I’ve reached the conclusion that a lot of courses have an unrealistically high number of evaluations. We have too many and we pretend that we are going to achieve outcomes for which we have no supporting evidence. Worse, in many cases, we are painfully aware that we cause last-minute lemming-like effects that do anything other than encourage learning. But why do we have so many? Because we’re trying to fit them into the term or semester size that we have: the administrative limit.
One the big challenges for authenticity in Computer Science is the nature of the software project. While individual programs can be small and easy to write, a lot of contemporary programming projects are:
- Large and composed of many small programs.
- Complex to a scale that may exceed one person’s ability to visualise.
- Built on platforms that provide core services; the programmers do not have the luxury to write all of the code in the system.
Many final year courses in Software Engineering have a large project courses, where students are forced to work with a (usually randomly assigned) group to produce a ‘large’ piece of software. In reality, this piece of software is very well-defined and can be constructed in the time available: it has been deliberately selected to be so.
Is a two month software task in a group of six people indicative of real software?
Yes and no. It does give a student experience in group management, except that they still have the safe framework of lecturers over the top. It’s more challenging than a lot of what we do because it is a larger artefact over a longer time.
But it’s not that realistic. Industry software projects live over years, with tens to hundreds of programmers ‘contributing’ updates and fixes… reversing changes… writing documentation… correcting documentation. This isn’t to say that the role of a university is to teach industry skills but these skill sets are very handy for helping programmers to take their code and make it work, so it’s good to encourage them.
I believe finally, that education must be conceived as a continuing reconstruction of experience; that the process and the goal of education are one and the same thing.
from John Dewey, “My Pedagogic Creed”, School Journal vol. 54 (January 1897)
I love the term ‘continuing reconstruction of experience’ as it drives authenticity as one of the aesthetic characteristics of good education.
Authentic, appropriate and effective learning and evaluation activities may not fit comfortably into a term. We already accept this for activities such as medical internship, where students must undertake 47 weeks of work to attain full registration. But we are, for many degrees, trapped by the convention of a semester of so many weeks, which is then connected with other semesters to make a degree that is somewhere between three to five years long.
The semester is an artefact of the artificial decomposition of the year, previously related to season in many places but now taking on a life of its own as an administrative mechanism. Jamming things into this space is not going to lead to an authentic experience and we can now reject this on aesthetic grounds. It might fit but it’s beautiful or true.
But wait! We can’t do that! We have to fit everything into neat degree packages or our students won’t complete on time!
Let’s now look at the ‘so many years degree’. This is a fascinating read and I’ll summarise the reported results for degree programs in the US, which don’t include private colleges and universities:
- Fewer than 10% of reporting institutions graduated a majority of students on time.
- Only 19% of students at public universities graduate on-time.
- Only 36% of state flagship universities graduate on-time
- 5% of community college students complete an associate degree on-time.
The report has a simple name for this: the four-year myth. Students are taking longer to do their degrees for a number of reasons but among them are poorly designed, delivered, administered or assessed learning experiences. And jamming things into semester blocks doesn’t seem to be magically translating into on-time completions (unsurprisingly).
It appears that the way we break up software into little pieces is artificial and we’re also often trying to carry out too many little assessments. It looks like a good model is to stretch our timeline out over more than one course to produce an experience that is genuinely engaging, more authentic and more supportive of long term collaboration. That way, our capstone course could be a natural end-point to a three year process… or however long it takes to get there.
Finally, in the middle of all of this, we need to think very carefully about why we keep using the semester or the term as a container. Why are degrees still three to four years long when everything else in the world has changed so much in the last twenty years?
There was a time before graphics dominated the way that you worked with computers and, back then, after punchcards and before Mac/Windows, the most common way of working with a computer was to use the Command Line Interface (CLI). Many of you will have seen this, here’s Terminal from the Mac OS X, showing a piece of Python code inside an editor.
Rather than use a rich Integrated Development Environment, where text is highlighted and all sorts of clever things are done for me, I would run some sort of program editor from the command line, write my code, close that editor and then see what worked.
