My friend and inspirational thought leader George gets well-deserved recognition in this in-depth Chronicle article that gives George’s background as well as an overview of some of his ideas, particularly as they relate to MOOCs. The article has one minor error: it’s Dr Siemens, not Mr Siemens – he has at least two doctorates, one earned, the other awarded.
Address of the bookmark: http://chronicle.com/article/George-Siemens-Gets-Connected/143959/?cid=wc&utm_source=wc&utm_medium=en
Interesting and free evolving handbook for learning with and from others without formal structures and courses.
It’s a little overblown in singing its own praises and a little lacking in much substance yet, not to mention having a cringeworthy (albeit memorable and descriptive) name. But it is still evolving and there are some very sound ideas from the connectivist family gathered together here in a very digestible, non-scholarly, practical form, from a number of excellent thinkers, and it is nice to see that it practices what it preaches. A worthwhile resource that should help to move things forward in useful ways.
Address of the bookmark: http://peeragogy.org/
Thanks to Jim and several others who have recently brought my attention to IBM’s rather grandiose claim that, in a few years, classrooms will learn us. The kinds of technology described in this article are not really very new. They have been just around the corner since the 60s and have been around in quantity since the early 90s when adaptive hypermedia (AH) and intelligent tutoring systems (ITS) rose to prominence, spawning a great many systems, and copious research reported on in hundreds of conferences, books and journal articles. A fair bit of my early work in the late 90s was on applying such things to an open corpus, which is the kind of thing that has blossomed (albeit indirectly) into the recently popular learning analytics movement. Learning analytics systems are essentially very similar to AH systems but mostly leave the adaptation stage of the process up to the learner and/or teacher and tend to focus more on presenting information about the learning process in a useful way than on acting on the results. I’ve maintained more than a passing interest in this area but I remain a little on the edge of the field because my ambitions for such tools have never been to direct the learning process. For me, this has always been about helping people to help one another to learn, not to tell them or advise them on how to learn, because people are, at least till now, the best teachers and an often-wasted resource. This seemed intuitively obvious to me from the start and, as a design pattern, it has served me well. Of late, I have begun to understand better why it works, hence this post.
The general principle behind any adaptive system for learning is that there are learners, some kind of content, and some means of adapting the content to the learners. This implies some kind of learner model and a means of mapping that to the content, although I believe (some disagree) that the learner model can be disembodied in constituent pieces and can even happily exist outside the systems we build, in the heads of learners. Learning analytics systems are generally all about the learner model and not much else, while adaptive systems also need a content model and a means of bringing the two together.
Beyond some dedicated closed-corpus systems, there are some big obstacles to building effective adaptive systems for learning, or that support the learning process by tracking what we are doing. It’s not that these are bad ideas in principle – far from it. The problem is more to do with how they are automated and what they automate. Automation is a great idea when it works. If the tasks are very well defined and can be converted into algorithms that won’t need to be changed too much over time, then it can save a lot of effort and let us do things we could not do before, with greater efficiency. If we automate the wrong things, use the wrong data, or get the automation a little wrong, we create at least as many problems as we solve. Learning management systems are a simple case in point: they automated abstracted versions of existing teaching practice, thus making it more likely that existing practices would be continued in an online setting, even though they had in many cases emerged for pragmatic rather than pedagogic reasons that made little sense in an online environment. In fact, the very process of abstraction made this more likely to happen. Worse, we make it very much harder to back out when we automate, because we tend to harden a system, making it less flexible and less resilient. We set in stone what used to be flexible and open. It’s worse still if we centralize that, because then whole systems depend on what we have set in stone and you cannot implement big changes in any area without scrapping the whole thing. If the way we teach is wrong then it is crazy to try to automate it. Again, learning management systems show this in spades, as do many of the more popular xMOOC systems. They automate at least some of the wrong things (e.g. courses, grading, etc). So we had better be mighty sure about what we are automating and why we are doing it. And this is where things begin to look a bit worrying for IBM’s ‘vision’. At the heart of it is the assumption that classrooms, courses, grades and other paraphenalia of educational systems are all good ideas that are worth preserving. The problem here is that these evolved in an ecosystem that made them a sensible set of technologies at the time but that have very little to do with best practice or research into learning. This is not about learning – it is about propping up a poorly adapted system.
