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’ve laid out some honest and effective approaches to the evaluation of student work that avoid late penalties and still provide high levels of feedback, genuine motivation and a scalable structure.
But these approaches have to fit into the realities of time that we have in our courses. This brings me to the discussion of mastery learning (Bloom). An early commenter noted how much my approach was heading towards mastery goals, where we use personalised feedback and targeted intervention to ensure that students have successfully mastered certain tiers of knowledge, before we move on to those that depend upon them.
A simple concept: pre-requisites must be mastered before moving on. It’s what much of our degree structure is based upon and is what determines the flow of students through courses, leading towards graduation. One passes 101 in order to go on to courses that assume such knowledge.
Within an individual course, we quickly realise that too many mastery goals starts to leave us in a precarious position. As I noted from my earlier posts, having enough time to do your job as designer or evaluator requires you to plan what you’re doing and keep careful track of your commitments. The issue that arises with mastery goals is that, if a student can’t demonstrate mastery, we offer remedial work and re-training with an eye to another opportunity to demonstrate that knowledge.
This can immediately lead to a backlog of work that must be completed prior to the student being considered to have mastered an area, and thus being ready to move on. If student A has completed three mastery goals while B is struggling with the first, where do we pitch our teaching materials, in anything approximating a common class activity, to ensure that everyone is receiving both what they need and what they are prepared for? (Bergmann and Sams’ Flipped Mastery is one such approach, where flipping and time-shifting are placed in a mastery focus – in their book “Flip Your Classroom”)
But even if we can handle a multi-speed environment (and we have to be careful because we know that streaming is a self-fulfilling prophecy) how do we handle the situation where a student has barely completed any mastery goals and the end of semester is approaching?
Mastery learning is a sound approach. It’s both ethically and philosophically pitched to prevent the easy out for a teacher of saying “oh, I’m going to fit the students I have to an ideal normal curve” or, worse, “these are just bad students”. A mastery learning approach tends to produces good results, although it can be labour intensive as we’ve noted. To me, Bloom’s approach is embodying one of my critical principles in teaching: because of the variable level of student preparation, prior experience and unrelated level of privilege, we have to adjust our effort and approach to ensure that all students can be brought to the same level wherever possible.
Equity is one of my principle educational aesthetics and I hope it’s one of yours. But now we have to mutter to ourselves that we have to think about limiting how many mastery goals there are because of administrative constraints. We cannot load up some poor student who is already struggling and pretend that we are doing anything other than delaying their ultimate failure to complete.
At the same time, we would be on shaky ground to construct a course where we could turn around at week 3 of 12 and say “You haven’t completed enough mastery goals and, because of the structure, this means that you have already failed. Stop trying.”
The core of a mastery-based approach is the ability to receive feedback, assimilate it, change your approach and then be reassessed. But, if this is to be honest, this dependency upon achievement of pre-requisites should have a near guarantee of good preparation for all courses that come afterwards. I believe that we can all name pre-requisite and dependency patterns where this is not true, whether it is courses where the pre-requisite course is never really used or dependencies where you really needed to have achieved a good pass in the pre-req to advance.
Competency-based approaches focus on competency and part of this is the ability to use the skill or knowledge where it is required, whether today or tomorrow. Many of our current approaches to knowledge and skill are very short-term-focussed, encouraging cramming or cheating in order to tick a box and move on. Mastering a skill for a week is not the intent but, unless we keep requiring students to know or use that information, that’s the message we send. This is honesty: you must master this because we’re going to keep using it and build on it! But trying to combine mastery and grades raises unnecessary tension, to the student’s detriment.
As Bloom notes:
Mastery and recognition of mastery under the present relative grading system is unattainable for the majority of students – but this is the result of the way in which we have “rigged” the educational system.
Bloom, Learning for Mastery, UCLA CSEIP Evaluation Comment, 1, 2, 1968.
Mastery learning is part and parcel of any competency based approach but, without being honest about the time constraints that are warping it, even this good approach is diminished.
The upshot of this is that any beautiful model of education adhering to the equity aesthetic has to think in a frame that is longer than a semester and in a context greater than any one course. We often talk about doing this but detailed alignment frequently escapes us, unless it is to put up our University-required ‘graduate attributes’ to tell the world how good our product will be.
