In our 50th research seminar, Nicholas Gardella, a PhD candidate at the University of Virginia, shared insights from his research on the effects of AIDEs on beginner programmers’ skills.

Measuring AI’s impact on students

AI tools are becoming a big part of software development, but what does that mean for students learning to code? As tools like GitHub Copilot become more common, it’s crucial to ask: Do these tools help students to learn better and work more effectively, especially when time is tight?

This is precisely what Nicholas’s research aims to identify by examining the impact of AIDEs on four key areas:

• Performance (how well students completed the tasks)
• Workload (the effort required)
• Emotion (their emotional state during the task)
• Self-efficacy (their belief in their own abilities to succeed)

Nicholas conducted his study with 17 undergraduate students from an introductory computer science course, who were mostly first-time programmers, with different genders and backgrounds.

The students completed programming tasks both with and without the assistance of GitHub Copilot. Nicholas selected the tasks from OpenAI’s human evaluation data set, ensuring they represented a range of difficulty levels. He also used a repeated measures design for the study, meaning that each student had the opportunity to program both independently and with AI assistance multiple times. This design helped him to compare individual progress and attitudes towards using AI in programming.

Less workload, more performance and self-efficacy in learning

The results were promising for those advocating AI’s role in education. Nicholas’s research found that participants who used GitHub Copilot performed better overall, completing tasks with less mental workload and effort compared to solo programming.

However, the immediate impact on students’ emotional state and self-confidence was less pronounced. Initially, participants did not report feeling more confident while coding with AI. Over time, though, as they became more familiar with the tool, their confidence in their abilities improved slightly. This indicates that students need time and practice to fully integrate AI into their learning process. Students increasingly attributed their progress not to the AI doing the work for them, but to their own growing proficiency in using the tool effectively. This suggests that with sustained practice, students can gain confidence in their abilities to work with AI, rather than becoming overly reliant on it.

A particularly important takeaway from the talk was the reduction in workload when using AI tools. Novice programmers, who often find programming challenging, reported that AI assistance lightened the workload. This reduced effort could create a more relaxed learning environment, where students feel less overwhelmed and more capable of tackling challenging tasks.

However, while workload decreased, use of the AI tool did not significantly boost emotional satisfaction or happiness during the coding process. Nicholas explained that although students worked more efficiently, using the AI tool did not necessarily make coding a more enjoyable experience. This highlights a key challenge for educators: finding ways to make learning both effective and engaging, even when using advanced tools like AI.

AI as a tool for collaboration, not replacement

Nicholas’s findings raise interesting questions about how AI should be introduced in computer science education. While tools like GitHub Copilot can enhance performance, they should not be seen as shortcuts for learning. Students still need guidance in how to use these tools responsibly. Importantly, the study showed that students did not take credit for the AI tool’s work — instead, they felt responsible for their own progress, especially as they improved their interactions with the tool over time.

Students might become better programmers when they learn how to work alongside AI systems, using them to enhance their problem-solving skills rather than relying on them for answers. This suggests that educators should focus on teaching students how to collaborate with AI, rather than fearing that these tools will undermine the learning process.

Bridging research and classroom realities

Moreover, the study touched on an important point about the limits of its findings. Since the experiment was conducted in a controlled environment with only 17 participants, researchers need to conduct further studies to explore how AI tools perform in real-world classroom settings. For example, the role of internet usage plays a fundamental role. It will be relevant to understand how factors such as class size, prior varying experience, and the age of students affect their ability to integrate AI into their learning.

In the follow-up discussion, Nicholas also demonstrated how AI tools are becoming more accessible within browsers and how teachers can integrate AI-driven development environments more easily into their courses. By making AI technology more readily available, these tools are democratising access to advanced programming aids, enabling students to build applications directly in their web browsers with minimal setup.

The path ahead

Nicholas’s talk provided an insightful look into the evolving relationship between AI tools and novice programmers. While AI can improve performance and reduce workload, it is not a magic solution to all the challenges of learning to code.

Based on the discussion after the talk, educators should support students in developing the skills to use these tools effectively, shaping an environment where they can feel confident working with AI systems. The researchers and educators agreed that more research is needed to expand on these findings, particularly in more diverse and larger-scale educational settings. 

