Tag Archives: research seminar

How useful do teachers find error message explanations generated by AI? Pilot research results

Post Syndicated from Veronica Cucuiat original https://www.raspberrypi.org/blog/error-message-explanations-large-language-models-teachers-views/

As discussions of how artificial intelligence (AI) will impact teaching, learning, and assessment proliferate, I was thrilled to be able to add one of my own research projects to the mix. As a research scientist at the Raspberry Pi Foundation, I’ve been working on a pilot research study in collaboration with Jane Waite to explore the topic of program error messages (PEMs). 

Computer science students at a desktop computer in a classroom.

PEMs can be a significant barrier to learning for novice coders, as they are often confusing and difficult to understand. This can hinder troubleshooting and progress in coding, and lead to frustration. 

Recently, various teams have been exploring how generative AI, specifically large language models (LLMs), can be used to help learners understand PEMs. My research in this area specifically explores secondary teachers’ views of the explanations of PEMs generated by a LLM, as an aid for learning and teaching programming, and I presented some of my results in our ongoing seminar series.

Understanding program error messages is hard at the start

I started the seminar by setting the scene and describing the current background of research on novices’ difficulty in using PEMs to fix their code, and the efforts made to date to improve these. The three main points I made were that:

  1. PEMs are often difficult to decipher, especially by novices, and there’s a whole research area dedicated to identifying ways to improve them.
  2. Recent studies have employed LLMs as a way of enhancing PEMs. However, the evidence on what makes an ‘effective’ PEM for learning is limited, variable, and contradictory.
  3. There is limited research in the context of K–12 programming education, as well as research conducted in collaboration with teachers to better understand the practical and pedagogical implications of integrating LLMs into the classroom more generally.

My pilot study aims to fill this gap directly, by reporting K–12 teachers’ views of the potential use of LLM-generated explanations of PEMs in the classroom, and how their views fit into the wider theoretical paradigm of feedback literacy. 

What did the teachers say?

To conduct the study, I interviewed eight expert secondary computing educators. The interviews were semi-structured activity-based interviews, where the educators got to experiment with a prototype version of the Foundation’s publicly available Code Editor. This version of the Code Editor was adapted to generate LLM explanations when the question mark next to the standard error message is clicked (see Figure 1 for an example of a LLM-generated explanation). The Code Editor version called the OpenAI GPT-3.5 interface to generate explanations based on the following prompt: “You are a teacher talking to a 12-year-old child. Explain the error {error} in the following Python code: {code}”. 

The Foundation’s Python Code Editor with LLM feedback prototype.
Figure 1: The Foundation’s Code Editor with LLM feedback prototype.

Fifteen themes were derived from the educators’ responses and these were split into five groups (Figure 2). Overall, the educators’ views of the LLM feedback were that, for the most part, a sensible explanation of the error messages was produced. However, all educators experienced at least one example of invalid content (LLM “hallucination”). Also, despite not being explicitly requested in the LLM prompt, a possible code solution was always included in the explanation.

Themes and groups derived from teachers’ responses.
Figure 2: Themes and groups derived from teachers’ responses.

Matching the themes to PEM guidelines

Next, I investigated how the teachers’ views correlated to the research conducted to date on enhanced PEMs. I used the guidelines proposed by Brett Becker and colleagues, which consolidate a lot of the research done in this area into ten design guidelines. The guidelines offer best practices on how to enhance PEMs based on cognitive science and educational theory empirical research. For example, they outline that enhanced PEMs should provide scaffolding for the user, increase readability, reduce cognitive load, use a positive tone, and provide context to the error.

Out of the 15 themes identified in my study, 10 of these correlated closely to the guidelines. However, the 10 themes that correlated well were, for the most part, the themes related to the content of the explanations, presentation, and validity (Figure 3). On the other hand, the themes concerning the teaching and learning process did not fit as well to the guidelines.

Correlation between teachers’ responses and enhanced PEM design guidelines.
Figure 3: Correlation between teachers’ responses and enhanced PEM design guidelines.

Does feedback literacy theory fit better?

However, when I looked at feedback literacy theory, I was able to correlate all fifteen themes — the theory fits.

Feedback literacy theory positions the feedback process (which includes explanations) as a social interaction, and accounts for the actors involved in the interaction — the student and the teacher — as well as the relationships between the student, the teacher, and the feedback. We can explain feedback literacy theory using three constructs: feedback types, student feedback literacy, and teacher feedback literacy (Figure 4). 

Feedback literacy at the intersection between feedback types, student feedback literacy, and teacher feedback literacy.
Figure 4: Feedback literacy at the intersection between feedback types, student feedback literacy, and teacher feedback literacy.

From the feedback literacy perspective, feedback can be grouped into four types: telling, guiding, developing understanding, and opening up new perspectives. The feedback type depends on the role of the student and teacher when engaging with the feedback (Figure 5). 

Feedback types as formalised by McLean, Bond, & Nicholson.
Figure 5: Feedback types as formalised by McLean, Bond, & Nicholson.

From the student perspective, the competencies and dispositions students need in order to use feedback effectively can be stated as: appreciating the feedback processes, making judgements, taking action, and managing affect. Finally, from a teacher perspective, teachers apply their feedback literacy skills across three dimensions: design, relational, and pragmatic. 

In short, according to feedback literacy theory, effective feedback processes entail well-designed feedback with a clear pedagogical purpose, as well as the competencies students and teachers need in order to make sense of the feedback and use it effectively.

A computer science teacher sits with students at computers in a classroom.

This theory therefore provided a promising lens for analysing the educators’ perspectives in my study. When the educators’ views were correlated to feedback literacy theory, I found that:

  1. Educators prefer the LLM explanations to fulfil a guiding and developing understanding role, rather than telling. For example, educators prefer to either remove or delay the code solution from the explanation, and they like the explanations to include keywords based on concepts they are teaching in the classroom to guide and develop students’ understanding rather than tell.
  1. Related to students’ feedback literacy, educators talked about the ways in which the LLM explanations help or hinder students to make judgements and action the feedback in the explanations. For example, they talked about how detailed, jargon-free explanations can help students make judgments about the feedback, but invalid explanations can hinder this process. Therefore, teachers talked about the need for ways to manage such invalid instances. However, for the most part, the educators didn’t talk about eradicating them altogether. They talked about ways of flagging them, using them as counter-examples, and having visibility of them to be able to address them with students.
  1. Finally, from a teacher feedback literacy perspective, educators discussed the need for professional development to manage feedback processes inclusive of LLM feedback (design) and address issues resulting from reduced opportunities to interact with students (relational and pragmatic). For example, if using LLM explanations results in a reduction in the time teachers spend helping students debug syntax errors from a pragmatic time-saving perspective, then what does that mean for the relationship they have with their students? 

Conclusion from the study

By correlating educators’ views to feedback literacy theory as well as enhanced PEM guidelines, we can take a broader perspective on how LLMs might not only shape the content of the explanations, but the whole social interaction around giving and receiving feedback. Investigating ways of supporting students and teachers to practise their feedback literacy skills matters just as much, if not more, than focusing on the content of PEM explanations. 

This study was a first-step exploration of eight educators’ views on the potential impact of using LLM explanations of PEMs in the classroom. Exactly what the findings of this study mean for classroom practice remains to be investigated, and we also need to examine students’ views on the feedback and its impact on their journey of learning to program. 

If you want to hear more, you can watch my seminar:

You can also read the associated paper, or find out more about the research instruments on this project website.

If any of these ideas resonated with you as an educator, student, or researcher, do reach out — we’d love to hear from you. You can contact me directly at [email protected] or drop us a line in the comments below. 

Join our next seminar

The focus of our ongoing seminar series is on teaching programming with or without AI. Check out the schedule of our upcoming seminars. 

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

I want to join the next seminar

You can also catch up on past seminars on our blog and on the previous seminars and recordings page.

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Adapting primary Computing resources for cultural responsiveness: Bringing in learners’ identity

Post Syndicated from Katharine Childs original https://www.raspberrypi.org/blog/adapting-computing-resources-cultural-responsiveness-research-with-primary-k5-teachers/

In recent years, the emphasis on creating culturally responsive educational practices has gained significant traction in schools worldwide. This approach aims to tailor teaching and learning experiences to better reflect and respect the diverse cultural backgrounds of students, thereby enhancing their engagement and success in school. In one of our recent research studies, we collaborated with a small group of primary school Computing teachers to adapt existing resources to be more culturally responsive to their learners.

Teachers work together to identify adaptations to Computing lessons.
At a workshop for the study, teachers collaborated to identify adaptations to Computing lessons

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.

An example slide from a culturally adapted activity to create a vector graphic emoji.
An example of an adapted Computing activity to create a vector graphic emoji.

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. 

Keywords for the funds of identity framework, drawing on work by Esteban-Guitart and Moll (2014) and Poole (2017).
Funds of identity framework, drawing on work by Esteban-Guitart and Moll (2014) and Poole (2017).

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.

An example of an identity artefact made by one of the students in a culturally adapted lesson on vector graphics.
An example of an identity artefact made by one of the students in the culturally adapted lesson on vector graphics. 

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.

Two learners do physical computing in the primary school classroom.

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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Empowering undergraduate computer science students to shape generative AI research

Post Syndicated from Bobby Whyte original https://www.raspberrypi.org/blog/empowering-undergraduate-computer-science-students-to-shape-generative-ai-research/

As use of generative artificial intelligence (or generative AI) tools such as ChatGPT, GitHub Copilot, or Gemini becomes more widespread, educators are thinking carefully about the place of these tools in their classrooms. For undergraduate education, there are concerns about the role of generative AI tools in supporting teaching and assessment practices. For undergraduate computer science (CS) students, generative AI also has implications for their future career trajectories, as it is likely to be relevant across many fields.

Dr Stephen MacNeil, Andrew Tran, and Irene Hou (Temple University)

In a recent seminar in our current series on teaching programming (with or without AI), we were delighted to be joined by Dr Stephen MacNeil, Andrew Tran, and Irene Hou from Temple University. Their talk showcased several research projects involving generative AI in undergraduate education, and explored how undergraduate research projects can create agency for students in navigating the implications of generative AI in their professional lives.

Differing perceptions of generative AI

Stephen began by discussing the media coverage around generative AI. He highlighted the binary distinction between media representations of generative AI as signalling the end of higher education — including programming in CS courses — and other representations that highlight the issues that using generative AI will solve for educators, such as improving access to high-quality help (specifically, virtual assistance) or personalised learning experiences.

Students sitting in a lecture at a university.

As part of a recent ITiCSE working group, Stephen and colleagues conducted a survey of undergraduate CS students and educators and found conflicting views about the perceived benefits and drawbacks of generative AI in computing education. Despite this divide, most CS educators reported that they were planning to incorporate generative AI tools into their courses. Conflicting views were also noted between students and educators on what is allowed in terms of generative AI tools and whether their universities had clear policies around their use.

