But [GPA calculation adjustment] have to be a method of avoidance, this can be a useful focusing device. If a student did really well in, say, Software Engineering but struggled with an earlier, unrelated, stream, why can’t we construct a GPA for Software Engineering that clearly states the area of relevance and degree of information? Isn’t that actually what employers and people interested in SE want to know?
This hits at the heart of my concerns over any kind of summary calculation that obscures the process. Who does this benefit? What use it is to anyone? What does it mean? Let’s look at one of the most obvious consumers of student GPAs: the employers and industry.
Feedback from the Australian industry tells us that employers are generally happy with the technical skills that we’re providing but it’s the softer skills (interpersonal skills, leadership, management abilities) that they would like to see more of and know more about. A general GPA doesn’t tell you this but a Software Engineering focused GPA (as I mentioned above) would show you how a student performed in courses where we would expect to see these skills introduced and exercised.
Putting everything into one transcript gives people the power to assemble this themselves, yes, but this requires the assembler to know what everything means. Most employers have neither the time nor inclination to do this for all 39 or so institutions in Australia. But if a University were to say “this is a summary of performance in these graduate attributes”, where the GAs are regularly focused on the softer skills, then we start to make something more meaningful out of an arbitrary number.
But let’s go further. If we can see individual assessments, rather than coarse subject grades, we can start to construct a model of an individual across the different challenges that they have faced and overcome. Portfolios are, of course, a great way to do this but they’re more work to read than single measures and, too often, such a portfolio is weighed against simpler, apparently meaningful measures such as high GPAs and found wanting. Portfolios also struggle if placed into a context of previous failure, even if recent activity clearly demonstrates that a student has moved on from that troubled or difficult time.
I have a deep ethical and philosophical objection to curve grading, as you probably know. The reason is simple: the actions of one student should not negatively affect the outcomes of another. This same objection is my biggest problem with GPA, although in this case the action and outcomes belong to the same student at different points in her or his life. Rather than using performance in one course to determine access to the learning upon which it depends, we make these grades a permanent effect and every grade that comes afterwards is implicitly mediated through this action.
Should Past Academic Nick have an inescapable impact on Now and Future Academic Nick’s life? When we look at all of the external influences on success, which make it clear how much totally non-academic things matter, it gets harder and harder to say “Yes, Past Academic Nick is inescapable.” Unfairness is rarely aesthetically pleasing.
An excellent comment on the previous post raised the issue of comparing GPAs in an environment where the higher GPA included some fails but the slightly lower GPA student had always passed. Which was the ‘best’ student from an award perspective? Student A fails three courses at the start of his degree, student B fails three courses at the end. Both pass with the same GPA, time to completion, and number of passes and fails. Is there even a sense of ‘better student’ here? B’s struggles are more immediate and, implicitly, concerns would be raised that these problems could still be active. A has, apparently, moved on in some way. But we’d never know this from simplistic calculations.
If we’re struggling to define ‘best’ and we’re not actually providing something that many people feel is useful, while burdening students with an inescapable past, then the least we can do is to sit down with the people who are affected by this and ask them what they really want.
And then, when they tell us, we do something about changing our systems.
If we are going to try and summarise a complicated, long-term process with a single number, and I don’t see such shortcuts going away anytime soon, then it helps to know:
- Exactly what the number represents.
- How it can be used.
- What the processes are that go into its construction.
We have conventions as to what things mean but, when we want to be precise, we have to be careful about our definition and our usage of the final value. As a simple example, one thing that often surprises people who are new to numerical analysis is that there is more than one way of calculating the average value of a group of numbers.
While average in colloquial language would usually mean that we take the sum of all of the numbers and divide them by their count, this is more formally referred to as the arithmetic mean. What we usually want from the average is some indication of what the typical value for this group would be. If you weigh ten bags of wheat and the average weight is 10 kilograms, then that’s what many people would expect the weight to be for future bags, unless there was clear early evidence of high variation (some 500g, some 20 kilograms, for example.)
But the mean is only one way to measure central tendency in a group of numbers. We can also measure the median, the number that separates the highest half of the data from the lowest, or the mode, the value that is the most frequently occurring value in the group.
(This doesn’t even get into the situation where we decide to aggregate the values in a different way.)