We have to accept that part of our job is asking a student to do something and then acknowledging that they have done it, while continuing to build systems where what they have done is useful, provides a foundation to further learning and, in key cases, is something that they could do again in the future to the approximate level of achievement.
We have to, again, ask not only why we grade but also why we grade in such strangely synchronous containers. Why is it that a degree for almost any subject is three to five years long? How is that, despite there being nearly thirty years between the computing knowledge in the degree that I did and the one that I teach, they are still the same length? How are we able to have such similarity when we know how much knowledge is changing?
A better model of education is not one that starts from the assumption of the structures that we have. We know a lot of things that work. Why are we constraining them so heavily?
I have been following the discussion about the ethics of the driverless car with some interest. This is close to a contemporary restatement of the infamous trolley problem but here we are instructing a trolley in a difficult decision: if I can save more lives by taking lives, should I do it? In the case of a driverless car, should the car take action that could kill the driver if, in doing so, it is far more likely to save more lives than would be lost?
While I find the discussion interesting, I worry that such discussion makes people unduly worried about driverless cars, potentially to a point that will delay adoption. Let’s look into why I think that. (I’m not going to go into whether cars, themselves, are a good or bad thing.)
Many times, the reason for a driverless car having to make such a (difficult) decision is that “a person leaps out from the kerb” or “driving conditions are bad” and “it would be impossible to stop in time.”
As noted in CACM:
The driverless cars of the future are likely to be able to outperform most humans during routine driving tasks, since they will have greater perceptive abilities, better reaction times, and will not suffer from distractions (from eating or texting, drowsiness, or physical emergencies such as a driver having a heart attack or a stroke).
In every situation where a driverless car could encounter a situation that would require such an ethical dilemma be resolved, we are already well within the period at which a human driver would, on average, be useless. When I presented the trolley problem, with driverless cars, to my students, their immediate question was why a dangerous situation had arisen in the first place? If the car was driving in a way that it couldn’t stop in time, there’s more likely to be a fault in environmental awareness or stopping-distance estimation.
If a driverless car is safe in varied weather conditions, then it has no need to be travelling at the speed limit merely because the speed limit is set. We all know the mantra of driving: drive to the conditions. In a driverless car scenario, the sensory awareness of the car is far greater than our own (and we should demand that it was) and thus we will eliminate any number of accidents before we arrived at an ethical problem.
Millions of people are killed in car accidents every year because of drink driving and speeding. In Victoria, Australia, close to 40% of accidents are tied to long distance driving and fatigue. We would eliminate most, if not all, of these deaths immediately with driverless technology adopted en masse.
What about people leaping out in front of the car? In my home city, Adelaide, South Australia, the average speed across the city is just under 30 kilometres per hour, despite the speed limit being 50 (traffic lights and congestion has a lot to do with this). The average human driver takes about 1.5 seconds to react (source), then braking deceleration is about 7 metres per second per second, less effectively in the wet. From that source, the actual stopping part of the braking, if we’re going 30km/h, is going to be less than 9 metres if it’s dry, 13 metres if wet. Other sources note that, with human reactions, the minimum overall braking is about 12 metres, 6 of which are braking. The good news is that 30km/h is already the speed at which only 10% of pedestrians are killed and, given how quickly an actively sensing car could react and safely coordinate braking without skidding, the driverless car is incredibly unlikely to be travelling fast enough to kill someone in an urban environment and still be able to provide the same average speed as we had.
The driverless car, without any ethics beyond “brake to avoid collisions”, will be causing a far lower level of injury and death. They don’t drink. They don’t sleep. They don’t speed. They will react faster than humans.
(That low urban speed thing isn’t isolated. Transport for London estimate the average London major road speed to be around 31 km/h, around 15km/h for Central London. Central Berlin is about 24 km/h, Warsaw is 26. Paris is 31 km/h and has a fraction of London’s population, about twice the size of my own city.)
Human life is valuable. Rather than focus on the impact on lives that we can see, as the Trolley Problem does, taking a longer view and looking at the overall benefits of the driverless car quickly indicates that, even if driverless cars are dumb and just slam on the brakes, the net benefit is going to exceed any decisions made because of the Trolley Problem model. Every year that goes by without being able to use this additional layer of safety in road vehicles is costing us millions of lives and millions of injuries. As noted in CACM, we already have some driverless car technologies and these are starting to make a difference but we do have a way to go.
