw on their practical funds of identity and create images about hobbies that were important to them, such as drawing or playing football. Others focused on existential  funds of identity and represented values that were central to their personalities, such as cool, chatty, or quiet.

2. Family and community connections

Many students also chose to include references to their family and community in their artefacts. Social funds of identity were displayed when students featured family members in their images. Some students also drew on their institutional funds, adding references to their school, or geographical funds, by showing places such as the local area or a particular country that held special significance for them. These references highlighted the importance of familial and communal ties in shaping the students’ identities.

3. Cultural representation

Another common theme was the way students represented their cultural backgrounds. Some students chose to highlight their cultural funds of identity, creating images that included their heritage, including their national flag or traditional clothing. Other students incorporated ideological aspects of their identity that were important to them because of their faith, including Catholicism and Islam. This aspect of the artefacts demonstrated how students viewed their cultural heritage as an integral part of their identity.

Implications for culturally responsive Computing teaching

The findings from this study have several important implications. Firstly, the spontaneous focus on identity by the teachers suggests that identity is a powerful entry point for culturally responsive Computing teaching. Secondly, the application of the funds of identity theory to the analysis of student work demonstrates the diverse cultural resources that students bring to the classroom and highlights ways to adapt Computing lessons in ways that resonate with students’ lived experiences.

However, we also found that teachers often had to carefully support students to illuminate their funds of identity. Sometimes students found it difficult to create images about their hobbies, particularly if they were from backgrounds with fewer social and economic opportunities. We also observed that when teachers modelled an identity artefact themselves, perhaps to show an example for students to aim for, students then sometimes copied the funds of identity revealed by the teacher rather than drawing on their own funds. These points need to be taken into consideration when using identity artefact activities. 

Finally, these findings relate to lessons about image editing and vector graphics that were taught to students aged 8- to 10-years old in England, and it remains to be explored how students in other countries or of different ages might reveal their funds of identity in the Computing classroom.

Moving forward with cultural responsiveness

The study demonstrated that when Computing teachers are given the opportunity to collaborate and reflect on their practice, they can develop innovative ways to make their teaching more culturally responsive. The focus on identity, as seen in the creation of identity artefacts, provided students with a platform to express themselves and connect their learning to their own lives. By understanding and valuing the funds of identity that students bring to the classroom, teachers can create a more equitable and empowering educational experience for all learners.

We’ve written about this study in more detail in a full paper and a poster paper, which will be published at the WiPSCE conference next week. 

We would like to thank all the researchers who worked on this project, including our collaborations with Lynda Chinaka from the University of Roehampton, and Alex Hadwen-Bennett from King’s College London. Finally, we are grateful to Cognizant for funding this academic research, and to the cohort of primary Computing teachers for their enthusiasm, energy, and creativity, and their commitment to this project.

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Sri Yash Tadimalla from the University of North Carolina and Dr Mary Lou Maher, Director of Research Community Initiatives at the Computing Research Association, are exploring how student identities affect their interaction with AI tools and their perceptions of the use of AI tools. They presented findings from two of their research projects in our March seminar.

How students interact with AI tools 

A common approach in research is to begin with a preliminary study involving a small group of participants in order to test a hypothesis, ways of collecting data from participants, and an intervention. Yash explained that this was the approach they took with a group of 25 undergraduate students on an introductory Java programming course. The research observed the students as they performed a set of programming tasks using an AI chatbot tool (ChatGPT) or an AI code generator tool (GitHub Copilot). 

The data analysis uncovered five emergent attitudes of students using AI tools to complete programming tasks: 

• Highly confident students rely heavily on AI tools and are confident about the quality of the code generated by the tool without verifying it
• Cautious students are careful in their use of AI tools and verify the accuracy of the code produced
• Curious students are interested in exploring the capabilities of the AI tool and are likely to experiment with different prompts 
• Frustrated students struggle with using the AI tool to complete the task and are likely to give up 
• Innovative students use the AI tool in creative ways, for example to generate code for other programming tasks

Whether these attitudes are common for other and larger groups of students requires more research. However, these preliminary groupings may be useful for educators who want to understand their students and how to support them with targeted instructional techniques. For example, highly confident students may need encouragement to check the accuracy of AI-generated code, while frustrated students may need assistance to use the AI tools to complete programming tasks.

An intersectional approach to investigating student attitudes

Yash and Mary Lou explained that their next research study took an intersectional approach to student identity. Intersectionality is a way of exploring identity using more than one defining characteristic, such as ethnicity and gender, or education and class. Intersectional approaches acknowledge that a person’s experiences are shaped by the combination of their identity characteristics, which can sometimes confer multiple privileges or lead to multiple disadvantages.