If we ignore the surrounding systems and start with a clean slate, then this should be a set of problems about learning. The first problem for learning analytics is to identify what are we should be analyzing, the second is to understand what the data mean and how to process them, the third to decide what to do about that. Our knowledge on all three stages is intermediate at best. There are issues concerning what to capture, what we can dicover about learners through the information we capture, and how we should use that knowledge to help them learn better. Central to all of this is what we actually know about education and what we have discovered works best – not just statistically or anecdotally, but for any and all individuals. Unfortunately, in education, the empirical knowledge we have to base this on is very weak indeed.
So far, the best we can come up with that is fairly generalizable (my favourite example being spaced learning) is typically only relevant to small and trivial learning tasks like memorization or simple skill acquisition. We’re pretty good at figuring out how to teach simple things well, and ITS and AH systems have done a pretty fair job under such circumstances, where goals (seldom learning goals – more often proxies like marks on tests or retention rates) are very clear and/or learning outcomes very simple. As soon as we aim for more complex learning tasks, the vast majority of studies of education are either specific, qualitative and anecdotal, or broad and statistical, or (more often than should be the case) both. Neither is of much value when trying to create an algorithmic teacher, which is the explicit goal of AH and ITS, and is implied in the teaching/learning support systems provided by learning analytics.
There are many patterns that we do know a lot about, though they don’t help much here. We know, for example, that one-to-one mastery teaching on average works really brilliantly – Bloom’s 2-sigma challenge still stands, about 30 years after it was first made. One-to-one teaching is not a process that can be replicated algorithmically: it is simply a configuration of people that allows the participants to adapt, interact and exchange or co-develop knowledge with each other more effectively than configurations where there is less direct contact between people. It lets learners express confusion or enthusiasm as directly as possible, and for the teacher to provide tailored responses, giving full and undistracted attention. It allows teachers to directly care both for the subject and for the student, and to express that caring effectively. It allows targeted teaching to occur, however that teaching might be enacted. It is great for motivation because it ticks all the boxes on what makes us self-motivated. But it is not a process and tells us nothing at all about how best to teach nor how best to learn in any way that can be automated, save that people can, on the whole, be pretty good at both, at least on average.
We also know that social constructivist models can, on average, be effective, for probably related reasons. it can also be a complete disaster. But fans of such approaches wilfully ignore the rather obvious fact that lots of people often learn very well indeed without them – the throwaway ‘on average’ covers a massive range of differences between real people, teachers and learners, and between the same people at different times in different contexts. This shouldn’t come as a surprise because a lot of teaching leads to some learning and most teaching is neither one-to-one nor inspired by social constructivist thinking. Personally, I have learned phenomenal amounts, been inspired and discovered many things through pretty dreadful teaching technologies and processes, including books and lectures and even examined quizzes. Why does it work? Partly because how we are taught is not the same thing at all as how we learn. How you and I learn from the same book is probably completely different in myriad ways. Partly it is because it ain’t what you do to teach but how you do it that makes the biggest difference. We do not yet have an effective algorithmic way of making or even identifying creative and meaningful decisions about what will help people to learn best – it is something that people and only people do well. Teachers can follow an identical course design with identical subject matter and turn it into a pile of junk or a work of art, depending on how they do it, how enthusiastic they are about it, how much eye contact they make, how they phrase it, how they pace it, their intonation, whether they turn to the wall, whether they remembered to shave, whether they stammer etc, etc, etc, and the same differentiators may work sometimes and not work others, may work for some people sometimes and not others. Sometimes, even awful teaching can lead to great learning, if the learners are interested and learn despite rather than because of the teacher, taking things into their own hands because the teaching is so awful. Teaching and learning, beyond simple memory and training tasks, are arts and not sciences. True, some techniques appear to work more often than not (but not always), but there is always a lot of mysterious stuff that is not replicable from one context to the next, save in general patterns and paradigms that are mostly not easily reduced to algorithms. It is over-ambitious to think that we can automate in software something we do not understand well enough to turn into an algorithm. Sure, we learn tricks and techniques, just like any artist, and it is possible to learn to be a good teacher just as it is possible to learn to be a good sculptor, painter or designer. We can learn much of what doesn’t work, and methods for dealing with tricky situations, and even a few rules of thumb to help us to do it better and processes for learning from our mistakes. But, when it comes down to basics, it is a creative process that can be done well, badly or with inspiration, whether we follow rules of thumb or not, and it takes very little training to become proficient. Some of the best teachers I’ve ever known have used the worst techniques. I quite like the emphasis that Alexandra Cristea and others have put on designing good authoring environments for adaptive systems because they then become creative tools rather than ends in themselves, but a good authoring tool has, to date, proved elusive and far too few people are working on this problem.
