Skills lost due to COVID-19 school closures will hit economic output for generations (hmmm)

Snippet from OECD report on covid-19 and education This CBC report is one of many dozens of articles in the world’s press highlighting one rather small but startling assertion in a recent OECD report on the effects of Covid-19 on education – that the ‘lost’ third of a year of schooling in many countries will lead to an overall lasting drop in GDP of 1.5% across the world. Though it contains many more fascinating and useful insights that are far more significant and helpful, the report itself does make this assertion quite early on and repeats it for good measure, so it is not surprising that journalists have jumped on it. It is important to observe, though, that the reasoning behind it is based on a model developed by Hanushek and Woessman over several years, and an unpublished article by the authors that tries to explain variations in global productivity according to amount and  – far more importantly – the quality of education: that long-run productivity is a direct consequence of the cognitive skills (or knowledge capital) of a nation, that can be mapped directly to how well and how much the population is educated.

As an educator I find this model, at a glance, to be reassuring and confirmatory because it suggests that we do actually have a positive effect on our students. However, there may be a few grounds on which it might be challenged (disclaimer: this is speculation). The first and most obvious is that correlation does not equal causation. The fact that countries that do invest in improving education consistently see productivity gains to match in years to come is interesting, but it raises the question of what led to that investment in the first place and whether that might be the ultimate cause, not the education itself.  A country that has invested in increasing the quality of education would, normally, be doing so as a result of values and circumstances that may lead to other consequences and/or be enabled by other things (such as rising prosperity, competition from elsewhere, a shift to more liberal values, and so on).  The second objection might be that, sure, increased quality of education does lead to greater productivity, but that it is not the educational process that is causing it, as such. Perhaps, for instance, an increased focus on attainment raises aspirations. A further objection might be that the definition of ‘quality’ does not measure what they think it measures. A brief skim of the model used suggests that it makes extensive use of scores from the likes of TIMSS, PIRLS and PISA, standardized test approaches used to compare educational ‘effectiveness’ in different regions that embody quite a lot of biases, are often manipulated at a governmental level, and that, as I have mentioned once or twice before, are extremely dubious indicators of learning: in fact, even when they are not manipulated, they may indicate willingness to comply with the demands of the powerful more than learning (does that improve GDP? Probably).  Another objection might be that absence of time spent in school does not equate to absence of education. Indeed, Hanushek and Woessman’s central thesis is that it is not the amount but the quality of schooling that matters, so it seems bizarre that they might fall back on quantifying learning by time spent in school. We know for sure that, though students may not have been conforming to curricula at the rate desired by schools and colleges, they have not stopped learning. In fact, in many ways and in many places, there are grounds to believe that there have been positive learning benefits: better family learning, more autonomy, more thoughtful pedagogies, more intentional learning community forming, and so on.  Out of this may spring a renewed focus on how people learn and how best to support them, rather than maintaining a system that evolved in mediaeval times to support very different learning needs, and that is so solidly packed with counter technologies and so embedded in so many other systems that have nothing to do with learning that we have lost sight of the ones that actually matter. If education improves as a result, then (if it is true that better and more education improves the bottom line) we may even see gains in GDP. I expect that there are other reasons for doubt: I have only skimmed the surface of the possible concerns.

I may be wrong to be sceptical –  in fairness, I have not read the many papers and books produced by Hanushek and Woessman on the subject, I am not an economist, nor do I have sufficient expertise (or interest) to analyze the regression model that they use. Perhaps they have fully addressed such concerns in that unpublished paper and the simplistic cause-effect prediction distorts their claims. But, knowing a little about complex adaptive systems, my main objection is that this is an entirely new context to which models that have worked before may no longer apply and that, even if they do, there are countless other factors that will affect the outcome in both positive and negative ways, so this is not so much a prediction as an observation about one small part of a small part of a much bigger emergent change that is quite unpredictable. I am extremely cautious at the best of times whenever I see people attempting to find simple causal linear relationships of this nature, especially when they are so precisely quantified, especially when past indicators are applied to something wholly novel that we have never seen before with such widespread effects, especially given the complex relationships at every level, from individual to national.  I’m glad they are telling the story – it is an interesting one that no doubt contains grains of important truths – but it is just an informative story, not predictive science.  The OECD has a bit of track record on this kind of misinterpretation, especially in education. This is the same organization that (laughably, if it weren’t so influential) claimed that educational technology in the classroom is bad for learning. There’s not a problem with the data collection or analysis, as such. The problem is with the predictions and recommendations drawn from it.