In the second research study, 50 undergraduate students participated in programming tasks and their approaches and attitudes were observed. The gathered data was analysed using intersectional groupings, such as:

• Students who were from the first generation in their family to attend university and female
• Students who were from an underrepresented ethnic group and female 

Although the researchers observed differences amongst the groups of students, there was not enough data to determine whether these differences were statistically significant.

Who thinks using AI tools should be considered cheating? 

Participating students were also asked about their views on using AI tools, such as “Did having AI help you in the process of programming?” and “Does your experience with using this AI tool motivate you to continue learning more about programming?”

The same intersectional approach was taken towards analysing students’ answers. One surprising finding stood out: when asked whether using AI tools to help with programming tasks should be considered cheating, students from more privileged backgrounds agreed that this was true, whilst students with less privilege disagreed and said it was not cheating.

This finding is only with a very small group of students at a single university, but Yash and Mary Lou called for other researchers to replicate this study with other groups of students to investigate further. 

You can watch the full seminar here:

Acknowledging differences to prevent deepening divides

As researchers and educators, we often hear that we should educate students about the importance of making AI ethical, fair, and accessible to everyone. However, simply hearing this message isn’t the same as truly believing it. If students’ identities influence how they view the use of AI tools, it could affect how they engage with these tools for learning. Without recognising these differences, we risk continuing to create wider and deeper digital divides. 

Join our next seminar

The focus of our ongoing seminar series is on teaching programming with or without AI. 

For our next seminar on Tuesday 16 April at 17:00 to 18:30 GMT, we’re joined by Brett A. Becker (University College Dublin), who will talk about how generative AI can be used effectively in secondary school programming education and how it can be leveraged so that students can be best prepared for continuing their education or beginning their careers. To take part in the seminar, click the button below to sign up, and we will send you information about how to join. We hope to see you there.

The schedule of our upcoming seminars is online. You can catch up on past seminars on our blog and on the previous seminars and recordings page.

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That’s why one of our areas of academic research aims to support Computing teachers to use culturally relevant pedagogy to design and deliver equitable learning experiences that enable all learners to enjoy and succeed in Computing and Computer Science at school. Our previous research projects within this area have involved developing guidelines for culturally relevant and responsive teaching, and exploring how a small group of primary and secondary Computing teachers used these guidelines in their teaching.

In our latest research study, funded by Cognizant, we worked with 13 primary school teachers in England on adapting computing lessons to incorporate culturally relevant and responsive principles and practices. Here’s an insight into the workshop we ran with them, and what the teachers and we have taken away from it.

Adapting lesson materials based on culturally relevant pedagogy

In the group of 13 England-based primary school Computing teachers we worked with for this study:

• One third were specialist primary Computing teachers, and the other two thirds were class teachers who taught a range of subjects
• Some acted as Computing subject lead or coordinator at their school
• Most had taught Computing for between three and five years 
• The majority worked in urban areas of England, at schools with culturally diverse catchment areas 

In November 2022, we held a one-day workshop with the teachers to introduce culturally relevant pedagogy and explore how to adapt two six-week units of computing resources.

The first part of the workshop was a collaborative, discussion-based professional development session exploring what culturally relevant pedagogy is. This type of pedagogy uses equitable teaching practices to:

• Draw on the breadth of learners’ experiences and cultural knowledge
• Facilitate projects that have personal meaning for learners
• Develop learners’ critical consciousness

The rest of the workshop day was spent putting this learning into practice while planning how to adapt two units of computing lessons to make them culturally relevant for the teachers’ particular settings. We used a design-based approach for this part of the workshop, meaning researchers and teachers worked collaboratively as equal stakeholders to decide on plans for how to alter the units.

We worked in four groups, each with three or four teachers and one or two researchers, focusing on one of two units of work from The Computing Curriculum for teaching digital skills: a unit on photo editing for Year 4 (ages 8–9), and a unit about vector graphics for Year 5 (ages 9–10).

In order to plan how the resources in these units of work could be made culturally relevant for the participating teachers’ contexts, the groups used a checklist of ten areas of opportunity. This checklist is a result of one of our previous research projects on culturally relevant pedagogy. Each group used the list to identify a variety of ways in which the units’ learning objectives, activities, learning materials, and slides could be adapted. Teachers noted down their ideas and then discussed them with their group to jointly agree a plan for adapting the unit.