The proponents of learning analytics reckon they have an answer to this problem, by simply providing more information, better aggregated and more easily analyzed. It is still a creative and responsive teacher doing the teaching and/or a learner doing learning, so none of the craft or art is lost, but now they have more information, more complete, more timely, better presented, to help them with the task so that they can do it better. The trouble is that, if the information is about the wrong things, it will be worse than useless. We have very little idea what works in education from a process point of view so we do not know what to collect or how to represent it, unless all we are doing is relying on proxies that are based on an underlying model that we know with absolute certainty is at least partly incorrect or, at best, is massively incomplete. Unless we can get a clearer idea of how education works, we are inevitably going to be making a system that we know to be flawed to be more efficient than it was. Unfortunately, it is not entirely clear where the flaws lie especially as what may be a flaw for one may not be for another, and a flaw in one context may be a positive benefit in another. When performing analytics or building adaptive systems of any kind, we focus on proxies like grades, attention, time-on-task, and so on – things that we unthinkingly value in the broken system and that mean different things to different people in different contexts. Peter Drucker made an important observation about this kind of thing:
‘Nothing is less productive than to make more efficient what should not be done at all‘.
A lot of systems of this nature improve the efficiency of bad ideas. Maybe they valorize behaviourist learning models and/or mediaeval or industrial forms of teaching. Maybe they increase the focus on grading. Maybe they rely on task-focused criteria that ignore deeper connective discoveries. Maybe they contain an implied knowledge model that is based on experts’ views of a subject area, which does not normally equate to the best way to come by that knowledge. Maybe they assume that time on task matters or, just as bad, that less time spent learning means the system is working better (both and neither are true). Maybe they track progress through a system that, at its most basic level, is anti-educational. I have seen all these flaws and then some. The vast majority of tools are doing education-process analytics, not learning analytics. Even those systems that use a more open form of analytics which makes fewer assumptions about what should be measured, using data mining techniques to uncover hidden patterns, typically have risky systemic effects: they afford plentiful opportunities for filter bubbles, path dependencies, Matthew Effects and harmful feedback loops, for example. But there is a more fundamental difficulty for these systems. Whenever you make a model it is, of necessity, a simplification, and the rules for simplification make a difference. Models are innately biased, but we need them, so the models have to be good. If we don’t know what it is that works in the first place then we cannot have any idea whether the patterns we pick out and use to help people guide their learning journeys are a cause, an effect or a by-product of something else entirely. If we lack an explicit and accurate or useful model in the first place, we could just again be making something more efficient that should never be done at all. This is not to suggest that we should abandon the effort, because it might be a step to finding a better model, but it does suggest we should treat all findings gathered this way with extreme scepticism and care, as steps towards a model rather than an end in themselves.
