UK and Ireland Computing Education Research Conference

This year, Brett Becker and I will be acting as program co-chairs of the new UK and Ireland Computing Education Research (UKICER) conference. It will be held at the University of Kent on 5-6th September 2019. The committee includes Janet Carter as conference chair, along with Sally Fincher, Quintin Cutts and Steven Bradley. Steven has been running the Computing Education Practice conference at Durham for the past few years, which has grown impressively. We believe that there is a growing community of computing education researchers in the UK and Ireland, but we do not have a local conference to support this community’s growth. Our hope is that this sister conference to CEP will provide a useful outlet to share Irish and British computing education research, and encourage research collaborations.

The conference will run roughly from lunchtime on the 5th to lunchtime on the 6th, with some collaboration-building events beforehand and some workshops afterwards. We thus invite submissions of research papers (max 6 pages, ACM format), and proposals for 1-2 hour workshops, by the beginning of June. More details are available on the conference website. Please feel free to send any questions to and please do share this news with anyone you think might be interested in submitting or attending. We hope to see a variety of researchers and educators for all age groups.

While My Guitar Gently Beeps

In an email this week, Greg Wilson asked me why music playing was centred around individual tutoring but programming education was not, i.e., why professional musicians still take lessons but professional programmers do not. That reminded me of this article I’d recently read about Fender ( in this BBC article and this [paywalled] FT article). I wondered about similarities and differences between learning music and learning programming — especially end-user programming where people do small bits of coding in larger systems, like formulas in spreadsheets, scripting in image editors and so on.

Gender Balance, and Goals

Like just about every other industry on the planet, guitar manufacturers have generally neglected the female demand for their product. Turns out half of all guitar buyers are women, and half of those buy electric (unlike the stereotype of women playing acoustic). It also turned out that many purchasers didn’t want to become performers — they just wanted to play by themselves. In his SIGCSE keynote, Mark Guzdial mentioned how many more end-user programmers there are than professional programmers, yet the former are treated like a pale imitation of the latter.

Drop-out Rates

You may think computing courses have high drop-out rates, but Fender discovered that within a year, 90% of purchasers have stopped trying to play, and many of those stopped within the first three months. In Fender’s case they have a clear economic motivation to fix this — if no-one can learn guitar they are not going to buy another (and they’re going to ebay the one they did buy). So Fender were driven to aid learning by making apps and tutorials. In this, perhaps, computing is not so bad — there is a wealth of material out there for learning to program.

One difference between music and programming is that we can change what programming looks like. We can’t change the core programming concepts but we have the ability to make our tools more helpful and less painful. However, the guitar is inherently unmodifiable. Sure, we can allow guitar music creation using simpler tools — keyboards and MPCs and computerised music generation — but that’s not a guitar.

The invention of those other tools did lead to an explosion in music creation though. A bunch of UK dance and electronica bands in the 90s got started by messing around with electronics and computers and seeing what came out, and similar patterns led to the rise in hip-hop producers in the US at the same time. The tools did make a difference in who could make music and how easily.

Reality Bites

My uncle said about his efforts to learn the guitar: “I pick it up for a few hours and I can make noise, but then I get frustrated that I’m not playing like Eric Clapton and put it down again.” We have expectations about what we can achieve and we all want a quick win rather than a long painful process. The programming equivalent is perhaps the young kids who want to make the next Call of Duty but then find they’re struggling to draw a turtle graphics hexagon on screen. Again, programming can build helper libraries and layers of abstraction to make this process much easier, more so than music playing where reality bites: when you pick up a violin for the first time, nothing is going to stop your ears bleeding as you try to hit a note. The musical alternative is to use an easier instrument, but in music and programming, such beginner’s tools are often sneered at.


Music teaching is a mixture of formal and informal. Some learn in schools, some have one-to-one tuition, some learn for themselves via the Internet. There is a temptation among those who learned programming by themselves to describe formal school teaching as unnecessary — why doesn’t everyone do what I did and teach themselves? But just because some learn themselves doesn’t mean that others wouldn’t benefit from widely available teaching. I learned programming by myself with a handful of books for years before I took any programming classes. But what I now wonder is: how much faster could I have learned with a teacher? Just because you can learn a certain way doesn’t mean it’s best.