By the end of the day, the groups had designed four really creative plans for:

• A Year 4 unit on photo editing that included creating an animal to represent cultural identity
• A Year 4 unit on photo editing that included creating a collage all about yourself 
• A Year 5 unit on vector graphics that guided learners to create their own metaverse and then add it to the class multiverse
• A Year 5 unit on vector graphics that contextualised the digital skills by using them in online activities and in video games

Outcomes from the workshop

Before and after the workshop, we asked the teachers to fill in a survey about themselves, their experiences of creating computing resources, and their views about culturally relevant resources. We then compared the two sets of data to see whether anything had changed over the course of the workshop.

After teachers had attended the workshop, they reported a statistically significant increase in their confidence levels to adapt resources to be culturally relevant for both themselves and others. 

Teachers explained that the workshop had increased their understanding of culturally relevant pedagogy and of how it could impact on learners. For example, one teacher said:

“The workshop has developed my understanding of how culturally adapted resources can support pupil progress and engagement. It has also highlighted how contextual appropriateness of resources can help children to access resources.” – Participating teacher

Some teachers also highlighted how important it had been to talk to teachers from other schools during the workshop, and how they could put their new knowledge into practice in the classroom:

“The dedicated time and value added from peer discourse helped make this authentic and not just token activities to check a box.” – Participating teacher

“I can’t wait to take some of the work back and apply it to other areas and subjects I teach.” – Participating teacher

What you can expect to see next from this project

After our research team made the adaptations to the units set out in the four plans made during the workshop, the adapted units were delivered by the teachers to more than 500 Year 4 and 5 pupils. We visited some of the teachers’ schools to see the units being taught, and we have interviewed all the teachers about their experience of delivering the adapted materials. This observational and interview data, together with additional survey responses, will be analysed by us, and we’ll share the results over the coming months.

In our next blog post about this work, we will delve into the fascinating realm of parental attitudes to culturally relevant computing, and we’ll explore how embracing diversity in the digital landscape is shaping the future for both children and their families. 

We’ve also written about this professional development activity in more detail in a paper to be published at the UKICER conference in September, and we’ll share the paper once it’s available.

Finally, we are grateful to Cognizant for funding this academic research, and to our cohort of primary computing teachers for their enthusiasm, energy, and creativity, and their commitment to this project.

The post Running a workshop with teachers to create culturally relevant Computing lessons appeared first on Raspberry Pi Foundation.

In our most recent research seminar, attendees heard about a research-based initiative called Data Education in Schools. The speakers, Kate Farrell and Professor Judy Robertson from the University of Edinburgh, Scotland, shared how this project aims to empower learners to develop data literacy skills and succeed in a data-driven world.

“Data literacy is the ability to ask questions, collect, analyse, interpret and communicate stories about data.”

– Kate Farrell & Prof. Judy Robertson

Being a data citizen

Scotland’s national curriculum does not explicitly mention data literacy, but the topic is embedded in many subjects such as Maths, English, Technologies, and Social Studies. Teachers in Scotland, particularly in primary schools, have the flexibility to deliver learning in an interdisciplinary way through project-based learning. Therefore, the team behind Data Education in Schools developed a set of cross-curricular data literacy projects. Educators and education policy makers in other countries who are looking to integrate computing topics with other subjects may also be interested in this approach.

The Data Education in Schools projects are aimed not just at giving learners skills they may need for future jobs, but also at equipping them as data citizens in today’s world. A data citizen can think critically, interpret data, and share insights with others to effect change.

Kate and Judy shared an example of data citizenship from a project they had worked on with a primary school. The learners gathered data about how much plastic waste was being generated in their canteen. They created a data visualisation in the form of a giant graph of types of rubbish on the canteen floor and presented this to their local council.

As a result, the council made changes that reduced the amount of plastic used in the canteen. This shows how data citizens are able to communicate insights from data to influence decisions.

A cycle for data literacy projects

Across its projects, the Data Education in Schools initiative uses a problem-solving cycle called the PPDAC cycle. This cycle is a useful tool for creating educational resources and for teaching, as you can use it to structure resources, and to concentrate on areas to develop learner skills.

The five stages of the cycle are: 

1. Problem: Identifying the problem or question to be answered
2. Plan: Deciding what data to collect or use to answer the question
3. Data: Collecting the data and storing it securely
4. Analysis: Preparing, modelling, and visualising the data, e.g. in a graph or pictogram
5. Conclusion: Reviewing what has been learned about the problem and communicating this with others 

Smaller data literacy projects may focus on one or two stages within the cycle so learners can develop specific skills or build on previous learning. A large project usually includes all five stages, and sometimes involves moving backwards — for example, to refine the problem — as well as forwards.

