The hand of an expert is visible in design

Posted: January 20, 2016 | Author: | Filed under: Education | Tags: , , , , , , , , , , , , , , , , , , , , , , , , , , | 1 Comment

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?

A panel from the Morris Snakeshead textile showing flowers with interwoven branches and leaves, from the Arts and Crafts movement.

William Morris: Snakeshead Textile

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.

Scheme2

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.

Four-tier assessment

Posted: January 19, 2016 | Author: | Filed under: Education, Opinion | Tags: , , , , , , , , , , , , , , , , , , , , , , | 1 Comment

We’ve looked at a classification of evaluators that matches our understanding of the complexity of the assessment tasks we could ask students to perform. If we want to look at this from an aesthetic framing then, as Dewey notes:

“By common consent, the Parthenon is a great work of art. Yet it has aesthetic standing only as the work becomes an experience for a human being.”

John Dewey, Art as Experience, Chapter 1, The Live Creature.

Having a classification of evaluators cannot be appreciated aesthetically unless we provide a way for it to be experienced. Our aesthetic framing demands an implementation that makes use of such an evaluator classification, applies to a problem where we can apply a pedagogical lens and then, finally, we can start to ask how aesthetically pleasing it is.

And this is what brings us to beauty.

A systematic allocation of tasks to these different evaluators should provide valid and reliable marking, assuming we’ve carried out our design phase correctly. But what about fairness, motivation or relevancy, the three points that we did not address previously? To be able to satisfy these aesthetic constraints, and to confirm the others, it now matters how we handle these evaluation phases because it’s not enough to be aware that some things are going to need different approaches, we have to create a learning environment to provide fairness, motivation and relevancy.

I’ve already argued that arbitrary deadlines are unfair, that extrinsic motivational factors are grossly inferior to those found within, and, in even earlier articles, that we too insist on the relevancy of the measurements that we have, rather than designing for relevancy and insisting on the measurements that we need.

To achieve all of this and to provide a framework that we can use to develop a sense of aesthetic satisfaction (and hence beauty), here is a brief description of a four-tier, penalty free, assessment.

Let’s say that, as part of our course design, we develop an assessment item, A1, that is one of the elements to provide evaluation coverage of one of the knowledge areas. (Thus, we can assume that A1 is not required to be achieved by itself to show mastery but I will come back to this in a later post.)

Recall that the marking groups are: E1, expert human markers; E2, trained or guided human markers; E3, complex automated marking; and E4, simple and mechanical automated marking.

A1 has four, inbuilt, course deadlines but rather than these being arbitrary reductions of mark, these reflect the availability of evaluation resource, a real limitation as we’ve already discussed. When the teacher sets these courses up, she develops an evaluation scheme for the most advanced aspects (E1, which is her in this case), an evaluation scheme that could be used by other markers or her (E2), an E3 acceptance test suite and some E4 tests for simplicity. She matches the aspects of the assignment to these evaluation groups, building from simple to complex, concrete to abstract, definite to ambiguous.

The overall assessment of work consists of the evaluation of four separate areas, associated with each of the evaluators. Individual components of the assessment build up towards the most complex but, for example, a student should usually have had to complete at least some of E4-evaluated work to be able to attempt E3.

Here’s a diagram of the overall pattern for evaluation and assessment.

Scheme

The first deadline for the assignment is where all evaluation is available. If students provide their work by this time, the E1 will look at the work, after executing the automated mechanisms, first E4 then E3, and applying the E2 rubrics. If the student has actually answered some E1-level items, then the “top tier” E1 evaluator will look at that work and evaluate it. Regardless of whether there is E1 work or not, human-written feedback from the lecturer on everything will be provided if students get their work in at that point. This includes things that would be of help for all other levels. This is the richest form of feedback, it is the most useful to the students and, if we are going to use measures of performance, this is the point at which the most opportunities to demonstrate performance can occur.

