I’m at the Australasian Computer Science Week at the moment and I’m dividing my time between attending amazing talks, asking difficult questions, catching up with friends and colleagues and doing my own usual work in the cracks. I’ve talked to a lot of people about my ideas on assessment (and beauty) and, as always, the responses have been thoughtful, challenging and helpful.
I think I know what the basis of my problem with assessment is, taking into account all of the roles that it can take. In an earlier post, I discussed Wolff’s classification of assessment tasks into criticism, evaluation and ranking. I’ve also made earlier (grumpy) notes about ranking systems and their arbitrary nature. One of the interesting talks I attended yesterday talked about the fragility and questionable accuracy of post-University exit surveys, which are used extensively in formal and informal rankings of Universities, yet don’t actually seem to meet many of the statistical or sensible guidelines for efficacy we already have.
But let’s put aside ranking for a moment and return to criticism and evaluation. I’ve already argued (successfully I hope) for a separation of feedback and grades from the criticism perspective. While they are often tied to each other, they can be separated and the feedback can still be useful. Now let’s focus on evaluation.
Remind me why we’re evaluating our students? Well, we’re looking to see if they can perform the task, apply the skill or knowledge, and reach some defined standard. So we’re evaluating our students to guide their learning. We’re also evaluating our students to indirectly measure the efficacy of our learning environment and us as educators. (Otherwise, why is it that there are ‘triggers’ in grading patterns to bring more scrutiny on a course if everyone fails?) We’re also, often accidentally, carrying out an assessment of the innate success of each class and socio-economic grouping present in our class, among other things, but let’s drill down to evaluating the student and evaluating the learning environment. Time for another thought experiment.
Thought Experiment 2
There are twenty tasks aligned with a particularly learning outcome. It’s an important task and we evaluate it in different ways but the core knowledge or skill is the same. Each of these tasks can receive a ‘grade’ of 0, 0.5 or 1. 0 means unsuccessful, 0.5 is acceptable, 1 is excellent. Student A attempts all tasks and is acceptable in 19, unsuccessful in 1. Student B attempts the first 10 tasks, receives excellent in all of them and stops. Student C sets up a pattern of excellent,unsuccessful, excellent, unsuccessful.. and so on to receive 10 “Excellent”s and 10 “unsuccessful”s. When we form an aggregate grade, A receives 47.5%, B receives 50% and C also receives 50%. Which of these students is the most likely to successfully complete the task?
This framing allows us to look at the evaluation of the student in a meaningful way. “Who will pass the course?” is not the question we should be asking, it’s “Who will be able to reliably demonstrate mastery of the skills or knowledge that we are imparting.” Passing the course has a naturally discrete attention focus: focus on n assignments and m exams and pass. Continual demonstration of mastery is a different goal. This framing also allows us to examine the learning environment because, without looking at the design, I can’t tell you if B and C’s behaviour is problematic or not.
A has undertaken the most tasks to an acceptable level but an artefact of grading (or bad luck) has dropped the mark below 50%, which would be a fail (aggregate less than acceptable) in many systems. B has performed excellently on every task attempted but, being aware of the marking scheme, optimising and strategic behaviour allows this student to walk away. (Many students who perform at this level wouldn’t, I’m aware, but we’re looking at the implications of this.) C has a troublesome pattern that provides the same outcome as B but with half the success rate.
Before we answer the original question (which is most likely to succeed), I can nominate C as the most likely to struggle because C has the most “unsuccessful”s. From a simple probabilistic argument, 10/20 success is worse than 19/20. It’s a bit tricker comparing 10/10 and 10/20 (because of confidence intervals) but 10/20 has an Adjusted Wald range of +/- 20% and 10/10 is -14%, so the highest possible ‘real’ measure for C is 14/20 and the lowest possible ‘real’ measure for B is (scaled) 15/20, so they don’t overlap and we can say that B appears to be more successful than C as well.