The role of generative AI tools in students’ help-seeking

There is growing interest in how undergraduate CS students are using generative AI tools. Irene presented a study in which her team explored the effect of generative AI on undergraduate CS students’ help-seeking preferences. Help-seeking can be understood as any actions or strategies undertaken by students to receive assistance when encountering problems. Help-seeking is an important part of the learning process, as it requires metacognitive awareness to understand that a problem exists that requires external help. Previous research has indicated that instructors, teaching assistants, student peers, and online resources (such as YouTube and Stack Overflow) can assist CS students. However, as generative AI tools are now widely available to assist in some tasks (such as debugging code), Irene and her team wanted to understand which resources students valued most, and which factors influenced their preferences. Their study consisted of a survey of 47 students, and follow-up interviews with 8 additional students. 

Undergraduate CS student use of help-seeking resources

Responding to the survey, students stated that they used online searches or support from friends/peers more frequently than two generative AI tools, ChatGPT and GitHub Copilot; however, Irene indicated that as data collection took place at the beginning of summer 2023, it is possible that students were not familiar with these tools or had not used them yet. In terms of students’ experiences in seeking help, students found online searches and ChatGPT were faster and more convenient, though they felt these resources led to less trustworthy or lower-quality support than seeking help from instructors or teaching assistants.

Two undergraduate students are seated at a desk, collaborating on a computing task.

Some students felt more comfortable seeking help from ChatGPT than peers as there were fewer social pressures. Comparing generative AI tools and online searches, one student highlighted that unlike Stack Overflow, solutions generated using ChatGPT and GitHub Copilot could not be verified by experts or other users. Students who received the most value from using ChatGPT in seeking help either (i) prompted the model effectively when requesting help or (ii) viewed ChatGPT as a search engine or comprehensive resource that could point them in the right direction. Irene cautioned that some students struggled to use generative AI tools effectively as they had limited understanding of how to write effective prompts.

Using generative AI tools to produce code explanations

Andrew presented a study where the usefulness of different types of code explanations generated by a large language model was evaluated by students in a web software development course. Based on Likert scale data, they found that line-by-line explanations were less useful for students than high-level summary or concept explanations, but that line-by-line explanations were most popular. They also found that explanations were less useful when students already knew what the code did. Andrew and his team then qualitatively analysed code explanations that had been given a low rating and found they were overly detailed (i.e. focusing on superfluous elements of the code), the explanation given was the wrong type, or the explanation mixed code with explanatory text. Despite the flaws of some explanations, they concluded that students found explanations relevant and useful to their learning.

Perceived usefulness of code explanation types

Using generative AI tools to create multiple choice questions

In a separate study, Andrew and his team investigated the use of ChatGPT to generate novel multiple choice questions for computing courses. The researchers prompted two models, GPT-3 and GPT-4, with example question stems to generate correct answers and distractors (incorrect but plausible choices). Across two data sets of example questions, GPT-4 significantly outperformed GPT-3 in generating the correct answer (75.3% and 90% vs 30.8% and 36.7% of all cases). GPT-3 performed less well at providing the correct answer when faced with negatively worded questions. Both models generated correct answers as distractors across both sets of example questions (GPT-3: 11.1% and 10% of cases; GPT-4: 9.9% and 17.8%). They concluded that educators would still need to verify whether answers were correct and distractors were appropriate.

An undergraduate student is raising his hand up during a lecture at a university.

Undergraduate students shaping the direction of generative AI research

With student concerns about generative AI and its implications for the world of work, the seminar ended with a hopeful message highlighting undergraduate students being proactive in conducting their own research and shaping the direction of generative AI research in computer science education. Stephen concluded the seminar by celebrating the undergraduate students who are undertaking these research projects.

You can watch the seminar here:

If you are interested to learn more about Stephen’s work on generative AI, you can read about how undergraduate students used generative AI tools to create analogies for recursion. If you would like to experiment with using generative AI tools to assist with debugging, you could try using Gemini, ChatGPT, or Copilot.

Join our next seminar

Our current seminar series is on teaching programming with or without AI. 

In our next seminar, on 16 July at 17:00 to 18:30 BST, we welcome Laurie Gale (Raspberry Pi Computing Education Research Centre, University of Cambridge), who will discuss how to teach debugging to secondary school students. 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.

I want to join the next seminar

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

The post Empowering undergraduate computer science students to shape generative AI research appeared first on Raspberry Pi Foundation.

Imagining students’ progression in the era of generative AI

Post Syndicated from Sarah Millar original https://www.raspberrypi.org/blog/students-progression-generative-ai-computing-education-brett-becker/

Generative artificial intelligence (AI) tools are becoming more easily accessible to learners and educators, and increasingly better at generating code solutions to programming tasks, code explanations, computing lesson plans, and other learning resources. This raises many questions for educators in terms of what and how we teach students about computing and AI, and AI’s impact on assessment, plagiarism, and learning objectives.

Brett Becker.

We were honoured to have Professor Brett Becker (University College Dublin) join us as part of our ‘Teaching programming (with or without AI)’ seminar series. He is uniquely placed to comment on teaching computing using AI tools, having been involved in many initiatives relevant to computing education at different levels, in Ireland and beyond.

In a computing classroom, two girls concentrate on their programming task.

Brett’s talk focused on what educators and education systems need to do to prepare all students — not just those studying Computing — so that they are equipped with sufficient knowledge about AI to make their way from primary school to secondary and beyond, whether it be university, technical qualifications, or work.

How do AI tools currently perform?

Brett began his talk by illustrating the increase in performance of large language models (LLMs) in solving first-year undergraduate programming exercises: he compared the findings from two recent studies he was involved in as part of an ITiCSE Working Group. In the first study — from 2021 — the results generated by GPT-3 were similar to those of students in the top quartile. By the second study in 2023, GPT-4’s performance matched that of a top student (Figure 1).

A graph comparing exam scores.

Figure 1: Student scores on Exam 1 and Exam 2, represented by circles. GPT-3’s 2021 score is represented by the blue ‘x’, and GPT-4’s 2023 score on the same questions is represented by the red ‘x’.

Brett also explained that the study found some models were capable of solving current undergraduate programming assessments almost error-free, and could solve the Irish Leaving Certificate and UK A level Computer Science exams.

What are challenges and opportunities for education?

This level of performance raises many questions for computing educators about what is taught and how to assess students’ learning. To address this, Brett referred to his 2023 paper, which included findings from a literature review and a survey on students’ and instructors’ attitudes towards using LLMs in computing education. This analysis has helped him identify several opportunities as well as the ethical challenges education systems face regarding generative AI. 

The opportunities include: 

  • The generation of unique content, lesson plans, programming tasks, or feedback to help educators with workload and productivity
  • More accessible content and tools generated by AI apps to make Computing more broadly accessible to more students
  • More engaging and meaningful student learning experiences, including using generative AI to enable creativity and using conversational agents to augment students’ learning
  • The impact on assessment practices, both in terms of automating the marking of current assessments as well as reconsidering what is assessed and how

Some of the challenges include:

  • The lack of reliability and accuracy of outputs from generative AI tools
  • The need to educate everyone about AI to create a baseline level of understanding
  • The legal and ethical implications of using AI in computing education and beyond
  • How to deal with questionable or even intentionally harmful uses of AI and mitigating the consequences of such uses

Programming as a basic skill for all subjects

Next, Brett talked about concrete actions that he thinks we need to take in response to these opportunities and challenges. 

He emphasised our responsibility to keep students safe. One way to do this is to empower all students with a baseline level of knowledge about AI, at an age-appropriate level, to enable them to keep themselves safe. 

Secondary school age learners in a computing classroom.

He also discussed the increased relevance of programming to all subjects, not only Computing, in a similar way to how reading and mathematics transcend the boundaries of their subjects, and the need he sees to adapt subjects and curricula to that effect. 

As an example of how rapidly curricula may need to change with increasing AI use by students, Brett looked at the Irish Computer science specification for “senior cycle” (final two years of second-level, ages 16–18). This curriculum was developed in 2018 and remains a strong computing curriculum in Brett’s opinion. However, he pointed out that it only contains a single learning outcome on AI. 

To help educators bridge this gap, in the book Brett wrote alongside Keith Quille to accompany the curriculum, they included two chapters dedicated to AI, machine learning, and ethics and computing. Brett believes these types of additional resources may be instrumental for teaching and learning about AI as resources are more adaptable and easier to update than curricula. 

Generative AI in computing education

Taking the opportunity to use generative AI to reimagine new types of programming problems, Brett and colleagues have developed Promptly, a tool that allows students to practise prompting AI code generators. This tool provides a combined approach to learning about generative AI while learning programming with an AI tool. 

Promptly is intended to help students learn how to write effective prompts. It encourages students to specify and decompose the programming problem they want to solve, read the code generated, compare it with test cases to discern why it is failing (if it is), and then update their prompt accordingly (Figure 2). 

An example of the Promptly interface.

Figure 2: Example of a student’s use of Promptly.

Early undergraduate student feedback points to Promptly being a useful way to teach programming concepts and encourage metacognitive programming skills. The tool is further described in a paper, and whilst the initial evaluation was aimed at undergraduate students, Brett positioned it as a secondary school–level tool as well. 

Brett hopes that by using generative AI tools like this, it will be possible to better equip a larger and more diverse pool of students to engage with computing.

Re-examining the concept of programming

Brett concluded his seminar by broadening the relevance of programming to all learners, while challenging us to expand our perspectives of what programming is. If we define programming as a way of prompting a machine to get an output, LLMs allow all of us to do so without the need for learning the syntax of traditional programming languages. Taking that view, Brett left us with a question to consider: “How do we prepare for this from an educational perspective?”

You can watch Brett’s presentation here:

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 11 June at 17:00 to 18:30 GMT, we’re joined by Veronica Cucuiat (Raspberry Pi Foundation), who will talk about whether LLMs could be employed to help understand programming error messages, which can present a significant obstacle to anyone new to coding, especially young people.  

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.

I want to join the next seminar

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.

The post Imagining students’ progression in the era of generative AI appeared first on Raspberry Pi Foundation.

Insights into students’ attitudes to using AI tools in programming education

Post Syndicated from Katharine Childs original https://www.raspberrypi.org/blog/insights-into-students-attitudes-to-using-ai-tools-in-programming-education/

Educators around the world are grappling with the problem of whether to use artificial intelligence (AI) tools in the classroom. As more and more teachers start exploring the ways to use these tools for teaching and learning computing, there is an urgent need to understand the impact of their use to make sure they do not exacerbate the digital divide and leave some students behind.

A teenager learning computer science.

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.

A student in a computing classroom.

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.