In conclusion, from a computing perspective, we don’t really know much about what to measure, we don’t have great grounds for deciding how to process what we have measured, and we don’t know much at all about how to respond to what we have processed. Real teachers and learners know this kind of thing and can make sense of the complexity because we don’t just rely on algorithms to think. Well, OK, that’s not necessarily entirely true, but the algorithms are likely at a neural network level as well as an abstract level and are probably combinatorially complex in ways we are not likely to understand for quite a while yet. It’s thus a little early to be predicting a new generation of education. But it’s a fascinating area to research that is full of opportunities to improve things, albeit with one important proviso: we should not be entrusting a significant amount of our learning to such systems just yet, at least not on a massive scale. If we do use them, it should be piecemeal and we should try diverse systems rather than centralizing or standardizing in ways that the likes of Knewton are trying to do. It’s bit like putting a computer in charge of decisions whether or not to launch nuclear missiles. If the computer were amazingly smart, reliable and bug-free, in a way that no existing computer even approaches, it might make sense. If not, if we do not understand all the processes and ramifications of decisions that have to be made along the way, including ways to avoid mistakes, accidents and errors, it might be better to wait. If we cannot wait, then using a lot of different systems and judging their different outputs carefully might be a decent compromise. Either way, adaptive teaching and learning systems are undoubtedly a great idea, but they are, have long been, and should remain on the fringes until we have a much clearer idea of what they are supposed to be doing.
Interesting report and interview on the relationship between test scores of ‘crystallized skills’ (what schools teach) and ‘fluid intelligence’ (basically, the ability to think). Of course, there is none. Futhermore, teaching makes almost no contribution to logical thinking and problem solving in novel situations, at least for the 1400 eighth graders being studied.
“where a school accounted for approximately 1/3 of the variation in state test scores, they accounted for very near zero of the variation on these fluid cognitive skill measures.”
This is hardly surprising in a world where the success of teaching is measured by standardized tests, and teaching is focused on achieving good results in those tests. The researchers are right to observe that crystallized skills are important, so this is not necessarily all bad news: schools appear to have some effect. However, I strongly suspect this is a short-term effect (as long as is needed to pass the test) and much less than it could be due to the extrinsic motivation designed into the system which actively degrades the students’ intrinsic motivation to learn. Whether or not that’s true, it’s a terrible indictment of an educational system that it affords no opportunities to develop the thinking skills that matter more. These skills are not measured in the standardized tests nor could they be measured in that way without destroying what they seek to observe. This doesn’t mean that we need better tests. We need better education.
Address of the bookmark: http://commonhealth.wbur.org/2013/12/standard-test-fluid-skills
An article from the Guardian that makes me glad my kids have already gone through the UK school system. The pigeon-brained fool in charge of UK education right now, Michael Gove, is doing his level best to set school education in that country back a hundred years, ignorantly or wilfully ignoring every shred of educational research over the past century. He is living proof that an expensive education doesn’t automatically lead to an educated person and might even lead to the reverse: allegedly, he was a somewhat intelligent child, at least before he went to an independent school. Surprising. Thank heavens for people like Tricia Kelleher, the main subject of this article, whose common-sense critique rings true. I particularly like her complementary observations:
“If Michael Gove is saying we should just value what is in Pisa, then we might as well just collapse the curriculum and teach what will come top.”
and
“My worry is we are now going to be driven towards Pisa because Pisa becomes the next altar we worship at. But it is really a cul-de-sac in learning terms.”
Well said.
It makes me wonder about why we allow elected representatives with much less than no knowledge of education to run/ruin our educational systems. There must be some appeal among a significant number of people in the lunatic measures of success that they latch onto but that actually guarantee failure, such as PISA, standardized testing and the deliberate teaching of things that alienate children, along with counter-productivity initiatives that seek efficiency but that liquidize the baby with the bathwater. I’m guessing that these ideas might resonate with and spring from some of those who were brought up under the long-discredited behaviourist regime that blighted the mid-twentieth century and that still refuses to die in some places, even among educators. Few of us are very rational beings and we suffer, amongst many other things, from irrational primacy biases, choice-supportive biases, confirmation biases, irrational escalation and endowment effects that together lead us to believe that what was done to us was the right way to do things, no matter how much the available evidence proves that it was not. Unfortunately, those who were damaged by behaviourist teaching approaches have been taught one of the best ways not to learn so, notwithstanding a good many who rise above it and/or who learned to learn in other ways, this may be a vicious cycle that is doomed to repeat itself for a while longer.