This is a good excuse to trot an anecdote about Jamie Smith, the producer member of the British band The xx:

[Smith] was already being hailed as a visionary figure by his label boss at XL Recordings, Richard Russell. “I found him really inspiring as a beatmaker in quite a specific way,” says Russell. “He was playing the MPC – which is a piece of studio equipment you’re supposed to use for recording and sequencing – as an instrument. That idea sort of blew my mind: that you could play something live and still have the sounds I love, the sample sounds. I started doing it myself straight away, on Bobby Womack’s album, on the stuff I was doing with Damon Albarn.” (“I didn’t realise you weren’t supposed to do that with it,” shrugs Smith. “I still don’t know how to use one properly.”)


You might say there’s three types of feedback in learning. One is the obvious signs of success — does the code compile? Does the guitar make the right note? Another is longer-term pain points — is the code hard to make changes to (e.g. because of lots of repeated code)? Is it hard to play the notes at the right pace? The third is higher-level features that aren’t obvious without external feedback — is your code well structured for others to understand? Are you using an interesting variety of musical techniques?

The lack of useful feedback for the second and third items often crop up in end-user programs. You find replicated chunks of near-identical code because the programmers don’t know about functions, repeated variables like x1, x2, x3 because they don’t know arrays and so on. It’s an unknown unknown — how do you get people to write better code when they don’t know what better is, and don’t know what techniques are available for doing so?


Learning has similar challenges in programming, music and other domains. There is a diverse set of ways to learn: self-taught, tutored, formally taught in classrooms, informally taught by peers, and so on. If we focus too much on one and assume it fits everyone, we can miss out on a lot of potential learners. What I’m struck by when observing some of our Blackbox participants is that programming can be a slow, frustrating and painful experience for many who are by themselves. There is no-one solution to improving learning: it needs to be a combination of language design, tool design, pedagogy, materials and more.

Congratulations to Mark Guzdial

Research is ultimately about discovering new knowledge, and sharing it. A long-standing criticism of academic researchers is that they tend to falter on the sharing part. Far too many researchers think that publishing a paper in a paywalled journal or conference is sufficient. The modern world allows us to engage in many more ways with other researchers and the larger public — blogs, twitter, youtube and so on — yet many researchers still do not publicise their research findings well.

I’m on my way to SIGCSE 2019, the biggest conference in computer science education. On Friday morning the keynote will be given by Mark Guzdial, who will receive the SIGCSE Award for Outstanding Contribution to CS Education. Mark is a prolific researcher in his own right — SIGCSE statistics this week showed he is the second most frequent author there:

I wouldn’t mind betting that Mark is one of the most well-known computer science education researchers worldwide, but the reason for that is not just his own papers — it’s his dedication to disseminating research.

Mark runs the blog as a solo effort. You might know the blog even if you don’t know his name — unlike my decision to plaster my face on the side of this blog, his name is modestly buried on the about page. It is a remarkable act of dedication to run a blog for over ten years as Mark has, with regular content several times a week for hundreds of weeks on end. In trying to find the age of his blog, I reached page 345 (!) to find his first wordpress post in 2009, let alone the ones before that on a different host. He not only posts about his own research but is generous in discussing and crediting the research of others, and is also active in discussions on twitter.

There is a surprising disconnect in education between research and teaching. Part of the reason that dodgy pedagogy proliferates — learning styles, etc — is that research findings rarely make it into the hands of teachers. Platforms like Mark’s blog are a way to correct this serious issue. It’s also something that universities rarely give credit for. Public engagement is too often a “nice to have” tickbox that’s rated well below citation counts, teacher ratings and the other matriculated metrics du jour. So it’s nice to view this SIGCSE award as recognition for something that Mark has done for so long without due appreciation.

Mark is very generous with his time and is a welcoming, even-handed presence in the field. At the end of every SIGCSE the feedback form asks for proposals for future SIGCSE award winners. I’ve been writing Mark’s name in for years, and he is a worthy winner. I look forward to his keynote, which he will no doubt blog about afterwards.