This feedback will be provided in enough time that the students can modify their work to meet the next deadline, which is the availability of E2 markers. Now TAs or casuals are marking instead or the lecturer is now doing easier evaluation from a simpler rubric. These human markers still start by running the automated scripts, E4 then E3, to make sure that they can mark something in E2. They also provide feedback on everything in E2 to E4, sent out in time for students to make changes for the next deadline.

Now note carefully what’s going on here. Students will get useful feedback, which is great, but because we have these staggered deadlines, we can pass on important messages as we identify problems. If the class is struggling with key complex or more abstract elements, harder to fix and requiring more thought, we know about it quickly because we have front-loaded our labour.

Once we move down to the fully automated systems, we’re losing opportunities for rich and human feedback to students who have not yet submitted. However, we have a list of students who haven’t submitted, which is where we can allocate human labour, and we can encourage them to get work in, in time for the E3 “complicated” script. This E3 marking script remains open for the rest of the semester, to encourage students to do the work sometime ahead of the exam. At this point, the discretionary allocation of labour for feedback is possible, because the lecturer has done most of the hard work in E1 and E2 and should, with any luck, have far fewer evaluation activities for this particular assignment. (Other things may intrude, including other assignments, but we have time bounds on this one, which is better than we often have!)

Finally, at the end of the teaching time (in our parlance, a semester’s teaching will end then we will move to exams), we move the assessment to E4 marking only, giving students the ability (if required) to test their work to meet any “minimum performance” requirements you may have for their eligibility to sit the exam. Eventually, the requirement to enter a record of student performance in this course forces us to declare the assessment item closed.

This is totally transparent and it’s based on real resource limitations. Our restrictions have been put in place to improve student feedback opportunities and give them more guidance. We have also improved our own ability to predict our workload and to guide our resource requests, as well as allowing us to reuse some elements of automated scripts between assignments, without forcing us to regurgitate entire assignments. These deadlines are not arbitrary. They are not punitive. We have improved feedback and provided supportive approaches to encourage more work on assignments. We are able to get better insight into what our students are achieving, against our design, in a timely fashion. We can now see fairness, intrinsic motivation and relevance.

I’m not saying this is beautiful yet (I think I have more to prove to you) but I think this is much closer than many solutions that we are currently using. It’s not hiding anything, so it’s true. It does many things we know are great for students so it looks pretty good.

Tomorrow, we’ll look at whether such a complicated system is necessary for early years and, spoilers, I’ll explain a system for first year that uses peer assessment to provide a similar, but easier to scale, solution.

Joi Ito on Now-ists

Posted: January 18, 2016 | Author: | Filed under: Education, Opinion | Tags: , , , , , , , , , , , , , , , , , | Leave a comment

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.)

Four tiers of evaluators

Posted: January 18, 2016 | Author: | Filed under: Education, Opinion | Tags: , , , , , , , , , , , , , , , , , | 1 Comment

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:

  1. Valid, based on the content. We should be evaluating things that we’ve taught.
  2. 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.
  3. Fair.
  4. 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.
  5. 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.

Some cast-iron wheels and gears, connected with a bicycle chain.

We’ve come a long, in terms of machine-based evaluation. It doesn’t have to be steam-driven.

If we think about it, we really have four separate tiers of evaluators to draw upon, who have different levels of ability. These are:

  1. 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.
  2. 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.)
  3. 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.
  4. 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.

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.

 

What are we assessing? How?

Posted: January 16, 2016 | Author: | Filed under: Education, Opinion | Tags: , , , , , , , , , , , , , , , , , , , , , | Leave a comment

How we can create a better assessment system, without penalties, that works in a grade-free environment? Let’s provide a foundation for this discussion by looking at assessment today.

fx_Bloom_New

Bloom’s Revised Taxonomy

We have many different ways of understanding exactly how we are assessing knowledge. Bloom’s taxonomy allows us to classify the objectives that we set for students, in that we can determine if we’re just asking them to remember something, explain it, apply it, analyse it, evaluate it or, having mastered all of those other aspects, create a new example of it. We’ve also got Bigg’s SOLO taxonomy to classify levels of increasing complexity in a student’s understanding of subjects. Now let’s add in threshold concepts, learning edge momentum, neo-Piagetian theory and …

Let’s summarise and just say that we know that students take a while to learn things, can demonstrate some convincing illusions of progress that quickly fall apart, and that we can design our activities and assessment in a way that acknowledges this.