Beyond methodological worries, though, and even if their predictions about GDP are correct (I am pretty sure they are not – there are too many other factors at play, including huge ones like the destruction of the environment that makes the odd 1.5% seem like a drop in the barrel) then it might be a good thing. It might be that we are moving – rather reluctantly – into a world in which GDP serves as an even less effective measure of success than it already is. There are already plentiful reasons to find it wanting, from its poor consideration of ecological consequences to its wilful blindness to (and causal effect upon) inequalities, to its simple inadequacy to capture the complexity and richness of human culture and wealth. I am a huge fan of the state of Bhutan’s rejection of the GDP, that it has replaced with the GNH happiness index. The GNH makes far more sense, and is what has led Bhutan to be one of the only countries in the world to be carbon positive, as well as being (arguably but provably) one of the happiest countries in the world. What would you rather have, money (at least for a few, probably not you), or happiness and a sustainable future? For Bhutan, education is not for economic prosperity: it is about improving happiness, which includes good governance, sustainability, and preservation of (but not ossification of) culture.

Many educators – and I am very definitely one of them – share Bhutan’s perspective on education. I think that my customer is not the student, or a government, or companies, but society as a whole, and that education makes (or should make) for happier, safer, more inventive, more tolerant, more stable, more adaptive societies, as well as many other good things. It supports dynamic meta-stability and thus the evolution of culture. It is very easy to lose sight of that goal when we have to account to companies, governments, other institutions, and to so many more deeply entangled sets of people with very different agendas and values, not to mention our inevitable focus on the hard methods and tools of whatever it is that we are teaching, as well as the norms and regulations of wherever we teach it. But we should not ever forget why we are here. It is to make the world a better place, not just for our students but for everyone. Why else would we bother?

Originally posted at: https://landing.athabascau.ca/bookmarks/view/6578662/skills-lost-due-to-covid-19-school-closures-will-hit-economic-output-for-generations-hmmm

Evaluating assessment

Exam A group of us at AU have begun discussions about how we might transform our assessment practices, in the light of the far-reaching AU Imagine plan and principles. This is a rare and exciting opportunity to bring about radical and positive change in how learning happens at the institution. Hard technologies influence soft more than vice versa, and assessments (particularly when tied to credentials) tend to be among the hardest of all technologies in any pedagogical intervention. They are therefore a powerful lever for change. Equally, and for the same reasons, they are too often the large, slow, structural elements that infest systems to stunt progress and innovation.

Almost all learning must involve assessment, whether it be of one’s own learning, or provided by other people or machines. Even babies constantly assess their own learning. Reflection is assessment. It is completely natural and it only gets weird when we treat it as a summative judgment, especially when we add grades or credentials to the process, thus normally changing the purpose of learning from achieving competence to achieving a reward. At best it distorts learning, making it seem like a chore rather than a delight, at worst it destroys it, even (and perhaps especially) when learners successfully comply with the demands of assessors and get a good grade. Unfortunately, that’s how most educational systems are structured, so the big challenge to all teachers must be to eliminate or at least to massively reduce this deeply pernicious effect. A large number of the pedagogies that we most value are designed to solve problems that are directly caused by credentials. These pedagogies include assessment practices themselves.

