I am a potato – heading towards caramelisation. (Programming Language Threshold Concepts Part II)
Posted: October 28, 2012 Filed under: Education | Tags: curriculum, design, education, educational problem, educational research, feedback, Generation Why, higher education, in the student's head, learning, measurement, principles of design, reflection, resources, student perspective, teaching, teaching approaches, thinking, threshold concepts, tools Leave a commentFollowing up on yesterday’s discussion of some of the chapters in “Threshold Concepts Within the Disciplines”, I finished by talking about Flanagan and Smith’s thoughts on the linguistic issues in learning computer programming. This led me to the theory of markedness, a useful way to think about some of the syntactic structures that we see in computer programs. Let me introduce the concept of markedness with an example. Consider the pair of opposing concepts big/small. If you ask how ‘big’ something is, then you’re not actually assuming that the thing you’re asking about is ‘big’, you’re asking about its size. However, ask someone how ‘small’ something is and there’s a presumption that it’s actually small (most of the time). The same thing happens for old/young. Asking someone how old they are, bad jokes aside, is not implying that they are old – the word “old” here is standing in for the concept of age. This is an example of markedness in the relationship between lexical opposites: the assumed meaning (the default) is referred to as the unmarked form, where the marked form is more restrictive (in that it doesn’t subsume both concepts) and it is generally not the default. You see this in gender and plural forms too. In Lions/Lionesses, Lions is an unmarked form because it’s the default and it doesn’t exclude the Lionesses, whereas Lionesses would not be the general form used (for whatever reasons, good or bad) and excludes the male lions.
Why is this important for programming languages? Because we often have syntactic elements (the structures and the tokens that we type) that take the form of opposing concepts where one is the default, and hence unmarked, form. Many modern languages employ object-oriented programming practices (itself a threshold concept) that allow programmers to specify how the data that they define inside their programs is going to be used, even within that program. These practices include the ability to set access controls, that strictly define how you can use your code, how other pieces of code that you write can use your code, and how other people’s code can use it, as well. The fundamental access control pairs are public and private, one of which says anyone can use this piece of code to calculate things or can change this value, the other restricts such use or change to the owner. In the Java programming language, public dominates, by far, and can be considered unmarked. Private, however, changes the way that you can work with your own code and it’s easy for students to get this wrong. (To make it more confusing, there is another type of access control that sits effectively between public and private, which is an even more cognitively complex concept and is probably the least well understood of the lot!) One of the issues with any programming language is that deviating from the default requires you to understand what you are doing because you are having to type more, think more and understand more of the implications of your actions.
However, it gets harder, because we sometimes have marked/unmarked pairs where the unmarked element is completely invisible. If we didn’t have the need to describe how people could use our code then we wouldn’t need the access modifiers – the absence of public, private or protected wouldn’t signify anything. There are some implicit modes of operation in programming languages that can be overridden with keywords but the introduction of these keywords just doesn’t illustrate a positive/negative asymmetry (as with big/small or private/public), these illustrate an asymmetry between “something” and “nothing”. Now, the presence of a specific and marked keyword makes it glaringly obvious that there has been an invisible assumption sitting in that spot the whole time.
One of these troublesome word/nothing pairs is found in several languages and consists of the keyword static, with no matching keyword. What do you think the opposite (and pair) of static is? If you’re like most humans, you’d think dynamic. However, not only is this not what this keyword actually means but there is no dynamic keyword that balances it. Let’s look at this in Java:
public static void main(String [] args) {...}
public static int numberOfObjects(int theFirst) {...}
public int getValues() {...}
You’ll see that static keyword twice.Where static isn’t used, however, there’s nothing at all, and this (by its absence) also has a definite meaning and this defines what the default expectation is of behaviour in the Java programming language. From a teaching perspective, this means that we now have a default context, with a separation between those tokens and concepts that are marked and unmarked, and it becomes easier to see why students will struggle with instance methods and fields (which is what we call things without static) if we start with static, and struggle with the concept of static if we start the other way around! What further complicates is this is that every single program we write must contain at least one static method, because it is the starting point for the program’s execution. Even if you don’t want to talk about static yet, you must use it anyway (unless you want to provide the students with some skeleton code or a harness that removes this – but now we’ve put the wizard behind the curtain even more).
One other point I found very interesting in Flanagan and Smith’s chapter was the discussion of barriers and traps in programming languages, from Thimbleby’s critique of Java (1999). Barriers are the limitations on expressiveness that mean that what you want to say in a programming language can only be said in a certain way or in a certain place – which limits how we can explain the language and therefore affects learnability. As students tend to write their lines of code as and when they think of them, at least initially, these barriers will lead the students to make errors because they haven’t developed the locally valid computational idiom. I could ask for food in German as “please two pieces ham thick tasty” and, while I’ll get some looks, I’ll also get ham. Students hitting a barrier get confusing error messages that are given back to them at a time when they barely have enough framework to understand what these messages mean, let alone how to fix them. No ham for them!
Traps are unknown and unexpected problems, such as those caused by not using the right way to compare two things in a program. In short, it is possible in many programming languages to ask “does this equal that” and return an answer of true or false that does not depend upon the values of this or that, but where they are being stored in memory. This is a trap. It is confusing for the novice to try to work out why the program is telling her that two containers that have the value “3” in them are not the same because they are duplicates rather than aliases for the same entity. These traps can seriously trip someone up as they attempt to form a correct mental model and, in the worst case, can lead to magical or cargo-cult thinking once again. (This is not helped by languages that, despite saying that they will take such-and-such an action, take actions that further undermine consistent mental models without being obvious about it. Sekrit Java String munging, I’m looking at you.)
This way of thinking about languages is of great interest to me because, instead of talking about usability in an abstract sense, we are now discussing concrete benefits and deficiencies in the language. Is it heavily restrictive on what goes where, such as Pascal’s pre-declaration of variables or Java’s package import restrictions? Does the language have a large number on unbalanced marked/unmarked pairs where one of them is invisible and possibly counterintuitive, such as static? Is it easy to turn a simple English statement into a programmatic equivalent that does not do what was expected?
The authors suggested ways to dealing with this, including teaching students about formal grammars for programming languages – effectively treating this as learning a new language because the grammar, syntax and semantics are very, very different from English.(Suggestions included Wittgenstein’s Sprachspiel, language game, which will be a post for another time.) Another approach is to start from logic and then work forwards, turning this into forms that will then match the programming languages and giving us a Rosetta stone between English speakers and program speakers.
I have found the whole book very interesting so far and, obviously, so too this chapter. Identifying the problems and their locations, regrettably, is only the starting point. Now I have to think about ways to overcome this, building on what these and other authors have already written.