I want to join the next seminar

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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Using an AI code generator with school-age beginner programmers

Post Syndicated from Bobby Whyte original https://www.raspberrypi.org/blog/using-an-ai-code-generator-with-school-age-beginner-programmers/

AI models for general-purpose programming, such as OpenAI Codex, which powers the AI pair programming tool GitHub Copilot, have the potential to significantly impact how we teach and learn programming. 

Learner in a computing classroom.

The basis of these tools is a ‘natural language to code’ approach, also called natural language programming. This allows users to generate code using a simple text-based prompt, such as “Write a simple Python script for a number guessing game”. Programming-specific AI models are trained on vast quantities of text data, including GitHub repositories, to enable users to quickly solve coding problems using natural language. 

As a computing educator, you might ask what the potential is for using these tools in your classroom. In our latest research seminar, Majeed Kazemitabaar (University of Toronto) shared his work in developing AI-assisted coding tools to support students during Python programming tasks.

Evaluating the benefits of natural language programming

Majeed argued that natural language programming can enable students to focus on the problem-solving aspects of computing, and support them in fixing and debugging their code. However, he cautioned that students might become overdependent on the use of ‘AI assistants’ and that they might not understand what code is being outputted. Nonetheless, Majeed and colleagues were interested in exploring the impact of these code generators on students who are starting to learn programming.

Using AI code generators to support novice programmers

In one study, the team Majeed works in investigated whether students’ task and learning performance was affected by an AI code generator. They split 69 students (aged 10–17) into two groups: one group used a code generator in an environment, Coding Steps, that enabled log data to be captured, and the other group did not use the code generator.

A group of male students at the Coding Academy in Telangana.

Learners who used the code generator completed significantly more authoring tasks — where students manually write all of the code — and spent less time completing them, as well as generating significantly more correct solutions. In multiple choice questions and modifying tasks — where students were asked to modify a working program — students performed similarly whether they had access to the code generator or not. 

A test was administered a week later to check the groups’ performance, and both groups did similarly well. However, the ‘code generator’ group made significantly more errors in authoring tasks where no starter code was given. 

Majeed’s team concluded that using the code generator significantly increased the completion rate of tasks and student performance (i.e. correctness) when authoring code, and that using code generators did not lead to decreased performance when manually modifying code. 

Finally, students in the code generator group reported feeling less stressed and more eager to continue programming at the end of the study.

Student perceptions when (not) using AI code generators

Understanding how novices use AI code generators

In a related study, Majeed and his colleagues investigated how novice programmers used the code generator and whether this usage impacted their learning. Working with data from 33 learners (aged 11–17), they analysed 45 tasks completed by students to understand:

  1. The context in which the code generator was used
  2. What learners asked for
  3. How prompts were written
  4. The nature of the outputted code
  5. How learners used the outputted code 

Their analysis found that students used the code generator for the majority of task attempts (74% of cases) with far fewer tasks attempted without the code generator (26%). Of the task attempts made using the code generator, 61% involved a single prompt while only 8% involved decomposition of the task into multiple prompts for the code generator to solve subgoals; 25% used a hybrid approach — that is, some subgoal solutions being AI-generated and others manually written.

In a comparison of students against their post-test evaluation scores, there were positive though not statistically significant trends for students who used a hybrid approach (see the image below). Conversely, negative though not statistically significant trends were found for students who used a single prompt approach.

A positive correlation between hybrid programming and post-test scores

Though not statistically significant, these results suggest that the students who actively engaged with tasks — i.e. generating some subgoal solutions, manually writing others, and debugging their own written code — performed better in coding tasks.

Majeed concluded that while the data showed evidence of self-regulation, such as students writing code manually or adding to AI-generated code, students frequently used the output from single prompts in their solutions, indicating an over-reliance on the output of AI code generators.

He suggested that teachers should support novice programmers to write better quality prompts to produce better code.  

If you want to learn more, you can watch Majeed’s seminar:

You can read more about Majeed’s work on his personal website. You can also download and use the code generator Coding Steps yourself.

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 17:00–18:30 GMT, we’re joined by Brett Becker (University College Dublin), who will discuss how generative AI may be effectively utilised in secondary school programming education and how it can be leveraged so that students can be best prepared for whatever lies ahead. To take part in the seminar, click the button below to sign up, and we will send you information about joining. We hope to see you there.

I want to join the next seminar

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

The post Using an AI code generator with school-age beginner programmers appeared first on Raspberry Pi Foundation.

Supporting learners with programming tasks through AI-generated Parson’s Problems

Post Syndicated from Veronica Cucuiat original https://www.raspberrypi.org/blog/supporting-learners-with-programming-tasks-through-ai-generated-parsons-problems/

The use of generative AI tools (e.g. ChatGPT) in education is now common among young people (see data from the UK’s Ofcom regulator). As a computing educator or researcher, you might wonder what impact generative AI tools will have on how young people learn programming. In our latest research seminar, Barbara Ericson and Xinying Hou (University of Michigan) shared insights into this topic. They presented recent studies with university student participants on using generative AI tools based on large language models (LLMs) during programming tasks. 

A girl in a university computing classroom.

Using Parson’s Problems to scaffold student code-writing tasks

Barbara and Xinying started their seminar with an overview of their earlier research into using Parson’s Problems to scaffold university students as they learn to program. Parson’s Problems (PPs) are a type of code completion problem where learners are given all the correct code to solve the coding task, but the individual lines are broken up into blocks and shown in the wrong order (Parsons and Haden, 2006). Distractor blocks, which are incorrect versions of some or all of the lines of code (i.e. versions with syntax or semantic errors), can also be included. This means to solve a PP, learners need to select the correct blocks as well as place them in the correct order.

A presentation slide defining Parson's Problems.

In one study, the research team asked whether PPs could support university students who are struggling to complete write-code tasks. In the tasks, the 11 study participants had the option to generate a PP when they encountered a challenge trying to write code from scratch, in order to help them arrive at the complete code solution. The PPs acted as scaffolding for participants who got stuck trying to write code. Solutions used in the generated PPs were derived from past student solutions collected during previous university courses. The study had promising results: participants said the PPs were helpful in completing the write-code problems, and 6 participants stated that the PPs lowered the difficulty of the problem and speeded up the problem-solving process, reducing their debugging time. Additionally, participants said that the PPs prompted them to think more deeply.

A young person codes at a Raspberry Pi computer.

This study provided further evidence that PPs can be useful in supporting students and keeping them engaged when writing code. However, some participants still had difficulty arriving at the correct code solution, even when prompted with a PP as support. The research team thinks that a possible reason for this could be that only one solution was given to the PP, the same one for all participants. Therefore, participants with a different approach in mind would likely have experienced a higher cognitive demand and would not have found that particular PP useful.

An example of a coding interface presenting adaptive Parson's Problems.

Supporting students with varying self-efficacy using PPs

To understand the impact of using PPs with different learners, the team then undertook a follow-up study asking whether PPs could specifically support students with lower computer science self-efficacy. The results show that study participants with low self-efficacy who were scaffolded with PPs support showed significantly higher practice performance and higher problem-solving efficiency compared to participants who had no scaffolding. These findings provide evidence that PPs can create a more supportive environment, particularly for students who have lower self-efficacy or difficulty solving code writing problems. Another finding was that participants with low self-efficacy were more likely to completely solve the PPs, whereas participants with higher self-efficacy only scanned or partly solved the PPs, indicating that scaffolding in the form of PPs may be redundant for some students.

Secondary school age learners in a computing classroom.

These two studies highlighted instances where PPs are more or less relevant depending on a student’s level of expertise or self-efficacy. In addition, the best PP to solve may differ from one student to another, and so having the same PP for all students to solve may be a limitation. This prompted the team to conduct their most recent study to ask how large language models (LLMs) can be leveraged to support students in code-writing practice without hindering their learning.

Generating personalised PPs using AI tools

This recent third study focused on the development of CodeTailor, a tool that uses LLMs to generate and evaluate code solutions before generating personalised PPs to scaffold students writing code. Students are encouraged to engage actively with solving problems as, unlike other AI-assisted coding tools that merely output a correct code correct solution, students must actively construct solutions using personalised PPs. The researchers were interested in whether CodeTailor could better support students to actively engage in code-writing.

An example of the CodeTailor interface presenting adaptive Parson's Problems.

In a study with 18 undergraduate students, they found that CodeTailor could generate correct solutions based on students’ incorrect code. The CodeTailor-generated solutions were more closely aligned with students’ incorrect code than common previous student solutions were. The researchers also found that most participants (88%) preferred CodeTailor to other AI-assisted coding tools when engaging with code-writing tasks. As the correct solution in CodeTailor is generated based on individual students’ existing strategy, this boosted students’ confidence in their current ideas and progress during their practice. However, some students still reported challenges around solution comprehension, potentially due to CodeTailor not providing sufficient explanation for the details in the individual code blocks of the solution to the PP. The researchers argue that text explanations could help students fully understand a program’s components, objectives, and structure. 

In future studies, the team is keen to evaluate a design of CodeTailor that generates multiple levels of natural language explanations, i.e. provides personalised explanations accompanying the PPs. They also aim to investigate the use of LLM-based AI tools to generate a self-reflection question structure that students can fill in to extend their reasoning about the solution to the PP.

Barbara and Xinying’s seminar is available to watch here: 

Find examples of PPs embedded in free interactive ebooks that Barbara and her team have developed over the years, including CSAwesome and Python for Everybody. You can also read more about the CodeTailor platform in Barbara and Xinying’s paper.

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 12 March at 17:00–18:30 GMT, we’re joined by Yash Tadimalla and Prof. Mary Lou Maher (University of North Carolina at Charlotte). The two of them will share further insights into the impact of AI tools on the student experience in programming courses. To take part in the seminar, click the button below to sign up, and we will send you information about joining. We hope to see you there.

I want to join the next seminar

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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Grounded cognition: physical activities and learning computing

Post Syndicated from Bonnie Sheppard original https://www.raspberrypi.org/blog/grounded-cognition/

Everyone who has taught children before will know the excited gleam in their eyes when the lessons include something to interact with physically. Whether it’s printed and painstakingly laminated flashcards, laser-cut models, or robots, learners’ motivation to engage with the topic will increase along with the noise levels in the classroom.

Two learners do physical computing in the primary school classroom.

However, these hands-on activities are often seen as merely a technique to raise interest, or a nice extra project for children to do before the ‘actual learning’ can begin. But what if this is the wrong way to think about this type of activity? 

How do children learn?

In our 2023 online research seminar series, focused on computing education for primary-aged (K–5) learners, we delved into the most recent research aimed at enhancing learning experiences for students in the earliest stages of education. From a deep dive into teaching variables to exploring the integration of computational thinking, our series has looked at the most effective ways to engage young minds in the subject of computing.

An adult on a plain background.