Code highlighting: the lowlights

Syntax highlighting is such a ubiquitous feature in program editors that we often give it very little thought. It’s even like an indicator of program code: you can tell something is code if it is in a fixed-width font and some of the words are consistently coloured. It’s clearly a popular feature but is it actually helpful?

The latest paper on this (paywalled, alas) is by Hannebauer et al, which I found via Greg Wilson. They set ~400 participants a variety of comprehension and editing tasks and found zero difference in correctness between having syntax highlighting on and off during the tasks. So it doesn’t look like it helps with programming.

There’s two ways to take this result. One is that since the feature is ineffective, we should stop wasting effort on building it into our IDEs. The other way to view null results is that since it makes no difference either way, we are free to choose based on other considerations. The authors of the paper imply that people seem to like syntax colouring, which may well be for aesthetic reasons. And if it doesn’t get in the way, why not make it look prettier?

The authors end with a suggestion that highlighting syntax keywords may not be most effective use of colour, and propose a few of their own schemes, such as using colouring to do a live git blame display. I’d say the obvious other use of colour is for scope highlighting, where coloured background indicates the extents of code blocks. BlueJ has both syntax highlighting and scope highlighting, which can be a bit busy:

When we made the Stride editor, we left out syntax highlighting and just kept the scope highlighting provided by the frame outlines, which seemed less visually noisy:

We haven’t done a study to look at the effect of this scope highlighting. But David Weintrop and Uri Wilensky did something similar when they looked at multiple choice questions shown in text-based form (with syntax highlighting) versus block-based form (which is effectively scope highlighting), and the non-null effects showed a superiority of blocks over text, although the highlighting is not the only difference:

Their paper is available online (unpaywalled).

So although syntax highlighting of tokens does not seem to make a difference, scope highlighting may aid comprehension. (If anyone wants to study this directly, you can toggle syntax and scope highlighting on and off in BlueJ, so be our guest…)

What makes a [computing education] research paper?

Yesterday on Twitter, Jens Moenig had some kind words to say about our journal paper on Stride and complained about its repeated rejection from other journals as a symptom of incorrect criteria for accepting computing education research papers (head to twitter for the full thread):

The core issue was this: the original Stride journal paper was a long detailed description of the design of our Stride editor and the decisions involved, with very minimal evaluation. In general, should this be accepted as a paper?

The case against accepting

Computing education is full of tools. There are lots of block-based editors and beginners’ IDEs and learning assistants and so on. I’ll admit that — even as I work on making new tools — when I come to review a paper with a new tool, I do roll my eyes briefly and wonder if yet another tool is needed. The problem for our field is that we have a lot of tool-makers, but few tool evaluators. There are much fewer researchers (like David Weintrop, for example) who perform detailed comparisons between tools that they did not write themselves. The field has a glut of unevaluated tools, which is surely not helpful for someone wondering which tool to use, and we can’t be sure that any of the tools actually aid learning. In this light, rejecting our paper seems reasonable: yet another paper on a new tool with no evaluation.

The case for accepting

There’s two main arguments I see for accepting the paper. One is that the design description itself can be of value. As someone who builds tools I find it very useful to talk to other designers, like Jens and John Maloney, to find out why they made certain design decisions. I can use their tools — Scratch, Snap, GP — but that doesn’t explain the full story behind the design choices. Jens’ point is that he found it useful to read our decisions in order to improve the decisions they make in their tools. This type of exchange is beneficial for the field — the question then is should these design descriptions be considered computing education research papers by themselves, or should they be put somewhere else (some kind of design journal? or things like a tools paper track?).