Imagine that you are a raw potato…
Posted: October 27, 2012 Filed under: Education | Tags: community, design, education, educational research, feedback, Generation Why, higher education, in the student's head, principles of design, resources, student perspective, teaching, teaching approaches, thinking, threshold concepts, tools Leave a commentThe words in the title of this post, surprisingly, are the first words in the Editors’ Preface to Land, Meyer and Smiths 2008 edited book “Threshold Concepts within the Disciplines”. Our group has been looking at the penetration of certain ideas through the discipline, examining how much the theory social constructivism accompanies the practice of group work for example, or, as in this case, seeing how many people identify threshold concepts in what they are trying to teach. Everyone who teaches first year Computer Science knows that some ideas seem to be sticking points and Meyer and Land’s two papers on “Threshold Concepts and Troublesome Knowledge” (2003 and 2005) provide a way of describing these sticking points by characterising why these particular aspects are hard – but also by identifying the benefits when someone actually gets it.
Threshold concept theory, in the words of Cousin, identifies the “the kind of complicated learner transitions learners undergo” and identifies portals that change the way that you think about a given discipline. This is deeply related to our goal of “Thinking as a discipline practitioner” because we must assume that a sound practitioner has passed through these portals and has transformed the way that they think in order to be able to practice correctly. Put simply, being a mathematician is more than plugging numbers into formulae.
As you can read, and I’ve mentioned in a previous post, threshold concepts are transformative, integrative, irreversible and (unfortunately) troublesome. Once you have passed through the hurdle then a new vista opens up before you but, my goodness, sometimes that’s a steep hurdle and, unsurprisingly, this is where many students fall.
The potato example in the preface describes the irreversible chemical process of cooking and how the way that we can use the potato changes at each stage. Potatoes, thankfully unaware, have no idea of what is going on nor can they oscillate on their pathway to transformation. Students, especially in the presence of the challenging, can and do oscillate on their transformational road. Anyone who teaches has seen this where we make great strides on one day and, the next, some of the progress ebbs away because a student has fallen back to a previous way of thinking. However, once we have really got the new concept to stick, then we can move forward on the basis of the new knowledge.
Threshold concepts can also be thought of as marking the boundary of areas within a discipline and, in this regard, have special interest to teachers and learners alike. Being able to subdivide knowledge into smaller sections to develop mastery that then allows further development makes the learning process easier to stage and scaffold. However, the looming and alien nature of the portal between sections introduces a range of problems that will apply to many of our students, so we have to ready to assist at these key points.
The book then provides a collection of chapters that discuss how these threshold concepts manifest inside different disciplines and in what forms the alien and troublesome nature can appear. It’s unsurprising again, for anyone teaching Computer Science or programming, that there are a large number of fundamental concepts in programming that are considered threshold concepts. These include the notion of program state, the collection of data that describes the information within a program. While state is an everyday concept (the light is on, the lift is on level 4), the concentration on state, the limitations and implications of manipulation and the new context raise this banal and everyday concepts into the threshold area. A large number of students can happily tell you which floor the lift is on, but cannot associate this physical state with the corresponding programmatic state in their own code.
Until students master some of these concepts, their questions will always appear facile, potentially ill-formed and (regrettably) may be interpreted as lazy. Flanagan and Smith raise an interesting point in that programming languages, which are written in pseudo-English with a precise but alien grammar, may be leading a linguistic problem, where the translation to a comprehensible form is one of the first threshold concepts that a student faces. As an example, consider this simple English set of instructions:
There are 10 apples in the basket. Take each apple out of the basket, polish it, and place it in the sink.
Now let’s look at what the ‘take each apple’ instruction looks like in the C programming language.
for (int i = 0; i < numberOfApples; i++) {
// commands here
}
This is second nature to me to read but a number of you have just looked at that and gone ‘huh’? If you don’t learn what each piece does, understand its importance and can then actually produce it when asked then the risk is that you will just reproduce this template whenever I ask you to count apples. However, there are two situations that humans understand readily: “do something so many times” and “do something UNTIL something happens”. In programs we write these two cases differently – but it’s a linguistic distinction that, from Flanagan and Smith’s work “From Playing to Understanding”, correlates quite well with an ability to pick the more appropriate way of writing the program. If the language itself is the threshold, and for some students it certainly appears that it is, then we are not even able to assume that the students will reach the first stage of ‘local thresholds’ found within the subdomain itself, they are stuck on the outside reading a menu in a foreign language trying to work out if it says “this way to the toilet”.
Such linguistic thresholds will make students appear very, very slow and this is a problem. If you ask a student a question and the words make no sense in the way that you’re presenting them, then they will either not respond (if they have a choice) as they don’t know what you asked, they will answer a different question (by taking a stab at the meaning) or they will ask you what you mean. If someone asks you what you mean when, to you, the problem is very simple, we run the risk of throwing up a barrier between teacher and learner, the teacher assuming that the learner is stupid or lazy, the student assuming that the teacher either doesn’t know what they’re saying or doesn’t care about them.
I’ll write more on the implications of all of this tomorrow.
A Difficult Argument: Can We Accept “Academic Freedom” In Defence of Poor Teaching?
Posted: October 26, 2012 Filed under: Education | Tags: advocacy, authenticity, community, curriculum, education, educational problem, educational research, ethics, feedback, Generation Why, higher education, measurement, principles of design, reflection, student perspective, teaching, teaching approaches, thinking, tools, vygotsky 3 CommentsLet me frame this very carefully, because I realise that I am on very, very volatile ground with any discussion that raises the spectre of a right or a wrong way of teaching. The educational literature is equally careful about this and, very sensibly, you read about rates of transfer, load issues, qualitative aspects and quantitative outcomes, without any hard and fast statements such as “You must never lecture again!” or “You must use formative assessment or bees will consume your people!”
I am aware, however, that we are seeing a split between those people who accept that educational research has something to tell them, which may possibly override personal experience or industry requirement, and those who don’t. But, and let me tread very carefully indeed, while those of us who accept that the traditional lecture is not always the right approach realise that the odd lecture (or even entire course of lectures) won’t hurt our students, there is far more damaging and fundamental disagreement.
Does education transform in the majority of cases or are most students ‘set’ by the time that they come to us?
This is a key question because it affects how we deal with our students. If there are ‘good’ and ‘bad’ students, ‘smart’ and ‘dumb’ or ‘hardworking’ and ‘lazy’, and this is something that is an immutable characteristic, then a lot of what we are doing in order to engage students, to assist them in constructing knowledge and placing into them collaborative environments, is a waste of their time. They will either get it (if they’re smart and hardworking) or they won’t. Putting a brick next to a bee doesn’t double your honey-making capacity or your ability to build houses. Except, of course, that students are not bees or bricks. In fact, there appears to be a vast amount of evidence that says that such collaborative activities, if set up correctly in accordance with the established work in social constructivism and cognitive apprenticeship, will actually have the desired effect and you will see positive transformations in students who take part.
