Most assessment’s ugly

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.)

  1. It’s not authentic. 
  2. There’s little design. 
  3. Wrong Bloom’s level.
  4. Weak links forward.
  5. Weak links backward. 
  6. Testing not evaluating.
  7. Marks not feedback. 
  8. Not learning focused. 
  9. Deadlines are rubbish. 
  10. Tradition dominates innovation. 

How was that? 

And I’m out.


The hand of an expert is visible in design

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

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

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

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?

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.


Can we do this? We already have.

How does one actually turn everything I’ve been saying into a course that can be taught? We already have examples of this working, whether in the performance/competency based models found in medical schools around the world or whether in mastery learning based approaches where do not measure anything except whether a student has demonstrated sufficient knowledge or skill to show an appropriate level of mastery.

An absence of grades, or student control over their grades, is not as uncommon as many people think. MIT in the United States give students their entire first semester with no grades more specific than pass or fail. This is a deliberate decision to ease the transition of students who have gone from being leaders at their own schools to the compressed scale of MIT. Why compressed? If we were to assess all school students then we would need a scale that could measure all levels of ability, from ‘not making any progress at school’ to ‘transcendent’. The tertiary entry band is somewhere between ‘passing school studies’ to ‘transcendent’ and, depending upon the college that you enter, can shift higher and higher as your target institution becomes more exclusive. If you look at the MIT entry requirements, they are a little coy for ‘per student’ adjustments, but when the 75th percentile for the SAT components is 800, 790, 790, and 800,800,800 would be perfect, we can see that any arguments on how demotivating simple pass/fail grades must be for excellent students have not just withered, they have caught fire and the ash has blown away. When the target is MIT, it appears the freshmen get their head around a system that is even simpler than Rapaport’s.

MIT_Dome_night1_Edit

Pictured: A highly prestigious University with some of the most stringent entry requirements in the world, which uses no grades in first semester.

Other universities, such as Brown, deliberately allow students to choose how their marks are presented, as they wish to deemphasise the numbers in order to focus on education. It is not a cakewalk to get into Brown, as these figures attest, and yet Brown have made a clear statement that they have changed their grading system in order to change student behaviour – and the world is just going to have to deal with that. It doesn’t seem to be hurting their graduates, from quotes on the website such as “Our 85% admission rate to medical school and 89% admission rate to law school are both far above the national average.

And, returning to medical schools themselves, my own University runs a medical program where the usual guidelines for grading do not hold. The medical school is running on a performance/competency scheme, where students who wish to practise medicine must demonstrate that they are knowledgable, skilful and safe to practice. Medical schools have identified the core problem in my thought experiment where two students could have the opposite set of knowledge or skills and they have come to the same logical conclusion: decide what is important and set up a scheme that works for it.

When I was a solider, I was responsible for much of the Officer Training in my home state for the Reserve. We had any number of things to report on for our candidates, across knowledge and skills, but one of them was “Demonstrate the qualities of an officer” and this single item could fail an otherwise suitable candidate. If a candidate could not be trusted to one day be in command of troops on the battlefield, based on problems we saw in peacetime, then they would be counselled to see if it could be addressed and, if not, let go. (I can assure you that this was not used often and it required a large number of observations and discussion before we would pull that handle. The power of such a thing forced us to be responsible.)

We know that limited scale, mastery-based approaches are not just working in the vocational sector but in allied sectors (such as the military), in the Ivy league (Brown) and in highly prestigious non-Ivy league institutions such as MIT. But we also know of examples such as Harvey Mudd, who proudly state that only seven students since 1955 have earned a 4.0 GPA and have a post on the career blog devoted to “explaining why your GPA is so low” And, be in no doubt, Harvey Mudd is an excellent school, especially for my discipline. I’m not criticising their program, I’ve only heard great things about them, but when you have to put up a page like that? You’re admitting that there’s a problem but you are pushing it on to the student to fix it. But contrast that with Brown, who say to employers “look at our students, not their grades” (at least on the website).

Feedback to the students on their progress is essential. Being able to see what your students are up to is essential for the teacher. Being able to see what your staff and schools are doing is important for the University. Employers want to know who to hire. Which of these is the most important?

The students. It has to be the students. Doesn’t it? (Arguments for the existence of Universities as a self-sustaining bureaucracy system in the comments, if you think that’s a thing you want to do.)

This is not an easy problem but, as we can see, we have pieces of the solution all over the place. Tomorrow, I’m going to put in a place a cornerstone of beautiful assessment that I haven’t seen provided elsewhere or explained in this way. (Then all of you can tell me which papers I should have read to get it from, I can publish the citation, and we can all go forward.)

 


Not just videos!

SMPTE_Color_Bars.svg

Just a quick note that on-line learning is not just videos! I am a very strong advocate of active learning in my face-to-face practice and am working to compose on-line systems that will be as close to this as possible: learning and doing and building and thinking are all essential parts of the process.