I attended a talk by Eric Mazur, of Peer Instruction fame, and he said a lot of what I’ve already said about assessment not working with how we know we should be teaching. His belief is that we rarely rise above remembering and understanding, when it comes to testing, and he’s at Harvard, where everyone would easily accept their practices as, in theory, being top notch. Eric proposed a number of approaches but his focus on outcomes was one that I really liked. He wanted to keep the coaching role he could provide separate from his evaluator role: another thing I think we should be doing more.

Eric is in Physics but all of these ideas have been extensively explored in my own field, especially where we start to look at which of the levels we teach students to and then what we assess. We do a lot of work on this in Australia and here is some work by our groups and others I have learned from:

  • Szabo, C., Falkner, K. & Falkner, N. 2014, ‘Experiences in Course Design using Neo-Piagetian Theory’
  • Falkner, K., Vivian, R., Falkner, N., 2013, ‘Neo-piagetian Forms of Reasoning in Software Development Process Construction’
  • Whalley, J., Lister, R.F., Thompson, E., Clear, T., Robbins, P., Kumar, P. & Prasad, C. 2006, ‘An Australasian study of reading and comprehension skills in novice programmers, using Bloom and SOLO taxonomies’
  • Gluga, R., Kay, J., Lister, R.F. & Teague, D. 2012, ‘On the reliability of classifying programming tasks using a neo-piagetian theory of cognitive development’

I would be remiss to not mention Anna Eckerdal’s work, and collaborations, in the area of threshold concepts. You can find her many papers on determining which concepts are going to challenge students the most, and how we could deal with this, here.

Let me summarise all of this:

  • There are different levels at which students will perform as they learn.
  • It needs careful evaluation to separate students who appear to have learned something from students who have actually learned something.
  • We often focus too much on memorisation and simple explanation, without going to more advanced levels.
  • If we want to assess advanced levels, we may have to give up the idea of trying to grade these additional steps as objectivity is almost impossible as is task equivalence.
  • We should teach in a way that supports the assessment we wish to carry out. The assessment we wish to carry out is the right choice to demonstrate true mastery of knowledge and skills.

If we are not designing for our learning outcomes, we’re unlikely to create courses to achieve those outcomes. If we don’t take into account the realities of student behaviour, we will also fail.

We can break our assessment tasks down by one of the taxonomies or learning theories and, from my own work and that of others, we know that we will get better results if we provide a learning environment that supports assessment at the desired taxonomic level.

But, there is a problem. The most descriptive, authentic and open-ended assessments incur the most load in terms of expert human marking. We don’t have a lot of expert human markers. Overloading them is not good. Pretending that we can mark an infinite number of assignments is not true. Our evaluation aesthetics are objectivity, fairness, effectiveness, timeliness and depth of feedback. Assignment evaluation should be useful to the students, to show progress, and useful to us, to show the health of the learning environment. Overloading the marker will compromise the aesthetics.

Our beauty lens tells us very clearly that we need to be careful about how we deal with our finite resources. As Eric notes, and we all know, if we were to test simpler aspects of student learning, we can throw machines at it and we have a near infinite supply of machines. I cannot produce more experts like me, easily. (Snickers from the audience) I can recruit human evaluators from my casual pool and train them to mark to something like my standard, using a rubric or using an approximation of my approach.