However, there are still many activities and teachers who continue to treat students as if they are always going to be bricks or bees. Why does this matter? Let me digress for a moment.
I don’t care if vampires, werewolves or zombies actually exist or not and, for the majority of my life, it is unlikely to make any difference to me. However, if someone else is convinced that she is a vampire and she attacks me and drain my blood, I am just as dead as if she were not a vampire – of course, I now will not rise from the dead but this is of little import to me. What matters is the impact upon me because of someone else’s practice of their beliefs.
If someone strongly believes that students are either ‘smart enough’ to take their courses or not, they don’t care who fails or how many, and that it is purely the role of the student to have or to spontaneously develop this characteristic then their impact will likely be high enough to have a negative impact on at least some students. We know about stereotype threat. We’re aware of inherent bias. In this case, we’re no longer talking about right or wrong teaching (thank goodness), we’re talking about a fundamentally self-fulfilling prophecy as a teaching philosophy. This will have as great an impact to those who fail or withdraw as the transformation pathway does to those who become better students and develop.
It is, I believe, almost never about the bright light of our most stellar successes. Perhaps we should always be held to answer (or at least explain) for the number and nature of those who fall away. I have been looking for statements of student rights across Australia and the Higher Education sites all seem to talk about ‘fair assessment’ and ‘right of appeal’, as well as all of the student responsibilities. The ACARA (Australian Curriculum and Reporting Authority) website talks a lot about opportunities and student needs in schools. What I haven’t yet found is something that I would like to see, along these lines:
“Educational is transformational. Students are entitled to be assessed on their own performance, in the context of their opportunities.”
Curve grading, which I’ve discussed before, immediately forces a false division of students into good and bad, merely by ‘better’ students existing. It is hard to think of something that is fundamentally less fair or appropriate to the task if we accept that our goal is improvement to a higher standard, regardless of where people start. In a curve graded system, the ‘best’ person can coast because all they have to do is stay one step ahead of their competition and natural alignment and inflation will do the rest. This is not the motivational framework that we wish to establish, especially when the lowest realise that all is lost.
I am a long distance runner and my performances will never set the world on fire. To come first in a race, I would have to be in a small race with very unfit people. But no-one can take away my actual times for my marathons and it is those times that have been used to allow me to enter other events. You’ll note that in the Olympics, too. Qualifying times are what are used because relative performance does not actually establish any set level of quality. The final race? Yes, we’ve established competitiveness and ranking becomes more important – but then again, entering the final heat of an Olympic race is an Olympian achievement. Let’s not quibble on this, because this is the equivalent of Nobel and Turing awards.
And here is the problem again. If I believe that education is transformative and set up all of my classes with collaborative work, intrinsic motivation and activities to develop self-regulation, then that’s great but what if it’s in third-year? If the ‘students were too dumb to get it’ people stand between me and my students for the first two years then I will have lost a great number of possibly good students by this stage – not to mention the fact that the ones who get through may need some serious de-programming.
Is it an acceptable excuse that another academic should be free to do what they want, if what they want to do is having an excluding and detrimental effect on students? Can we accept that if it means that we have to swallow that philosophy? If I do, does it make me complicit? I would like nothing more than to let people do what they want, hey, I like that as much as the next person, but in thinking about the effect of some decisions being made, is the notion of personal freedom in what is ultimately a public service role still a sufficiently good argument for not changing practice?
Recursive Tutorial: A tutorial on writing a tutorial
Posted: October 24, 2012 Filed under: Education | Tags: authenticity, community, curriculum, data visualisation, education, educational research, Generation Why, grand challenge, higher education, in the student's head, learning, principles of design, reflection, student perspective, teaching, teaching approaches, thinking, tools Leave a commentI assigned the Grand Challenge students a slightly strange problem for yesterday’s tutorial: “How would you write an R tutorial for Year 11 High School Students?” R is an open source statistics package that is incredibly powerful and versatile but it is nowhere near as friendly to use or accessible as traditional GUI tools such as Microsoft Excel. R has some menus and buttons on it but most of these are used to control the environment, rather than applying the statistical and mathematical functions. R Studio is an associated Integrated Development Environment (IDE) that makes working with R easier but, at its core, R relies upon you knowing enough R to type the right commands.
Discussing this with students, we compared Excel and R to find out what the core differences were and some of them are not important early on but become more important later. Excel, for example, allows you to quickly paste and move around data, apply some functions, draw some graphs and come to a result quickly, mostly by pushing buttons and using on-line help with a little typing. But, and it’s an important but, unless you write a program in Excel (and not that many people do), re-applying all of that manipulation to a new data source requires you to click and push and move across the screen all over again. You have to recreate a long and complicated combination of mechanical and cognitive functions. R, by contrast, requires you to type commands to get things to happen but it remembers them by default and you can easily extract them. Because of how R works, you drag in data (from a file, say) and then execute a set of manipulation steps. If you’re familiar with R then this is straight-forward. If not, then steep learning curve. However, re-using these instructions and manipulations on a new data source is trivial. You change the file and re-run all of the steps.
Why am I talking about new data sources? Because it’s often the case that you want to do the same thing with new data OR you realise that the data you were working with was incomplete or in error. Unless you write a lot of Visual Basic in Excel (and that no longer works on Macs so it’s not a transferable option), your Excel spreadsheet with changed data requires you to potentially reapply or check the application of everything in the spreadsheet, especially if there is any sorting of data, creation of new columns or summary data – and let’s not even start talking about pivot tables! But, for single run, for finance, for counting stuff, Excel is almost always going to be more easy to teach people to use than R. For scientists, however, R is better to use for two very important reasons: it is less likely to do something that is irreversible to your data and the vast majority of its default choices are sensible.
The students came up with a list of things that Excel does (good and bad): it’s strongly visual, lay-user friendly, tells you what you can do, does what it damn well wants to, data changes may require manual reapplication. There’s a corresponding list for R: steep learning curve, visual display for R environment but command-line interface for commands, does what you tell it to do (except when it’s too smart). I surveyed the class to find out who was using R rather than Excel and the majority of students were using R for their analysis but, and again it’s an important but, only because they had to. In situations where Excel was enough (simple manipulation, straight forward analysis), then Excel got used because Excel is far easier to use and far friendlier.
The big question for the students was “How do I start doing something?” In Excel, you type numbers into the spreadsheet and then can just start selecting things using a relatively good on-line help system. In R you are faced with a blinking prompt and you have to know enough to type streams of commands like this:
newtab <-read.csv("~/days.txt",header=FALSE)
plot(seq(1,nrow(newtab)),newtab$V1)
boxplot(newtab)
abline(a=1500,b=0)
mean(newtab)

And, with a whole set of other commands, you can get graphs like this. (I realise that this is not a box plot!)