Data literacy for primary school learners

At primary school, the aim of data literacy projects is to give learners an intuitive grasp of what data looks like and how to make sense of graphs and tables. Our speakers gave some great examples of playful approaches to data. This can be helpful because younger learners may benefit from working with tangible objects, e.g. LEGO bricks, which can be sorted by their characteristics. Kate and Judy told us about one learner who collected data about their clothes and drew the results in the form of clothes on a washing line — a great example of how tangible objects also inspire young people’s creativity.

As learners get older, they can begin to work with digital data, including data they collect themselves using physical computing devices such as micro:bit microcontrollers or Raspberry Pi computers.

You can access the seminar slides here.

Free resources for primary (and secondary) schools

For many attendees, one of the highlights of the seminar was seeing the range of high-quality teaching resources for learners aged 3–18 that are part of the Data Education in Schools project. These include: 

• Data 101 videos: A set of 11 videos to help primary and secondary teachers understand data literacy better.
• Data literacy live lessons: Data-related activities presented through live video.
• Lesson resources: Lots of projects to develop learners’ data literacy skills. These are mapped to the Scottish primary and secondary curriculum, but can be adapted for use in other countries too.

More resources are due to be published later in 2023, including a set of prompt cards to guide learners through the PPDAC cycle, a handbook for teachers to support the teaching of data literacy, and a set of virtual data-themed escape rooms.  

You may also be interested in the units of work on data literacy skills that are part of The Computing Curriculum, our complete set of classroom resources to teach computing to 5- to 16-year-olds.

Join our next seminar on primary computing education

At our next seminar we welcome Aim Unahalekhaka from Tufts University, USA, who will share research about a rubric to evaluate young learners’ ScratchJr projects. If you have a tablet with ScratchJr installed, make sure to have it available to try out some activities. The seminar will take place online on Tuesday 6 June at 17.00 UK time, sign up now to not miss out.

To find out more about connecting research to practice for primary computing education, you can see a list of our upcoming monthly seminars on primary (K–5) teaching and learning and watch the recordings of previous seminars in this series.

The post Introducing data science concepts and skills to primary school learners appeared first on Raspberry Pi Foundation.

Gender inequality in the digital and computing sector

As the UN Women’s announcement for IWD 2023 says: “Growing inequalities are becoming increasingly evident in the context of digital skills and access to technologies, with women being left behind as the result of this digital gender divide. The need for inclusive and transformative technology and digital education is therefore crucial for a sustainable future.”

According to the UN, women currently hold only 2 in every 10 science, engineering, and information and communication technology jobs globally. Women are a minority of university-level students in science, technology, engineering, and mathematics (STEM) courses, at only 35%, and in information and communication technology courses, at just 3%. This is especially concerning since the WEF predicts that by 2050, 75% of jobs will relate to STEM.

We see this situation reflected in England: computer science is the secondary school subject with the largest gender gap at A level, with girls accounting for only 15% of students. That’s why over the past three years, we have run a research programme to trial ways to encourage more young women to study Computer Science. The programme, Gender Balance in Computing, has produced useful insights for designing equitable computing education around the world.

Who belongs in computing?

The UN says that “across countries, girls are systematically steered away from science and math careers. Teachers and parents, intentionally or otherwise, perpetuate biases around areas of education and work best ‘suited’ for women and men.” There is strong evidence to suggest that the representation of women and girls in computing can be improved by introducing them to computing role models such as female computing students or women in tech careers.

Presenting role models was central to the Belonging trial in our Gender Balance in Computing programme. One arm of this trial used resources developed by WISE called My Skills My Life to explore the effect of introducing role models into computing lessons for primary school learners. The trial provided opportunities for learners to speak to women who work in technology. It also offered a quiz to help learners identify their strengths and characteristics and to match them with role models who were similar to them, which research shows is more effective for increasing learners’ confidence.

Teachers who used the resources reported learners’ increased understanding of the types and range of technology jobs, and a widening of learners’ career aspirations. 

“Learning about computing makes me feel good because it helps me think more about what I want to be.” — Primary school learner in the Belonging trial

“When [the resources were] showing all of the females in the jobs, nobody went ‘Oh, I didn’t know that a female could do that’, but I think they were amazed by the role of jobs and the fact it was all females doing it.“ — Primary school teacher in the Belonging trial

Learning together to give everyone a voice

When teachers and students enter a computing classroom, they bring with them diverse social identities that affect the dynamics of the classroom. Although these dynamics are often unspoken, they can become apparent in which students answer questions or succeed visibly in activities. Without intervention, a dominant group of confident speakers can emerge, and students who are not in this dominant group may lose confidence in their abilities. When teachers set collaborative learning activities that use defined roles or structured discussions, this gives a wider range of students the opportunity to speak up and participate.