As AI continues to shape the future of programming education, the role of educators will remain crucial in guiding students towards responsible and effective use of these technologies, as we are only at the beginning.

Join our next seminar

In our current seminar series, we are exploring how to teach programming with and without AI technology. Join us at our next seminar on Tuesday, 10 December at 17:00–18:30 GMT to hear Leo Porter (UC San Diego) and Daniel Zingaro (University of Toronto) discuss how they are working to create an introductory programming course for majors and non-majors that fully incorporates generative AI into the learning goals of the course. 

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

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

The post Does AI-assisted coding boost novice programmers’ skills or is it just a shortcut? appeared first on Raspberry Pi Foundation.

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

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

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

Using generative AI tools to create personalised programming exercises

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

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

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

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

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

Responding to help requests using large language models

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

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

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

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

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

Teaching students how to prompt large language models

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

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

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

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

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

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

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

Further resources

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

Join our next seminar

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

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

The post Using generative AI to teach computing: Insights from research appeared first on Raspberry Pi Foundation.

At times, it can seem like everything is being automated with AI. However, there are some parts of learning to program that cannot (and probably should not) be automated, such as understanding errors in code and how to fix them. Manually typing code might not be necessary in the future, but it will still be crucial to understand the code that is being generated and how to improve and develop it. 

As important as debugging might be for the future of programming, it’s still often the task most disliked by novice programmers. Even if program error messages can be explained in the future or tools like LitterBox can flag bugs in an engaging way, actually fixing the issues involves time, effort, and resilience — which can be hard to come by at the end of a computing lesson in the late afternoon with 30 students crammed into an IT room. 

But what is it about debugging that young people find so hard, even when they’re given enough time to do it? And how can we make debugging a more motivating experience for young people? These are two of the questions that Laurie Gale, a PhD student at the Raspberry Pi Computing Education Research Centre, focused on in our July seminar.

Why do students find debugging hard?

Laurie has spent the past two years talking to teachers and students and developing tools (a visualiser of students’ programming behaviour and PRIMMDebug, a teaching process and tool for debugging) to understand why many secondary school students struggle with debugging. It has quickly become clear through his research that most issues are due to problematic debugging strategies and students’ negative experiences and attitudes.

When students first start learning how to program, they have to remember a vast amount of new information, such as different variables, concepts, and program designs. Utilising this knowledge is often challenging because they’re already busy juggling all the content they’ve previously learnt and the challenges of the programming task at hand. When error messages inevitably appear that are confusing or misunderstood, it can become extremely difficult to debug effectively. 

Given this information overload, students often don’t develop efficient strategies for debugging. When Laurie analysed the debugging efforts of 12- to 14-year-old secondary school students, he noticed some interesting differences between students who were more and less successful at debugging. While successful students generally seemed to make less frequent and more intentional changes, less successful students tinkered frequently with their broken programs, making one- or two-character edits before running the program again. In addition, the less successful students often ran the program soon after beginning the debugging exercise without allowing enough time to actually read the code and understand what it was meant to do. 

The issue with these behaviours was that they often resulted in students adding errors when changing the program, which then compounded and made debugging increasingly difficult with each run. 74% of students also resorted to spamming, pressing ‘run’ again and again without changing anything. This strategy resonated with many of our seminar attendees, who reported doing the same thing after becoming frustrated. 

Educators need to be aware of the negative consequences of students’ exasperating and often overwhelming experiences with debugging, especially if students are less confident in their programming skills to begin with. Even though spending 15 minutes on an exercise shows a remarkable level of tenaciousness and resilience, students’ attitudes to programming — and computing as a whole — can quickly go downhill if their strategies for identifying errors prove ineffective. Debugging becomes a vicious circle: if a student has negative experiences, they are less confident when having to bug-fix again in the future, which can lead to another set of unsuccessful attempts, which can further damage their confidence, and so on. Avoiding this downward spiral is essential. 

Approaches to help students engage with debugging

Laurie stresses the importance of understanding the cognitive challenges of debugging and using the right tools and techniques to empower students and support them in developing effective strategies.