Once you’re used to it, this is meaningful, powerful and re-applicable. I can update the data and re-run this to my heart’s content, analysing vast quantities of data without having to keep mouse clicking into cells. But let’s remember our context. I’m not talking about higher education students, I’m talking about school students and it’s important to remember that teaching people something before they’re ready to use it or before they have an opportunity to use it is potentially not the best use of effort.
My students pointed out that the school students of today are all learning how to use graphing calculators, with giant user manuals, and (in some cases) the students switch on their calculators to see a menu rather than the traditional calculator single line. But the syntax and input modes for calculators vary widely. Some use ( ) for operations like sin, so a student will see sin(30) when they start doing trig, whereas some don’t. This means that some of the students I might want to teach R to have not necessarily got their head around the fact that functions exist, except as something that Excel requires them to do. Let’s go to the why here, because it’s important. Why are students learning how to use these graphing calculators? So they can pass their exams, where the competent and efficient use of these things will help them. Yes, it appears that students may be carrying out the kind of operations I would like them to put into a more powerful tool, but why should they?
If a teach a high school student about Excel then there are many places that they might use this kind of software: micro-budgeting, keeping track of things, the ‘simple’ approximation of a database storing books or things like that. However, the general practice of using Excel is familiarisation with a GUI interface that is very, very common and that most students need experience with. If I teach them R then I might be extending their knowledge but (a) the majority are probably not yet ready for it and (b) they are highly unlikely to need to use it for anything in the near future.
The conclusion that my students reached was that, if we really wanted to provide exposure to an industry-like scientific or engineering tool at the earlier stage, then why not use one that was friendlier, more helpful but still had a more scientific focus. They suggested Matlab (as a number of them had been exposed) or Mathematica. Now this whole exercise was designed to get them to practice their thinking about outreach, community, communication and sharing knowledge, so I wasn’t ever actually planning to run an R tutorial at Year 11. But these students thought through and asked the very important questions:
- Who is this aimed at?
- What do they already know?
- What do they need to know?
- Why are we doing this?
Of course, I have also learned a great deal from this as well – I had no idea that the calculators had quite got to this point, nor that there were schools were students would have to select through a graphical menu to get to the simple “3+3 EXE” section of the calculator! Don’t tell my Grand Challenge students but I think I’m learning roughly as much as they are!
Polymaths, Philomaths and Teaching Philosophy: Why we can’t have the first without the second, and the second should be the goal of the third.
Posted: October 22, 2012 Filed under: Education | Tags: advocacy, authenticity, collaboration, community, education, educational problem, ethics, Generation Why, higher education, philosophy, principles of design, reflection, resources, teaching, teaching approaches, thinking, tools, universal principles of design, vygotsky 1 CommentYou may have heard the term polymath, a person who possesses knowledge across multiple fields, or if you’re particularly unlucky, you’ve been at one of those cocktail parties where someone hands you a business card that says, simply, “Firstname Surname, Polymath” and you have formed a very interesting idea of what a polymath is. We normally reserve this term for people who excel across multiple fields such as, to drawn examples from this Harvard Business Review blog by Kyle Wiens, Leonard da Vinci (artist and inventor), Benjamin Franklin, Paul Robeson or Steve Jobs. (Let me start to address the article’s gender imbalance with Hypatia of Alexandria, Natalie Portman, Maya Angelou and Mayim Bialik, to name a small group of multidisciplinary women, admittedly focussing on the Erdös-Bacon intersection.) By focusing on those who excel, we do automatically associate a higher degree of assumed depth of knowledge across these multiple fields. The term “Renaissance [person]” is often bandied about as well.

Da Vinci, seen here inventing the cell phone. Sadly, it was to be over 500 years before the cell phone tower was invented so he never received a call. His monthly bill was still enormous.
Now, I have worked as a system administrator and programmer, a winemaker and I’m now an academic in Computer Science, being slowly migrated into some aspects of managerialism, who hopes shortly to start a PhD in Creative Writing. Do I consider myself to be a polymath? No, absolutely not, and I struggle to think of anyone who would think of me that way, either. I have a lot of interests but, while I have had different areas of expertise over the years, I’ve never managed the assumed highly parallel nature of expertise that would be required to be considered a polymath, of any standing. I have academic recognition of some of these interests but this changes neither the value (to me or others) nor has it ever been required to be well-lettered to be in the group mentioned above.
I describe myself, if I have to, as a philomath, someone who is a lover of learning. (For both of the words, the math suffix comes from the Greek and means to learn, but poly means much/many and philo means loving, so a polymath is ‘many learnéd’.) The immediate pejorative for someone who leans lots of things across areas is the infamous “Jack of all trades” and its companion “master of none”. I love to learn new things, I like studying but I also like applying it. I am confident that the time I spent in each discipline was valuable and that I knew my stuff. However, the main point I’d like to state here is that you cannot be a polymath without first having been a philomath – I don’t see how you can develop good depth in many areas unless you have a genuine love of learning. So every polymath was first a philomath.
Now let’s talk about my students. If they are at all interested in anything I’m teaching them, and let’s assume that at least some of them love various parts of a course at some stage, then they are looking to develop more knowledge in one area of learning. However, looking at my students as mono-cultural beings who only exist when they are studying, say, the use of the linked list in programming, is to sell them very, very short indeed. My students love doing a wide range of things. Yes, those who love learning in my higher educational context will probably do better but I guarantee you that every single student you have loves doing something, and most likely that’s more than one thing! So every single one of my students is inherently a philomath – but the problems arise when what they love to learn is not what I want to teach!
This leads me to the philosophy of learning and teaching, how we frame, study and solve the problems of trying to construct knowledge and transform it to allow its successful transfer to other people, as well as how we prepare students to receive, use and develop it. It makes sense that the state that we wish to develop on our students is philomathy. Students are already learning from, interested and loving their lives and the important affairs of the world as they see them, so to get them interested in what we want to teach them requires us to acknowledge that we are only one part of their lives. I rarely meet a student who cannot provide a deep, accurate and informative discourse on something in their lives. If we accept this then, rather than demanding an unnatural automaton who rewrites their entire being to only accept our words on some sort of diabolical Turing Tape of compliance, we now have a much easier path, in some respects, because accepting this means that our students will spend time on something in the depth that we want – it is now a matter of finding out how to tap into this. At this point, the yellow rag of populism is often raised, unfairly in most cases, because it is assumed that students will only study things which are ‘pop’ or ‘easy’. There is nothing ‘easy’ about most of the pastimes at which our students excel and they will expend vast amount of efforts on tasks if they can see a clear reason to do so, it appears to be a fair return on investment, and they feel that they have reasonable autonomy in the process. Most of my students work harder for themselves than they ever will for me: all I do is provide a framework that allows them to achieve something and this, in turn, allows them to develop a love. Once the love has been generated, the philomathic wheel turns and knowledge (most of the time) develops.