With that in mind, before the group’s first meeting I compiled a list of some of the main principles that I adhere to when designing assessments, most of which are designed to reduce or eliminate the structural failings of educational systems. The meeting caused me to reflect a bit more. This is the result:

Principles applying to all assessments

  • The primary purpose of assessment is to help the learner to improve their learning. All assessment should be formative.
  • Assessment without feedback (teacher, peer, machine, self) is judgement, not assessment, pointless.
  • Ideally, feedback should be direct and immediate or, at least, as prompt as possible.
  • Feedback should only ever relate to what has been done, never the doer.
  • No criticism should ever be made without also at least outlining steps that might be taken to improve on it.
  • Grades (with some very rare minor exceptions where the grade is intrinsic to the activity, such as some gaming scenarios or, arguably, objective single-answer quizzes with T/F answers) are not feedback.
  • Assessment should never ever be used to reward or punish particular prior learning behaviours (e.g. use of exams to encourage revision, grades as goals, marks for participation, etc) .
  • Students should be able to choose how, when and on what they are assessed.
  • Where possible, students should participate in the assessment of themselves and others.
  • Assessment should help the teacher to understand the needs, interests, skills, and gaps in knowledge of their students, and should be used to help to improve teaching.
  • Assessment is a way to show learners that we care about their learning.

Specific principles for summative assessments

A secondary (and always secondary) purpose of assessment is to provide evidence for credentials. This is normally described as summative assessment, implying that it assesses a state of accomplishment when learning has ended. That is a completely ridiculous idea. Learning doesn’t end. Human learning is not in any meaningful way like programming a computer or storing stuff in a database. Knowledge and skills are active, ever-transforming, forever actively renewed, reframed, modified, and extended. They are things we do, not things we have.

With that in mind, here are my principles for assessment for credentials (none of which supersede or override any of the above core principles for assessment, which always apply):

  • There should be no assessment task that is not in itself a positive learning activity. Anything else is at best inefficient, at worst punitive/extrinsically rewarding.
  • Assessment for credentials must be fairly applied to all students.
  • Credentials should never be based on comparisons between students (norm-referenced assessment is always, unequivocally, and unredeemably wrong).
  • The criteria for achieving a credential should be clear to the learner and other interested parties (such as employers or other institutions), ideally before it happens, though this should not forestall the achievement and consideration of other valuable outcomes.
  • There is no such thing as failure, only unfinished learning. Credentials should only celebrate success, not punish current inability to succeed.
  • Students should be able to choose when they are ready to be assessed, and should be able to keep trying until they succeed.
  • Credentials should be based on evidence of competence and nothing else.
  • It should be impossible to compromise an assessment by revealing either the assessment or solutions to it.
  • There should be at least two ways to demonstrate competence, ideally more. Students should only have to prove it once (though may do so in many ways and many times, if they wish).
  • More than one person should be involved in judging competence (at least as an option, and/or on a regularly taken sample).
  • Students should have at least some say in how, when, and where they are assessed.
  • Where possible (accepting potential issues with professional accreditation, credit transfer, etc) they should have some say over the competencies that are assessed, in weighting and/or outcome.
  • Grades and marks should be avoided except where mandated elsewhere. Even then, all passes should be treated as an ‘A’ because students should be able to keep trying until they excel.
  • Great success may sometimes be worthy of an award – e.g. a distinction – but such an award should never be treated as a reward.
  • Assessment for credentials should demonstrate the ability to apply learning in an authentic context. There may be many such contexts.
  • Ideally, assessment for credentials should be decoupled from the main teaching process, because of risks of bias, the potential issues of teaching to the test (regardless of individual needs, interests and capabilities) and the dangers to motivation of the assessment crowding out the learning. However, these risks are much lower if all the above principles are taken on board.

I have most likely missed a few important issues, and there is a bit of redundancy in all this, but this is a work in progress. I think it covers the main points.

Further random reflections

There are some overriding principles and implied specifics in all of this. For instance, respect for diversity, accessibility, respect for individuals, and recognition of student control all fall out of or underpin these principles. It implies that we should recognize success, even when it is not the success we expected, so outcome harvesting makes far more sense than measurement of planned outcomes. It implies that failure should only ever be seen as unfinished learning, not as a summative judgment of terminal competence, so appreciative inquiry is far better than negative critique. It implies flexibility in all aspects of the activity. It implies, above and beyond any other purpose, that the focus should always be on learning. If assessment for credentials adversely affects learning then it should be changed at once.