Pair programming is one such activity that has been used in research studies to improve learner attitudes and confidence towards computing. In pair programming, one learner is the ‘driver’.  They control the keyboard and mouse to write the code. The other learner is the ‘navigator’. They read out the instructions and monitor the code for errors. Learners swap roles regularly, so that both can participate equitably. The Pair Programming trial we conducted as part of Gender Balance in Computing explored the use of this teaching approach with students aged 8 to 11. Feedback from the teachers showed that learners found working in structured pairs engaging. 

“Even those who are maybe a little bit more reluctant… those who put their hands up today and said they still prefer to work independently, they are still all engaging quite clearly in that with their pair and doing it really, really well. However much they say they prefer working independently, I think they clearly showed how much they enjoy it, engage with it. And you know they’re achieving with it — so we should be doing this.” – Primary school teacher in the Pair Programming trial

Another collaborative teaching approach is peer instruction. In lessons that use peer instruction, students work in small groups to discuss the answer to carefully constructed multiple choice questions. A whole-class discussion then follows. In the Peer Instruction trial with learners aged 12 to 13 in our Gender Balance in Computing programme, we found that this approach was welcomed by the learners, and that it changed which learners offered answers and ideas. 

“I prefer talking in a group because then you get the other side of other people’s thoughts.” – Secondary school learner (female) in the Peer Instruction trial

“[…] you can have a bit of time to think for yourself then you can bounce ideas off other people.” – Secondary school learner (male) in the Peer Instruction trial

“I was very pleased that a lot of the girls were doing a lot of the talking.” – Secondary school teacher in the Peer Instruction trial

We need to do more, and sooner

Our Gender Balance in Computing research programme showed that no single intervention we trialled significantly increased girls’ engagement in computing or their intention to study it further. Combining several of the approaches we tested may be more impactful. If you’re part of an educational setting where you’d like to adopt multiple approaches at the same time, you can freely access the materials associated with the research programme (see our blog posts about the trials for links).

The research programme also showed that age matters: across Gender Balance in Computing, we observed a big difference in intent to study Computing between primary school and secondary school learners (data from ages 8–11 and 12–13). Fewer secondary school learners reported intent to study the subject further, and while this difference was apparent for both girls and boys, it was more marked for girls.

This finding from England is mirrored by a study the UN Women’s Gender Snapshot 2022 refers to: “A 2020 study of Filipina girls demonstrated that loss of interest in STEM subjects started as early as age 10, when girls began perceiving STEM careers as male-dominated and believing that girls are naturally less adept in STEM subjects. The relative lack of female STEM role models reinforced such perceptions.” That’s why it’s necessary that all primary school learners — no matter what their gender is — have a successful start in the computing classroom, that they encounter role models they can relate to, and that they are supported to engage in computing and creating with technology by their parents, teachers, and communities.

The Foundation’s vision is that every young person develops the knowledge, skills, and confidence to use digital technologies effectively, and to be able to critically evaluate these technologies and confidently engage with technological change. While making changes inside the computing classroom will be beneficial for gender equality, this is just one aspect of building an equitable digital future. We all need to contribute to creating a world where innovation and technology support gender equity.

What do you think is needed?

In all our work, we make sure gender equity is at the forefront, whether that’s in programmes we run for young people, in resources we create for schools, or in partnerships we have, such as with Pratham Education Foundation in India or Team4Tech and Kenya Connect in Wamunyu, Kenya. Computing education is a global challenge, and we are proud to be part of a community that is committed to making it equitable.

This IWD, we invite you to share your thoughts on what equitable computing education means to you, and what you think is needed to achieve it, whether that’s in your school or club, in your local community, or in your country. 

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We are hearing from a diverse range of speakers in our current series of monthly online research seminars focused on primary (K-5) computing education. Many of them work closely with educators to translate research findings into classroom practice to make sure that all our younger learners have positive first experiences of learning computing. An important goal of their research is to impact the development of pedagogy, resources, and professional development to support educators to deliver computing concepts with confidence.

Variables in computing and mathematics

Dr Katie Rich (American Institutes of Research) and Carla Strickland (UChicago STEM Education) are both part of a team that worked on a research project called Everyday Computing, which aims to integrate computational thinking into primary mathematics lessons. A key part of the Everyday Computing project was to develop coherent learning resources across a number of school years. During the seminar, Katie and Carla presented on a study in the project that revolved around teaching variables in Grade 3 and 4 (age 8 to 10) by linking this computing concept to mathematical concepts such as area, perimeter, and fractions.

