There’s seems to be something about rainy weekends in May that stimulates academics in Computing departments to have e-mail discussions about programming languages and teaching. The key ingredients probably include houseboundness, the lull between the end of formal teaching and the start of exams, the beginning of contemplation of next year’s workload, and enthusiasm; of course, the academic administrative timescale is such that any changes that we contemplate now, in May 2014, can only be put in place for the intake in September 2015... if you’ve ever wondered why University programmes in fashionable subjects seem to lag about two years behind the fashion (e.g. the high growth of Masters programmes in “Financial Engineering” or similar around 2007 – I suspect that demand for paying surprisingly high tuition fees for a degree in Synthetic Derivative Construction weakened shortly after those programmes came on stream, but I don’t actually have the data to be certain).
Meanwhile, I was at the European Lisp Symposium in Paris last week, where there was a presentation of a very apposite nature: the Computing Department at Middlesex university has implemented an integrated first-year of undergraduate teaching, covering a broad range of the computing curriculum (possibly not as broad as at Goldsmiths, though) through an Arduino and Raspberry Pi robot with a Racket-based programmer interface. Students’ progress is evaluated not through formal tests, courseworks or exams, but through around 100 binary judgments in the natural context of “student observable behaviours” at three levels (‘threshold’, which students must exhibit to progress to the second year; ‘typical’, and ‘excellent’).
This approach has a number of advantages, I think, over a more traditional division of the year into four thirty-credit modules (e.g. Maths, Java, Systems, and Profession): for one, it pretty much guarantees a coherent approach to the year, where in the divided modules case it is surprisingly easy for one module to be updated in syllabus or course content without adjustments to the others, leaving for example some programming material insufficiently supported by the maths (and some maths taught without motivation). The assessment method is in principle transparent to the students, who know what they have to do to progress (and to get better marks); I'm not convinced that this is always a good thing, but for an introductory and core course I think the benefits substantially outweigh the disadvantages. The use of Racket as the teaching language has an equalising effect – it’s unlikely that students will have prior experience with it, so everyone starts off at the same point at least with respect to the language – and the use of a robot provides visceral feedback and a sense of achievement when it is made to do something in a way that text and even pixels on a screen might not. (This feedback and tangible sense of achievement is perhaps why the third-year option of Physical Computing at Goldsmiths is so popular: often oversubscribed by a huge margin).
With these thoughts bubbling around in my head, then, when the annual discussion kicked off at the weekend I decided to try to articulate my thoughts in a less ephemeral way than in the middle of a hydra-like discussion: so I wrote a, and circulated that. One of the points of having a personal wiki is that the content on it can evolve, but before I eradicate the evidence of what was there before, and since it got at least one response (beyond “why don’t you allow comments on your wiki?”) it's worth trying to continue the dialogue.
Firstly, Chris Cannam pulls me up on not including an Understand goal, or one like it: teaching students to understand and act on their understanding of computing artifacts, hardware and software. I could make the argument that this lives at the intersection of my Think and Experiment goals, but I think that would be retrospective justification and that there is a distinct aim there. I’m not sure why I left it out; possibly, I am slightly hamstrung in this discussion about pedagogy by a total absence of formal computing education; one course in fundamentals of computing as a 17-year-old, and one short course on Fortran and numerical methods as an undergraduate, and that’s it. It's in some ways ironic that I left out Understand, given that in my use of computers as a hobby it’s largely what I do: Lisp software maintenance is often a cross between debugger-oriented programming and software archaeology. But maybe that irony is not as strong as it might seem; I learnt software and computing as a craft, apprenticed to a master; I learnt the shibboleths (“OAOO! OAOO! OAOO!”) and read the training manuals, but I learnt by doing, and I’m sure I’m far from alone, even in academic Computing let alone among programmers or computing professionals more generally.
Maybe the Middlesex approach gets closer, in the University setting, to the apprenticeship (or pre-apprenticeship) period; certainly, there are clear echoes in their approach of the switch by MIT from the SICP- and Scheme-based 6.001 to the Robotics- and Python-based introduction to programming – and Sussman’s argument from the time of the switch points to a qualitative difference in the role of programmers, which happens to dovetail with current research on active learning. In my conversation with the lecturers involved after their presentation, they said that the students take a more usual set of languages in their second-years (Java, C++); obviously, since this is the first year of their approach, they don’t yet know how the transition will go.
And then there was the slightly cynical Job vs Career distinction that I drew, being the difference between a graduate-level job six months after graduation as distinct from a fulfilling career. One can of course lead to the other, but it’s by no means guaranteed, and I would guess that if asked most people would idealistically say we as teachers in Universities should be attempting to optimize for the latter. Unfortunately, we are measured in our performance in the former; one of the “Key Information Sets” collected by higher-education agencies and presented to prospective undergraduates is the set of student ‘destinations’. Aiming to optimize this statistic is somewhat akin to schools optimizing their GCSE results with respect to the proportion of pupils gaining at least 5 passes: ideally, the measurement should be a reflection of the pedagogical practice, but the foreknowledge of the measurement has the potential to distort the allocation of effort and resources. In the case of the school statistic, there’s evidence that extra effort is concentrated on borderline pupils, at the expense of both the less able and potential high-fliers; the distortion isn’t so stark in the case of programming languages, because the students have significant agency between teaching and measurement, but there is certainly pressure to ensure that we teach the most common language used in assessment centres.
In Chris’ case, C++ might have had a positive effect on both; the programming language snob in me, though, wants to believe that there are hordes of dissatisfied programmers out there, having got a job using their competence in some “industry-standard” language, desperately wanting to know about a better way of doing things. I might be overprojecting the relationship of a professional programmer with their tools, of course: for many a career in programming and development is just that, rather than a love-hate relationship with their compiler and linker. (Also, there’s the danger of showing the students that there is a better way, but that the market doesn’t let them use it...)
Is it possible to conclude anything from all of this? Well, as I said in my initial thoughts, this is an underspecified problem; I think that a sensible decision can only be taken in practice once the priorities for teaching programming at all have been established, in combination with the resources available for delivering teaching. I’m also not enough of a polyglot in practice to offer a mature judgment on many fashionable languages; I know enough of plenty of languages to modify and maintain code, but am comfortable writing from scratch in far fewer.
So with all those caveats, an ideal programming curriculum (reflecting my personal preferences and priorities) might include in its first two years: something in the Lisp family, for Think and Experiment; ARM Assembler, for Think and Understand; C++, for Job and Career (and all three together for Society). Study should probably be handled in individual third-year electives, and I would probably also include a course hybrid between programming, database and system administration to cover a Web programming stack for more Job purposes. Flame on (but not here, because system administration is hard).