Address of the bookmark: http://www.theguardian.com/politics/2013/dec/19/headteacher-michael-gove-tricia-kelleher-education-reforms
I love Alfie Kohn – his writing is consistently clear, constructive and filled with sound arguments based on bulletproof research that continue to surprise even though the conclusions are completely obvious to anyone who spends a moment thinking about it. In this essay he shows how we, the teachers and our institutions, are the principle cause of cheating, creating elaborate and demotivating gotchas and systems designed to make cheating rewarding and, perhaps, inevitable. As a result, we are cheating students out of the joy learning. We are teaching them not to learn. Full of useful insights and simple but not simplistic solutions.
Address of the bookmark: http://www.alfiekohn.org/teaching/cheating.htm
Donald once again in brilliant form cracking open a bunch of academic memes that still pervade the education system and have way too much influence on those that fund it. Especially good on challenging the awful data underlying standardization and comparisons like university league tables and PISA scores on which governments and journalists thrive.
Address of the bookmark: http://donaldclarkplanb.blogspot.co.uk/2013/11/when-big-data-goes-bad-6-epic-fails.html
In case the news has not got through to anyone yet, research into learning styles is pointless. The research that proves this is legion but, for instance, see (for just a tiny sample of the copious and damning evidence):
Riener, C., & Willingham, D. (2010). The Myth of Learning Styles. Change: The Magazine of Higher Learning Change: The Magazine of Higher Learning, 42(5), 32-35. doi:doi: 10.1080/00091383.2010.503139
Derribo, M. H., & Howard, K. (2007). Advice about the use of learning styles: A major myth in education. Journal of college reading and learning, 37, 2.
Coffield, F., Moseley, D., Hall, E., & Ecclestone, K. (2004). Learning styles and pedagogy in post-16 learning: A systematic and critical review. 041543).
No one denies that it is possible to classify people in all sorts of ways with regards to things that might affect how they learn, nor that everyone is different, nor that there are some similarities and commonalities between how people prefer to or habitually go about learning. When these elaborately constructed theories claim no more than that people are different in interesting and sometimes identifiably consistent ways, then I have little difficult accepting them in principle, though it’s always worth observing that there are well over 100 of these theories and they cannot all be right. There is typically almost nothing in any of them that could prove them wrong either. This is a hallmark of pseudo-science and should set our critical sensors on full alert. The problem comes when the acolytes of whatever nonsense model is their preferred flavour try to take the next step and tell us that this means we should teach people in particular ways to match their particular learning styles. There is absolutelly no plausible evidence that knowing someone’s learning style, however it is measured, should have any influence whatsoever on how we should teach them, apart from the obvious requirement that we should cater for diversity and provide multiple paths to success. None. This is despite many decades spent trying to prove that it makes a difference. It doesn’t.
It is consequently a continual source of amazement to me when people pipe up in conversations to say that we should consider student learning styles when designing courses and learning activities. Balderdash. There is a weak case to be made that, like astrology (exactly like astrology), such theories serve a useful purpose of encouraging people to reflect on what they do and how they behave. They remind teachers to consider the possibility that there might be more than one way to learn something and so they are more likely to produce useful learning experiences that cater for diverse needs, to try different things and build flexibility into their teaching. Great – I have no objection to that at all, it’s what we should be aiming for. But it would be a lot more efficient to simply remind people of that simple and obvious fact rather than to sink vast sums of money and human resources into perpetuating these foolish myths. And there is a darker side to this. If we tell people that they are (just a random choice) ‘visual’, or ‘sensing’ or ‘intuitive’ or ‘sequential’ learners then they will inevitably be discouraged from taking different approaches. If we teach them in a way that we think fits a mythical need, we do not teach them in other ways. This is harmful. It is designed to put learners in a filter bubble. The worst of it is that learners then start to believe it themselves and ignore or undervalue other ways of learning.