Whether you agree on the nature of the tasks or not, I hope that you can see why the love of learning should be a core focus of our philosophy. Our students should engage because they want to and not just because we force them to do so. Only one of these approaches will persist when you remove the rewards and the punishments and, while Skinner may disagree, we appear to be more than rats, especially when we engage our delightfully odd brains to try and solve tasks that are not simply rote learned. Inspiring the love of learning in any one of our disciplines puts a student on the philomathic path but this requires us to accept that their love of learning may have manifested in many other areas, that may be confusedly described as without worth, and that all we are doing is to try and get them to bring their love to something that will be of benefit to them in their studies and, assuming we’ve set the course up correctly, their lives in our profession.
Sources of Knowledge: Stickiness and the Chasm Between Theory and Practice.
Posted: October 21, 2012 Filed under: Education | Tags: blogging, collaboration, design, education, educational research, higher education, in the student's head, principles of design, reflection, resources, student perspective, teaching, teaching approaches, thinking, tools, universal principles of design, vygotsky Leave a commentMy head is still full of my current crop of research papers and, while I can’t go into details, I can discuss something that I’m noticing more and more as I read into the area of Computer Science Education. Firstly, how much I have left to learn and, secondly, how difficult it is sometimes to track down ideas and establish novelty, provenance and worth. I read Mark Guzdial’s blog a lot because Mark has spent a lot of time being very clever in this area (Sorry, Mark, it’s true) but he is also an excellent connecter of the reader to good sources of information, as well as reminding us when something pops up that is effectively a rehash of an old idea. This level of knowledge and ability to discuss ideas is handy when we keep seeing some of the same old ideas pop up, from one source or another, over time. I’ve spoken before about how the development of the mass-accessible library didn’t end the importance of the University or school, and Mark makes a similar note in a recent post on MOOCs when he points us to an article on mail delivery lessons from a hundred years before and how this didn’t lead to the dissolution of the education system. Face-to-face continues to be important, as do bricks and mortar, so while the MOOC is a fascinating new tool and methodology with great promise, the predicted demise of the school and college may (once again) turn out to be premature.
If you’ve read Malcolm Gladwell’s “The Tipping Point”, you’ll be familiar with the notion that ideas need to have certain characteristics, and certain human agents, before they become truly persuasive and widely adopted. If you’ve read Dawkin’s “Selfish Gene” (published over a decade before) then you’ll understand that Gladwell’s book would be stronger if it recognised a debt to Dawkins’ coining of the term meme, for self-replicating beliefs and behaviours. Gladwell’s book, as a source, is a fairly unscientific restatement of some existing ideas with a useful narrative structure, despite depending on some now questionable case studies. In many ways, it is an example of itself because Gladwell turned existing published information into a form where, with his own additions, he has identified a useful way to discuss certain systems of behaviour. Better still, people do (still) read it.
(A quick use of Google Trends shows me that people search for “The Tipping Point” roughly twice as much as “The Selfish Gene” but for “Richard Dawkins” twice as much as “Malcolm Gladwell”. Given Dawkins’ very high profile in belligerent atheism, this is not overly surprising.)
Gladwell identified the following three rules of epidemics (in terms of the spread of ideas):
- The Law of the Few: There are a small group of people who make a big difference to the proliferation of an idea. The mavens accumulate knowledge and know a lot about the area. The connectors are the gregarious and sociable people who know a lot of other people and, in Gladwell’s words, “have a gift for bringing the word together”. The final type of people are salespeople or (more palatably) persuaders, the people who convince us that something is a good idea. Gladwell’s thesis is that it is not just about the message, but that the messenger matters.
- The Stickiness Factor: Ideas have to be memorable in order to spread effectively so there is something about the specific content of the message that will determine its impact. Content matters.
- The Power of Context: We are all heavily influenced by and sensitive to our environment. Context matters.
Dawkins’ meme is a very sticky idea and, while there’s a lot of discussion about the Selfish Gene, we now have the field of memetics and the fact that the word ‘meme’ is used (almost correctly) thousands, if not millions, of times a day. Every time that you’ve seen a prawn running on a treadmill while Yakity Sax plays, you can think of Richard Dawkins and thank him for giving you a word to describe this.
My early impressions of some of the problem with the representation of earlier ideas in CS Ed, as if they are new, makes me wonder if there is a fundamental problem with the stickiness of some of these ideas. I would argue that the most successful educational researchers, and I’ve had the privilege to see some of them, are in fact strong combinations of Gladwell’s few. Academics must be, by definition, mavens, information specialists in our domains. We must be able to reach out to our communities and spread our knowledge – is this enough for us to be called connectors? We have to survive peer review, formal discussions and criticism and we have to be able to argue our ideas, on the reasonable understanding that it is our ideas and not ourselves that is potentially at fault. Does this also make us persuaders? If we can find all of these “few” in our community, and we already a community of the few, where does it leave us in terms of explaining why we, in at least some areas, keep rehashing the same old ideas. Do we fail to appreciate the context of those colleagues we seek to reach or are our ideas just not sticky enough? (Context is crucial here, in my opinion, because it is very easy to to explain a new idea in a way that effectively says “You’ve been doing it wrong all these years. Now fix it or you’re a bad person.” This is going to create a hostile environment. Once again, context matters but this time it is in terms of establishing context.)
I wonder if this is compounded in Computer Science by the ability to separate theory from practice, and to draw in new practice from both an educational research focus and an industrial focus? To explain why teamwork actually works, we move into social constructivism and to Vygotsky, via Ben-Ari in many cases, Bandura, cognitive apprenticeship – that’s an educational research focus. To say that teamwork works, because we’ve got some good results from industry and we’re supported by figures such as Brooks, Boehm and Humphrey and their case studies in large-scale development – that’s an industrial focus. The practice of teamwork is sticky, that ship has sailed in software development, but does the stickiness of the practice transfer to the stickiness of the underlying why? The answer, I believe, is ‘no’ and I’m beginning to wonder if a very sticky “what” is actually acting against the stickiness of the “why”. Why ask “why?” when you know that it works? This seems to be a running together of the importance of stickiness and the environment of the CS Ed researcher as a theoretical educationalist, working in a field that has a strong industrial focus, with practitioner feedback and accreditation demands pushing a large stream of “what do to”.
It has been a thoughtful week and, once again, I admit my novice status here. Is this the real problem? If so, how can we fix it?
A Study in Ethics: Lance Armstrong and Why You Shouldn’t Burn Your Bracelet.