It’s only fitting that in our final seminar in the series, Anaclara Gerosa from the University of Glasgow tackled one of the most fundamental questions in education: how do children actually learn? Beyond the conventional methods, emerging research has been shedding light on a fascinating approach — the concept of grounded cognition. This theory suggests that children don’t merely passively absorb knowledge; they physically interact with it, quite literally ‘grasping’ concepts in the process.

Grounded cognition, also known in variations as embodied and situated cognition, offers a new perspective on how we absorb and process information. At its core, this theory suggests that all cognitive processes, including language and thought, are rooted in the body’s dynamic interactions with the environment. This notion challenges the conventional view of learning as a purely cognitive activity and highlights the impact of action and simulation.

A group of learners do physical computing in the primary school classroom.

There is evidence from many studies in psychology and pedagogy that using hands-on activities can enhance comprehension and abstraction. For instance, finger counting has been found to be essential in understanding numerical systems and mathematical concepts. A recent study in this field has shown that children who are taught basic computing concepts with unplugged methods can grasp abstract ideas from as young as 3. There is therefore an urgent need to understand exactly how we could use grounded cognition methods to teach children computing — which is arguably one of the most abstract subjects in formal education.

A recent study in this field has shown that children who are taught basic computing concepts with unplugged methods can grasp abstract ideas from as young as 3.

A new framework for teaching computing

Anaclara is part of a group of researchers at the University of Glasgow who are currently developing a new approach to structuring computing education. Their EIFFEL (Enacted Instrumented Formal Framework for Early Learning in Computing) model suggests a progression from enacted to formal activities.

Following this model, in the early years of computing education, learners would primarily engage with activities that allow them to work with tangible 3D objects or manipulate intangible objects, for instance in Scratch. Increasingly, students will be able to perform actions in an instrumented or virtual environment which will require the knowledge of abstract symbols but will not yet require the knowledge of programming languages. Eventually, students will have developed the knowledge and skills to engage in fully formal environments, such as writing advanced code.

A graph illustrating the EIFFEL model for early computing.

In a recent literature review, Anaclara and her colleagues looked at existing research into using grounded cognition theory in computing education. Although several studies report the use of grounded approaches, for instance by using block-based programming, robots, toys, or construction kits, the focus is generally on looking at how concrete objects can be used in unplugged activities due to specific contexts, such as a limited availability of computing devices.

The next steps in this area are looking at how activities that specifically follow the EIFFEL framework can enhance children’s learning. 

You can watch Anaclara’s seminar here: 

You can also access the presentation slides here.

Try grounded activities in your classroom

Research into grounded cognition activities in computer science is ongoing, but we encourage you to try incorporating more hands-on activities when teaching younger learners and observing the effects yourself. Here are a few ideas on how to get started:

Join us at our next seminar

In 2024, we are exploring different ways to teach and learn programming, with and without AI tools. In our next seminar, on 13 February at 17:00 GMT, Majeed Kazemi from the University of Toronto will be joining us to discuss whether AI-powered code generators can help K–12 students learn to program in Python. All of our online seminars are free and open to everyone. Sign up and we’ll send you the link to join on the day.

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Integrating computational thinking into primary teaching

Post Syndicated from Veronica Cucuiat original https://www.raspberrypi.org/blog/integrating-computational-thinking-into-primary-teaching/

“Computational thinking is really about thinking, and sometimes about computing.” – Aman Yadav, Michigan State University

Young people in a coding lesson.

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.

A child at a laptop

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:

  1. Debugging
  2. Abstraction
  3. Decomposition
  4. 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.

Teacher and young student at a laptop.

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.  

A graph from Aman's seminar.

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.

A graph from Aman's seminar.

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. 

Watch the recording of Aman’s presentation:

You can access Aman’s seminar slides as well.

You can find out more about connecting research to practice for primary computing education by watching the recordings of the other seminars in our series on primary (K–5) teaching and learning. In particular, Bobby Whyte discusses similar concepts to Aman in his talk on integrating primary computing and literacy through multimodal storytelling. 

Sign up for our seminars

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.

On 13 February, we’ll hear from Majeed Kazemi (University of Toronto) about his work investigating whether AI code generator tools can support K-12 students to learn Python programming.

Sign up now to join the seminar:

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Engaging primary Computing teachers in culturally relevant pedagogy through professional development

Post Syndicated from Claire Johnson original https://www.raspberrypi.org/blog/culturally-relevant-pedagogy-areas-opportunity-adapting-lessons/

Underrepresentation in computing is a widely known issue, in industry and in education. To cite some statistics from the UK: a Black British Voices report from August 2023 noted that 95% of respondents believe the UK curriculum neglects black lives and experiences; fewer students from working class backgrounds study GCSE Computer Science; when they leave formal education, fewer female, BAME, and white working class people are employed in the field of computer science (Kemp 2021); only 21% of GCSE Computer Science students, 15% at A level, and 22% at undergraduate level are female (JCQ 2020, Ofqual 2020, UCAS 2020); students with additional needs are also underrepresented.

In a computing classroom, two girls concentrate on their programming task.

Such statistics have been the status quo for too long. Many Computing teachers already endeavour to bring about positive change where they can and engage learners by including their interests in the lessons they deliver, so how can we support them to do this more effectively? Extending the reach of computing so that it is accessible to all also means that we need to consider what formal and informal values predominate in the field of computing. What is the ‘hidden’ curriculum in computing that might be excluding some learners? Who is and who isn’t represented?

Katharine Childs.
Katharine Childs (Raspberry Pi Foundation)

In a recent research seminar, Katharine Childs from our team outlined a research project we conducted, which included a professional development workshop to increase primary teachers’ awareness of and confidence in culturally relevant pedagogy. In the workshop, teachers considered how to effectively adapt curriculum materials to make them culturally relevant and engaging for the learners in their classrooms. Katharine described the practical steps teachers took to adapt two graphics-related units, and invited seminar participants to apply their learning to a graphics activity themselves.

What is culturally relevant pedagogy?

Culturally relevant pedagogy is a teaching framework which values students’ identities, backgrounds, knowledge, and ways of learning. By drawing on students’ own interests, experiences and cultural knowledge educators can increase the likelihood that the curriculum they deliver is more relevant, engaging and accessible to all.

The idea of culturally relevant pedagogy was first introduced in the US in the 1990s by African-American academic Gloria Ladson-Billings (Ladson-Billings 1995). Its aim was threefold: to raise students’ academic achievement, to develop students’ cultural competence and to promote students’ critical consciousness. The idea of culturally responsive teaching was later advanced by Geneva Gay (2000) and more recently  brought into focus in US computer science education by Kimberly Scott and colleagues (2015). The approach has been localised for England by Hayley Leonard and Sue Sentance (2021) in work they undertook here at the Foundation.

Ten areas of opportunity

Katharine began her presentation by explaining that the professional development workshop in the Primary culturally adapted resources for computing project built on two of our previous research projects to develop guidelines for culturally relevant and responsive computing and understand how teachers used them in practice. This third project ran as a pilot study funded by Cognizant, starting in Autumn 2022 with a one-day, in-person workshop for 13 primary computing teachers

The research structure was a workshop followed by research adaption, then delivery of resources, and evaluation through a parent survey, teacher interviews, and student focus groups.

Katharine then introduced us to the 10 areas of opportunity (AO) our research at the Raspberry Pi Computing Education Research Centre had identified for culturally relevant pedagogy. These 10 areas were used as practical prompts to frame the workshop discussions:

  1. Find out about learners
  2. Find out about ourselves as teachers
  3. Review the content
  4. Review the context
  5. Make the learning accessible to all
  6. Provide opportunities for open-ended and problem solving activities
  7. Promote collaboration and structured group discussion
  8. Promote student agency through choice
  9. Review the learning environment
  10. Review related policies, processes, and training in your school and department

At first glance it is easy to think that you do most of those things already, or to disregard some items as irrelevant to the computing curriculum. What would your own cultural identity (see AO2) have to do with computing, you might wonder. But taking a less complacent perspective might lead you to consider all the different facets that make up your identity and then to think about the same for the students you teach. You may discover that there are many areas which you have left untapped in your lesson planning.

Two young people learning together at a laptop.

Katharine explained how this is where the professional development workshop showed itself as beneficial for the participants. It gave teachers the opportunity to reflect on how their cultural identity impacted on their teaching practices — as a starting point to learning more about other aspects of the culturally relevant pedagogy approach.

Our researchers were interested in how they could work alongside teachers to adapt two computing units to make them more culturally relevant for teachers’ specific contexts. They used the Computing Curriculum units on Photo Editing (Year 4) and Vector Graphics (Year 5).

A slide about adapting an emoji teaching activity to make it culturally relevant.

Katharine illustrated some of the adaptations teachers and researchers working together had made to the emoji activity above, and which areas of opportunity (AO) had been addressed; this aspect of the research will be reported in later publications.

Results after the workshop

Although the numbers of participants in this pilot study was small, the findings show that the professional development workshop significantly increased teachers’ awareness of culturally relevant pedagogy and their confidence in adapting resources to take account of local contexts:

  • After the workshop, 10/13 teachers felt more confident to adapt resources to be culturally relevant for their own contexts, and 8/13 felt more confident in adapting resources for others.
  • Before the workshop, 5/13 teachers strongly agreed that it was an important part of being a computing teacher to examine one’s own attitudes and beliefs about race, gender, disabilities, sexual orientation. After the workshop, the number in agreement rose to 12/13.
  • After the workshop, 13/13 strongly agreed that part of a computing teacher’s responsibility is to challenge teaching practices which maintain social inequities (compared to 7/13 previously).
  • Before the workshop, 4/13 teachers strongly agreed that it is important to allow student choice when designing computing activities; this increased to 9/13 after the workshop.

These quantitative shifts in perspective indicate a positive effect of the professional development pilot. 

Katharine described that in our qualitative interviews with the participating teachers, they expressed feeling that their understanding of culturally relevant pedagogy had increased and they recognized the many benefits to learners of the approach. They valued the opportunity to discuss their contexts and to adapt materials they currently used with other teachers, because it made it a more ‘authentic’ and practical professional development experience.

The seminar ended with breakout sessions inviting viewers to consider possible adaptations that could be made to the graphics activities which had been the focus of the workshop.

In the breakout sessions, attendees also discussed specific examples of culturally relevant teaching practices that had been successful in their own classrooms, and they considered how schools and computing educational initiatives could support teachers in their efforts to integrate culturally relevant pedagogy into their practice. Some attendees observed that it was not always possible to change schemes of work without a ‘whole-school’ approach, senior leadership team support, and commitment to a research-based professional development programme.

Where do you see opportunities for your teaching?

The seminar reminds us that the education system is not culture neutral and that teachers generally transmit the dominant culture (which may be very different from their students’) in their settings (Vrieler et al, 2022). Culturally relevant pedagogy is an attempt to address the inequities and biases that exist, which result in many students feeling marginalised, disenfranchised, or underachieving. It urges us to incorporate learners’ cultures and experiences in our endeavours  to create a more inclusive computing curriculum; to adopt an intersectional lens so that all can thrive.