The occasion for this rant came up in a meeting yesterday, where it was revealed that a surprising number of our students describe their learning style (by which they actually mean their learning preference) to be to listen to a video lecture. I’m not sure where to begin with that. I would have been flabbergasted had I not heard similar things before. Even learning style believers would have trouble with that one. One of the main things that is worth noting, however, is that this is actually a description not of a learning preference but of a ‘being-taught habit’. Not as catchy, but that’s what it is.
I have spent much of my teaching career not so much teaching as unteaching: trying to break the appalling habits that our institutional education systems beat into us until we come to believe that the way we are being taught is actually a good way to learn. This is seldom the case – on the whole, educational systems have to achieve a compromise between cost-efficiency and effective teaching – but, luckily, people are often smart enough to learn despite poor teaching systems. Indeed, sometimes, people learn because of poor teaching systems, inasmuch as (if they are interested and have not had the passion sucked out of them) they have to find alternative ways to learn, and so become more motivated and more experienced in the process of learning itself. Indeed, problem-based and enquiry-based techniques (which are in principle a good idea) sometimes intentionally make use of that kind of dynamic, albeit usually with a design that supports it and offers help and guidance where needed.
If nothing else, one of the primary functions of an educational system should be to enable people to become self-directed, capable lifelong learners. Learning the stuff itself and gaining competence in a subject area or skill in doing something is part of that – we need foundations on which to build. But it is at least as much about learning ways of learning. There are many many ways to learn, and different ways work better for different people learning different things. We need to be able to choose from a good toolkit and use approaches that work for the job in hand, not that match the demands of some pseudo-scientific claptrap.
Rant over.
A report on the findings of this year’s Equinox Summit. Amongst the more interesting:
“the summit’s conclusion was that, in less than 20 years, “knowing facts will have little value”, meaning that schools will have to scrap conventional examinations and grades and replace them with more “qualitative assessment”. This would measure a student’s all-round ability, rather than testing their knowledge in a particular subject.”
A lot of other sound and common-sense ideas are reported on here. All good stuff.
Address of the bookmark: http://www.tes.co.uk/article.aspx?storyCode=6365265
I’m preparing for a presentation and discussion tomorrow with some doctoral students on the orchestration of lifelong learning. Having come up with the topic some time ago on a whim I’m not entirely sure what I’ll be talking about, so this is mostly an attempt to focus my thinking a little and is very much a work in progress.
In brief, the central jumping off point for this discussion is that teachers are not isolated actors but are instead are gestalts formed from
For it to work, everything must harmonize or must make the right kinds of dischord to bring about learning. There are various things that shake out of this perpsective, not least of which being that there are many ways to organize this teaching gestalt that do not involve an educational system of the sort we are used to, and that do not involve individuals labelled as teachers. This matters because most of the learning we do throughout our lives does not take place in or result from formal education.
Even and perhaps particularly in a traditional educational system, teachers are not just the ones that stand (metaphorically or actually) in front of classes and explicitly perform an act that we label as teaching. Teachers are also the authors, editors, illustrators, designers and publishers of textbooks, the builders of websites, the writers of articles and so on. Teachers are designers of school systems, timetablers, architects, designers and furniture builders. Teachers are makers of videos, programmers of online environments, system administrators, TV producers, designers of door handles and technicians. And, above all, learners are teachers – of themselves and of one another. In short, teaching is always a distributed role.
Unpicking this a little further, almost all learning transactions involve at least two teachers – the one with knowledge of content, process, etc, and the learner. Learning is always an active process of knowledge construction, linking, and sense-making in which we constantly reflect, reorientate, examine, and adjust our knowledge in the light of new information or new ways of seeing. We always teach ourselves at least as much as we are taught. We are not given knowledge – we make it. Another person may help to guide us, shape the directions we go, correct us when we are confused or wrong, and motivate us to go the extra mile, but we are always a teacher in this process, whether we like it or not.