At my University, we almost always taught Computer Science using command line tools, rather than rich development environments such as Eclipse or the Visual Studio tools. Why? The reasoning was that the CLI developed skills required to write code, compile it, debug it and run it, without training students into IDE-provided shortcuts. The CLI was the approach that would work anywhere. That knowledge was, as we saw it, fundamental.
But, remember that Processing example? We clearly saw where the error was. This is what a similar error looks like for the Java programming language in a CLI environment.
Same message (and now usefully on the right line because 21st Century) but it is totally divorced from the program itself. That message has to give me a line number (5) in the original program because it has no other way to point to the problem.
And here’s the problem. The cognitive load increases once we separate code and errors. Despite those Processing errors looking like the soft option, everything we know about load tells us that students will find fixing their problems easier if they don’t have to mentally or physically switch between code and error output.
Everything I said about CLIs is still true but that’s only a real consideration if my students go out into the workplace and need some CLI skills. And, today, just about every workplace has graphics based IDEs for production software. (Networking is often an exception but we’ll skip over that. Networking is special.)
The best approach for students learning to code is that we don’t make things any harder than we need to. The CLI approach is something I would like students to be able to do but my first task is to get them interested in programming. Then I have to make their first experiences authentic and effective, and hopefully pleasant and rewarding.
I have thought about this for years and I started out as staunchly CLI. But as time goes by, I really have to wonder whether a tiny advantage for a small number of graduates is worth additional load for every new programmer.
And I don’t think it is worth it. It’s not fair. It’s the opposite of equitable. And it goes against the research that we have on cognitive load and student workflows in these kinds of systems. We already know of enough load problems in graphics based environments if we make the screens large enough, without any flicking from one application to another!
You don’t have to accept my evaluation model to see this because it’s a matter of common sense that forcing someone to unnecessarily switch tasks to learn a new skill is going to make it harder. Asking someone to remember something complicated in order to use it later is not as easy as someone being able to see it when and where they need to use it.
The world has changed. CLIs still exist but graphical user interfaces (GUIs) now rule. Any of my students who needs to be a crack programmer in a text window of 80×24 will manage it, even if I teach with all IDEs for the entire degree, because all of the IDEs are made up of small windows. Students can either debug and read error messages or they can’t – a good IDE helps you but it doesn’t write or fix the code for you, in any deep way. It just helps you to write code faster, without having to wait and switch context to find silly mistakes that you could have fixed in a split second in an IDE.
When it comes to teaching programming, I’m not a CLI guy anymore.
Earlier, I split the evaluation resources of a course into:
- E1 (the lecturer and course designer),
- E2 (human work that can be based on rubrics, including peer assessment and casual markers),
- E3 (complicated automated evaluation mechanisms)
- E4 (simple automated evaluation mechanisms, often for acceptance testing)
E1 and E2 everyone tends to understand, because the culture of Prof+TA is widespread, as is the concept of peer assessment. In a Computing Course, we can define E3 as complex marking scripts that perform amazing actions in response to input (or even carry out formal analysis if we’re being really keen), with E4 as simple file checks, program compilation and dumb scripts that jam in a set of data and see what comes out.
But let’s get back to my first year, first exposure, programming class. What I want is hands-on, concrete, active participation and constructive activity and lots of it. To support that, I want the best and most immediate feedback I can provide. Now I can try to fill a room with tutors, or do a lot of peer work, but there will come times when I want to provide some sort of automated feedback.
Given how inexperienced these students are, it could be a quite a lot to expect them to get their code together and then submit it to a separate evaluation system, then interpret the results. (Remember I noted earlier on how code tracing correlates with code ability.)
Thus, the best way to get that automated feedback is probably working with the student in place. And that brings us to the Integrated Development Environment (IDE). An IDE is an application that provides facilities to computer programmers and helps them to develop software. They can be very complicated and rich (Eclipse), simple (Processing) or aimed at pedagogical support (Scratch, BlueJ, Greenfoot et al) but they are usually made up of a place in which you can assemble code (typing or dragging) and a set of buttons or tools to make things happen. These are usually quite abstract for early programmers, built on notional machines rather than requiring a detailed knowledge of hardware.