Secondary school age learners in a computing classroom.

As a pilot study, the workshop was offered to a small cohort of 13, yet the findings show that the intervention significantly increased participants’ awareness of culturally relevant pedagogy and their confidence in adapting resources to take account of local contexts.

Of course there are many ways in which teachers already adapt resources to make them interesting and accessible to their pupils. Further examples of the sort of adaptations you might make using these areas of opportunity include:

  • AO1: You could find out to what extent learners feel like they ‘belong’ or are included in a particular computing-related career. This is sure to yield valuable insights into learners’ knowledge and/or preconceptions of computing-related careers. 
  • AO3: You could introduce topics such as the ethics of AI, data bias, investigations of accessibility and user interface design. 
  • AO4: You might change the context of a unit of work on the use of conditional statements in programming, from creating a quiz about ‘Vikings’ to focus on, for example, aspects of youth culture which are more engaging to some learners such as football or computer games, or to focus on religious celebrations, which may be more meaningful to others.
  • AO5: You could experiment with a particular pedagogical approach to maximise the accessibility of a unit of work. For example, you could structure a programming unit by using the PRIMM model, or follow the Universal Design for Learning framework to differentiate for diversity.
  • AO6/7: You could offer more open-ended and collaborative activities once in a while, to promote engagement and to allow learners to express themselves autonomously.
  • AO8: By allowing learners to choose topics which are relevant or familiar to their individual contexts and identities, you can increase their feeling of agency. 
  • AO9: You could review both your learning materials and your classroom to ensure that all your students are fully represented.
  • AO10: You can bring colleagues on board too; the whole enterprise of embedding culturally relevant pedagogy will be more successful when school- as well as department-level policies are reviewed and prioritised.

Can you see an opportunity for integrating culturally relevant pedagogy in your classroom? We would love to hear about examples of culturally relevant teaching practices that you have found successful. Let us know your thoughts or questions in the comments below.

You can watch Katharine’s seminar here:

You can download her presentation slides on our ‘previous seminars’ page, and you can read her research paper.

To get a practical overview of culturally relevant pedagogy, read our 2-page Quick Read on the topic and download the guidelines we created with a group of teachers and academic specialists.

Tomorrow we’ll be sharing a blog about how the learners who engaged with the culturally adapted units found the experience, and how it affected their views of computing. Follow us on social media to not miss it!

Join our upcoming seminars live

On 12 December we’ll host the last seminar session in our series on primary (K-5) computing. Anaclara Gerosa will share her work on how to design and structure early computing activities that promote and scaffold students’ conceptual understanding. As always, the seminar is free and takes place online at 17:00–18:30 GMT / 12:00–13:30 ET / 9:00–10:30 PT / 18:00–19:30 CET. Sign up and we’ll send you the link to join on the day.

I want to sign up

In 2024, our new seminar series will be about teaching and learning programming, with and without AI tools. If you’re signed up to our seminars, you’ll receive the link to join every monthly seminar.

The post Engaging primary Computing teachers in culturally relevant pedagogy through professional development appeared first on Raspberry Pi Foundation.

Spotlight on teaching programming with and without AI in our 2024 seminar series

Post Syndicated from Bonnie Sheppard original https://www.raspberrypi.org/blog/teaching-programming-ai-seminar-series-2024/

How do you best teach programming in school? It’s one of the core questions for primary and secondary computing teachers. That’s why we’re making it the focus of our free online seminars in 2024. You’re invited to attend and hear about the newest research about the teaching and learning of programming, with or without AI tools.

Two smiling adults learn about computing at desktop computers.

Building on the success and the friendly, accessible session format of our previous seminars, this coming year we will delve into the latest trends and innovative approaches to programming education in school.

Secondary school age learners in a computing classroom.

Our online seminars are for everyone interested in computing education

Our monthly online seminars are not only for computing educators but also for everyone else who is passionate about teaching young people to program computers. The seminar participants are a diverse community of teachers, technology enthusiasts, industry professionals, coding club volunteers, and researchers.

Two adults learn about computing at desktop computers.

With the seminars we aim to bridge the gap between the newest research and practical teaching. Whether you are an educator in a traditional classroom setting or a mentor guiding learners in a CoderDojo or Code Club, you will gain insights from leading researchers about how school-age learners engage with programming. 

What to expect from the seminars

Each online seminar begins with an expert presenter delivering their latest research findings in an accessible way. We then move into small groups to encourage discussion and idea exchange. Finally, we come back together for a Q&A session with the presenter.

Here’s what attendees had to say about our previous seminars:

“As a first-time attendee of your seminars, I was impressed by the welcoming atmosphere.”

“[…] several seminars (including this one) provided valuable insights into different approaches to teaching computing and technology.”

“I plan to use what I have learned in the creation of curriculum […] and will pass on what I learned to my team.”

“I enjoyed the fact that there were people from different countries and we had a chance to see what happens elsewhere and how that may be similar and different to what we do here.”

January seminar: AI-generated Parson’s Problems

Computing teachers know that, for some students, learning about the syntax of programming languages is very challenging. Working through Parson’s Problem activities can be a way for students to learn to make sense of the order of lines of code and how syntax is organised. But for teachers it can be hard to precisely diagnose their students’ misunderstandings, which in turn makes it hard to create activities that address these misunderstandings.

A group of students and a teacher at the Coding Academy in Telangana.

At our first 2024 seminar on 9 January, Dr Barbara Ericson and Xinying Hou (University of Michigan) will present a promising new approach to helping teachers solve this difficulty. In one of their studies, they combined Parsons Problems and generative AI to create targeted activities for students based on the errors students had made in previous tasks. Thus they were able to provide personalised activities that directly addressed gaps in the students’ learning.

Sign up now to join our seminars

All our seminars start at 17:00 UK time (18:00 CET / 12:00 noon ET / 9:00 PT) and are held online on Zoom. To ensure you don’t miss out, sign up now to receive calendar invitations, and access links for each seminar on the day.

I want to sign up

If you sign up today, we’ll also invite you to our 12 December seminar with Anaclara Gerosa (University of Glasgow) about how to design and structure of computing activities for young learners, the final session in our 2023 series about primary (K-5) computing education.

The post Spotlight on teaching programming with and without AI in our 2024 seminar series appeared first on Raspberry Pi Foundation.

Support for new computing teachers: A tool to find Scratch programming errors

Post Syndicated from Bonnie Sheppard original https://www.raspberrypi.org/blog/support-new-computing-teachers-debugging-scratch-litterbox/

We all know that learning to program, and specifically learning how to debug or fix code, can be frustrating and leave beginners overwhelmed and disheartened. In a recent blog article, our PhD student Lauria at the Raspberry Pi Computing Education Research Centre highlighted the pivotal role that teachers play in shaping students’ attitudes towards debugging. But what about teachers who are coding novices themselves?

Two adults learn about computing at desktop computers.

In many countries, primary school teachers are holistic educators and often find themselves teaching computing despite having little or no experience in the field. In a recent seminar of our series on computing education for primary-aged children, Luisa Greifenstein told attendees that struggling with debugging and negative attitudes towards programming were among the top ten challenges mentioned by teachers.

Luisa Greifenstein.

Luisa is a researcher at the University of Passau, Germany, and has been working closely with both teacher trainees and experienced primary school teachers in Germany. She’s found that giving feedback to students can be difficult for primary school teachers, and especially for teacher trainees, as programming is still new to them. Luisa’s seminar introduced a tool to help.

A unique approach: Visualising debugging with LitterBox

To address this issue, the University of Passau has initiated the primary::programming project. One of its flagship tools, LitterBox, offers a unique solution to debugging and is specifically designed for Scratch, a beginners’ programming language widely used in primary schools.

A screenshot from the LitterBox tool.
You can upload Scratch program files to LitterBox to analyse them. Click to enlarge.

LitterBox serves as a static code debugging tool that transforms code examination into an engaging experience. With a nod to the Scratch cat, the tool visualises the debugging of Scratch code as checking the ‘litterbox’, categorising issues into ‘bugs’ and ‘smells’:

  • Bugs represent code patterns that have gone wrong, such as missing loops or specific blocks
  • Smells indicate that the code couldn’t be processed correctly because of duplications or unnecessary elements
A screenshot from the LitterBox tool.
The code patterns LitterBox recognises. Click to enlarge.

What sets LitterBox apart is that it also rewards correct code by displaying ‘perfumes’. For instance, it will praise correct broadcasting or the use of custom blocks. For every identified problem or achievement, the tool provides short and direct feedback.

A screenshot from the LitterBox tool.
LitterBox also identifies good programming practice. Click to enlarge.

Luisa and her team conducted a study to gauge the effectiveness of LitterBox. In the study, teachers were given fictitious student code with bugs and were asked to first debug the code themselves and then explain in a manner appropriate to a student how to do the debugging.

The results were promising: teachers using LitterBox outperformed a control group with no access to the tool. However, the team also found that not all hints proved equally helpful. When hints lacked direct relevance to the code at hand, teachers found them confusing, which highlighted the importance of refining the tool’s feedback mechanisms.

A bar chart showing that LitterBox helps computing teachers.

Despite its limitations, LitterBox proved helpful in another important aspect of the teachers’ work: coding task creation. Novice students require structured tasks and help sheets when learning to code, and teachers often invest substantial time in developing these resources. While LitterBox does not guide educators in generating new tasks or adapting them to their students’ needs, in a second study conducted by Luisa’s team, teachers who had access to LitterBox not only received support in debugging their own code but also provided more scaffolding in task instructions they created for their students compared to teachers without LitterBox.

How to maximise the impact of new tools: use existing frameworks and materials

One important realisation that we had in the Q&A phase of Luisa’s seminar was that many different research teams are working on solutions for similar challenges, and that the impact of this research can be maximised by integrating new findings and resources. For instance, what the LitterBox tool cannot offer could be filled by:

  • Pedagogical frameworks to enhance teachers’ lessons and feedback structures. Frameworks such as PRIMM (Predict, Run, Investigate, Modify, and Make) or TIPP&SEE for Scratch projects (Title, Instructions, Purpose, Play & Sprites, Events, Explore) can serve as valuable resources. These frameworks provide a structured approach to lesson design and teaching methodologies, making it easier for teachers to create engaging and effective programming tasks. Additionally, by adopting semantic waves in the feedback for teachers and students, a deeper understanding of programming concepts can be fostered. 
  • Existing courses and materials to aid task creation and adaptation. Our expert educators at the Raspberry Pi Foundation have not only created free lesson plans and courses for teachers and educators, but also dedicated non-formal learning paths for Scratch, Python, Unity, web design, and physical computing that can serve as a starting point for classroom tasks.