In terms of implementation, while objective quizzes and their cousins can play a useful formative role in helping students to self-assess and to build confidence, machines (whether implemented by computers or rule-following humans) should normally be kept out of credentialling. There’s a place for AI but only when it augments and informs human intelligence, never when it behaves autonomously. Written exams and their ilk should be avoided, unless they conform to or do not conflict with all the above principles: I have found very few examples like this in the real world, though some practical demonstrations of competence in an authentic setting (e.g. lab work and reporting) and some reflective exercises on prior work can be effective.

A portfolio of evidence, including a reflective commentary, is usually going to be the backbone of any fair, humane, effective assessment: something that lets students highlight successes (whether planned or not), that helps them to consolidate what they have learned, and that is flexible enough to demonstrate competence shown in any number of ways. Outputs or observations of authentic activities are going to be important contributors to that. My personal preference in summative assessments is to only use the intended (including student-generated) and/or harvested outcomes for judging success, not for mandated assignments. This gives flexibility, it works for every subject, and it provides unquivocal and precise evidence of success. It’s also often good to talk with students, perhaps formally (e.g. a presentation or oral exam), in order to tease out what they really know and to give instant feedback. It is worth noting that, unlike written exams and their ilk, such methods are actually fun for all concerned, albeit that the pleasure comes from solving problems and overcoming challenges, so it is seldom easy.

Interestingly, there are occasions in traditional academia where these principles are, for the most part, already widely applied. A typical doctoral thesis/dissertation, for example, is often quite close to it (especially in more modern professional forms that put more emphasis on recording the process), as are some student projects. We know that such things are a really good idea, and lead to far richer, more persistent, more fulfilling learning for everyone. We do not do them ubiquitously for reasons of cost and time. It does take a long time to assess something like this well, and it can take more time during the rest of the teaching process thanks to the personalization (real personalization, not the teacher-imposed form popularized by learning analytics aficionados) and extra care that it implies. It is an efficient use of our time, though, because of its active contribution to learning, unlike a great many traditional assessment methods like teacher-set assignments (minimal contribution) and exams (negative contribution).  A lot of the reason for our reticence, though, is the typical university’s schedule and class timetabling, which makes everything pile on at once in an intolerable avalanche of submissions. If we really take autonomy and flexibility on board, it doesn’t have to be that way. If students submit work when it is ready to be submitted, if they are not all working in lock-step, and if it is a work of love rather than compliance, then assessment is often a positively pleasurable task and is naturally staggered. Yes, it probably costs a bit more time in the end (though there are plenty of ways to mitigate that, from peer groups to pedagogical design) but every part of it is dedicated to learning, and the results are much better for everyone.

Some useful further reading

This is a fairly random selection of sources that relate to the principles above in one way or another. I have definitely missed a lot. Sorry for any missing URLs or paywalled articles: you may be able to find downloadable online versions somewhere.

Boud, D., & Falchikov, N. (2006). Aligning assessment with long-term learning. Assessment & Evaluation in Higher Education, 31(4), 399-413. Retrieved from https://www.jhsph.edu/departments/population-family-and-reproductive-health/_docs/teaching-resources/cla-01-aligning-assessment-with-long-term-learning.pdf

Boud, D. (2007). Reframing assessment as if learning were important. Retrieved from https://www.researchgate.net/publication/305060897_Reframing_assessment_as_if_learning_were_important

Cooperrider, D. L., & Srivastva, S. (1987). Appreciative inquiry in organizational life. Research in organizational change and development, 1, 129-169.

Deci, E. L., Vallerand, R. J., Pelletier, L. G., & Ryan, R. M. (1991). Motivation and education: The self-determination perspective. Educational Psychologist, 26(3/4), 325-346.

Hussey, T., & Smith, P. (2002). The trouble with learning outcomes. Active Learning in Higher Education, 3(3), 220-233.

Kohn, A. (1999). Punished by rewards: The trouble with gold stars, incentive plans, A’s, praise, and other bribes (Kindle ed.). Mariner Books. (this one is worth forking out money for).

Kohn, A. (2011). The case against grades. Educational Leadership, 69(3), 28-33.