Variables are used in both mathematics and computing, but in significantly different ways. In mathematics, a variable, often represented by a single letter such as x or y, corresponds to a quantity that stays the same for a given problem. However, in computing, a variable is an identifier used to label data that may change as a computer program is executed. A variable is one of the programming constructs that can be used to generalise programs to make them work for a range of inputs. Katie highlighted that the research team was keen to explore the synergies and tensions that arise when curriculum subjects share terms, as is the case for ‘variable’. 

Defining a learning trajectory

At the start of the project, in order to be able to develop coherent learning resources across school years, the team reviewed research papers related to teaching the programming construct of variables. In the papers, they found a variety of learning goals that related to facts (what learners need to know) and skills (what learners need to be able to do). They grouped these learning goals and arranged the groups into ‘levels of thinking’, which were then mapped onto a learning trajectory to show progression pathways for learning.

Learning materials about variables

Carla then shared three practical examples of learning resources their research team created that integrated the programming construct of variables into a maths curriculum. The three activities, described below, form part of a series of lessons called Action Fractions. You can read more about the series of lessons in this research paper.

Robot Boxes is an unplugged activity that is positioned at the Data User level of thinking. It relates to creating instructions for a fictional robot. Learners have to pay attention to different data the robot needs in order to draw a box, such as the length and width, and also to the value that the robot calculates as area of the box. The lesson uses boxes on paper as concrete representations of variables to which learners can physically add values.

Ambling Animals is set at the ‘Data Storer’ and ‘Variable Interpreter’ levels of thinking. It includes a Scratch project to help students to locate and compare fractions on number lines. During this lesson, find a variable that holds the value of the animal that represents the larger of two fractions.

Adding Fractions draws on facts and skills from the ‘Variable Interpreter’ and ‘Variable Implementer’ levels of thinking and also includes a Scratch project. The Scratch project visualises adding fractions with the same denominator on a number line. The lesson starts to explain why variables are so important in computer programs by demonstrating how using a variable can make code more efficient. 

Takeaways: Cross-curricular teaching, collaborative research

Teaching about the programming construct of variables can be challenging, as it requires young learners to understand abstract ideas. The research Katie and Carla presented shows how integrating these concepts into a mathematics curriculum is one way to highlight tangible uses of variables in everyday problems. The levels of thinking in the learning trajectory provide a structure helping teachers to support learners to develop their understanding and skills; the same levels of thinking could be used to introduce variables in other contexts and curricula.

Many primary teachers use cross-curricular learning to increase children’s engagement and highlight real-world examples. The seminar showed how important it is for teachers to pay attention to terms used across subjects, such as the word ‘variable’, and to explicitly explain a term’s different meanings. Katie and Carla shared a practical example of this when they suggested that computing teachers need to do more to stress the difference between equations such as xy = 45 in maths and assignment statements such as length = 45 in computing.

The Everyday Computing project resources were created by a team of researchers and educators who worked together to translate research findings into curriculum materials. This type of collaboration can be really valuable in driving a research agenda to directly improve learning outcomes for young people in classrooms. 

How can this research influence your classroom practice or other activities as an educator? Let us know your thoughts in the comments. We’ll be continuing to reflect on this question throughout the seminar series.

You can watch Katie’s and Carla’s full presentation here:

Join our seminar series on primary computing education

Our monthly seminar series on primary (K–5) teaching and learning is of interest to a global audience of educators, including those who want to understand the prior learning experiences of older learners.

We continue on Tuesday 7 February at 17.00 UK time, when we will hear from Dr Jean Salac, University of Washington. Jean will present her work in identifying inequities in elementary computing instruction and in developing a learning strategy, TIPP&SEE, to address these inequities. Sign up now, and we will send you a joining link for the session.

The post Combining computing and maths to teach primary learners about variables appeared first on Raspberry Pi Foundation.

We have been working on Gender Balance in Computing since 2018, together with partner organisations Behavioural Insights Team, Apps for Good, and WISE, to conduct research studies exploring ways to encourage more girls and young women to engage with Computing in school. The research programme has been funded by the Department for Education, and we deliver it as part of the National Centre for Computing Education. The report we share today is about the penultimate study in the programme.

Components of a Computing curriculum

A typical Computing curriculum is built around content: a list of concepts, knowledge, and skills that will be covered during the course. For some learners, that list will be enough to motivate and engage them in Computing. But other learners require more to engage with the subject, such as context about how they can use the computing skills they learn in the real world. Crucially, this difference between learners is often gendered. Research has shown that many boys become absorbed by the content in Computing courses, whereas for many girls the context for using computing skills is more important, and this context needs to relate to a variety of relevant scenarios where computing can solve problems.