Exploring innovative ideas in computing education

As we navigate the evolving landscape of programming education, it’s clear that innovative tools like LitterBox can make a significant difference in the journey of both educators and students. By equipping educators with effective debugging and task creation solutions, we can create a more positive and engaging learning experience for students.

If you’re an educator, consider exploring how such tools can enhance your teaching and empower your students in their coding endeavours.

You can watch the recording of Luisa’s seminar here:

Sign up now to join our next seminar

If you’re interested in the latest developments in computing education, join us at one of our free, monthly seminars. In these sessions, researchers from all over the world share their innovative ideas and are eager to discuss them with educators and students. In our December seminar, Anaclara Gerosa (University of Edinburgh) will share her findings about how to design and structure early-years computing activities.

I want to sign up

This will be the final seminar in our series about primary computing education. Look out for news about the theme of our 2024 seminar series, which are coming soon.

The post Support for new computing teachers: A tool to find Scratch programming errors appeared first on Raspberry Pi Foundation.

Young children’s ScratchJr coding projects: Assessment and support

Post Syndicated from Diana Kirby original https://www.raspberrypi.org/blog/childrens-scratchjr-projects-assessment-support/

Block-based programming applications like Scratch and ScratchJr provide millions of children with an introduction to programming; they are a fun and accessible way for beginners to explore programming concepts and start making with code. ScratchJr, in particular, is designed specifically for children between the ages of 5 and 7, enabling them to create their own interactive stories and games. So it’s no surprise that they are popular tools for primary-level (K–5) computing teachers and learners. But how can teachers assess coding projects built in ScratchJr, where the possibilities are many and children are invited to follow their imagination?

Aim Unahalekhala
Aim Unahalekhala

In the latest seminar of our series on computing education for primary-aged children, attendees heard about two research studies that explore the use of ScratchJr in K–2 education. The speaker, Apittha (Aim) Unahalekhala, is a graduate researcher at the DevTech Research Group at Tufts University. The two studies looked at assessing young children’s ScratchJr coding projects and understanding how they create projects. Both of the studies were part of the Coding as Another Language project, which sees computer science as a new literacy for the 21st century, and is developing a literacy-based coding curriculum for K–2.

How to evaluate children’s ScratchJr projects

ScratchJr offers children 28 blocks to choose from when creating a coding project. Some of these are simple, such as blocks that determine the look of a character or setting, while others are more complex, such as messaging blocks and loops. Children can combine the blocks in many different ways to create projects of different levels of complexity.

A child select blocks for a ScratchJr project on a tablet.
Selecting blocks for a ScratchJr project

At the start of her presentation, Aim described a rubric that she and her colleagues at DevTech have developed to assess three key aspects of a ScratchJr coding project. These aspects are coding concepts, project design, and purposefulness.

  • Coding concepts in ScratchJr are sequencing, repeats, events, parallelism, coordination, and the number parameter
  • Project design includes elaboration (number of settings and characters, use of speech bubbles) and originality (character and background customisation, animated looks, sounds)

The rubric lets educators or researchers:

  • Assess learners’ ability to use their coding knowledge to create purposeful and creative ScratchJr projects
  • Identify the level of mastery of each of the three key aspects demonstrated within the project
  • Identify where learners might need more guidance and support
The elements covered by the ScratchJr project evaluation rubric.
The elements covered by the ScratchJr project evaluation rubric. Click to enlarge.

As part of the study, Aim and her colleagues collected coding projects from two schools at the start, middle, and end of a curriculum unit. They used the rubric to evaluate the coding projects and found that project scores increased over the course of the unit.

They also found that, overall, the scores for the project design elements were higher than those for coding concepts: many learners enjoyed spending lots of time designing their characters and settings, but made less use of other features. However, the two scores were correlated, meaning that learners who devoted a lot of time to the design of their project also got higher scores on coding concepts.

The rubric is a useful tool for any teachers using ScratchJr with their students. If you want to try it in your classroom, the validated rubric is free to download from the DevTech research group’s website.

How do young children create a project?

The rubric assesses the output created by a learner using ScratchJr. But learning is a process, not just an end outcome, and the final project might not always be an accurate reflection of a child’s understanding.

By understanding more about how young children create coding projects, we can improve teaching and curriculum design for early childhood computing education.

In the second study Aim presented, she set out to explore this question. She conducted a qualitative observation of children as they created coding projects at different stages of a curriculum unit, and used Google Analytics data to conduct a quantitative analysis of the steps the children took.

A Scratch project creation process involving iteration.
A project creation process involving iteration

Her findings highlighted the importance of encouraging young learners to explore the full variety of blocks available, both by guiding them in how to find and use different blocks, and by giving them the time and tools they need to explore on their own.

She also found that different teaching strategies are needed at different stages of the curriculum unit to support learners. This helps them to develop their understanding of both basic and advanced blocks, and to explore, customise, and iterate their projects.

Early-unit strategy:

  • Encourage free play to self-discover different functions, especially basic blocks

Mid-unit strategy:

  • Set plans on how long children will need on customising vs coding
  • More guidance on the advanced blocks, then let children explore

End-of-unit strategy:

  • Provide multiple sessions to work
  • Promote iteration by encouraging children to keep improving code and adding details
Teaching strategies for different stages of a ScratchJr curriculum.
Teaching strategies for different stages of the curriculum

You can watch Aim’s full presentation here:

You can also access the seminar slides here.

Join our next seminar on primary computing education

At our next seminar, we welcome Aman Yadav (Michigan State University), who will present research on computational thinking in primary school. The session will take place online on Tuesday 7 November at 17:00 UK time. Don’t miss out and sign up now:

I want to sign up to join

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

The post Young children’s ScratchJr coding projects: Assessment and support appeared first on Raspberry Pi Foundation.

Apply for a free UK teacher’s place at the WiPSCE conference

Post Syndicated from Bonnie Sheppard original https://www.raspberrypi.org/blog/free-uk-teacher-places-wipsce-conference-2023/

From 27 to 29 September 2023, we and the University of Cambridge are hosting the WiPSCE International Workshop on Primary and Secondary Computing Education Research for educators and researchers. This year, this annual conference will take place at Robinson College in Cambridge. We’re inviting all UK-based teachers of computing subjects to apply for one of five ‘all expenses paid’ places at this well-regarded annual event.

Educators and researchers mingle at a conference.

You could attend WiPSCE with all expenses paid

WiPSCE is where teachers and researchers discuss research that’s relevant to teaching and learning in primary and secondary computing education, to teacher training, and to related topics. You can find more information about the conference, including the preliminary programme, at wipsce.org. 

Educators and researchers listen to a talk at a conference.
Educators and researchers mingle at a conference.

As a teacher at the conference, you will:

  • Engage with high-quality international research in the field where you teach
  • Learn ways to use that research to develop your own classroom practice
  • Find out how to become an advocate in your professional community for research-informed approaches to the teaching of computing.

We are delighted that, thanks to generous funding from a funder, we can offer five free places to UK computing teachers, covering:

  • The registration fee
  • Two nights’ accommodation at Robinson College
  • Up to £500 supply costs paid to your school to cover your teaching
  • Up to £100 travel costs
I want to apply for a free place

The application deadline is Wednesday 19 July.

The application details

To be eligible to apply:

  1. You need to be a currently practising, UK-based teacher of Computing (England), Computing Science (Scotland), ICT or Digital Technologies (N. Ireland), or Computer Science (Wales)
  2. Your headteacher needs to be able to provide written confirmation that they are happy for you to attend WiPSCE
  3. You need to be available to attend the whole conference from Wednesday lunchtime to Friday afternoon
  4. You need to be willing to share what you learn from the conference with your colleagues at school and with your broader teaching community, including through writing an article about your experience and its relevance to your teaching for this blog or Hello World magazine

The application form will ask your for:

  • Your name and contact details
  • Demographic and school information
  • Your teaching experience
  • A statement of up to 500 words on why you’re applying and how you think your teaching practice, your school and your colleagues will benefit from your attendance at WiPSCE (500 words is the maximum, feel free to be concise)

After the 19 July deadline, we’re aiming to inform you of the outcome of your application on Friday 21 July. 

Apply today
Information materials at a conference.
Participants at the Clubs Conference.

Your application will be reviewed by the 2023 WiPSCE Chairs:

Sue and Mareen will:

  • Use the information you share in your form, particularly in your statement
  • Select applicants from a mix of primary and secondary schools, with a mix of years of computing teaching experience, and from a mix of geographic areas

Join us in strengthening research-informed computing classroom practice

We’d be delighted to receive your application. Being able to facilitate teachers’ attendance at the conference is very much aligned with our approach to research. Both at the Foundation and the Raspberry Pi Computing Education Research Centre, we’re committed to conducting research that’s directly relevant to schools and teachers, and to working in close collaboration with teachers.

We hope you are interested in attending WiPSCE and becoming an advocate for research-informed computing education practice. If your application is unsuccessful, we hope you consider coming along anyway. We’re looking forward to meeting you there. In the meantime, you can keep up with WiPSCE news on Twitter.

The post Apply for a free UK teacher’s place at the WiPSCE conference appeared first on Raspberry Pi Foundation.

Running a workshop with teachers to create culturally relevant Computing lessons

Post Syndicated from Katharine Childs original https://www.raspberrypi.org/blog/research-teacher-workshop-culturally-relevant-computing-lessons/

Who chooses to study Computing? In England, data from GCSE and A level Computer Science entries in 2019 shows that the answer is complex. Black Caribbean students were one of the most underrepresented groups in the subject, while pupils from other ethnic backgrounds, such as White British, Chinese, and Asian Indian, were well-represented. This picture is reflected in the STEM workforce in England, where Black people are also underrepresented.

Two young girls, one of them with a hijab, do a Scratch coding activity together at a desktop computer.

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.

A tree symbolising culturally relevant pedagogy,with the roots labeled 'curriculum, the trunk labeled 'teaching approaches', and the crown labeled 'learning materials'.
Learning materials, teaching approaches, and the curriculum as a whole are three areas where culturally relevance is important.

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.

An example of a collaborative activity from a teacher-focused workshop around culturally relevant pedagogy.
An example of a collaborative activity from the workshop

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).

Descriptions of a classroom unit of teaching materials about photo editing for Year 4 (ages 8–9), and a unit about vector graphics for Year 5 (ages 9–10).
We based the workshop around two Computing Curriculum units that cover digital literacy skills.

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.

A teacher attending a training workshop laughs as she works through an activity.
The workshop was a positive experience for the teachers.

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.

A computing classroom filled with learners
As part of the project, we observed teachers delivering the adapted units to their learners.

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.