Posted: October 19, 2012 Filed under: Education | Tags: advocacy, blogging, community, education, ethics, feedback, Generation Why, higher education, in the student's head, lance armstrong, principles of design, resources, student perspective, teaching, teaching approaches, thinking 2 CommentsIf you haven’t heard about the recent USADA release of new evidence against Lance Armstrong, former star of cycling and Chairman for his own LIVESTRONG Cancer Foundation, then let me summarise it: it’s pretty damning. After reviewing this and other evidence, I have little doubt that Lance Armstrong systematically and deliberately engaged in the procurement, distribution, promotion and consumption of banned substances while he was engaged in an activity that explicitly prohibited this. I also have very little doubt that he engaged in practices, such as blood transfusion, intimidation and the manipulation of colleagues and competitors, again in a way that contravened the rules of his sport and in a way that led the sport into disrepute. The USADA report contains a lot of the missing detail, witness reports, accounts and evidence that, up until now, has allowed Lance Armstrong to maintain that delightful state of grace that is plausible deniability. He has now been banned for life, although he can appeal, his sponsors are leaving him and he has stepped down as the Chairman of his charity.
I plan to use Armstrong in my discussions of ethics over the next year for a number of reasons and this is an early musing, so it’ll be raw and I welcome discussion. Here are my initial reasons and thoughts:
- It’s general knowledge and everyone knows enough about this case to have formed an opinion. Many of the other case studies I use refer to the past or situations that are not as widely distributed.
- It’s a scenario that (either way) is easy to believe and grounded in the experience of my students.
- Lance Armstrong appears to have been making decisions that impacted his team, his competitors, his entire sport. His area of influence is large.
- There is an associated entity that is heavily linked with Lance’s personal profile, the LIVESTRONG Cancer Charity.
Points 1 and 2 allows me to talk about Lance Armstrong and have everyone say “Oh, yeah!” as opposed to other classic discussions such as Tuskegee, Monster Study, Zimbardo, etc, where I first have to explain the situation, then the scenario and they try to make people believe that this could happen! Believing that a professional sports person may have taken drugs is, in many ways, far easier to get across than complicated stories of making children stutter. Point 3 allows me to get away from thee “So what if someone decides to do X to themselves?” argument – which is a red herring anyway in a competitive situation based (even in theory) on a level playing field. Rationalisations of the actions taken by an individual do not apply when they are imposed on another group, so many of the “my right to swing my arm ends at your nose” arguments that students effectively bring up in discussing moral and ethical behaviour will not stand up against the large body of evidence that Armstrong intimidated other riders, forced their silence, and required team members to follow the same regime. I expect that we’ll still have to have the “So what if everyone dopes” argument in terms of “are people choosing?” and “what are the ethical implications if generalised?” approaches.
But it is this last theme that I really wish to explore. I read a Gawker article telling everyone to rip off their yellow wristbands and that I strongly disagree with. Lance Armstrong is, most likely, a systematic cheat who has been, and still is, lying about his ongoing cheating in order to continue as many of his activities as possible, as well as maintaining some sense of fan base. The time where he could have apologised for his actions, stood up and taken a stand, is pretty much over. Sponsors who have stood by other athletes at difficult times have left him, because the evidence is so overwhelming.
But to say that this has anything to do with LIVESTRONG is an excellent example of the Genetic Fallacy – that is, because something came from Lance Armstrong, it is now somehow automatically bad. Would I drink from a Coke he gave me? Probably not. Do I still wish his large and influential cancer charity all the success in the world? Yes, of course. LIVESTRONG gave out roughly $30,000,000 last year across its programs and that’s a good thing.
It’s a terrible shame that, for so many years, Armstrong’s work with the charity was, more than slightly cynically, used to say what a good person he was despite the allegations. (There’s a great Onion piece from a couple of years ago that now seems bizarrely prescient). Much as LIVESTRONG is not guaranteed to be bad because Armstrong is a doper, running and setting up LIVESTRONG doesn’t absolve Armstrong from actions in other spheres. A Yahoo sports article describes his charity as being used as a ‘moral cloak’, although smokescreen might be the better word. But we need to look further.
To what does LIVESTRONG owe its success? Would it be as popular and successful if Armstrong hadn’t come back from cancer (he continues to be a cancer survivor) and then hadn’t won all of those tours? Given that his success was, apparently, completely dependent upon illegal activity, aren’t we now indebted to Armstrong’s illegal activity for the millions of dollars that have gone to help people with cancer?
We can talk about moral luck, false dichotomy and false antecedent/consequent (depending on which way around you wish to frame it) in this and this leads us into all sorts of weird and wonderful discussions, from a well-known and much discussed current affairs issue. But the core is quite simple: Armstrong’s actions had a significantly negative effect upon his world but at least one of the actions that he took has had a positive outcome. Whatever his motivation and intention, the outcome is beneficial. LIVESTRONG now has a challenge to see if it is big enough to survive this reversal of fortune but this is, most definitely, not the time to burn the bracelet. Turn it around, if you want, but, until it turns out that LIVESTRONG is some sort of giant front for clubbing baby harp seals, we can’t just lump this in with the unethical actions of one man.
I was thinking about what Armstrong could do now and, while I believe that he will never be able to do many of the things that he used to do (pro cycling/speaking arrangements/public figure), we know that he is quite good at two things:
- Riding a bike
- Getting drugs into difficult places.
One of the major problems in the world is getting the right pharmaceuticals to the right people because of government issues, instability and poverty. There are probably worse things for Armstrong to do than cycle from point to point, sneaking medicine past border guards, shinning down drain pipes to provide retrovirals to the poor in the slums of a poor city and hiking miles so that someone doesn’t die today. (I know, that’s all a bit hair shirt – I’m not suggesting that seeking atonement is either required or sensible.) More seriously, the end of my ethical study in Armstrong will only be written when he works out what he wants to do next. Then my students can look at it, scratch their heads and try to work out where that now places him in terms of morality and ethics.
Authenticity and Challenge: Software Engineering Projects Where Failure is an Option
Posted: October 17, 2012 Filed under: Education | Tags: authenticity, collaboration, community, curriculum, design, education, educational problem, fred brooks, Generation Why, higher education, in the student's head, learning, principles of design, reflection, resources, sigcse, software engineering, student perspective, teaching, teaching approaches, thinking, tools, universal principles of design 2 CommentsIt’s nearly the end of semester and that means that a lot of projects are coming to fruition – or, in a few cases, are still on fire as people run around desperately trying to put them out. I wrote a while about seeing Fred Brooks at a conference (SIGCSE) and his keynote on building student projects that work. The first four of his eleven basic guidelines were:
- Have real projects for real clients.
- Groups of 3-5.
- Have lots of project choices
- Groups must be allowed to fail.
We’ve done this for some time in our fourth year Software Engineering option but, as part of a “Dammit, we’re Computer Science, people should be coming to ask about getting CS projects done” initiative, we’ve now changed our third year SE Group Project offering from a parallel version of an existing project to real projects for real clients, although I must confess that I have acted as a proxy in some of them. However, the client need is real, the brief is real, there are a lot of projects on the go and the projects are so large and complex that:
- Failure is an option.
- Groups have to work out which part they will be able to achieve in the 12 weeks that they have.