Thus I have a framework of assignments, divide by level, and I appear to have assignment evaluation resources. And the more expert and human the marker, the more … for want of a better word … valuable the resource. The better feedback it can produce. Yet the more valuable the resource, the less of it I have because it takes time to develop evaluation skills in humans.

Tune in tomorrow for the penalty free evaluation and feedback that ties all of this together.

Teaching for (current) Humans

Posted: January 13, 2016 | Author: | Filed under: Education, Opinion | Tags: , , , , , , , , , , , , , , , , , , , , , , , , , , | 5 Comments
da Vinci's Vitrvuian Man. Human figure with arms and legs outstretched showing the ratios of the perfect form.

Leonardo’s experiments in human-octopus engineering never received appropriate recognition.

I was recently at a conference-like event where someone stood up and talked about video lectures. And these lectures were about 40 minutes long.

Over several million viewing sessions, EdX have clearly shown that watchable video length tops out at just over 6 minutes. And that’s the same for certificate-earning students and the people who have enrolled for fun. At 9 minutes, students are watching for fewer than 6 minutes. At the 40 minute mark, it’s 3-4 minutes.

I raised this point to the speaker because I like the idea that, if we do on-line it should be good on-line, and I got a response that was basically “Yes, I know that but I think the students should be watching these anyway.” Um. Six minutes is the limit but, hey, students, sit there for this time anyway.

We have never been able to unobtrusively measure certain student activities as well as we can today. I admit that it’s hard to measure actual attention by looking at video activity time but it’s also hard to measure activity by watching students in a lecture theatre. When we add clickers to measure lecture activity, we change the activity and, unsurprisingly, clicker-based assessment of lecture attentiveness gives us different numbers to observation of note-taking. We can monitor video activity by watching what the student actually does and pausing/stopping a video is a very clear signal of “I’m done”. The fact that students are less likely to watch as far on longer videos is a pretty interesting one because it implies that students will hold on for a while if the end is in sight.

In a lecture, we think students fade after about 15-20 minutes but, because of physical implications, peer pressure, politeness and inertia, we don’t know how many students have silently switched off before that because very few will just get up and leave. That 6 minute figure may be the true measure of how long a human will remain engaged in this kind of task when there is no active component and we are asking them to process or retain complex cognitive content. (Speculation, here, as I’m still reading into one of these areas but you see where I’m going.) We know that cognitive load is a complicated thing and that identifying subgoals of learning makes a difference in cognitive load (Morrison, Margulieux, Guzdial)  but, in so many cases, this isn’t what is happening in those long videos, they’re just someone talking with loose scaffolding. Having designed courses with short videos I can tell you that it forces you, as the designer and teacher, to focus on exactly what you want to say and it really helps in making your points, clearly. Implicit sub-goal labelling, anyone? (I can hear Briana and Mark warming up their keyboards!)

If you want to make your videos 40 minutes long, I can’t stop you. But I can tell you that everything I know tells me that you have set your materials up for another hominid species because you’re not providing something that’s likely to be effective for current humans.

 

A quick note on direction

Posted: January 11, 2016 | Author: | Filed under: Education, Opinion | Tags: , , , , , , , , , , , , , , , , , , | Leave a comment

Wind vane

I’m getting some great comments, on and off the blog, about possible solutions to the problems I’m putting up, as well as thoughts on some of my examples.

Firstly, thank you, everyone! Secondly, I am deliberately starting slowly and building up, to reframe all of these arguments in terms of aesthetics, fitness for purpose and clarity. (Beauty, goodness and truth, again.) I am not trying to make anything appear worse than it is but I’m teasing out some points to show why we should be seeking to change practice that is both widespread and ingrained.

I will make a quick note that Raymond Lister raised about my thought experiment with the two students who split the knowledge, in that I don’t differentiate between skills and knowledge (true) and I am talking about an educational design where no work has been done to identify which areas have to be mastered in order to progress (also true). This is totally deliberate on my part, because it reflects a lot of current practice, not because I think it’s what we should be doing. I will be returning to, and extending this, example over time.