From a learning design perspective, do our evaluation artefacts have an implicit design that explains C’s pattern? Is there a difference we’re not seeing? Taking apart any ranking of likeliness to pass our evaluatory framework, C’s pattern is so unusual (high success/lack of any progress) that we learn something immediately from the pattern, whether it’s that C is struggling or that we need to review mechanisms we thought to be equivalent!
But who is more likely to succeed out of A and B? 19/20 and 10/10 are barely distinguishable in statistical terms! The question for us now is how many evaluations of a given skill or knowledge mastery are required for us to be confident of competence. This totally breaks the discrete cramming for exams and focus on assignment model because all of our science is built on the notion that evidence is accumulated through observation and the analysis of what occurred, in order to be able to construct models to predict future behaviour. In this case, our goal is to see if our students are competent.
I can never be 100% sure that my students will be able to perform a task but what is the level I’m happy with? How many times do I have to evaluate them at a skill so that I can say that x successes in y attempts constitutes a reliable outcome?
If we say that a student has to reliably succeed 90% of the time, we face the problem that just testing them ten times isn’t enough for us to be sure that they’re hitting 90%.
But the level of performance we need to be confident is quite daunting. By looking at some statistics, we can see that if we provide a student with 150 opportunities to demonstrate knowledge and they succeed at this 143 times, then it is very likely that their real success level is at least 90%.
If we say that competency is measured by a success rate that is greater than 75%, a student who achieves 10/10 has immediately met that but even succeeding at 9/9 doesn’t meet that level.
What this tells us (and reminds us) is that our learning environment design is incredibly important and it must start from a clear articulation of what success actually means, what our goals are and how we will know when our students have reached that point.
There is a grade separation between A and B but it’s artificial. I noted that it was hard to distinguish A and B statistically but there is one important difference in the lower bound of their confidence interval. A is less than 75%, B is slightly above.
Now we have to deal with the fact that A and B were both competent (if not the same) for the first ten tests and A was actually more competent than B until the 20th failed test. This has enormous implications for we structure evaluation, how many successful repetitions define success and how many ‘failures’ we can tolerate and still say that A and B are competent.
Confused? I hope not but I hope that this is making you think about evaluation in ways that you may not have done so before.
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?
A literate and numerate society is an excellent goal. I’d say it’s probably our least goal for a happy, safe and stable society. But the rise in the number of programmable machines and objects has meant that being able to program or being able to think about programming can make a great deal of difference in the jobs you can hold and in the way that you can amplify your own human effort. Cars help us to go faster but computers help us to get more thinking work done. Being able to program, or knowing when it would be a good idea and how to approach it, will be essential for getting things done.
In fact, having some computer science or programming is handy right now because so many pieces of software can be much more useful if you use their programmatic extensions.
To give you an example, yesterday I was proof reading my first novel. I’m using the Scrivener software package and, among other features, it allows you to use Regular Expressions to search and replace text. A Regular Expression (RegEx) is a type of pattern; once defined, the computer looks for everything that matches that pattern.
I wanted to see if, while writing, I’d accidentally written the same word twice. (Believe me, it happens over 100,000 words.) Instead of searching for duplicate words by having to type ‘of of’ or ‘and and’ into a search field and looking for hits, I can use my knowledge of CS to enter the RegEx:
And this will go looking for any repeated pattern of the form ‘ it it ‘ or ‘ and an d’. (The RegEx should be read as ‘find all the times that I have put two words next to the other, separated by a space, where the words are the same.) Now my hit list is every possible occurrence of this!
By using a RegEx, I found that I had written ‘some some’, a pattern I never would have thought to check for. But that’s the power of programming. When I know how to tell a computer what I actually want, I can use its power to amplify the impact of my thoughts with reduced effort on my part.
Many of today’s applications become much more usable with a little programming. Microsoft Excel is another example where a little CS goes a long way.