Some ideas of how to improve debugging skills that were mentioned by Laurie and our attendees included:

• Using frame-based editing tools for novice programmers because such tools encourage students to focus on logical errors rather than accidental syntax errors, which can distract them from understanding the issues with the program. Teaching debugging should also go hand in hand with understanding programming syntax and using simple language. As one of our attendees put it, “You wouldn’t give novice readers a huge essay and ask them to find errors.”
• Making error messages more understandable, for example, by explaining them to students using Large Language Models.
• Teaching systematic debugging processes. There are several different approaches to doing this. One of our participants suggested using the scientific method (forming a hypothesis about what is going wrong, devising an experiment that will provide information to see whether the hypothesis is right, and iterating this process) to methodically understand the program and its bugs. 

Most importantly, debugging should not be a daunting or stressful experience. Everyone in the seminar agreed that creating a positive error culture is essential. 

Some ideas you could explore in your classroom include:

• Normalising errors: Stress how normal and important program errors are. Everyone encounters them — a professional software developer in our audience said that they spend about half of their time debugging. 
• Rewarding perseverance: Celebrate the effort, not just the outcome.
• Modelling how to fix errors: Let your students write buggy programs and attempt to debug them in front of the class.

In a welcoming classroom where students are given support and encouragement, debugging can be a rewarding experience. What may at first appear to be a failure — even a spectacular one — can be embraced as a valuable opportunity for learning. As a teacher in Laurie’s study said, “If something should have gone right and went badly wrong but somebody found something interesting on the way… you celebrate it. Take the fear out of it.” 

Watch the recording of Laurie’s presentation:

Join our next seminar

In our current seminar series, we are exploring how to teach programming with and without AI.

Join us at our next seminar on Tuesday, 12 November at 17:00–18:30 GMT to hear Nicholas Gardella (University of Virginia) discuss the effects of using tools like GitHub Copilot on the motivation, workload, emotion, and self-efficacy of novice programmers. To sign up and take part in the seminar, click the button below — we’ll then send you information about joining. We hope to see you there.

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

The post How to make debugging a positive experience for secondary school students appeared first on Raspberry Pi Foundation.

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}”. 

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.

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.

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). 

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). 

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.

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.

2. 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.

3. 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 veronica.cucuiat@raspberrypi.org 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.

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

The post How useful do teachers find error message explanations generated by AI? Pilot research results appeared first on Raspberry Pi Foundation.

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.

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. 

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.

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.

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.

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.

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.

However, we know that busy teachers do not have time to keep abreast of all the latest research. This is where our Pedagogy Quick Reads come in. They show teachers how an area of current research either has been or could be applied in practice. 

Our new Pedagogy Quick Read summarises the central tenets of culturally relevant pedagogy (the theory) and then lays out 10 areas of opportunity as concrete ways for you to put the theory into practice.

Why is culturally relevant pedagogy necessary?

Computing remains an area where many groups of people are underrepresented, including those marginalised because of their gender, ethnicity, socio-economic background, additional educational needs, or age. For example, recent stats in the BCS’ Annual Diversity Report 2023 record that in the UK, the proportion of women working in tech was 20% in 2021, and Black women made up only 0.7% of tech specialists. Beyond gender and ethnicity, pupils who have fewer social and economic opportunities ‘don’t see Computing as a subject for somebody like them’, a recent report from Teach First found. 

The fact that in the UK, 94% of girls and 79% of boys drop Computing at age 14 should be of particular concern for Computing educators. This last statistic makes it painfully clear that there is much work to be done to broaden the appeal of Computing in schools. One approach to make the subject more inclusive and attractive to young people is to make it more culturally relevant. 

As part of our research to help teachers effectively adapt their curriculum materials to make them culturally relevant and engaging for their learners, we’ve identified 10 areas of opportunity — areas where teachers can choose to take actions to bring the latest research on culturally relevant pedagogy into their classrooms, right here, right now. 

Applying the areas of opportunity in your classroom

The Pedagogy Quick Read gives teachers ideas for how they can use the areas of opportunity (AOs) to begin to review their own curriculum, teaching materials, and practices. We recommend picking one area initially, and focusing on that perhaps for a term. This helps you avoid being overwhelmed, and is particularly useful if you are trying to reach a particular group, for example, Year 9 girls, or low-attaining boys, or learners who lack confidence or motivation. 