If you’ve got ten bags of wheat and nine have 10 kilograms in there, but one has only 5 kilograms, which of these ways of calculating the average is the one you want? The mode is 10kg but the mean is 9.5kg. If you tried to distribute the bags based on the expectation that everyone gets 9.5, you’re going to make nine people very happy and one person unhappy.
Most Grade Point Average calculations are based on a simple arithmetic mean of all available grades, with points allocated from 0 to an upper bound based on the grade performance. As a student adds more courses, these contributions are added to the calculation.
In yesterday’s post, I mused on letting students control which grades go into a GPA calculation and, to explore that, I now have to explain what I mean and why that would change things.
As it stands, because a GPA is an average across all courses, any lower grades will permanently drop the GPA contribution of any higher grades. If a student gets a 7 (A+ or High Distinction) for 71 of her courses and then a single 4 (a Passing grade) for one, her GPA will be 6.875. It can never return to 7. The clear performance band of this student is at the highest level, given that just under 99% of her marks are at the highest level, yet the inclusion of all grades means that a single underperformance, for whatever reason, in three years has cost her standing for those people who care about this figure.
My partner and I discussed some possible approaches to GPA that would be better and, by better, we mean approaches that encourage students to improve, that clearly show what the GPA figure means, and that are much fairer to the student. There are too many external factors contributing to resilience and high performance for me to be 100% comfortable with the questionable representation provided by the GPA.
Before we even think about student control over what is presented, we can easily think of several ways to make a GPA reflect what you have achieved, rather than what you have survived.
- We could only count a percentage of the courses for each student. Even having 90% counted means that students who stumble a little once or twice do not have this permanently etched into a dragging grade.
- We could allow a future attempt at a course with an improved to replace the previous grade. Before we get too caught up in the possibility of ‘gaming’, remember that students would have to pay for this (even if delayed) in most systems and it will add years to their degree. If a student can reach achievement level X in a course then it’s up to us to make sure that does correspond to the achievement level!
- We could only count passes. Given that a student has to assemble sufficient passing grades to be awarded a degree, why then would we include the courses that do not count in a calculation of GPA?
- We could use the mode and report the most common mark the student receives.
- We could do away with it totally. (Not going to happen any time soon.)
- We could pair the GPA with a statistical accompaniment that tells the viewer how indicative it is.
Options 1 and 2 are fairly straight-forward. Option 3 is interesting because it compresses the measurement band to a range of (in my system) 4-7 and this then implicitly recognises that GPA measures for students who graduate are more likely to be in this tighter range: we don’t actually have the degree of separation that we’d assume from a range of 0-7. Option 4 is an interesting way to think about the problem: which grade is the student most likely to achieve, across everything? Option 5 is there for completeness but that’s another post.
Option 6 introduces the idea that we stop GPA being a number and we carefully and accurately contextualise it. A student who receives all high distinctions in first semester still has a number of known hurdles to get over. The GPA of 7 that would be present now is not as clear an indicator of facility with the academic system as a GPA of 7 at the end of a degree, whichever other GPA adjustment systems are in play.
More evidence makes it clearer what is happening. If we can accompany a GPA (or similar measure) with evidence, then we are starting to make the process apparent and we make the number mean something. However, this also allows us to let students control what goes into their calculation, from the grades that they have, as a clear measure of the relevance of that measure can be associated.
But this doesn’t have to be a method of avoidance, this can be a useful focusing device. If a student did really well in, say, Software Engineering but struggled with an earlier, unrelated, stream, why can’t we construct a GPA for Software Engineering that clearly states the area of relevance and degree of information? Isn’t that actually what employers and people interested in SE want to know?
Handing over an academic transcript seems to allow anyone to do this but human cognitive biases are powerful, subtle and pervasive. It is harder for most humans to recognise positive progress in the areas that they are interested in, if there is evidence of less stellar performance elsewhere. I cite my usual non-academic example: Everyone thought Anthony La Paglia’s American accent was too fake until he stopped telling people he was Australian.
If we have to use numbers like this, then let us think carefully about what they mean and, if they don’t mean that much, then let’s either get rid of them or make them meaningful. These should, at a fundamental level, be useful to the students first, us second.
Yesterday, I wrote:
We need assessment systems that work for the student first and everyone else second.
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.
A friend and colleague responded to my post about driverless cars and noted that the social change aspects would be large, considering the role of driving as a key employer of many people. I had noted that my original post was not saying whether cars were good or bad, delaying such discussion to later.