That’s why I’m excited by the US President’s announcement on CS for all. You’ll know that our own work in Australia is towards empowering creators and building confidence in all educators and students. It’s great to see such a large and funded initiative being declared for the US. Armed with more knowledge, people can use computers to help themselves and so many more.
You don’t have to be an aesthetic philosopher or educational rebel to know that an empowered and knowledgable generation of school kids is a beautiful thing. As Mark put it, this is huge!
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 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.
Ed challenged me: distill my thinking! In three words? Ok, Ed, fine: most assessment’s ugly.
Why is that? (Three word answers. Yes, I’m cheating.)
- It’s not authentic.
- There’s little design.
- Wrong Bloom’s level.
- Weak links forward.
- Weak links backward.
- Testing not evaluating.
- Marks not feedback.
- Not learning focused.
- Deadlines are rubbish.
- Tradition dominates innovation.
How was that?
In yesterday’s post, I laid out an evaluation scheme that allocated the work of evaluation based on the way that we tend to teach and the availability, and expertise, of those who will be evaluating the work. My “top” (arbitrary word) tier of evaluators, the E1s, were the teaching staff who had the subject matter expertise and the pedagogical knowledge to create all of the other evaluation materials. Despite the production of all of these materials and designs already being time-consuming, in many cases we push all evaluation to this person as well. Teachers around the world know exactly what I’m talking about here.
Our problem is time. We move through it, tick after tick, in one direction and we can neither go backwards nor decrease the number of seconds it takes to perform what has to take a minute. If we ask educators to undertake good learning design, have engaging and interesting assignments, work on assessment levels well up in the taxonomies and we then ask them to spend day after day standing in front of a class and add marking on top?
Forget it. We know that we are going to sacrifice the number of tasks, the quality of the tasks or our own quality of life. (I’ve written a lot about time before, you can search my blog for time or read this, which is a good summary.) If our design was good, then sacrificing the number of tasks or their quality is going to compromise our design. If we stop getting sleep or seeing our families, our work is going to suffer and now our design is compromised by our inability to perform to our actual level of expertise!
When Henry Ford refused to work his assembly line workers beyond 40 hours because of the increased costs of mistakes in what were simple, mechanical, tasks, why do we keep insisting that complex, delicate, fragile and overwhelmingly cognitive activities benefit from us being tired, caffeine-propped, short-tempered zombies?
We’re not being honest. And thus we are not meeting our requirement for truth. A design that gets mangled for operational reasons without good redesign won’t achieve our outcomes. That’s not going to achieve our results – so that’s not good. But what of beauty?
What are the aesthetics of good work? In Petts’ essay on the Arts and Crafts movement, he speaks of William Morris, Dewey and Marx (it’s a delightful essay) and ties the notion of good work to work that is authentic, where such work has aesthetic consequences (unsurprisingly given that we were aiming for beauty), and that good (beautiful) work can be the result of human design if not directly the human hand. Petts makes an interesting statement, which I’m not sure Morris would let pass un-challenged. (But, of course, I like it.)
It is not only the work of the human hand that is visible in art but of human design. In beautiful machine-made objects we still can see the work of the “abstract artist”: such an individual controls his labor and tools as much as the handicraftsman beloved of Ruskin.
Jeffrey Petts, Good Work and Aesthetic Education: William Morris, the Arts and Crafts Movement, and Beyond, The Journal of Aesthetic Education, Vol. 42, No. 1 (Spring, 2008), page 36
Petts notes that it is interesting that Dewey’s own reflection on art does not acknowledge Morris especially when the Arts and Crafts’ focus on authenticity, necessary work and a dedication to vision seems to be a very suitable framework. As well, the Arts and Crafts movement focused on the rejection of the industrial and a return to traditional crafting techniques, including social reform, which should have resonated deeply with Dewey and his peers in the Pragmatists. However, Morris’ contribution as a Pragmatist aesthetic philosopher does not seem to be recognised and, to me, this speaks volumes of the unnecessary separation between cloister and loom, when theory can live in the pragmatic world and forms of practice can be well integrated into the notional abstract. (Through an Arts and Crafts lens, I would argue that there is are large differences between industrialised education and the provision, support and development of education using the advantages of technology but that is, very much, another long series of posts, involving both David Bowie and Gary Numan.)