Developing teaching materials to highlight the relevance of Computing

In the Relevance study, we worked together with colleagues from Apps for Good to create teaching materials that present Computing in contexts that were relevant to pupils’ own interests. To do this, we drew on a research concept called identification. This states that when learners become interested in a topic because it relates to part of their own identity, that makes the subject more personally meaningful to them, which in turn means that they are more likely to continue studying it. In the materials we created, we drew on learners’ identities based on the communities that they belonged to (see image below). The materials asked them to identify the connections they had to their own communities, and to then use this as the context to design and create a mobile phone app.

“I feel a sense of achievement in Computing when making your ideas a reality makes you proud of your creation, which is rewarding.” (Female learner, Relevance study evaluation report p. 57)

The Relevance research study

Between January 2022 and April 2022, more than 95 secondary schools were part of our study investigating the effect that learning with these resources might have on the attitudes of Year 8 pupils (aged 12–13) towards Computing. We are very grateful to all the schools, pupils, and teachers who took part in this study.

To enable evaluation of the study as a randomised controlled trial, the schools were randomly divided into two groups: a ‘control’ group that taught standard Computing lessons, and a ‘treatment’ group that delivered the intervention materials we had developed. The impact of the intervention was independently evaluated by the Behavioural Insights Team using data collected from pupils via surveys at the start and end of the intervention. The evaluators also collected data while conducting lesson observations, pupil group discussions, teacher interviews, and teacher surveys to understand how the intervention was delivered.

The girls who took part in the intervention chose an interesting range of contexts for their apps, including: 

• Solving problems in the school community, such as homework timetabling and public transport
• Interest-based communities, such as melody-making and interior design 
• Issues in wider communities, such as sea life population and mental health

“I feel like it’s an important subject, and I feel like sea life is at risk right now, and I want to help people realise that.” (Female learner, Relevance study evaluation report p. 60)

“I feel like computing can create apps to do with solving mental health problems, which I think are very important and personally need a lot of improvement on the way we can cope with mental health.” (Female learner, Relevance study evaluation report p. 60)

What we learned from the Relevance study

The start of this blog refers to the core components of a Computing curriculum: concepts, knowledge, and skills. One way of building a curriculum is to list these components and develop a scheme of work which covers them all. However, in a recent computing education paper, researchers present an alternative way: developing curricula around the possible endpoints of learners. For computing, one endpoint could be the economic opportunities of a programming career, but equally, another could be using digital technologies for creative expression. The researchers argue that when learners have the opportunity to use computing as a tool related to personally meaningful contexts, a more diverse group of learners can become engaged in the subject.

Girls who took part in our Relevance study expressed the importance of creativity. “I think last term we had instructions and you follow them, whereas now it’s like your own ideas and your own creativity and whatever you make,” said one female learner (report, p. 56). The series of lessons where learners designed a prototype of their app was particularly popular among girls because this activity included creative expression. Girls who see themselves as creative align their interests with subjects that allow them to express this part of their identity.

Based on learner responses to a ‘yes/no’ question about whether they were likely to choose GCSE Computer Science, the evaluators of the study found no statistically significant differences between the students who were part of the treatment and control groups. However, when learners were asked instead to select from a list which subjects they were likely to choose at GCSE, there was a statistically significant difference in the results: girls from schools in the treatment group were more likely to choose GCSE Computer Science as one of their options than girls in the control group. This finding suggests that it would be beneficial to gender balance in Computing if educators who design Computing curricula consider multiple endpoints for learners and include personally meaningful contexts to create learning experiences that are relevant to diverse groups of learners.

Find out more about making computing relevant for your learners

• Read the full evaluation report about this study
• Watch this video to find out more about the relevance of Computing in a range of careers
• Learn more about how to make Computing relevant for learners in our special edition of Hello World, The Big Book of Computing Pedagogy

Gender Balance in Computing is now complete — read our summary of the findings.

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To find out what works in non-formal computing learning, we’ve conducted two research projects recently: a systematic literature review, and a set of two interventions that were applied and evaluated as part of our Gender Balance in Computing programme. In this blog, we outline these two research projects.

What is non-formal learning?

When you think of young people learning computing, do you think of schools, classrooms, and curricula? You’d be right that lots of computing education for young people takes place in classrooms as part of national curricula. However, a lot of learning can take place outside of formal schooling. When we talk about non-formal computing education, we mean structured or semi-structured learning environments such as clubs or community groups, often set up by volunteers. These may take place in a school, library, or community venue; but we’ve also heard of some of our communities running non-formal learning activities on buses, in fire stations, or at football grounds  — there really is no limit to where learning can happen.