And I want this interesting discussion of ethics to continue but I don’t want it to be a reason not to go ahead, because it’s not an honest comparison and saying that it’s important just because there’s no human in the car is hypocrisy.
I wish to apply the beauty lens to this. When we look at a new approach, we often find things that are not right with it and, given that we have something that works already, we may not adopt a new approach because we are unsure of it or there are problems. The aesthetics of such a comparison, the characteristics we wish to maximise, are the fair consideration of evidence, that the comparison be to the same standard, and a commitment to change our course if the evidence dictates that it be so. We want a better outcome and we wish to make sure that any such changes made support this outcome. We have to be honest about our technology: some things that are working now and that we are familiar with are not actually that good or they are solving a problem that we might no longer need to solve.
Human drivers do not stand up to many of the arguments presented as problems to be faced by driverless cars. The reason that the trolley problems exists in so many different forms, and the fact that it continues to be debated, shows that this is not a problem that we have moved on from. You would also have to be highly optimistic in your assessment of the average driver to think that a decision such as “am I more valuable than that evil man standing on the road” is going through anyone’s head; instead, people jam on the brakes. We are holding driverless cars to a higher standard than we accept for driving when it’s humans. We posit ‘difficult problems’ that we apparently ignore every time we drive in the rain because, if we did not, none of us would drive!
Humans are capable of complex ethical reasoning. This does not mean that they employ it successfully in the 1.5 seconds of reaction time before slamming on the brakes.
We are not being fair in this assessment. This does not diminish the value of machine ethics debate but it is misleading to focus on it here as if it really matters to the long term impact of driverless cars. Truck crashes are increasing in number in the US, with over 100,000 people injured each year, and over 4,000 killed. Trucks follow established routes. They don’t go off-road. This makes them easier to bring into an automated model, even with current technology. They travel long distances and the fatigue and inattention effects upon human drivers kill people. Automating truck fleets will save over a million lives in the US alone in the first decade, reducing fleet costs due to insurance payouts, lost time, and all of those things.
We have a long way to go before we have the kind of vehicles that can replace what we have but let’s focus on what is important. Getting a reliable sensory rig that works better than a human and can brake faster is the immediate point at which any form of adoption will start saving lives. Then costs come down. Then adoption goes up. Then millions of people live happier lives because they weren’t killed or maimed by cars. That’s being fair. That’s being honest. That will lead to good.
Your driverless car doesn’t need to be prepared to kill you in order to save lives.
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.
I’ve been talking about why late penalties are not only not useful but they don’t work, yet I keep talking about getting work in on time and tying it to realistic resource allocation. Does this mean I’m really using late penalties?
No, but let me explain why, starting from the underlying principle of fairness that is an aesthetic pillar of good education. One part of this is that the actions of one student should not unduly affect the learning journey of another student. That includes evaluation (and associated marks).
This is the same principle that makes me reject curve grading. It makes no sense to me that someone else’s work is judged in the context of another, when we have so little real information with which we could establish any form of equivalence of human experience and available capacity.
I don’t want to create a market economy for knowledge, where we devaluate successful demonstrations of knowledge and skill for reasons that have nothing to do with learning. Curve grading devalues knowledge. Time penalties devalue knowledge.
I do have to deal with resource constraints, in that I often have (some) deadlines that are administrative necessities, such as degree awards and things like this. I have limited human resources, both personally and professionally.
Given that I do not have unconstrained resources, the fairness principle naturally extends to say that individual students should not consume resources to the detriment of others. I know that I have a limited amount of human evaluation time, therefore I have to treat this as a constrained resource. My E1 and E2 evaluations resources must be, to a degree at least, protected to ensure the best outcome for the most students. (We can factor equity into this, and should, but this stops this from being a simple linear equivalence and makes the terms more complex than they need to be for explanation, so I’ll continue this discussion as if we’re discussing equality.)
You’ve noticed that the E3 and E4 evaluation systems are pretty much always available to students. That’s deliberate. If we can automate something, we can scale it. No student is depriving another of timely evaluation and so there’s no limitation of access to E3 and E4, unless it’s too late for it to be of use.