(Raymond does great work in a lot of areas dear to my heart and we will be returning to some of his work in our peregrinations, especially the SOLO taxonomy and Bloom’s mappings. Until then, here is his Google Scholar link for you to read some very interesting papers. And I could not agree more that there is no programming gene!)

No numbers

Posted: January 11, 2016 | Author: | Filed under: Education, Opinion | Tags: , , , , , , , , , , , , , , , , , , | 5 Comments

We know that grades are really quite arbitrary and that turning numbers into letters, while something we can do, is actually not that strongly coupled to evaluating learning or demonstrating mastery. Why? Because having the appropriate level of knowledge and being able to demonstrate it are not necessarily the same as being able to pass tests or produce solutions to assignments.

For example, if we look at Rapaport’s triage approach as a way to evaluate student interaction with assignments, we can then design our learning environment to provide multiple opportunities to construct and evaluate knowledge on the understanding that we are seeking clear evidence that a student cannot just perform tasks of this nature but, more important, can do reliably. We can do this even if we use “Good, getting there, wrong and no submission” rather than numbers. The duality of grades (a symbol and its meaning) degenerates to something other than numbers anyway. Students at my University didn’t care about 84 versus 85 until we put a new letter grade in at 85 (High Distinction). But even these distinctions are arbitrary scales when it comes to evaluating actual learning.

A very arbitrary scale.

Why are numbers not important in this? Because they’re rarely important anyway. Have you ever asked your surgeon what her grades were in school? What about your accountant? Perhaps you’ve questioned the percentage that your favourite Master of Wine achieved in the tasting exams? Of course you haven’t. You’ve assumed that a certification (of some sort) indicates sufficient knowledge to practise. And what we have to face is that we are currently falling back onto numbers to give us false confidence that we are measuring learning. They don’t map. They’re not objective. They’re often mathematically nonsensical. No-one cares about them except to provide yet another way of sorting human beings and, goodness knows, we already have enough of those.

Ah, but “but students like to know how they’re going”, right? Yes. Which is where critique and evaluation come in, as well as may other authentic and appropriate ways to recognise progress and encourage curiosity and further development. None of which require numbers.

Let me ask you a question:

Does every student who accumulates enough pass tokens to graduate from your program have a clearly demonstrated ability to perform tasks to the requisite level in all of the knowledge areas of your program?

If the answer is no, then numbers and grades didn’t help, did they? I suspect that, for you as for many others including me, you can probably think of students who managed to struggle through but, in reality, were probably never going to be much good in the field. Perhaps 50% doesn’t magically cover competency? If 50% doesn’t, then raising the bar to 75% won’t solve the problem either. For reasons already mentioned, many of the ways we combine numbers to get grades just don’t make any real sense and they certainly don’t provide much insight into how well the student actually learned what you were trying to teach.

If numbers/grades don’t have much solid foundation, don’t always reflect ability to perform the task, and aren’t actually going to be used in the future? Then they are neither good nor true. And they cannot be beautiful.

Thus, let me strip Rapaport back one notch and provide a three-tier grade-free system, commonly used in many places already, that is closer to what we probably want:

  1. Nothing submitted,
  2. Work in progress, resubmit if possible, and
  3. Work to competent standard.

I know that there are concerns about the word ‘competency’ but I think it’s something we’re going to have think about moving on from. I teach engineers and computer scientists and they have to go out and perform tasks successfully if people are going to employ them or work with them. They have to be competent. Right now, I can tell you which of them have passed but, for a variety of grading reasons, I can’t tell you which one of them, from an academic transcript alone, will be able to sit down and solve your problem. I can see which ones pass exams but I don’t know if this is fixed knowledge or swotting. But what if you made it easy and said “ok, just point to the one who will build me the best bridge”? No. I can’t tell you that. (The most likely worst bridge is easier, as I can identify who does and doesn’t have Civil Engineering qualifications.)