Introducing data science concepts and skills to primary school learners

Post Syndicated from Katharine Childs original https://www.raspberrypi.org/blog/data-science-data-literacy-primary-school-scotland/

Every day, most of us both consume and create data. For example, we interpret data from weather forecasts to predict our chances of a good weather for a special occasion, and we create data as our carbon footprint leaves a trail of energy consumption information behind us. Data is important in our lives, and countries around the world are expanding their school curricula to teach the knowledge and skills required to work with data, including at primary (K–5) level.

Kate Farrell
Kate Farrell
Prof. Judy Robertson

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.

Becoming a data citizen involves finding meaning in data, controlling your personal data trail, being a critical consumer of data, and taking action based on data.
Data citizens have skills they need to thrive in a world shaped by digital technology.

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.

A child arranges objects to visualise data.
Sorting food waste from lunch by type of material

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 PPDAC project cycle.
The PPDAC data problem-solving cycle

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.

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

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

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.

I want to sign up for the next seminar

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.

Integrating primary computing and literacy through multimodal storytelling

Post Syndicated from Veronica Cucuiat original https://www.raspberrypi.org/blog/primary-computing-programming-literacy-storytelling/

Broadening participation and finding new entry points for young people to engage with computing is part of how we pursue our mission here at the Raspberry Pi Foundation. It was also the focus of our March online seminar, led by our own Dr Bobby Whyte. In this third seminar of our series on computing education for primary-aged children, Bobby presented his work on ‘designing multimodal composition activities for integrated K-5 programming and storytelling’. In this research he explored the integration of computing and literacy education, and the implications and limitations for classroom practice.

Young learners at computers in a classroom.

Motivated by challenges Bobby experienced first-hand as a primary school teacher, his two studies on the topic contribute to the body of research aiming to make computing less narrow and difficult. In this work, Bobby integrated programming and storytelling as a way of making the computing curriculum more applicable, relevant, and contextualised.

Critically for computing educators and researchers in the area, Bobby explored how theories related to ‘programming as writing’ translate into practice, and what the implications of designing and delivering integrated lessons in classrooms are. While the two studies described here took place in the context of UK schooling, we can learn universal lessons from this work.

What is multimodal composition?

In the seminar Bobby made a distinction between applying computing to literacy (or vice versa) and true integration of programming and storytelling. To achieve true integration in the two studies he conducted, Bobby used the idea of ‘multimodal composition’ (MMC). A multimodal composition is defined as “a composition that employs a variety of modes, including sound, writing, image, and gesture/movement [… with] a communicative function”.

Storytelling comes together with programming in a multimodal composition as learners create a program to tell a story where they:

  • Decide on content and representation (the characters, the setting, the backdrop)
  • Structure text they’ve written
  • Use technical aspects (i.e. motion blocks, tension) to achieve effects for narrative purposes
A screenshot showing a Scratch project.
Defining multimodal composition (MMC) for a visual programming context

Multimodality for programming and storytelling in the classroom

To investigate the use of MMC in the classroom, Bobby started by designing a curriculum unit of lessons. He mapped the unit’s MMC activities to specific storytelling and programming learning objectives. The MMC activities were designed using design-based research, an approach in which something is designed and tested iteratively in real-world contexts. In practice that means Bobby collaborated with teachers and students to analyse, evaluate, and adapt the unit’s activities.

A list of learning objectives that could be covered by a multimodal composition activity.
Mapping of the MMC activities to storytelling and programming learning objectives

The first of two studies to explore the design and implementation of MMC activities was conducted with 10 K-5 students (age 9 to 11) and showed promising results. All students approached the composition task multimodally, using multiple representations for specific purposes. In other words, they conveyed different parts of their stories using either text, sound, or images.

Bobby found that broadcast messages and loops were the least used blocks among the group. As a consequence, he modified the curriculum unit to include additional scaffolding and instructional support on how and why the students might embed these elements.

A list of modifications to the MMC curriculum unit based on testing in a classroom.
Bobby modified the classroom unit based on findings from his first study

In the second study, the MMC activities were evaluated in a classroom of 28 K-5 students led by one teacher over two weeks. Findings indicated that students appreciated the longer multi-session project. The teacher reported being satisfied with the project work the learners completed and the skills they practised. The teacher also further integrated and adapted the unit into their classroom practice after the research project had been completed.

How might you use these research findings?

Factors that impacted the integration of storytelling and programming included the teacher’s confidence to teach programming as well as the teacher’s ability to differentiate between students and what kind of support they needed depending on their previous programming experience.

In addition, there are considerations regarding the curriculum. The school where the second study took place considered the activities in the unit to be literacy-light, as the English literacy curriculum is ‘text-heavy’ and the addition of multimodal elements ‘wastes’ opportunities to produce stories that are more text-based.

Woman teacher and female student at a laptop.

Bobby’s research indicates that MMC provides useful opportunities for learners to simultaneously pursue storytelling and programming goals, and the curriculum unit designed in the research proved adaptable for the teacher to integrate into their classroom practice. However, Bobby cautioned that there’s a need to carefully consider both the benefits and trade-offs when designing cross-curricular integration projects in order to ensure a fair representation of both subjects.

Can you see an opportunity for integrating programming and storytelling in your classroom? Let us know your thoughts or questions in the comments below.

You can watch Bobby’s full presentation:

And you can read his research paper Designing for Integrated K-5 Computing and Literacy through Story-making Activities (open access version).

You may also be interested in our pilot study on using storytelling to teach computing in primary school, which we conducted as part of our Gender Balance in Computing programme.

Join our next seminar on primary computing education

At our next seminar, we welcome Kate Farrell and Professor Judy Robertson (University of Edinburgh). This session will introduce you to how data literacy can be taught in primary and early-years education across different curricular areas. It will take place online on Tuesday 9 May at 17.00 UK time, don’t miss out and sign up now.

I want to sign up to join the seminar

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

The post Integrating primary computing and literacy through multimodal storytelling appeared first on Raspberry Pi Foundation.

Combining computing and maths to teach primary learners about variables

Post Syndicated from Katharine Childs original https://www.raspberrypi.org/blog/variables-primary-school-computing-maths-education-seminar/

In our first seminar of 2023, we were delighted to welcome Dr Katie Rich and Carla Strickland. They spoke to us about teaching the programming construct of variables in Grade 3 and 4 (age 8 to 10).

Dr Katie Rich
Dr Katie Rich
Carla Strickland
Carla Strickland

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.

Young person using Scratch.

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.

Four of the five levels of thinking identified in the study: Data storer, data user, variable user, variable creator.
Four of the five levels of thinking identified in the study: Data Storer, Data User, Variable User, Variable Creator. Click to enlarge.

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.

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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.

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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.

A learner does physical computing in the primary school classroom.

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.

I want to sign up to join a seminar

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.

Gender Balance in Computing — the big picture

Post Syndicated from Sue Sentance original https://www.raspberrypi.org/blog/gender-balance-in-computing-big-picture/

Improving gender balance in computing is part of our work to ensure equitable learning opportunities for all young people. Our Gender Balance in Computing (GBIC) research programme has been the largest effort to date to explore ways to encourage more girls and young women to engage with Computing.

A girl in a university computing classroom.

Commissioned by the Department for Education in England and led by the Raspberry Pi Foundation as part of our National Centre for Computing Education work, the GBIC programme was a collaborative effort involving the Behavioural Insights Team, Apps for Good, and the WISE Campaign.

Gender Balance in Computing ran from 2019 to 2022 and comprised seven studies relating to five different research areas:

  • Teaching Approach:
  • Belonging: Supporting learners to feel that they “belong” in computer science
  • Non-formal Learning: Establishing the connections between in-school and out-of-school computing
  • Relevance: Making computing relatable to everyday life
  • Subject Choice: How computer science is presented to young people as a subject choice 

In December we published the last of seven reports describing the results of the programme. In this blog post I summarise our overall findings and reflect on what we’ve learned through doing this research.

Gender balance in computing is not a new problem

I was fascinated to read a paper by Deborah Butler from 2000 which starts by summarising themes from research into gender balance in computing from the 1980s and 1990s, for example that boys may have access to more role models in computing and may receive more encouragement to pursue the subject, and that software may be developed with a bias towards interests traditionally considered to be male. Butler’s paper summarises research from at least two decades ago — have we really made progress?

A computing classroom filled with learners.

In England, it’s true that making Computing a mandatory subject from age 5 means we have taken great strides forward; the need for young people to make a choice about studying the subject only arises at age 14. However, statistics for England’s externally assessed high-stakes Computer Science courses taken at ages 14–16 (GCSE) and 16–18 (A level) clearly show that, although there is a small upwards trend in the proportion of female students, particularly for A level, gender balance among the students achieving GCSE/A level qualifications remains an issue:

Computer Science qualification (England): In 2018: In 2021: In 2022:
GCSE (age 16) 20.41% 20.77% 21.37%
A level (age 18) 11.74% 14.71% 15.17%
Percentage of girls among the students achieving Computer Science qualifications in England’s secondary schools

What did we do in the Gender Balance in Computing programme?

In GBIC, we carried out a range of research studies involving more than 14,500 pupils and 725 teachers in England. Implementation teams came from the Foundation, Apps For Good, the WISE Campaign, and the Behavioural Insights Team (BIT). A separate team at BIT acted as the independent evaluators of all the studies.

In total we conducted the following studies:

  • Two feasibility studies: Storytelling; Relevance, which led to a full randomised controlled trial (RCT)
  • Five RCTs: Belonging; Peer Instruction; Pair Programming; Relevance, which was preceded by a feasibility study; Non-formal Learning (primary)
  • One quasi-experimental study: Non-formal Learning (secondary)
  • One exploratory research study: Subject Choice (Subject choice evenings and option booklets)

Each study (apart from the exploratory research study) involved a 12-week intervention in schools. Bespoke materials were developed for all the studies, and teachers received training on how to deliver the intervention they were a part of. For the RCTs, randomisation was done at school level: schools were randomly divided into treatment and control groups. The independent evaluators collected both quantitative and qualitative data to ensure that we gained comprehensive insights from the schools’ experiences of the interventions. The evaluators’ reports and our associated blog posts give full details of each study.

The impact of the pandemic

The research programme ran from 2019 to 2022, and as it was based in schools, we faced a lot of challenges due to the coronavirus pandemic. Many research programmes meant to take place in school were cancelled as soon as schools shut during the pandemic.

A learner and a teacher in a computing classroom.

Although we were fortunate that GBIC was allowed to continue, we were not allowed to extend the end date of the programme. Thus our studies were compressed into the period after schools reopened and primarily delivered in the academic year 2021/2022. When schools were open again, the implementation of the studies was affected by teacher and pupil absences, and by schools necessarily focusing on making up some of the lost time for learning.