In an educational context, a vast array of actors add their own contributions to the teaching whole. Some, like authors of textbooks, or creators of curricula, or other students sharing ideas and (mis)conceptions are very obviously playing a teaching role. Others are less obviously so, but they do matter. The people that made decisions about where to place a whiteboard, which tools to enable in an LMS, or what wattage of lightbulb to include in a classroom may make a huge contribution to the success of failure of a particular learning transaction. The designer of the timetable, the legislator who demanded a particular kind of content or a particular kind of behaviour, the setter of normalized tests, the curriculum designer and the person who cleaned the classroom, all play significant and sometimes crucial roles as part of the teaching gestalt. Timetables teach, LMSs teach, hallways teach. In an educational system it is the system that educates, not just the individual teacher. I particularly like the timetable example because it is a great rejoinder to those who rather naively suggest that teachers should put pedagogy first. Sure: but first you must do it only at these times, over this period, for this amount of time, in this physical or virtual place, on this subject. Whatever. Anyway, within this context, the person who is performing the explicit role of a teacher is thus just one of the teaching gestalt but, potentially, quite a special one, sometimes (but not always) second only to the learner in importance. He or she typically acts as a filter, conduit and interpreter that orchestrates this whole, that responds, gives feedback, shows caring. It’s not too surprising that we label this person differently from the rest of the gestalt.
Since we are talking about a process of orchestration, it is natural to think of music at this point, and the analogy works quite well. A teacher may be an orchestrator, adapting to a context in which many constraints and structures have already been determined by others, using the tools, techniques and technologies to play a part in the construction of knowledge that is hopefully the outcome. Some are conductors, trying to elicit harmonious learning through tight control of the process. Like the best conductors, the best teachers of this sort make use of the materials they are working with, fitting the strengths and weaknesses of the players, the acoustics of the venue, the nature of the instruments, to the demands of the piece to be played and the intended audience. Other teachers are more like arrangers, who organize the pieces and leave the playing to someone else. Some are like players in a band, maybe drummers or bassists providing a rhythm to keep learners on track, or perhaps as soloists showing virtuosity and improvisational skills that inspire the learners to new heights. Some are content to play second fiddle, bringing out the best in the soloist but always in the background. And then there are the ones who sit in a recording studio who play all the instruments themselves, sometimes even making the instruments, and arrange everything the way they want it to be arranged. Some play blues, using the same three chords and often simple technique to play an infinite and subtle range of tunes. Some play classically, sticking closely to but always interpreting a score. Some are composers. Some are jazz improvisors, modern or trad. Some go for unusual scales, exotic rhythms and peculiar blends, others prefer the folk traditions that they learned as children. The sounds that musicians make are a function of many things, including most notably the instrument itself as well as the surroundings in which it is played and the reactions of an audience. And, in most cases, there are many instruments to consider. A lot of the process of teaching is about the technologies tools and techniques, incredibly diverse, all of which have to work to a common purpose.
But whatever the tools, genres, blends and roles that teachers play, when it comes down to basics, teachers (that is to say, the players in the teaching gestalt) have to be skilled and creative, whatever and however they try to play. Above all, teaching (emerging from all the many contributors to that role) is a broad set of human practices, not a science, not just a set of techniques. It is, moreover, a creative, active and inventive practice that cannot be emptied of soul and programmed into a machine without losing the vitality and expression that makes it wonderful. This is not to suggest that machines cannot or should not be a big part of the process, however, any more than that an orchestra should try to play without instruments or a venue. Putting aside more blatant technologies like classrooms and LMSs, for better or worse, our educational systems are machines that, depending on your perspective and the aspect you are looking at, either enable or disable our ability to learn. Likewise, Google Search and Wikipedia (my two favourite e-learning technologies) have a very large and conspicuous machine element. And, of course, the creativity and inspiration can be distributed too. A bad teacher can be saved by a good textbook, for instance, and vice versa.