Even simple IDEs will tell you things that provide immediate feedback. We know how these environments can have positive reception, with some demonstrated benefits, although I recommend reading Sorva et al’s “A Review of Generic Program Visualization Systems for Introductory Programming Education” to see the open research questions. In particular, people employing IDEs in teaching often worry about the time to teach the environment (as well as the language), software visualisations, concern about time on task, lack of integration and the often short lifespan of many of the simpler IDEs that are focused on pedagogical outcomes. Even for well-established systems such as BlueJ, there’s always concern over whether the investment of time in learning it is going to pay off.
In academia, time is our currency.
But let me make an aesthetic argument for IDEs, based on the feedback that I’ve already put into my beautiful model. We want to maximise feedback in a useful way for early programmers. Early programmers are still learning the language, still learning how to spell words, how to punctuate, and are building up to a grammatical understanding. An IDE can provide immediate feedback as to what the computer ‘thinks’ is going on with the program and this can help the junior programmer make immediate changes. (Some IDEs have graphical representations for object systems but we won’t discuss these any further here as the time to introduce objects is a subject of debate.)
Now there’s a lot of discussion over the readability of computer error messages but let me show you an example. What’s gone wrong in this program?
See where that little red line is, just on the end of the first line? Down the bottom there’s a message that says “missing a semicolon”. In the Processing language, almost all lines end with a “;” so that section of code should read:
Did you get that? That missing semicolon problem has been an issue for years because many systems report the semicolon missing on the next line, due to the way that compilers work. Here, Processing is clearly saying: Oi! Put a semi-colon on the red squiggle.
I’m an old programmer, who currently programs in Java, C++ and Processing, so typing “;” at the end of a line is second nature to me. But it’s an easy mistake for a new programmer to make because, between all of the ( and the ) and the , and the numbers and the size and the rect… what do I do with the “;”?
The Processing IDE is functioning in at least an E4 mode: simple acceptance testing that won’t let anything happen until you fix that particular problem. It’s even giving you feedback as to what’s wrong. Now this isn’t to say that it’s great but it’s certainly better than a student sitting there with her hand up for 20 minutes waiting for a tutor to have the time to come over and say “Oh, you’re missing a semicolon.”
We don’t want shotgun coding, where random fixes and bashed-in attempts are made desperately to solve a problem. We want students to get used to getting feedback on how they’re going and using this to improve what they do.
Because of Processing’s highly visual mode, I think it’s closer to E3 (complex scripting) in many ways because it can tell you if it doesn’t understand what you’re trying to do at all. Beyond just not doing something, it can clearly tell you what’s wrong.
But what if it works and then the student puts something up on the screen, a graphic of some sort and it’s not quite right? Then the student has started to become their own E2, evaluating what has happened in response to the code and using human insight to address the shortfall and make changes. Not as an expert but, with support and encouragement, a developing expertise.
Feedback is good. Immediacy is good. Student involvement is good. Code tracing is linked to coding ability. A well-designed IDE can be simple and engage the student to an extent that is potentially as high as E2, although it won’t be as rich, without using any other human evaluation resources. Even if there is no other benefit, the aesthetic argument is giving us a very strong nudge to adopt an appropriate IDE.
Maybe it’s time to hang up the command line and live in a world where IDEs can help us to get things done faster, support our students better and make our formal human evaluation resources go further.
What do you think?
If we want to give feedback, then the time it takes to give feedback is going to determine how often we can do it. If the core of our evaluation is feedback, rather than some low-Bloom’s quiz-based approach giving a score of some sort, then we have to set our timelines to allow us to:
- Get the work when we are ready to work on it
- Undertake evaluation to the required level
- Return that feedback
- Do this at such a time that our students can learn from it and potentially use it immediately, to reinforce the learning
A commenter asked me how I actually ran large-scale assessment. The largest class I’ve run detailed feedback/evaluation on was 360 students with a weekly submission of a free-text (and graphics) solution to a puzzle. The goal was to have the feedback back within a week – prior to the next lecture where the solution would be delivered.