Kohn, A. (2015). Four Reasons to Worry About “Personalized Learning”. Retrieved from http://www.alfiekohn.org/blogs/personalized/ (check out Alfie Kohn’s whole site for plentiful other papers and articles – consistently excellent).

Reeve, J. (2002). Self-determination theory applied to educational settings. In E. L. Deci & R. M. Ryan (Eds.), Handbook of Self-Determination research (pp. 183-203). Rochester, NY: The University of Rochester Press.

Ryan, R. M., & Deci, E. L. (2017). Self-determination theory: Basic psychological needs in motivation, development, and wellness. Guilford Publications. (may be worth paying for if such things interest you).

Wilson-Grau, R., & Britt, H. (2012). Outcome harvesting. Cairo: Ford Foundation. http://www.managingforimpact.org/sites/default/files/resource/outome_harvesting_brief_final_2012-05-2-1.pdf.

Technology, technique, and teaching

These are the slides from my recent talk with students studying the philosophy of education at Pace University.

This is a mashup of various talks I have given in recent years, with a little new stuff drawn from my in-progress book. It starts with a discussion of the nature of technology, and the distinction between hard and soft technologies that sees relative hardness as the amount of pre-orchestration in a technology (be it a machine or a legal system or whatever). I observe that pedagogical methods (‘pedagogies’ for short) are soft technologies to those who are applying them, if not to those on the receiving end. It is implied (though I forgot to explicitly mention) that hard technologies are always more structurally significant than soft ones: they frame what is possible.

All technologies are assemblies, and (in education), the pedagogies applied by learners are always the most important parts of those assemblies. However, in traditional in-person classrooms, learners are (by default) highly controlled due to the nature of physics – the need to get a bunch of people together in one place at one time, scarcity of resources,  the limits of human voice and hearing, etc – and the consequent power relationships and organizational constraints that occur.  The classroom thus becomes the environment that frames the entire experience, which is very different from what are inaccurately described as online learning environments (which are just parts of a learner’s environment).

Because of physical constraints, the traditional classroom context is inherently very bad for intrinsic motivation. It leads to learners who don’t necessarily want to be there, having to do things they don’t necessarily want to do, often being either bored or confused. By far the most common solution to that problem is to apply externally regulated extrinsic motivation, such as grades, punishments for non-attendance, rules of classroom behaviour, and so on. This just makes matters much worse, and makes the reward (or the avoidance of punishment) the purpose of learning. Intelligent responses to this situation include cheating, short-term memorization strategies, satisficing, and agreeing with the teacher. It’s really bad for learning. Such issues are not at all surprising: all technologies create as well as solve problems, so we need to create counter technologies to deal with them. Thus, what we normally recognize as good pedagogy is, for the most part, a set of solutions to the problems created by the constraints of in-person teaching, to bring back the love of learning that is destroyed by the basic set-up. A lot of good teaching is therefore to do with supporting at least better, more internally regulated forms of extrinsic motivation.

Because pedagogies are soft technologies, skill is needed to use them well. Harder pedagogies, such as Direct Instruction, that are more prescriptive of method tend (on average) to work better than softer pedagogies such as problem-based learning, because most teachers tend towards being pretty average: that’s implicit in the term, after all. Lack of skill can be compensated for through the application of a standard set of methods that only need to be done correctly in order to work. Because such methods can also work for good teachers as well as the merely average or bad, their average effectiveness is, of course, high. Softer pedagogical methods such as active learning, problem-based learning, inquiry-based learning, and so on rely heavily on passionate, dedicated, skilled, time-rich teachers and so, on average, tend to be less successful. However, when done well, they outstrip more prescriptive methods by a large margin, and lead to richer, more expansive outcomes that go far beyond those specified in a syllabus or test. Softer technologies, by definition, allow for greater creativity, flexibility, adaptability, and so on than harder technologies but are therefore difficult to implement. There is no such thing as a purely hard or purely soft technology, though, and all exist on a spectrum,. Because all pedagogies are relatively soft technologies, even those that are quite prescriptive, almost any pedagogical method can work if it is done well: clunky, ugly, weak pedagogies used by a fantastic teacher can lead to great, persistent, enthusiastic learning. As Hattie observes, almost everything works – at least, that’s true of most things that are reported on in educational research studies :-). But (and this is the central message of my book, the consequences of which are profound) it ain’t what you do, it’s the way that you do it, that’s what gets results.