The three-tier scale is simple. The feedback approach that the marker should take is pretty clear in each place and the result is clear to the student. If we build our learning environment correctly, then we can construct a pathway where a student has to achieve tier 3 for all key activities and, at that point, we can actually say “Yes, this student can perform this task or apply this knowledge to the required level”. We do this enough times, we may even start to think that the student could perform this at the level of the profession.

Wait. Have we just re-invented competency-based assessment? There’s an immediate urge to say “but that’s not a University level thing” and I do understand that. CBA has a strong vocational focus but anyone who works in an engineering faculty is already in that boat. We have industry linked accreditation to allow our students to practise as engineers and they have to demonstrate the achievement of a certified program, as well as work experience. That program is taught at University but, given that all you need is to get the degree, you can do it on raw passes and be ‘as accredited’ as the next person.

Now, I’d be the first person to say that not only are many aspects of the University not vocationally focussed but I’d go further and say that they shouldn’t be vocationally focussed. The University is a place that allows for the unfettered exploration of knowledge for knowledge’s sake and I wouldn’t want to change that. (And, yet, so often, we still grade such abstract ideals…) But let’s take competency away from the words job and vocational for a moment. I’m not suggesting we turn Universities into vocational study centres or shut down “non-Industry” programs and schools. (I’d like to see more but that’s another post.) Let’s look at focusing on clarity and simplicity of evaluation.

A student writes an essay on Brecht and submits it for assessment. All of the rich feedback on language use, referencing and analysis still exists without the need to grade it as A, B or C. The question is whether the work should be changed in response to the feedback (if possible) or whether it is, recognisably, an appropriate response to the question ‘write an essay on Brecht’ that will allow the student to develop their knowledge and skills. There is no job focus here but pulling back to separate feedback and identifying whether knowledge has been sufficiently demonstrated is, fundamentally, a competency argument.

The PhD, the pinnacle of the University system, is essentially not graded. You gain vast amounts of feedback over time, you write in response and then you either defend it to your prospective peers or have it blind-assessed by external markers. Yes, there are degrees of acceptance but, ultimately, what you end up with is “Fine as it is”, “Do some more work”, and “Oh, no. Just no.” If we can extend this level of acceptance of competency to our highest valued qualification, what is the consistent and sound reasoning that requires us to look at a student group and say “Hmm, 73. And this one is… yes, 74.”? If I may, cui bono? Who is benefitting here?

But what would such a program look like, you ask? (Hey, and didn’t Nick say he was going to talk about late penalties?) Yes, indeed. Come back tomorrow!

The Illusion of a Number

Posted: January 10, 2016 | Author: | Filed under: Education, Opinion | Tags: , , , , , , , , , , , , , , , , , , , | 3 Comments

Rabbit? Duck? Paging Wittgenstein!

I hope you’ve had a chance to read William Rapaport’s paper, which I referred to yesterday. He proposed a great, simple alternative to traditional grading that reduces confusion about what is signalled by ‘grade-type’ feedback, as well as making things easier for students and teachers. Being me, after saying how much I liked it, I then finished by saying “… but I think that there are problems.” His approach was that we could break all grading down into: did nothing, wrong answer, some way to go, pretty much there. And that, I think, is much better than a lot of the nonsense that we pretend we hand out as marks. But, yes, I have some problems.

I note that Rapaport’s exceedingly clear and honest account of what he is doing includes this statement. “Still, there are some subjective calls to make, and you might very well disagree with the way that I have made them.” Therefore, I have license to accept the value of the overall scholarship and the frame of the approach, without having to accept all of the implementation details given in the paper.  Onwards!