Now it is later. Let’s talk.
As we continue to automate certain industries, we are going to reduce opportunities for humans to undertake those tasks. The early stages of the industrial revolution developed to the production lines and, briefly in the history of our species, there was employment to be found for humans who were required to do the same thing, over and over, without necessarily having to be particularly skilled. This separation marked a change of the nature of work from that of the artisan, the crafter, the artist and that emerging aspect of the middle class: the professional.
While the late 20th and early 21st century versions of such work are relatively safe and, until recently, relatively stable employers, early factory work was harsh, dangerous, often unfair and, until regulation was added, unethical. If you haven’t read Upton Sinclair’s novel on the meat-packing industry, “The Jungle”, or read of the New York milk scandals, you may have a vision of work that is far tidier than the reality for many people over the years. People died, for centuries, because they were treated as organic machine parts, interchangeable and ultimately disposable. Why then did people do this work?
Because they had to. Because they lacked the education or opportunity to do anything else. Because they didn’t want to starve. Because they wanted to look after their families. This cycle plays out over and over again and reinforces the value of education. Education builds opportunity for this generation and every one that comes afterwards. Education breaks poverty traps and frees people.
No-one is saying that, in the post-work utopia, there will not be a place for people to perform a work-like task in factories because, for some reason, they choose to but the idea is that this is a choice and the number of choices that you have, right now, tend to broaden as you have more recognised skills and qualifications. Whether it is trade-based or professional, it’s all education and, while we have to work, your choices tend to get more numerous with literacy, numeracy and the other benefits of good education.
Automation and regulation made work safer over time but it also slowly reduced the requirement to use humans, as machines became more involved, became more programmable and became cheaper. Manual labour has been disappearing for decades. Everywhere you look, there are dire predictions of 40% of traditional jobs being obsolete in as short as ten years.
The driverless car will, in short order, reduce the need for a human trucking industry from a driver in every truck to a set of coordinators and, until we replace them in turn, loading/unloading staff. While driving may continue for some time, insurance costs alone are going to restrict its domain and increase the level of training required to do it. I can see a time when people who want to drive have to go through almost as much training as pilots and for the same reason: their disproportionate impact on public safety.
What will those people do who left school, trained as drivers, and then spent the rest of their lives driving from point A to point B? Driving is a good job and can be one that people can pursue out to traditional retirement age, unlike many manual professions where age works against you more quickly. Sadly, despite this, a driverless car will, if we let it on the roads, be safer for everyone as I’ve already argued and we now have a tension between providing jobs for a group of people or providing safety for them and every other driver on the road.
The way to give people options is education but, right now, a lot of people choose to leave the education system because they see no reason to participate, they aren’t ready for it, they have a terrible school to go to, they don’t think it’s relevant or so many other reasons that they made a totally legitimate choice to go and do something else with their lives.
But, for a lot of people, that “something else” is going to disappear as surely as blacksmiths slowly diminished in number. You can still be a blacksmith but it’s not the same trade that it was and, in many places, it’s not an option at all anymore. The future of human work is, day by day, less manual and more intellectual. While this is heavily focussed on affluent nations, the same transition is going on globally, even if at different speeds.
We can’t just say “education is the answer” unless we accept how badly education has failed entire countries of people and, within countries, enter communities, racial sub-groups or people who don’t have money. Education has to be made an answer that can reach billions and be good while it does it. When we take away opportunities, even for good reasons, we have to accept that people just don’t go away. They still want to live, to thrive, to look after their families, to grow, and to benefit from living in the time and place that they do. The driverless car is just a more obvious indicator of the overall trend. That trend won’t stop and, thus, we’re going to have to deal with it.
A good educational system is essential for dealing with providing options to the billions of people who will need to change direction in the future but we’re not being honest until we accept that we need to talk about opportunity in terms of equity. We need to focus on bringing everyone up to equal levels of opportunity. Education is one part of that but we’re going to need society, politicians, industry and educators working together if we’re going to avoid a giant, angry, hopeless unemployed group of people in the near future.
Education is essential to support opportunity but we have to have enough opportunities to provide education. Education has to be attractive, relevant, appropriate and what everyone needs to make the most of their lives. The future of our civilisation depends upon it.
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?