It is tempting to say that most of the intentional learning we do is self-guided – that we teach ourselves anything from cooking to philosophy. I know it’s tempting, because I’ve been known to say it, and have read many research studies purporting to show this. However, this is nearly always massively wrong. What we actually do, in almost all cases, is to orchestrate teaching done by others. In some cases this is blatant and obvious. If we learn something by reading a Wikipedia article, or a book, or by watching a video, this is very clearly not a case of us teaching ourselves. At least, not totally. We are merely picking our teachers and exercising a bit of control over the pace, time and place that they teach us. We don’t get all the benefits of teaching that way by any means – importantly, we seldom get much in the way of feedback, for example, and any tailoring that happens is up to us. These kinds of things do not show us that they care about us. Such things are co-teachers, part of the teaching gestalt. But it is all a matter of degree: we are always our own teachers to some extent, and there are almost always others involved in teaching us, no matter how informal or formal the setting. Even when we learn by dabbling and experimenting, we are not exactly pure autodidacts. Partly this is because we often have some kind of target to aspire to because we have seen, read, heard or otherwise encountered terminal behaviours of the sort we are aiming for. For many competences, it is because the things we try to learn or learn with are typically designed by humans who have other humans in mind when they design them – this is true of learning that makes use of things like pencils, paints, cookware, computers, cars, musical instruments, exercise machines, calculators and yachts. Learning in a vacuum is not possible, unless we are learning about the vacuum which might be, incidentally, one of those rare occasions where no other teacher is directly involved in the process.
By way of example, in recent years, I have been ‘teaching myself’ to play a new instrument at least once a year. I know what these instruments sound like when they are played well, so I can recognize the gaps between what I can do with them and what they can do. Many teachers have taught me. I have seen other people playing them so I have a fair idea how to hold them but, on the whole, they are designed to be held and manipulated so it seldom takes too long to figure that out by trial and error. Their designers have taught me. That said, I challenge anyone to watch someone else play the flute and, based on what you get out of that, to make the flute sound the same. It’s mighty hard. You might get the odd note and you might even figure out how to shape your mouth differently to switch octaves, but simply copying is probably not quite enough. Most instruments have quirks like that and it would not normally be very wise to simply rely on trial and error. The actual process I generally follow usually involves reading a bit about fingerings, tunings, breathing, embouchure and so on, usually with instrument in hand so that I can check what it all means, then a lot of trial and error, lots of YouTube videos and a great deal of practice until I reach a plateau, after which the cycle repeats again as I learn how to do more advanced stuff like overtones, harmonics, complex chords, intonation, picking or bowing styles, etc. I am never going to become a virtuoso this way, sure, but it is loosely structured in a way that leads to a bit more than the outcome of a chopsticks culture (this refers to Alan Kay’s delightful analogy of what happens when you simply put a computer in a classroom and hope for the best). Eventually I need to play with other people who play better or differently, to get a bit of coaching, to find others who will challenge me to go beyond my comfort zone, but I generally wind up being competent to carry a tune reasonably enough before getting to that point. Part of the reason that I can do this kind of thing because I have learned to teach myself and, of course, I am building on a foundation of existing knowledge. I can read music. I’ve grappled with most families of musical instrument at some point. I know the difference between 3/4 and 4/4 time, and a little bit about harmony. And I know a little about how people learn. All of this is because I have had many teachers, very few of whom were intentionally playing that role.
This all leads to what will, in my talk tomorrow, be the jumping off point for the real discussion, and some questions to which I have some answers but mostly not the best ones. What do all the things that go up to make teachers actually do? What is the value professional teachers add? How can we manage our teachers? How can we replace them? As professional teachers, how can we allow our students to manage us? What aspects of educational systems teach? What alternative ways of organizing and orchestrating learning might we discover, invent or adapt? I’m particularly interested in exploring ways to overcome some of the manifestly awful teaching that our educational systems do to our students like grading, for instance, and what to do when the tunes we want to play are not in harmony with those played by the systems we are working in. But I am also interested in exploring ways that we can enable people to be better orchestrators of their own inner and outer teachers, beyond institutional contexts, beyond xMOOCs, beyond simple tutorials. I’m hoping it will be a fun discussion. How best to characterize what I’m aiming for? A bit of jazz improvisation, perhaps.