For the most part, this approach has been a resounding success. The groups have developed their team maturity faster, they have delivered useful and evolving prototypes, they have started to develop entire tool suites and solve quite complex side problems because they’ve run across areas that no-one else is working in and, most of all, the pride that they are taking in their work is evident. We have lit the blue touch paper and some of these students are skyrocketing upwards. However, let me not lose sight of one our biggest objectives, that we be confident that these students will be able to work with clients. In the vast majority of cases, I am very happy to say that I am confident that these students can make a useful, practical and informed contribution to a software engineering project – and they still have another year of projects and development to go.
The freedom that comes with being open with a client about the possibility of failure cannot be overvalued. This gives both you and the client a clear understanding of what is involved- we do not need to shield the students, nor does the client have to worry about how their satisfaction with software will influence things. We scaffold carefully but we have to allow for the full range of outcomes. We, of course, expect the vast majority of projects to succeed but this experience will not be authentic unless we start to pull away the scaffolding over time and see how the students stand by themselves. We are not, by any stretch, leaving these students in the wilderness. I’m fulfilling several roles here: proxying for some clients, sharing systems knowledge, giving advice, mentoring and, every so often, giving a well-needed hairy eyeball to a bad idea or practice. There is also the main project manager and supervisor who is working a very busy week to keep track of all of these groups and provide all of what I am and much, much more. But, despite this, sometimes we just have to leave the students to themselves and it will, almost always, dawn on them that problem solving requires them to solve the problem.
I’m really pleased to see this actually working because it started as a brainstorm of my “Why aren’t we being asked to get involved in more local software projects” question and bouncing it off the main project supervisor, who was desperate for more authentic and diverse software projects. Here is a distillation of our experience so far:
- The students are taking more ownership of the projects.
- The students are producing a lot of high quality work, using aggressive prototyping and regular consultation, staged across the whole development time.
- The students are responsive and open to criticism.
- The students have a better understanding of Software Engineering as a discipline and a practice.
- The students are proud of what they have achieved.
None of this should come as much of a surprise but, in a 25,000+ person University, there are a lot of little software projects on the 3-person team 12 month scale, which are perfect for two half-year project slots because students have to design for the whole and then decide which parts to implement. We hope to give these projects back to them (or similar groups) for further development in the future because that is the way of many, many software engineers: the completion, extension and refactoring of other people’s codebases. (Something most students don’t realise is that it only takes a very short time for a codebase you knew like the back of your hand to resemble the product of alien invaders.)
I am quietly confident, and hopeful, that this bodes well for our Software Engineers and that we still start to seem them all closely bunched towards the high achieving side of the spectrum in terms of their ability to practice. We’re planning to keep running this in the future because the early results have been so promising. I suppose the only problem now is that I have to go and find a huge number of new projects for people to start on for 2013.
As problems go, I can certainly live with that one!
Industry Speaks! (May The Better Idea Win)
Posted: October 16, 2012 Filed under: Education | Tags: alan noble, community, data visualisation, design, education, entrepreneurship, Generation Why, grand challenge, higher education, learning, measurement, MIKE, principles of design, teaching, teaching approaches, thinking, tools, universal principles of design Leave a commentAlan Noble, Director of Engineering for Google Australia and an Adjunct Professor with my Uni, generously gave up a day today to give a two hour lecture of distributed systems and scale to our third-year Distributed Systems course, and another two-hour lecture on entrepreneurship to my Grand Challenge students. Industry contact is crucial for my students because the world inside the Uni and the world outside the Uni can be very, very different. While we try to keep industry contact high in later years, and we’re very keen on authentic assignments that tackle real-world problems, we really need the people who are working for the bigger companies to come in and tell our students what life would be like working for Google, Microsoft, Saab, IBM…
My GC students have had a weird mix of lectures that have been designed to advance their maturity in the community and as scientists, rather than their programming skills (although that’s an indirect requirement), but I’ve been talking from a position of social benefit and community-focused ethics. It is essential that they be exposed to companies, commercialisation and entrepreneurship as it is not my job to tell them who to be. I can give them skills and knowledge but the places that they take those are part of an intensely personal journey and so it’s great to have an opportunity for Alan, a man with well-established industry and research credentials, to talk to them about how to make things happen in business terms.
The students I spoke to afterwards were very excited and definitely saw the value of it. (Alan, if they all leave at the end of this year and go to Google, you’re off the Christmas Card list.) Alan focused on three things: problems, users and people.
Problems: Most great companies find a problem and solve it but, first, you have to recognise that there is a problem. This sometimes just requires putting the right people in front of something to find out what these new users see as a problem. You have to be attentive to the world around you but being inventive can be just as important. Something Alan said really resonated with me in that people in the engineering (and CS) world tend to solve the problems that they encounter (do it once manually and then set things up so it’s automatic thereafter) and don’t necessarily think “Oh, I could solve this for everyone”. There are problems everywhere but, unless we’re looking for them, we may just adapt and move on, instead of fixing the problem.
Users: Users don’t always know what they want yet (the classic Steve Jobs approach), they may not ask for it or, if they do ask for something, what they want may not yet be available for them. We talked here about a lot of current solutions to problems but there are so many problems to fix that would help users. Simultaneous translation, for example, over telephone. 100% accurate OCR (while we’re at it). The risk is always that when you offer the users the idea of a car, all they ask for is a faster horse (after Henry Ford). The best thing for you is a happy user because they’re the best form of marketing – but they’re also fickle. So it’s a balancing act between genuine user focus and telling them what they need.
People: Surround yourself with people who are equally passionate! Strive of a culture of innovation and getting things done. Treasure your agility as a company and foster it if you get too big. Keep your units of work (teams) smaller if you can and match work to the team size. Use structures that encourage a short distance from top to bottom of the hierarchy, which allows for ideas to move up, down and sideways. Be meritocratic and encourage people to contest ideas, using facts and articulating their ideas well. May the Better Idea Win! Motivating people is easier when you’re open and transparent about what they’re doing and what you want.
Alan then went on to speak a lot about execution, the crucial step in taking an idea and having a successful outcome. Alan had two key tips.
Experiment: Experiment, experiment, experiment. Measure, measure, measure. Analyse. Take it into account. Change what you’re doing if you need to. It’s ok to fail but it’s better to fail earlier. Learn to recognise when your experiment is failing – and don’t guess, experiment! Here’s a quote that I really liked:
When you fail a little every day, it’s not failing, it’s learning.
Risk goes hand-in-hand with failure and success. Entrepreneurs have to learn when to call an experiment and change direction (pivot). Pivot too soon, you might miss out on something good. Pivot too late, you’re in trouble. Learning how to be agile is crucial.
Data: Collect and scrutinise all of the data that you get – your data will keep you honest if you measure the right things. Be smart about your data and never copy it when you can analyse it in situ.
(Alan said a lot more than this over 2 hours but I’m trying to give you the core.)