Problems can occur, though, when we use the same methods that work in person in a different context for which they were not designed. Online learning is by far the most dominant mode of learning (for those with an Internet connection – some big social, political, economic, and equity issues here) on the planet. Google, YouTube, Wikipedia, Reddit, StackExchange, Quora, etc, etc, etc, not to mention email, social networking sites, and so on, are central to how most of us in the online world learn anything nowadays. The weird thing about online education (in the institutional sense) is that online learning is far less obviously dominant, and tends to be viewed in a far less favourable light when offered as an option. Given the choice, and without other constraints, most students would rather learn in-person than online. At least in part, this is due to the fact that those of us working in formal online education continue to apply pedagogies and organizational methods that solved problems in in-person classrooms, especially with regard to teacher control: the rewards and punishments of grades, fixed length courses, strictly controlled pathways, and so on are solutions to problems that do not exist or that exist in very different forms for online learners, whose learning environment is never entirely controlled by a teacher.

The final section of the presentation is concerned with what – in very broad terms – native distance pedagogies might look like. Distance pedagogies need to acknowledge the inherently greater freedoms of distance learners and the inherently distributed nature of distance learning. Truly learner-centric teaching does not seek to control, but to support, and to acknowledge the massively distributed nature of the activity, in which everyone (including emergent collective and networked forms arising from their interactions) is part of the gestalt teacher, and each learner is – from their perspective – the most important part of all of that. To emphasize that none of this is exactly new (apart from the massive scale of connection, which does matter a lot), I include a slide of Leonardo’s to-do list that describes much the same kinds of activity as those that are needed of modern learners and teachers.

For those seeking more detail, I list a few of what Terry Anderson and I described as ‘Connectivist-generation’ pedagogical models. These are far more applicable to native online learning than earlier pedagogical generations that were invented for an in-person context. In my book I am now describing this new, digitally native generation as ‘complexivist’ pedagogies, which I think is a more accurate and less confusing name. It also acknowledges that many theories and models in the family (such as John Seely Brown’s distributed cognitive apprenticeship) predate Connectivism itself. The term comes from Davis’s and Sumara’s 2006 book, ‘Complexity and Education‘, which is a great read that deserves more attention than it received when it was published.

Slides: Technology, technique and teaching

Beyond learning outcomes

What we teach, what a student learns, what we assess This is a slide deck for a talk I’m giving today, at a faculty workshop, on the subject of learning outcomes.

I think that well-considered learning outcomes can be really helpful when planning and designing learning activities, especially where there is a need to assess learning. They can help keep a learning designer focused, and to remember to ensure that assessment activities actually make a positive contribution to learning. They can also be helpful to teachers while teaching, as a framework to keep them on track (if they wish to remain on track).  However, that’s about it. Learning outcomes are not useful when applied to bureaucratic ends, they are very poor descriptors of what learning actually happens, as a rule, and they are of very little (if any) use to students under most circumstances (there are exceptions – it’s a design issue, not a logical flaw).

The big point of my talk, though, is that we should be measuring what students have actually learned, not whether they have learned what we think we have taught, and that the purpose of everything we do should be to support learning, not to support bureaucracy.

I frame this in terms of the relationships between:

  • what we teach (what we actually teach, not just what we think we are teaching, including stuff like attitudes, beliefs, methods of teaching, etc),
  • what a student learns in the process (an individual student, not students as a whole), and
  • what we assess (formally and summatively, not necessarily as part of the learning process).

There are many things that we teach that any given student will not learn, albeit that (arguably) we wouldn’t be teaching at all if learning were not happening for someone. Most students get a small subset of that. There are also many things that we teach without intentionally teaching, not all of them good or useful.

There are also very many things that students learn that we do not teach, intentionally or otherwise. In fact, it is normal for us to mandate this as part of a learning design: any mildly creative or problem-solving/inquiry-oriented activity will lead to different learning outcomes for every learner. Even in the most horribly regimented teaching contexts, students are the ones that connect everything together, and that’s always going to include a lot more than what their teachers teach.