But here is beauty. The educational designer who carries out good design and manages to hold on to enough of her time resources to execute the design well is more aesthetically pleasing in terms of any notion of creative good works. By going through a development process to stage evaluations, based on our assessment and learning environment plans, we have created “made objects” that reflect our intention and, if authentic, then they must be beautiful.
We now have a strong motivating factor to consider both the often over-looked design role of the educator as well as the (easier to perceive) roles of evaluation and intervention.
I’ve revisited the diagram from yesterday’s post to show the different roles during the execution of the course. Now you can clearly see that the course lecturer maintains involvement and, from our discussion above, is still actively contributing to the overall beauty of the course and, we would hope, it’s success as a learning activity. What I haven’t shown is the role of the E1 as designer prior to the course itself – but that’s another post.
Even where we are using mechanical or scripted human markers, 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.
Do I need to personally look at each of the many works all of my first years produce? In our biggest years, we had over 400 students! It is beyond the scale of one person and, much as I’d love to have 40 expert academics for that course, a surplus of E1 teaching staff is unlikely anytime soon. However, if I design the course correctly and I continue to monitor and evaluate the course, then the monster of scale that I have can be defeated, if I can make a successful argument that the E2 to E4 marker tiers are going to provide the levels of feedback, encouragement and detailed evaluation that are required at these large-scale years.
Tomorrow, we look at the details of this as it applies to a first-year programming course in the Processing language, using a media computation approach.
This is a great TED talk. Joi Ito, director of the MIT media lab, talks about the changes that technological innovation have made to the ways that we can work on problems and work together.
I don’t agree with everything, especially the pejorative cast on education, but I totally agree that the way that we construct learning environments has to take into the way that our students will work, rather than trying to prepare them for the world that we (or our parents) worked in. Pretending that many of our students will have to construct simple things by hand, when that is what we were doing fifty years ago, takes up time that we could be using for more authentic and advanced approaches that cover the same material. Some foundations are necessary. Some are tradition. Being a now-ist forces us to question which is which and then act on that knowledge.
Your students will be able to run enterprises from their back rooms that used to require the resources of multinational companies. It’s time to work out what they actually need to get from us and, once we know that, deliver it. There is a place for higher education but it may not be the one that we currently have.
A lot of what I talk about on this blog looks as if I’m being progressive but, really, I’m telling you what we already know to be true right now. And what we have known to be true for decades, if not centuries. I’m not a futurist, at all. I’m a now-ist with a good knowledge of history who sees a very bleak future if we don’t get better at education.
(Side note: yes, this is over twelve minutes long. Watch our around the three minute mark for someone reading documents on an iPad up the back, rather than watching him talk. I think this is a little long and staged, when it could have been tighter, but that’s the TED format for you. You know what you’re getting into and, because it’s not being formally evaluated, it doesn’t matter as much if you recall high-level rather than detail.)
We know that we can, and do, assess different levels of skill and knowledge. We know that we can, and do, often resort to testing memorisation, simple understanding and, sometimes, the application of the knowledge that we teach. We also know that the best evaluation of work tends to come from the teachers who know the most about the course and have the most experience, but we also know that these teachers have many demands on their time.
The principles of good assessment can be argued but we can probably agree upon a set much like this:
- Valid, based on the content. We should be evaluating things that we’ve taught.
- Reliable, in that our evaluations are consistent and return similar results for different evaluators, that re-evaluating would give the same result, that we’re not unintentionally raising or lowering difficulty.
- Motivating, in that we know how much influence feedback and encouragement have on students, so we should be maximising the motivation and, we hope, this should drive engagement.