I think my biggest concern with the approach given is not in how it works for individual assessment elements. In that area, I think it shines, as it makes clear what has been achieved. A marker can quickly place the work into one of four boxes if there are clear guidelines as to what has to be achieved, without having to worry about one or two percentage points here or there. Because the grade bands are so distinct, as Rapaport notes, it is very hard for the student to make the ‘I only need one more point argument’ that is so clearly indicative as a focus on the grade rather than the learning. (I note that such emphasis is often what we have trained students for, there is no pejorative intention here.) I agree this is consistent and fair, and time-saving (after Walvoord and Anderson), and it avoids curve grading, which I loathe with a passion.

However, my problems start when we are combining a number of these triaged grades into a cumulative mark for an assignment or for a final letter grade, showing progress in the course. Sections 4.3 and 4.4 of the paper detail the implementation of assignments that have triage graded sub-tasks. Now, instead of receiving a “some way to go” for an assignment, we can start getting different scores for sub-tasks. Let’s look at an example from the paper, note 12, to describe programming projects in CS.

  • Problem definition 0,1,2,3
  • Top-down design 0,1,2,3
  • Documented code
    • Code 0,1,2,3
    • Documentation 0,1,2,3
  • Annotated output
    • Output 0,1,2,3
    • Annotations 0,1,2,3

Total possible points = 18

Remember my hypothetical situation from yesterday? I provided an example of two students who managed to score enough marks to pass by knowing the complement of each other’s course knowledge.  Looking at the above example, it appears (although not easily) to be possible for this situation to occur and both students to receive a 9/18, yet for different aspects. But I have some more pressing questions:

  1. Should it be possible for a student to receive full marks for output, if there is no definition, design or code presented?
  2. Can a student receive full marks for everything else if they have no design?

The first question indicates what we already know about task dependencies: if we want to build them into numerical grading, we have to be pedantically specific and provide rules on top of the aggregation mathematics. But, more subtly, by aggregating these measures, we no longer have an ‘accurately triaged’ grade to indicate if the assignment as a whole is acceptable or not. An assignment with no definition, design or code can hardly be considered to be a valid submission, yet good output, documentation and annotation (with no code) will not give us the right result!

The second question is more for those of us who teach programming and it’s a question we all should ask. If a student can get a decent grade for an assignment without submitting a design, then what message are we sending? We are, implicitly, saying that although we talk a lot about design, it’s not something you have to do in order to be successful. Rapaport does go on to talk about weightings and how we can emphasis these issues but we are still faced with an ugly reality that, unless we weight our key aspects to be 50-60% of the final aggregate, students will be able to side-step them and still perform to a passing standard. Every assignment should be doing something useful, modelling the correct approaches, demonstrating correct techniques. How do we capture that?

Now, let me step back and say that I have no problem with identifying the sub-tasks and clearly indicating the level of performance using triage grading, but I disagree with using it for marks. For feedback it is absolutely invaluable: triage grading on sub-tasks will immediately tell you where the majority of students are having trouble, quickly. That then lets you know an area that is more challenging than you thought or one that your students were not prepared for, for some reason. (If every student in the class is struggling with something, the problem is more likely to lie with the teacher.) However, I see three major problems with sub-task aggregation and, thus, with final grade aggregation from assignments.

The first problem is that I think this is the wrong kind of scale to try and aggregate in this way. As Rapaport notes, agreement on clear, linear intervals in grading is never going to be achieved and is, very likely, not even possible. Recall that there are four fundamental types of scale: nominal, ordinal, interval and ratio. The scales in use for triage grading are not interval scales (the intervals aren’t predictable or equidistant) and thus we cannot expect to average them and get sensible results. What we have here are, to my eye, ordinal scales, with no objective distance but a clear ranking of best to worst. The clearest indicator of this is the construction of a B grade for final grading, where no such concept exists in the triage marks for assessing assignment quality. We have created a “some way to go but sometimes nearly perfect” that shouldn’t really exist. Think of it like runners: you win one race and you come third in another. You never actually came second in any race so averaging it makes no sense.