For example, one simple intervention is AO1 ‘Finding out more about our learners’. It’s all too easy for teachers to assume that they know what their students’ interests are. And getting to know your students can be especially tricky at secondary level, when teachers might only see a class once a fortnight or in a carousel. 

However, finding out about your learners can be easily achieved in an online survey homework task, set at the beginning of a new academic year or term or unit of work. Using their interests, along with considerations of their backgrounds, families, and identities as inputs in curriculum planning can have tangible benefits: students may begin to feel an increased sense of belonging when they see their interests or identities reflected in the material later used. 

How we’re using the AOs

The Quick Read presents two practical case studies of how we’ve used the 10 AO to adapt and assess different lesson materials to increase their relevance for learners. 

Case study 1: Teachers in UK primary school adapt resources

As we’ve shared before, we implemented culturally relevant pedagogy as part of UK primary school teachers’ professional development in a recent research project. The Quick Read provides details of how we supported teachers to use the AOs to adapt teaching material to make it more culturally relevant to learners in their own contexts. Links to the resources used to review 2 units of work, lesson by lesson, to adapt tasks, learning material, and outcomes are included in the Quick Read. 

Case study 2: Reflecting on the adaption of resources for a vocational course for young adults in a Kenyan refugee camp

In a different project, we used the AOs to reflect on our adaptation of classroom materials from The Computing Curriculum, which we had designed for schools in England originally. Partnering with Amala Education, we adapted Computing Curriculum materials to create a 100-hour course for young adults at Kakuma refugee camp in Kenya who wanted to develop vocational digital literacy skills. 

The diagram below shows our ratings of the importance of applying each AO while adapting materials for this particular context. In this case, the most important areas for making adaptations were to make the context more culturally relevant, and to improve the materials’ accessibility in terms of readability and output formats (text, animation, video, etc.). 

You can use this method of reflection as a way to evaluate your progress in addressing different AOs in a unit of work, across the materials for a whole year group, or even for your school’s whole approach. This may be useful for highlighting those areas which have, perhaps, been overlooked. 

Applying research to practice with the AOs

The ‘Areas of opportunity’ Pedagogy Quick Read aims to help teachers apply research to their practice by summarising current research and giving practical examples of evidence-based teaching interventions and resources they can use.

The set of AOs was developed as part of a wider research project, and each one is itself research-informed. The Quick Read includes references to that research for everyone who wants to know more about culturally relevant pedagogy. This supporting evidence will be useful to teachers who want to address the topic of culturally relevant pedagogy with senior or subject leaders in their school, who often need to know that new initiatives are evidence-based.

Our goal for the Quick Read is to raise awareness of tried and tested pedagogies that increase accessibility and broaden the appeal of Computing education, so that all of our students can develop a sense of belonging and enjoyment of Computing.

Let us know if you have a story to tell about how you have applied one of the areas of opportunity in your classroom.

To date, our research in the field of culturally relevant pedagogy has been generously supported by funders including Cognizant and Google. We are very grateful to our partners for enabling us to learn more about how to make computing education inclusive for all.

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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.

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.

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.

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.

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.

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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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.

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.

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.

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.

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.

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.

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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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.

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.

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.

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:

• Try out one of our hands-on lessons and learning activities for young learners from The Computing Curriculum. For example, in this Programming unit for Year 1 learners (aged 5–6), students learn how to program with the help of reenactments and classroom robots.
• Explore the ‘Teach Data Literacy’ guide, developed by the Data Education in Schools team, which offers some practical activities to support young learners to develop their data literacy skills. You can find out more about the Data Education in Schools initiative in Kate Farrell and Judy Robertson’s seminar on teaching primary learners how to be data citizens from May 2023.
• Check out Barefoot Computing, which offers a range of resources for early years education that involve physical manipulation and simulation.

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

Why research matters

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

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

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

The Raspberry Pi Computing Education Research Centre 

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

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

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

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

We’re starting with three broad themes: 

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

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

Get involved

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

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

You can also join our free monthly research seminars.

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