I love a challenge.
This scale is, obviously, impossible for one person to achieve reliably (we estimated it as at least forty hours of work). Instead, we allocated a marking team to this task, coordinated by the lead educator. (E1 and E2 model again. There was, initially, no automated capacity for this at the time although we added some later.)
Coordinating a team takes time. Even when you start with a rubric, free text answers can turn up answer themes that you didn’t anticipate and we would often carry our simple checks to make sure that things were working. But, looking at the marking time I was billed for (a good measure), I could run an entire cycle of this in three days, including briefing time, testing, marking, and oversight. But this is with a trained team, a big enough team, good conceptual design and a senior educator who’s happy to take a more executive role.
In this case, we didn’t give the students a chance to refactor their work but, if we had, we could have done this with a release 3 days after submission. To ensure that we then completed the work again by the ‘solution release’ deadline, we would have had to set the next submission deadline to only 24 hours after the feedback was released. This sounds short but, if we assume that some work has been done, then refactoring and reworking should take less time.
But then we have to think about the cost. By running two evaluation cycles we are providing early feedback but we have doubled our cost for human markers (a real concern for just about everyone these days).
My solution was to divide the work into two components. The first was quiz-based and could be automatically and immediately assessed by the Learning Management System, delivering a mark at a fixed deadline. The second part was looked at by humans. Thus, students received immediate feedback on part of the problem straight away (or a related problem) while they were waiting for humans.
But I’d be the first to admit that I hadn’t linked this properly, according to my new model. It does give us insight for a staged hybrid model where we buffer our human feedback by using either smart or dumb automated assessment component to highlight key areas and, better still, we can bring these forward to help guide time management.
I’m not unhappy with that early attempt at large-scale human feedback as the students were receiving some excellent evaluation and feedback and it was timely and valuable. It also gave me a lot of valuable information about design and about what can work, as well as how to manage marking teams.
I also realised that some courses could never be assessed the way that they claimed unless they had more people on task or only delivered at a time when the result wasn’t usable anymore.
How much time should we give students to rework things? I’d suggest that allowing a couple of days takes into account the life beyond Uni that many students have. That means that we can do a cycle in a week if we can keep our human evaluation stages under 2 days. Then, without any automated marking, we get 2 days (E1 or E2) + 2 days (student) + 2 days (second evaluation, possibly E2) + 1 day (final readjustment) and then we should start to see some of the best work that our students can produce.
Assuming, of course, that all of us can drop everything to slot into this. For me, this motivates a cycle closer to two to three weeks to allow for everything else that both groups are doing. But that then limits us to fewer than five big assessment items for a twelve week course!
What’s better? Twelve assessment items that are “submit and done” or four that are “refine and reinforce to best practice”? Is this even a question we can ask? I know which one is aesthetically pleasing, in terms of all of the educational aesthetics we’ve discussed so far but is this enough for an educator to be able to stand up to a superior and say “We’re not going to do X because it just doesn’t make any sense!”
What do you think?
One of the problems with any model that builds in more feedback is that we incur both the time required to produce the feedback and we also have an implicit requirement to allow students enough time to assimilate and make use of it. This second requirement is still there even if we don’t have subsequent attempts at work, as we want to build upon existing knowledge. The requirement for good feedback makes no sense without a requirement that it be useful.
But let me reiterate that pretty much all evaluation and feedback can be very valuable, no matter how small or quick, if we know what we are trying to achieve. (I’ll get to more complicated systems in later posts.)
Novice programmers often struggle with programming and this early stage of development is often going to influence if they start off thinking that they can program or not. Given that automated evaluation only really provides useful feedback once the student has got something working, novice programming classes are an ideal place to put human markers. If we can make students think “Yes, I can do this” early on, this is the emotion that they will remember. We need to get to big problems quickly, turn them into manageable issues that can be overcome, and then let motivation and curiosity take the rest.
There’s an excellent summary paper on computer programming visualisation systems aimed at novice programmers, which discusses some of the key problems novices face on their path to mastery:
- Novices can see some concepts as code rather than the components of a dynamic process. For example, they might see objects as simply a way of containing things rather than modelling objects and their behaviours. These static perceptions prevent the students from understanding that they are designing behaviours, not just writing magic formulas.