The overall results of Gender Balance in Computing

Quantitatively, none of the RCTs showed a statistically significant impact on the primary outcome measured, which was different in different trials but related to either learners’ attitudes to computer science or their intention to study computer science. Most of the RCTs showed a positive impact that fell just short of statistical significance. The evaluators went to great lengths to control for pandemic-related attrition, and the implementation teams worked hard to support teachers in still delivering the interventions as designed, but attrition and disruptions due to the pandemic may have played a part in the results.

Woman teacher and female students at a computer

The qualitative research results were more encouraging. Teachers were enthusiastic about the approaches we had chosen in order to address known barriers to gender balance, and the qualitative data indicated that pupils reacted positively to the interventions. One key theme across the Teaching Approach (and other) studies was that girls valued collaboration and teamwork. The data also offered insights that enable us to improve on the interventions.

We designed the studies so they could act as pilots that may be rolled out at a national scale. While we have gained sufficient understanding of what works to be able to run the interventions at a larger scale, two particular learnings shape our view of what a large-scale study should look like:

1. A single intervention may not be enough to have an impact

The GBIC results highlight that there is no quick fix and suggest that we should combine some of the approaches we’ve been trialling to provide a more holistic approach to teaching Computing in an equitable way. We would recommend that schools adopt several of the approaches we’ve tested; the materials associated with each intervention are freely available (see our blog posts for links).

2. Age matters

One of the very interesting overall findings from this research programme was the difference in intent to study Computing between primary school and secondary school learners; fewer secondary school learners reported intent to study the subject further. This difference was observed for both girls and boys, but was more marked for girls, as shown in the graph below. This suggests that we need to double down on supporting children, especially girls, to maintain their interest in Computing as they enter secondary school at age 11. It also points to a need for more longitudinal research to understand more about the transition period from primary to secondary school and how it impacts children’s engagement with computer science and technology in general.

Bar graph showing that in the Gender Balance in Computing research programme, learners intent to continue studying computing was lower in secondary school than primary school, and that this difference is more pronounced for girls.
Compared to primary school age girls, girls aged 12 to 13 show dramatically reduced intent to continue studying computing.

What’s next?

We think that more time (in excess of 12 weeks) is needed to both deliver the interventions and measure their outcome, as the change in learners’ attitudes may be slow to appear, and we’re hoping to engage in more longitudinal research moving forward.

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

We know that an understanding of computer science can improve young people’s access to highly skilled jobs involving technology and their understanding of societal issues, and we need that to be available to all. However, gender balance relating to computing and technology is a deeply structural issue that has existed for decades throughout the computing education and workplace ecosystem. That’s why we intend to pursue more work around a holistic approach to improving gender balance, aligning with our ongoing research into making computing education culturally relevant.

Stay in touch

We are very keen to continue to build on our research on gender balance in computing. If you’d like to support us in any way, we’d love to hear from you. To explore the research projects we’re currently involved in, check out our research pages and visit the website of the Raspberry Pi Computing Education Research Centre at the University of Cambridge.

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Combining research and practice to evaluate and improve computing education in non-formal settings

Post Syndicated from Bonnie Sheppard original https://www.raspberrypi.org/blog/research-practice-evaluate-improve-computing-education-non-formal-settings-seminar/

In the final seminar in our series on cross-disciplinary computing, Dr Tracy Gardner and Rebecca Franks, who work here at the Foundation, described the framework underpinning the Foundation’s non-formal learning pathways. They also shared insights from our recently published literature review about the impact that non-formal computing education has on learners.

Dr Tracy Gardner.
Dr Tracy Gardner
Rebecca Franks
Rebecca Franks

Tracy and Rebecca both have extensive experience in teaching computing, and they are passionate about inspiring young learners and broadening access to computing education. In their work here, they create resources and content for learners in coding clubs and young people at home.

How non-formal learning creates opportunities for computing education

UNESCO defines non-formal learning as “institutionalised, intentional, and planned… an addition, alternative, and/or complement to formal education within the process of life-long learning of individuals”. In terms of computing education, this kind of learning happens in after-school programmes or children’s homes as they engage with materials that have been carefully designed by education providers.

Two children code on laptops while an adult supports them.
Three young people code at laptops in a CoderDojo.

At the Raspberry Pi Foundation, we support two global networks of free, volunteer-led coding clubs where regular non-formal learning takes place: Code Club, teacher- and volunteer-led coding clubs for 9- to 13-year-olds taking place in schools in more than160 countries; and CoderDojo, volunteer-led programming clubs for young people aged 7–17 taking place in community venues and offices in 100 countries. Through free learning resources and other support, we enable volunteers to run their club sessions, offering versatile opportunities and creative, inclusive spaces for young people to learn about computing outside of the school curriculum. Volunteers who run Code Clubs or CoderDojos report that participating in the club sessions positively impacts participants’ programming skills and confidence.

Rebecca and Tracy are part of the team here that writes the learning resources young people in Code Clubs and CoderDojos (and beyond) use to learn to code and create technology. 

Helping learners make things that matter to them

Rebecca started the seminar by describing how the team reviewed existing computing pedagogy research into non-formal learning, as well as large amounts of website visitor data and feedback from volunteers, to establish a new framework for designing and creating coding resources in the form of learning paths.

What the Raspberry Pi Foundation takes into account when creating non-formal learning resources: what young people are making, young people's interests, research, user data, our own experiences as educators, the Foundation's other educational offers, ideas of purpose-driven computing.
What the Raspberry Pi Foundation takes into account when creating non-formal learning resources. Click to enlarge.

As Rebecca explained, non-formal learning paths should be designed to bridge the so-called ‘Turing tar-pit’: the gap between what learners want to do, and what they have the knowledge and resources to achieve.

The Raspberry Pi Foundation's non-formal learning resources bridge the so-called Turing tar pit, in which learners get stuck when they feel everything is possible to create, but nothing is easy.

To prevent learners from getting frustrated and ultimately losing interest in computing, learning paths need to:

  • Be beginner-friendly
  • Include scaffolding
  • Support learner’s design skills
  • Relate to things that matter to learners

When Rebecca and Tracy’s team create new learning paths, they first focus on the things that learners want to make. Then they work backwards to bridge the gap between learners’ big ideas and the knowledge and skills needed to create them. To do this, they use the 3…2…1…Make! framework they’ve developed.

An illustration of the 3-2-1 structure of the new Raspberry Pi Foundation coding project paths.
An illustration of the 3…2…1…Make! structure of the new Raspberry Pi Foundation non-formal learning paths.

Learning paths designed according to the framework are made up of three different types of project in a 3-2-1 structure:

  • Three Explore projects to introduce creators to a set of skills and provide step-by-step instructions to help them develop initial confidence
  • Two Design projects to allow creators to practise the skills they learned in the previous Explore projects, and to express themselves creatively while they grow in independence
  • One Invent project where creators use their skills to meet a project brief for a particular audience

You can learn more about the framework in this blog post and this guide for adults who run sessions with young people based on the learning paths. And you can explore the learning paths yourself too.

Rebecca and Tracy’s team have created several new learning pathways based on the 3…2…1…Make! framework and received much positive feedback on them. They are now looking to develop more tools and libraries to support learners, to increase the accessibility of the paths, and also to conduct research into the impact of the framework. 

New literature review of non-formal computing education showcases its positive impact

In the second half of the seminar, Tracy shared what the research literature says about the impact of non-formal learning. She and researchers at the Foundation particularly wanted to find out what the research says about computing education for K–12 in non-formal settings. They systematically reviewed 421 papers, identifying 88 papers from the last seven years that related to empirical research on non-formal computing education for young learners. Based on these 88 papers, they summarised the state of the field in a literature review.

So far, most studies of non-formal computing education have looked at knowledge and skill development in computing, as well as affective factors such as interest and perception. The cognitive impact of non-formal education has been generally positive. The papers Tracy and the research reviewed suggested that regular learning opportunities, such as weekly Code Clubs, were beneficial for learners’ knowledge development, and that active teaching of problem solving skills can lead to learners’ independence.

In the literature review the Raspberry Pi Foundation team conducted, most research studies were university-organised on projects to broaden participation and interest development in immersive multi-day settings.

Non-formal computing education also seems to be beneficial in terms of affective factors (although it is unclear yet whether the benefits remain long-term, since most existing research studies conducted have been short-term ones). For example, out-of-school programmes can lead to more positive perception and increased awareness of computing for learners, and also boost learners’ confidence and self-efficacy if they have had little prior experience of computing. The social aspects of participating in coding clubs should not be underestimated, as learners can develop a sense of belonging and support as they work with their peers and mentors.

The affordances of non-formal computing activities that complement formal education: access and awareness, cultural relevance and equity, practice and personalisation, fun and engagement, community and identity, immediate impact.

The literature review showed that non-formal computing complements formal in-school education in many ways. Not only can Code Clubs and CoderDojos be accessible and equitable spaces for all young people, because the people who run them can tailor learning to the individuals. Coding clubs such as these succeed in making computing fun and engaging by enabling a community to form and allowing learners to make things that are meaningful to them.

What existing studies in non-formal computing aren’t telling us

Another thing the literature review made obvious is that there are big gaps in the existing understanding of non-formal computing education that need to be researched in more detail. For example, most of the studies the papers in the literature review described took place with female students in middle schools in the US.

That means the existing research tells us little about non-formal learning:

  • In other geographic locations
  • In other educational settings, such as primary schools or after-school programmes
  • For a wider spectrum of learners

We would also love to see studies that hone in on:

  • The long-term impact of non-formal learning
  • Which specific factors contribute to positive outcomes
  • Non-formal learning about aspects of computing beyond programming

3…2…1…research!

We’re excited to continue collaborating within the Foundation so that our researchers and our team creating non-formal learning content can investigate the impact of the 3…2…1…Make! framework.

At Coolest Projects, a group of people explore a coding project.
The aim of the 3…2…1…Make! framework is to enable young people to create things and solve problems that matter to them using technology.

This collaboration connects two of our long-term strategic goals: to engage millions of young people in learning about computing and how to create with digital technologies outside of school, and to deepen our understanding of how young people learn about computing and how to create with digital technologies, and to use that knowledge to increase the impact of our work and advance the field of computing education. Based on our research, we will iterate and improve the framework, in order to enable even more young people to realise their full potential through the power of computing and digital technologies. 

Join our seminar series on primary computing education

From January, you can join our new monthly seminar series on primary (K–5) teaching and learning. In this series, we’ll hear insights into how our youngest learners develop their computing knowledge, so whether you’re a volunteer in a coding club, a teacher, a researcher, or simply interested in the topic, we’d love to see you at one of these monthly online sessions.

The first seminar, on Tuesday 10 January at 5pm UK time, will feature researchers and educators Dr Katie Rich and Carla Strickland. They will share findings on how to teach children about variables, one of the most difficult aspects of computing for young learners. Sign up now, and we will send you notifications and joining links for each seminar session.

I want to sign up to attend

We look forward to seeing you soon, and to discussing with you how we can apply research results to better support all our learners.

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