The other argument for accepting design by itself is the amount of work. Our paper with design alone was 25-30 pages, which pushes the limit for most journals, and was the summation of three years’ work. A full evaluation would add another year and another 10 pages. Should this be one mega piece of work, or two separate bits of work? It can only be two papers if the first design paper can get accepted by itself. (The counter-argument is that there’s no guarantee the second paper ever appears…)

I will say that there are differences in quality of writing about design. A lot of papers I see on tools fall into the trap of describing technical details which do not generalise (e.g. we used web server X and hooked it up to cloud Y for storage) rather than discussing design decisions and trade-offs and user considerations. They also tend, due to page limits, to have minimal descriptions and pictures of the system as a whole. I had to look quite far back in time for the related work section in the Stride paper, and I can confirm that your paper will outlast your tool, so it needs to be useful to someone who does not have the tool itself. I’ve written in more detail in a previous post about this issue.


I’m not interested in grousing about one particular paper, but this is an issue that we run into repeatedly in our research. Our team has a lot of expertise in building tools but not much in evaluation. Should we be able to publish our designs as a computing education research paper, or should it always be coupled with an evaluation? It would be much easier for us if we were able to only publish designs, but I’m sympathetic to arguments on both sides.

At the ICER doctoral consortium a few years ago, Sally Fincher and Mark Guzdial ran an exercise to ask the students and discussants what computing education research comprised. I said that it was investiations of student learning and that our tools-only approach was on the periphery. What surprised was that all the people doing such investigations said that computing education research was tool-building, and their investigations were peripheral. I think perhaps this tension between tools and evaluation is inevitable in our field — but maybe it’s also useful.

Pedagogy of Programming Tools

If you want to teach programming, you have several decisions you need to make. You need to choose:

  • a programming language, such as Java, Python, Javascript, Ruby,
  • a programming environment, which may be something like Notepad + command-line, or a full-blown IDE like Visual Studio,
  • a context, such as making games, media computation, website creation, robotics, and
  • a pedagogical approach, such as what you will teach, in what order, and using which activities.

Not everyone thinks about that last item in terms of explicitly deciding on a pedagogical approach. But as soon as you start making decisions such as: “what task do I start with?”, you are implicitly deciding. Do you start with “What is a variable?” or “Here’s how to print ‘Hello World'” or “This is the syntax of a function call”? Do you teach automated testing? Do you start with a blank program or start by modifying an existing program? You have always chosen a pedagogical approach, whether you realise it or not.

What’s interesting about the four items above is that they all interact with each other. The top three clearly so: you can’t write Java in IDLE, for example, and you may find your robot of choice doesn’t support Javascript. But the tool and the language you choose will affect the available pedagogy and vice versa. Programming tools are not pedagogy-neutral. Your tool determines which programming-related activities are easy and which are hard, which in turn will affect how you use the tool to teach.

Code tracing is a useful skill but doing it an environment with a debugger that shows variable values step-by-step makes it much easier than in Notepad+command-line. Parsons problems (where you drag bits of pre-written code into order) are easier in Scratch than in a text editor. BlueJ lets you call methods on objects via the context menu without writing any code, whereas an IDE like IntelliJ does not. It’s useful to understand what pedagogies your tool supports or makes difficult when making a choice.

In our latest Greenfoot Live video, my colleague Hamza and I sat down for half an hour to do some Greenfoot programming and talk about pedagogical strategies in Greenfoot: ways you can use it to teach, and what pedagogical approaches we have in mind when designing the tool. I’m quite pleased with how it turned out, and I think it’s worth watching:

Whether you agree with our particular pedagogical philosophies or not, next time you choose a programming language and tool, be aware of its impact on what teaching approaches and activities it can support well, and which activities it will make hard for you to engage in.

Frame-Based Editing: The Paper

Frame-based editing is our work which combines blocks and text-based programming into a single method of editing. Our frame-based language, Stride, is available for use right now in the public releases of both Greenfoot and BlueJ.

Now, our large paper on frame-based editing has been published. You can freely download the individual paper, or the whole special issue with several other interesting-looking papers on block-based programming.

This paper is the canonical description of our frame-based editing work, describing its features and our design choices. I’m also quite pleased with section 13, which tries to explain why structured editing failed to take off, and yet block-based programming became a great success, despite being very similar concepts.

Thanks are due to John Maloney and Jens Moenig who have been very supportive of our work, as has the editor Franklyn Turbak, who did a very thorough job of editing the paper.