Please, once again, check out Mark’s CACM blog on the 10 myths of teaching computer science. There’s great stuff here that extends everything I’m talking about with short video sequences and attention spans. I wrote something ages ago about not turning ‘chalk and talk’ into ‘watch and scratch (your head)’. It’s a little dated but I include it for completeness.


Collaboration and community are beautiful

There are many lessons to be learned from what is going on in the MOOC sector. The first is that we have a lot to learn, even for those of us who are committed to doing it ‘properly’ whatever that means. I’m not trying to convince you of “MOOC yes” or “MOOC no”. We can have that argument some other time. I’m talking about we already know from using these tools.

We’ve learned (again) that producing a broadcast video set of boring people reading the book at you in a monotone is, amazingly, not effective, no matter how fancy the platform. We know that MOOCs are predominantly taken by people who have already ‘succeeded’ at learning, often despite our educational system, and are thus not as likely to have an impact in traditionally disadvantaged areas, especially without an existing learning community and culture. (No references, you can Google all of this easily.)

We know that online communities can and do form. Ok, it’s not the same as twenty people in a room with you but our own work in this space confirms that you can have students experiencing a genuine feeling of belonging, facilitated through course design and forum interaction.

“Really?” you ask.

In a MOOC we ran with over 25,000 students, a student wrote a thank you note to us at the top of his code, for the final assignment. He had moved from non-coder to coder with us and had created some beautiful things. He left a note in his code because he thought that someone would read it. And we did. There is evidence of this everywhere in the forums and their code. No, we don’t have a face-to-face relationship. But we made them feel something and, from what we’ve seen so far, it doesn’t appear to be a bad something.

But we, as in the wider on-line community, have learned something else that is very important. Students in MOOCs often set their own expectations of achievement. They come in, find what they’re after, and leave, much like they are asking a question on Quora or StackExchange. Much like you check out reviews on-line before you start watching a show or you download one or two episodes to check it out. You know, 21st Century life.

Once you see that self-defined achievement and engagement, a lot of things about MOOCs, including drop rates and strange progression, suddenly make sense. As does the realisation that this is a total change from what we have accepted for centuries as desirable behaviour. This is something that we are going to have a lot of trouble fitting into our existing system. It also indicates how much work we’re going to have to do in order to bring in traditionally disadvantaged communities, first-in-family and any other under-represented group. Because they may still believe that we’re offering Perry’s nightmare in on-line form: serried ranks with computers screaming facts at you.

We offer our students a lot of choice but, as Universities, we mostly work on the idea of ‘follow this program to achieve this qualification’. Despite notionally being in the business of knowledge for the sake of knowledge, our non-award and ‘not for credit’ courses are dwarfed in enrolments by the ‘follow the track, get a prize’ streams. And that, of course, is where the diminishing bags of dollars come from. That’s why retention is such a hot-button issue at Universities because even 1% more retained students is worth millions to most Universities. A hunt and peck community? We don’t even know what retention looks like in that context.

Pretending that this isn’t happening is ignoring evidence. It’s self-deceptive, disingenuous, hypocritical (for we are supposed to be the evidence junkies) and, once again, we have a failure of educational aesthetics. Giving people what they don’t want isn’t good. Pretending that they just don’t know what’s good for them is really not being truthful. That’s three Socratic strikes: you’re out.

The Eiffel Tower, Paris, at night being struck at the apex by three bolts of lightning simultaneously.

We had better be ready to redirect that energy or explode.

We have a message from our learning community. They want some control. We have to be aware that, if we really want them to do something, they have to feel that it’s necessary. (So much research supports this.) By letting them run around in the MOOC space, artificial and heavily instrumented, we can finally see what they’re up to without having to follow them around with clipboards. We see them on the massive scale, individuals and aggregates. Remember, on average these are graduates; these are students who have already been through our machine and come out. These are the last people, if we’ve convinced them of the rightness of our structure, who should be rocking the boat and wanting to try something different. Unless, of course, we haven’t quite been meeting their true needs all these years.

I often say that the problem we have with MOOC enrolments is that we can see all of them. There is no ‘peeking around the door’ in a MOOC. You’re in or you’re out, in order to be signed up for access or updates.

If we were collaborating with all of our students to produce learning materials and structures, not just the subset who go into MOOC, I wonder what we would end up turning out? We still need to apply our knowledge of pedagogy and psychology, of course, to temper desire with what works but I suspect that we should be collaborating with our learner community in a far more open way. Everywhere else, technology is changing the relationship between supplier and consumer. Name any other industry and we can probably find a new model where consumers get more choice, more knowledge and more power.

No-one (sensible) is saying we should raze the Universities overnight. I keep being told that allowing more student control is going to lead to terrible things but, frankly, I don’t believe it and I don’t think we have enough evidence to stop us from at least exploring this path. I think it’s scary, yes. I think it’s going to challenge how we think about tertiary education, absolutely. I also think that we need to work out how we can bring together the best of face-to-face with the best of on-line, for the most people, in the most educationally beautiful way. Because anything else just isn’t that beautiful.


Teaching for (current) Humans

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.