- Finally, we want our assessment to be as relevant to us, in terms of being able to use the knowledge gained to improve or modify our courses, as it is to our student. Better things should come from having run this assessment.
Notice that nothing here says “We have to mark or give a grade”, yet we can all agree on these principles, and any scheme that adheres to them, as being a good set of characteristics to build upon. Let me label these as aesthetics of assessment, now let’s see if I can make something beautiful. Let me put together my shopping list.
- Feedback is essential. We can see that. Let’s have lots of feedback and let’s put it in places where it can be the most help.
- Contextual relevance is essential. We’re going to need good design and work out what we want to evaluate and then make sure we locate our assessment in the right place.
- We want to encourage students. This means focusing on intrinsics and support, as well as well-articulated pathways to improvement.
- We want to be fair and honest.
- We don’t want to overload either the students or ourselves.
- We want to allow enough time for reliable and fair evaluation of the work.
What are the resources we have?
- Course syllabus
- Course timetable
- The teacher’s available time
- TA or casual evaluation time, if available
- Student time (for group work or individual work, including peer review)
- Rubrics for evaluation.
- Computerised/automated evaluation systems, to varying degree.
Wait, am I suggesting automated marking belongs in a beautiful marking system? Why, yes, I think it has a place, if we are going to look at those things we can measure mechanistically. Checking to see if someone has ticked the right box for a Bloom’s “remembering” level activity? Machine task. Checking to see if an essay has a lot of syntax or grammatical errors? Machine task. But we can build on that. We can use human markers and machine markers, in conjunction, to the best of their strengths and to overcome each other’s weaknesses.
If we think about it, we really have four separate tiers of evaluators to draw upon, who have different levels of ability. These are:
- E1: The course designers and subject matter experts who have a deep understanding of the course and could, possibly with training, evaluate work and provide rich feedback.
- E2: Human evaluators who have received training or are following a rubric provided by the E1 evaluators. They are still human-level reasoners but are constrained in terms of breadth of interpretation. (It’s worth noting that peer assessment could fit in here, as well.)
- E3: High-level machine evaluation includes machine-based evaluation of work, which could include structural, sentiment or topic analysis, as well as running complicated acceptance tests that look for specific results, coverage of topics or, in the case of programming tasks, certain output in response to given input. The E3 evaluation mechanisms will require some work to set up but can provide evaluation of large classes in hours, rather than days.
- E4: Low-level machine evaluation, checking for conformity in terms of length of assignment, names, type of work submitted, plagiarism detection. In the case of programming assignments, E4 would check that the filenames were correct, that the code compiled and also may run some very basic acceptance tests. E4 evaluation mechanisms should be quick to set up and very quick to execute.
This separation clearly shows us a graded increase of expertise that corresponds to an increase of time spent and, unfortunately, a decrease in time available. E4 evaluation is very easy to set up and carry out but it’s not fantastic for detailed feedback or higher Bloom’s level. Yet we have an almost infinite amount of this marking time available. E1 markers will (we hope) give the best feedback but they take a long time and this immediately reduces the amount of time to be spent on other things. How do we handle this and select the best mix?
While we’re thinking about that, let’s see if we are meeting the aesthetics.
- Valid? Yes. We’ve looked at our design (we appear to have a design!) and we’ve specifically set up evaluation into different areas while thinking about outcomes, levels and areas that we care about.
- Reliable? Looks like it. E3 and E4 are automated and E2 has a defined marking rubric. E1 should also have guidelines but, if we’ve done our work properly in design, the majority of marks, if not all of them, are going to be assigned reliably.
- Fair? We’ve got multiple stages of evaluation but we haven’t yet said how we’re going to use this so we don’t have this one yet.
- Motivating? Hmm, we have the potential for a lot of feedback but we haven’t said how we’re using that, either. Don’t have this one either.
- Relevant to us and the students. No, for the same reasons as 3 and 4, we haven’t yet shown how this can be useful to us.
It looks like we’re half-way there. Tomorrow, we finish the job.