- There can be significant difficulties in understanding the computer, seeing the notional machine that is the abstraction, forming a basis upon which knowledge of one language or platform could be used elsewhere.
- Misunderstanding fundamental concepts is common and such misconceptions can easily cause weak understanding, leaving the students in the liminal state, unable to assimilate a threshold concept and move on.
- Students struggle to trace programs and work out what state the program should be in. In my own community, Raymond Lister, Donna Teague, Simon, and others have clearly shown that many students struggle with the tracing of even simple programs.
If we have put human markers (E1 or E2) into a programming class and identified that these are the problems we’re looking for, we can provide immediate targeted evaluation that is also immediate constructive feedback. On the day, in response to actual issues, authentic demonstration of a solution process that students can model. This is the tightest feedback and reward loop we can offer. How does this work?
- Program doesn’t work because of one of the key problem areas.
- Human evaluator intervenes with student and addresses the issue, encouraging discovery inside the problem area.
- Student tries to identify problem and explains it to evaluator in context, modelling evaluator and based on existing knowledge.
- Evaluator provides more guidance and feedback.
- Student continues to work on problem.
- We hope that the student will come across the solution (or think towards it) but we may have to restart this loop.
Note that we’re not necessarily giving the solution here but we can consider leading towards this if the student is getting visibly frustrated. I’d suggest never telling a student what to type as it doesn’t address any of the problems, it just makes the student dependent upon being told the answer. Not desirable. (There’s an argument here for rich development environments that I’ll expand on later.)
Evaluation like this is formative, immediate and rich. We can even streamline it with guidelines to help the evaluators although much of this will amount to supporting students as they learn to read their own code and understand the key concepts. We should develop students simple to complex, concrete to abstract, so some problems with abstraction are to be expected, especially if we are playing near any threshold concepts.
But this is where learning designers have to be ready to say “this may cause trouble” and properly brief the evaluators who will be on the ground. If we want our evaluators to work efficiently and effectively, we have to brief them on what to expect, what to do, and how to follow up.
If you’ve missed it so far, one of our big responsibilities is training our evaluation team. It’s only by doing this that we can make sure that our evaluators aren’t getting bogged down in side issues or spending too much time with one student and doing the work for them. This training should include active scenario-based training to allow the evaluators to practise with the oversight of the educators and designers.
We have finite resources. If we want to support a room full of novices, we have to prepare for the possibility of all of them having problems at once and the only way to support that at scale is to have an excellent design and train for it.
I’m about to start a new thread of discussion, once I’ve completed the assessment posts, and this seemed to be good priming for thinking ahead.
“The true business of people should be to go back to school and think about whatever it was they were thinking about before somebody came along and told them they had to earn a living.”
Buckminster Fuller, reference.
I drew up a picture to show how many people appear to think about art. Now this is not to say that this is my thinking on art but you only have to go to galleries for a while to quickly pick up the sotto voce (oh, and loud) discussions about what constitutes art. Once we move beyond representative art (art that looks like real things), it can become harder for people to identify what they consider to be art.
I drew up this diagram in response to reading early passages from Dewey’s “Art as Experience”:
“An instructive history of modern art could be written in terms of the formation of the distinctively modern institutions of museum and exhibition gallery. (p8)
The growth of capitalism has been a powerful influence in the development of the museum as the proper home for works of art, and in the promotion of the idea that they are apart from the common life. (p8)
Why is there repulsion when the high achievements of fine art are brought into connection with common life, the life that we share with all living creatures?” (p20)
Dewey’s thinking is that we have moved from a time when art was deeply integrated into everyday life to a point where we have corralled “worthy” art into buildings called art galleries and museums, generally in response to nationalistic or capitalistic drivers, in order to construct an artefact that indicates how cultured and awesome we are. But, by doing this, we force a definition that something is art if it’s the kind of thing you’d see in an art gallery. We take art out of life, making valuable relics of old oil jars and assigning insane values to collections of oil on canvas that please the eye, and by doing so we demand that ‘high art’ cannot be part of most people’s lives.