If we ask students to get their work in at time X, it should be on the expectation that we are ready to leap into action at second X+(prep time), or that the students should be engaged in some other worthwhile activity from X+1, because otherwise we have made up a nonsense figure. In order to be fair, we should release all of our evaluations back at the same time, to avoid accidental advantages because of the order in which things were marked. (We may wish to vary this for time banking but we’ll come back to this later.) As many things are marked in surname or student number order, the only way to ensure that we don’t accidentally keep granting an advantage is to release everything at the same time.
Remember, our whole scheme is predicated on the assumption that we have designed and planned for how long it will take to go through the work and provide feedback in time for modification before another submission. When X+(prep time) comes, we should know, roughly to the hour or day, at worst, when this will be done.
If a student hands up fifteen minutes late, they have most likely missed the preparation phase. If we delay our process to include this student, then we will delay feedback to everyone. Here is a genuine motivation for students to submit on time: they will receive rich and detailed feedback as soon as it is ready. Students who hand up late will be assessed in the next round.
That’s how the real world actually works. No-one gives you half marks for something that you do a day late. It’s either accepted or not and, often, you go to the back of the queue. When you miss the bus, you don’t get 50% of the bus. You just have to wait for the next opportunity and, most of the time, there is another bus. Being late once rarely leaves you stranded without apparent hope – unlucky Martian visitors aside.
But there’s more to this. When we have finished with the first group, we can immediately release detailed feedback on what we were expecting to see, providing the best results to students and, from that point on, anyone who submits would have the benefit of information that the first group didn’t have before their initial submission. Rather than make the first group think that they should have waited (and we know students do), we give them the best possible outcome for organising their time.
The next submission deadline is done by everyone with the knowledge gained from the first pass but people who didn’t contribute to it can’t immediately use it for their own benefit. So there’s no free-riding.
There is, of course, a tricky period between the submission deadline and the release, where we could say “Well, they didn’t see the feedback” and accept the work but that’s when we think about the message we want to send. We would prefer students to improve their time management and one part of this is to have genuine outcomes from necessary deadlines.
If we let students keep handing in later and later, we will eventually end up having these late submissions running into our requirement to give feedback. But, more importantly, we will say “You shouldn’t have bothered” to those students who did hand up on time. When you say something like this, students will learn and they will change their behaviour. We should never reinforce behaviour that is the opposite of what we consider to be valuable.
Fairness is a core aesthetic of education. Authentic time management needs to reflect the reality of lost opportunity, rather than diminished recognition of good work in some numerical reduction. Our beauty argument is clear: we can be firm on certain deadlines and remove certain tasks from consideration and it will be a better approach and be more likely to have positive outcomes than an arbitrary reduction scheme already in use.
In my earlier post, I wrote:
Even where we are using mechanical or scripted human [evaluators], the hand of the designer is still firmly on the tiller and it is that control that allows us to take a less active role in direct evaluation, while still achieving our goals.
and I said I’d discuss how we could scale up the evaluation scheme to a large first year class. Finally, thank you for your patience, here it is.
The first thing we need to acknowledge is that most first-year/freshman classes are not overly complex nor heavily abstract. We know that we want to work concrete to abstract, simple to complex, as we build knowledge, taking into account how students learn, their developmental stages and the mechanics of human cognition. We want to focus on difficult concepts that students struggle with, to ensure that they really understand something before we go on.
In many courses and disciplines, the skills and knowledge we wish to impart are fundamental and transformative, but really quite straight-forward to evaluate. What this means, based on what I’ve already laid out, is that my role as a designer is going to be crucial in identifying how we teach and evaluate the learning of concepts, but the assessment or evaluation probably doesn’t require my depth of expert knowledge.
The model I put up previously now looks like this:
My role (as the notional E1) has moved entirely to design and oversight, which includes developing the E3 and E4 tests and training the next tier down, if they aren’t me.
As an example, I’ve put in two feedback points, suitable for some sort of worked output in response to an assignment. Remember that the E2 evaluation is scripted (or based on rubrics) yet provides human nuance and insight, with personalised feedback. That initial feedback point could be peer-based evaluation, group discussion and demonstration, or whatever you like. The key here is that the evaluation clearly indicates to the student how they are travelling; it’s never just “8/10 Good”. If this is a first year course then we can capture much of the required feedback with trained casuals and the underlying automated systems, or by training our students on exemplars to be able to evaluate each other’s work, at least to a degree.