Alan finished by summarising all of this as his Three As of Entrepreneurship, then why we seem to be hitting an entrepreneurship growth spurt in Australia at the moment. The Three As are:
- Audit your data
- Having Audited, Admit when things aren’t working
- Once admitted, you can Adapt (or pivot)
As to why we’re seeing a growth of entrepreneurship, Australia has a population who are some of the highest early adopters on the planet. We have a high technical penetration, over 20,000,000 potential users, a high GDP and we love tech. 52% of Australians have smart phones and we had so many mobile phones, pre-smart, that it was just plain crazy. Get the tech right and we will buy it. Good tech, however, is hardware+software+user requirement+getting it all right.
It’s always a pleasure to host Alan because he communicates his passion for the area well but he also puts a passionate and committed face onto industry, which is what my students need to see in order to understand where they could sit in their soon-to-be professional community.
Dealing with Plagiarism: Punishment or Remediation?
Posted: October 15, 2012 Filed under: Education | Tags: advocacy, community, curriculum, design, education, educational problem, educational research, ethics, feedback, Generation Why, higher education, in the student's head, learning, measurement, plagiarism, principles of design, reflection, student perspective, teaching, teaching approaches, thinking, tools, work/life balance 6 CommentsI have written previously about classifying plagiarists into three groups (accidental, panicked and systematic), trying to get the student to focus on the journey rather than the objective, and how overwork can produce situations in which human beings do very strange things. Recently, I was asked to sit in on another plagiarism hearing and, because I’ve been away from the role of Assessment Coordinator for a while, I was able to look at the process with an outsider’s eye, a slightly more critical view, to see how it measures up.
Our policy is now called an Academic Honesty Policy and is designed to support one of our graduate attributes: “An awareness of ethical, social and cultural issues within a global context and their importance in the exercise of professional skills and responsibilities”. The principles are pretty straight-forward for the policy:
- Assessment is an aid to learning and involves obligations on the part of students to make it effective.
- Academic honesty is an essential component of teaching, learning and research and is fundamental to the very nature of universities.
- Academic writing is evidence-based, and the ideas and work of others must be acknowledged and not claimed or presented as one’s own, either deliberately or unintentionally.
The policy goes on to describe what student responsibilities are, why they should do the right thing for maximum effect of the assessment and provides some handy links to our Writing Centre and applying for modified arrangements. There’s also a clear statement of what not to do, followed by lists of clarifications of various terms.
Sitting in on a hearing, looking at the process unfolding, I can review the overall thrust of this policy and be aware that it has been clearly identified to students that they must do their own work but, reading through the policy and its implementation guide, I don’t really see what it provides to sufficiently scaffold the process of retraining or re-educating students if they are detected doing the wrong thing.
There are many possible outcomes from the application of this policy, starting with “Oh, we detected something but we turned out to be wrong”, going through “Well, you apparently didn’t realise so we’ll record your name for next time, now submit something new ” (misunderstanding), “You knew what you were doing so we’re going to give you zero for the assignment and (will/won’t) let you resubmit it (with a possible mark cap)” (first offence), “You appear to make a habit of this so we’re giving you zero for the course” (second offence) and “It’s time to go.” (much later on in the process after several confirmed breaches).
Let me return to my discussions on load and the impact on people from those earlier posts. If you accept my contention that the majority of plagiarism cheating is minor omission or last minute ‘helmet fire’ thinking under pressure, then we have to look at what requiring students to resubmit will do. In the case of the ‘misunderstanding’, students may also be referred to relevant workshops or resources to attend in order to improve their practices. However, considering that this may have occurred because the student was under time pressure, we have just added more work and a possible requirement to go and attend extra training. There’s an old saying from Software Development called Brook’s Law:
“…adding manpower to a late software project makes it later.” (Brooks, Mythical Man Month, 1975)
In software it’s generally because there is ramp up time (the time required for people to become productive) and communication overheads (which increases with the square of the number of people again). There is time required for every assignment that we set which effectively stands in for the ramp-up and, as plagiarising/cheating students have probably not done the requisite work before (or could just have completed the assignment), we have just added extra ramp-up into their lives for any re-issued assignments and/or any additional improvement training. We have also greatly increased the communication burden because the communication between lecturers and peers has implicit context based on where we are in the semester. All of the student discussion (on-line or face-to-face) from points A to B will be based around the assignment work in that zone and all lecturing staff will also have that assignment in their heads. An significantly out-of-sequence assignment not only isolates the student from their community, it increases the level of context switching required by the staff, decreasing the amount of effective time that have with the student and increasing the amount of wall-clock time. Once again, we have increased the potential burden on a student that, we suspect, is already acting this way because of over-burdening or poor time management!
Later stages in the policy increase the burden on students by either increasing the requirement to perform at a higher level, due to the reduction of available marks through giving a zero, or by removing an entire course from their progress and, if they wish to complete the degree, requiring them to overload or spend an additional semester (at least) to complete their degree.
My question here is, as always, are any of these outcomes actually going to stop the student from cheating or do they risk increasing the likelihood of either the student cheating or the student dropping out? I complete agree with the principles and focus of our policy, and I also don’t believe that people should get marks for work that they haven’t done, but I don’t see how increasing burden is actually going to lead to the behaviour that we want. (Dan Pink on TED can tell you many interesting things about motivation, extrinsic factors and cognitive tasks, far more effectively than I can.)
This is, to many people, not an issue because this kind of policy is really treated as being punitive rather than remedial. There are some excellent parts in our policy that talk about helping students but, once we get beyond the misunderstanding, this language of support drops away and we head swiftly into the punitive with the possibility of controlled resubmission. The problem, however, is that we have evidence that light punishment is interpreted as a licence to repeat the action, because it doesn’t discourage. This does not surprise me because we have made such a risk/reward strategy framing with our current policy. We have resorted to a punishment modality and, as a result, we have people looking at the punishments to optimise their behaviour rather than changing their behaviour to achieve our actual goals.
This policy is a strange beast as there’s almost no way that I can take an action under the current approach without causing additional work to students at a time when it is their ability to handle pressure that is likely to have led them here. Even if it’s working, and it appears that it does, it does so by enforcing compliance rather than actually leading people to change the way that they think about their work.
My conjecture is that we cannot isolate the problems to just this policy. This spills over into our academic assessment policies, our staff training and our student support, and the key difference between teaching ethics and training students in ethical behaviour. There may not be a solution in this space that meets all of our requirements but if we are going to operate punitively then let us be honest about it and not over-burden the student with remedial work that they may not be supported for. If we are aiming for remediation then let us scaffold it properly. I think that our policy, as it stands, can actually support this but I’m not sure that I’ve seen the broad spread of policy and practice that is required to achieve this desirable, but incredibly challenging, goal of actually changing student behaviour because the students realise that it is detrimental to their learning.