But the gallery container is not enough to define art. We know that many people resist modernism (and post-modernism) almost reflexively, whether it’s abstract, neo-primitivist, pop, or simply that the viewer doesn’t feel convinced that they are seeing art. Thus, in the diagram above, real art is found in galleries but there are many things found in galleries that are not art. To steal an often overheard quote: “my kids could do that”. (I’m very interested in the work of both Rothko and Malevich so I hear this a lot.)
But let’s resist the urge to condemn people because, after we’ve wrapped art up in a bow and placed it on a pedestal, their natural interpretation of what they perceive, combined with what they already know, can lead them to a conclusion that someone must be playing a joke on them. Aesthetic sensibilities are inherently subjective and evolve over time, in response to exposure, development of depth of knowledge, and opportunity. The more we accumulate of these guiding experiences, the more likely we are to develop the cultural capital that would allow us to stand in any art gallery in the world and perceive the art, mediated by our own rich experiences.
Cultural capital is a term used to describe the assets that we have that aren’t money, in its many forms, but can still contribute to social mobility and perception of class. I wrote a long piece on it and perception here, if you’re interested. Dewey, working in the 1930s, was reacting to the institutionalisation of art and was able to observe people who were attempting to build a cultural reputation, through the purchase of ‘art that is recognised as art’, as part of their attempts to construct a new class identity. Too often, when people who are grounded in art history and knowledge look at people who can’t recognise ‘art that is accepted as art by artists’ there is an aspect of sneering, which is both unpleasant and counter-productive. However, such unpleasantness is easily balanced by those people who stand firm in artistic ignorance and, rather than quietly ignoring things that they don’t like, demand that it cannot be art and loudly deride what they see in order to challenge everyone around them to accept the art of an earlier time as the only art that there is.
Neither of these approaches is productive. Neither support the aesthetics of real discussion, nor are they honest in intent beyond a judgmental and dismissive approach. Not beautiful. Not true. Doesn’t achieve anything useful. Not good.
If this argument is seeming familiar, we can easily apply it to education because we have, for the most part, defined many things in terms of the institutions in which we find them. Everyone else who stands up and talks at people over Power Point slides for forty minutes is probably giving a presentation. Magically, when I do it in a lecture theatre at a University, I’m giving a lecture and now it has amazing educational powers! I once gave one of my lectures as a presentation and it was, to my amusement, labelled as a presentation without any suggestion of still being a lecture. When I am a famous professor, my lectures will probably start to transform into keynotes and masterclasses.
I would be recognised as an educator, despite having no teaching qualifications, primarily because I give presentations inside the designated educational box that is a University. The converse of this is that “university education” cannot be given outside of a University, which leaves every newcomer to tertiary education, whether face-to-face or on-line, with a definitional crisis that cannot be resolved in their favour. We already know that home-schooling, while highly variable in quality and intention, is a necessity in some places where the existing educational options are lacking, is often not taken seriously by the establishment. Even if the person teaching is a qualified teacher and the curriculum taught is an approved one, the words “home schooling” construct tension with our assumption that schooling must take place in boxes labelled as schools.
What is art? We need a better definition than “things I find in art galleries that I recognise as art” because there is far too much assumption in there, too much infrastructure required and there is not enough honesty about what art is. Some of the works of art we admire today were considered to be crimes against conventional art in their day! Let me put this in context. I am an artist and I have, with 1% of the talent, sold as many works as Van Gogh did in his lifetime (one). Van Gogh’s work was simply rubbish to most people who looked at it then.
And yet now he is a genius.
What is education? We need a better definition than “things that happen in schools and universities that fit my pre-conceptions of what education should look like.” We need to know so that we can recognise, learn, develop and improve education wherever we find it. The world population will peak at around 10 billion people. We will not have schools for all of them. We don’t have schools for everyone now. We may never have the infrastructure we need for this and we’re going need a better definition if we want to bring real, valuable and useful education to everyone. We define in order to clarify, to guide, and to tell us what we need to do next.