The same pattern as before lies underneath: meaningful timing with real implications. To get access to human evaluation, that work has to go in by a certain date, to allow everyone involved to allow enough time to perform the task. Let’s say the first feedback is a peer-assessment. Students can be trained on exemplars, with immediate feedback through many on-line and electronic systems, and then look at each other’s submissions. But, at time X, they know exactly how much work they have to do and are not delayed because another student handed up late. After this pass, they rework and perhaps the next point is a trained casual tutor, looking over the work again to see how well they’ve handled the evaluation.
There could be more rework and review points. There could be less. The key here is that any submission deadline is only required because I need to allocate enough people to the task and keep the number of tasks to allocate, per person, at a sensible threshold.
Beautiful evaluation is symmetrically beautiful. I don’t overload the students or lie to them about the necessity of deadlines but, at the same time, I don’t overload my human evaluators by forcing them to do things when they don’t have enough time to do it properly.
As for them, so for us.
Throughout this process, the E1 (supervising evaluator) is seeing all of the information on what’s happening and can choose to intervene. At this scale, if E1 was also involved in evaluation, intervention would be likely last-minute and only in dire emergency. Early intervention depends upon early identification of problems and sufficient resources to be able to act. Your best agent of intervention is probably the person who has the whole vision of the course, assisted by other human evaluators. This scheme gives the designer the freedom to have that vision and allows you to plan for how many other people you need to help you.
In terms of peer assessment, we know that we can build student communities and that students can appreciate each other’s value in a way that enhances their perceptions of the course and keeps them around for longer. This can be part of our design. For example, we can ask the E2 evaluators to carry out simple community-focused activities in classes as part of the overall learning preparation and, once students are talking, get them used to the idea of discussing ideas rather than having dualist confrontations. This then leads into support for peer evaluation, with the likelihood of better results.
Some of you will be saying “But this is unrealistic, I’ll never get those resources.” Then, in all likelihood, you are going to have to sacrifice something: number of evaluations, depth of feedback, overall design or speed of intervention.
You are a finite resource. Killing you with work is not beautiful. I’m writing all of this to speak to everyone in the community, to get them thinking about the ugliness of overwork, the evil nature of demanding someone have no other life, the inherent deceit in pretending that this is, in any way, a good system.
We start by changing our minds, then we change the world.
$6.9M Federal Funding for CSER Digital Technologies @cseradelaide @UniofAdelaide @birmo @cpyne @sallyannwPosted: January 21, 2016
Our research group, the Computer Science Education Research Group, has been working to support teachers involved in digital technologies for some time. The initial project was a collaboration between Google and the University of Adelaide, with amazing work from Sally-Ann Williams of Google to support us, to produce a support course that was free, open and recognised as professional development for teachers who were coming to terms with the new Digital Technologies (draft) curriculum. Today we are amazed and proud to announce $6.9 million dollars in Federal Funding over the next four years to take this project … well … just about everywhere.
You can read about what we’ve been doing here
I’ll now share Katrina’s message, slightly edited, to the rest of the school.
Today we hosted a visit from Ministers Birmingham and Pyne to announce a new funding agreement to support a national support program for Australian teachers within the Digital Technologies space.
Ministers Birmingham and Pyne confirmed that the Australian Government is providing $6.9 million over four years to the Computer Science Education Research Group at the University of Adelaide to support the roll out, on a national basis, of the teacher professional learning Massive Open Online Course (MOOC) supporting Australian primary and junior secondary teachers in developing skills in implementing the Australian Curriculum: Digital Technologies.
The CSER MOOC program provides free professional development for Australian teachers in the area of Computer Science, and supports research into the learning and teaching of Computer Science in the K-12 space. As part of this new program, we will be able to support teachers in disadvantaged schools and Indigenous schools across Australia in accessing the CSER MOOCs. We will also be able to establish a national lending library program to provide access to the most recent and best digital technologies education equipment to every school.
The Ministers, along with our Executive Dean and the Vice-Chancellor accompanied us to visit a coding outreach event for children run this morning as part of the University’s Bright Sparks STEM holiday program.
Here’s the ministerial announcement.