The second problem is that aggregation masks the beauty of triage in terms of identifying if a task has been performed to the pre-determined level. In an ideal world, every area of knowledge that a student is exposed to should be an important contributor to their learning journey. We may have multiple assignments in one area but our assessment mechanism should provide clear opportunities to demonstrate that knowledge. Thus, their achievement of sufficient assignment work to demonstrate their competency in every relevant area of knowledge should be a necessary condition for graduating. When we take triage grading back to an assignment level, we can then look at our assignments grouped by knowledge area and quickly see if a student has some way to go or has achieved the goal. This is not anywhere near as clear when we start aggregating the marks because of the mathematical issues already raised.

Finally, the reduction of triage to mathematical approximation reduces the ability to specify which areas of an assessment are really valuable and, while weighting is a reasonable approximation to this, it is very hard to use a mathematical formula with more and more ‘fudge factors’, a term Rapaport uses, to make up for the fact that this is just a little too fragile.

To summarise, I really like the thrust of this paper. I think what is proposed is far better, even with all of the problems raised above, at giving a reasonable, fair and predictable grade to students. But I think that the clash with existing grading traditions and the implicit requirement to turn everything back into one number is causing problems that have to be addressed. These problems mean that this solution is not, yet, beautiful. But let’s see where we can go.

Tomorrow, I’ll suggest an even more cut-down version of grading and then work on an even trickier problem: late penalties and how they affect grades.

Beauty Attack I: Assessment

Posted: January 8, 2016 | Author: | Filed under: Education, Opinion | Tags: , , , , , , , , , , , , , , , | 3 Comments

For the next week, I’m going to be applying an aesthetic lens to assessment and, because I’m in Computer Science, I’ll be focusing on the assessment of Computer Science knowledge and practice.

How do we know if our students know something? In reality, the best way is to turn them loose, come back in 25 years and ask the people in their lives, their clients, their beneficiaries and (of course) their victims, the same question: “Did the student demonstrate knowledge of area X?”

This is not available to us as an option because my Dean, if not my Head of School, would probably peer at me curiously if I were to suggest that all measurement of my efficacy be moved a generation from now. Thus, I am forced to retreat to the conventions and traditions of assessment: it is now up to the student to demonstrate to me, within a fixed timeframe, that he or she has taken a firm grip of the knowledge.

We know that students who are prepared to learn and who are motivated to learn will probably learn, often regardless of what we do. We don’t have to read Vallerand et al to be convinced that self-motivated students will perform, as we can see it every day. (But it is an enjoyable paper to read!) Yet we measure these students in the same assessment frames as students who do not have the same advantages and, thus, may not yet have the luxury or capacity of self-motivation: students from disadvantaged backgrounds, students who are first-in-family and students who wouldn’t know auto-didacticism if it were to dance in front of them.

How, then, do we fairly determine what it means to pass, what it means to fail and, even more subtly, what it means to pass or fail well? I hesitate to invoke Foucault, especially when I speak of “Discipline and Punish” in an educational setting, but he is unavoidable when we gaze upon a system that is dedicated to awarding ranks, graduated in terms of punishment and reward. It is strange, really, that were many patients to die under the hand of a surgeon for a simple surgery, we would ask for an inquest, but many students failing under the same professor in a first-year course is merely an indicator of “bad students”. So many of our mechanisms tell us that students are failing but often too late to be helpful and not in a way that encourages improvement. This is punishment. And it is not good enough.

A picture of Michel Foucault, mostly head shot, with his hand on his forehead, wearing glasses, apparently deep in contemplation.

Foucault: thinking about something very complicated, apparently.

Our assessment mechanisms are not beautiful. They are barely functional. They exist to provide a rough measure to separate pass from fail, with a variety of other distinctions that owe more to previous experience and privilege in many cases than any higher pedagogical approach.

Over the next week, I shall conduct an attack upon the assessment mechanisms that are currently used in my field, including my own, in the hope of arriving at a mechanism of design, practice and validation that is pedagogically pleasing (the aesthetic argument again) and will lead to outcomes that are both good and true.

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