It’s harder to assess the impact and effectiveness of non-formal computing activities than formal computing education: we have to think outside of the traditional measures such as grades and formal exams or assessments. Instead, we estimate outcomes according to measures such as level of participant engagement, attendance, attrition rates, and changes in participants’ attitudes towards computing. We have previously also piloted non-formal assessments such as quizzes and found that these were well-received by adult facilitators and children alike. 

Project 1: Researching the impact of non-formal computing education

Earlier this year, we conducted a systematic literature review into computing education for K–12 learners in non-formal settings. We identified 88 relevant research studies, which we read, compared, and synthesised to provide an overview of what is already known about the effectiveness of non-formal computing activities and to identify opportunities for further research. 

Our analysis looked for common themes within existing studies and suggested some benefits that non-formal learning offers, including: 

• Access to advanced and innovative topics
• Awareness about computing careers 
• The chance to personalise projects according to learner interests
• The opportunity for learners to progress at their own pace
• The chance for learners to develop a sense of community through peers and role models

We presented this research at an international computing education conference called ICER 2022, and you can read about it in our open-access paper in the ICER conference proceedings.

Project 2: Making links between non-formal learning and formal computing study skills 

One particularly interesting characteristic of non-formal learning is that it tends to attract a broader range of learners than formal computing lessons. For example, a 2019 survey found that about 40% of the young people who attend Code Clubs were female. This is a high percentage compared with the proportion of girls among the learners choosing Computer Science GCSE in England, which is currently around 20%. We believe this points to an opportunity to capitalise on girls’ interest in learning activities outside of the classroom, and we hope to use non-formal activities to encourage more girls to take an interest in formal computer science education.

As part of our Gender Balance in Computing research programme in England, we worked with Apps for Good and the Behavioual Insights Team (BIT) to run two interventions in school-based non-formal settings, for which we adapted non-formal resources and used behavioural science concepts to strengthen the links the resources make between non-formal learning and studying computing more formally. One intervention ran in secondary schools for learners aged 13–14 years old, who used an adapted Apps for Good course, and the other ran in primary school for learners aged 8–11 year olds, who took part in Code Clubs using adapted versions of our projects.

The interventions were evaluated independently by a separate team from BIT, based on data from surveys completed by learners before and after the interventions, and interviews with teachers and learners. This data was analysed by the independent team to explore the impact the interventions had on learners’ attitudes towards computing and intention to study the subject in the future. 

What did we learn from these research projects? 

Our literature review concluded that future research in this area would benefit from experimenting with a variety of approaches to designing, and measuring the impact of, computing activities in a non-formal setting. For example, this could include comparing the short-term and long-term impact of specific interventions, aiming to cater for different types of participants, and offering different types of learning experiences.

In these two Gender Balance in Computing interventions, there was limited statistical evidence of an improvement in participants’ attitude towards computing or in their stated intention to study computer programming in the future. The independent evaluators recommended that the learning content that was created for the interventions could be adapted further to make the link between non-formal and formal learning even more salient. On the other hand, as is often the case with research, some interesting themes — ones that we weren’t looking for — emerged from the data, including: 

• In the secondary school intervention, there was a small, positive change in girls’ attitudes toward computing when they saw that it was relevant to real-world problems
• In the primary school intervention, some teachers also reported an increased confidence to pursue computing among girls who had used the adapted Code Club resources, and they highlighted the importance of positive female role models in computing

In both projects, the findings suggest that it is beneficial for learners to participate in non-formal learning activities that link to real-world situations, and that this could be particularly beneficial for girls to help them see computing as a subject that is relevant to their own interests and goals. Another common theme in both projects is that non-formal learning activities play an important role in showing what a “computer person” looks like and who belongs in computing. This suggests there’s a need for a diverse range of volunteers to run non-formal computing activities, and that we should make sure that non-formal learning resources include representations of a diverse range of learners.

Undertaking these research projects has provided evidence that the work the Foundation does is on the right track and suggested opportunities to use these themes in our future non-formal work and resources. 

Find out more about our work on non-formal computing education

• Read the full evaluation reports from the non-formal learning interventions in primary schools and secondary schools 
• Find out more about the Raspberry Pi Foundation’s non-formal learning initiatives: Code Club, CoderDojo, the European Astro Pi  Challenge, and the Coolest Projects showcases
• Sign up for our free seminar on Tuesday 8 November to hear about further research work on non-formal learning from Dr Tracy Gardner and Rebecca Franks on our team

More information about research projects at the Raspberry Pi Foundation and our newly launched Raspberry Pi Computing Education Research Centre can be found on our research pages and on the Research Centre’s website.

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