Similarly, there are lots of things that we assess that we do not teach, even with great constructive alignment. For example, the students’ ability to string a sentence together tends to be not just a prerequisite but something that is actively graded in typical assessments.

My main points are that, though it is good to have a teaching plan (albeit that it should be flexible,  reponsive to student needs, and should accommodate serendipity)learning :

  • students should be participants in planning outcomes and
  • we should assess what students actually learn, not what we think we are teaching.

From a learning perspective, there’s less than no point in summatively judging what learners have not learned. However, that’s exactly what most institutions actually do. Assessment should be about how learners have positively changed, not whether they have met our demands.

This also implies that students should be participants in the planning and use of learning outcomes: they should be able to personalize their learning, and we should recognize their needs and interests. I use andragogy to frame this, because it is relatively uncontroversial, is easily understood, and doesn’t require people to change everything in their world view to become better teachers, but I could have equally used quite a large number of other models. Connectivism, Communities of Practice, and most constructivist theories, for instance, force us to similar conclusions.

I suggest that appreciative inquiry may be useful as an approach to assessment, inasmuch as the research methodology is purpose-built to bring about positive change, and its focus on success rather than failure makes sense in a learning context.

I also suggest the use of outcome mapping (and its close cousin, outcome harvesting) as a means of capturing unplanned as well as planned outcomes. I like these methods because they only look at changes, and then try to find out what led to those changes. Again, it’s about evaluation rather than judgment.

DT&L2018 spotlight presentation: The Teaching Gestalt

The teaching gestalt  presentation slides (PDF, 9MB)

This is my Spotlight Session from the 34th Distance Teaching & Learning Conference, at Wisconsin Madison, August 8th, 2018. Appropriately enough, I did this online and at a distance thanks to my ineptitude at dealing with the bureaucracy of immigration. Unfortunately my audio died as we moved to the Q&A session so, if anyone who was there (or anyone else) has any questions or observations, do please post them here! Comments are moderated.

The talk was concerned with how online learning is fundamentally different from in-person learning, and what that means for how (or even whether) we teach, in the traditional formal sense of the word.

Teaching is always a gestalt process, an emergent consequence of the actions of many teachers, including most notably the learners themselves, which is always greater than (and notably different from) the sum of its parts. This deeply distributed process is often masked by the inevitable (thanks to physics in traditional classrooms) dominance of an individual teacher in the process. Online, the mask falls off. Learners invariably have both far greater control and far more connection with the distributed gestalt. This is great, unless institutional teachers fight against it with rewards and punishments, in a pointless and counter-productive effort to try to sustain the level of control that is almost effortlessly attained by traditional in-person teachers, and that is purely a consequence of solving problems caused by physical classroom needs, not of the needs of learners. I describe some of the ways that we deal with the inherent weaknesses of in-person teaching especially relating to autonomy and competence support, and observe how such pedagogical methods are a solution to problems caused by the contingent side effects of in person teaching, not to learning in general.

The talk concludes with some broad characterization of what is different when teachers choose to let go of that control.  I observe that what might have been Leonardo da Vinci’s greatest creation was his effective learning process, without which none of the rest of his creations could have happened. I am hopeful that now, thanks to the connected world that we live in, we can all learn like Leonardo, if and only if teachers can learn to let go.

Turns out the STEM ‘gender gap’ isn’t a gap at all

Grace Hopper and Univac, image from en.wikipedia.org/wiki/Grace_HopperAt least in Ontario, it seems that there are about as many women as men taking STEM programs at undergraduate level. This represents a smaller percentage of women taking STEM subjects overall because there are way more women entering university in the first place. A more interesting reading of this, therefore, is not that we have a problem attracting women to science, technology, engineering, and mathematics, but that we have a problem attracting men to the humanities, social sciences, and the liberal arts. As the article puts it:

“it’s not that women aren’t interested in STEM; it’s that men aren’t interested in poetry—or languages or philosophy or art or all the other non-STEM subjects.”

That’s a serious problem.

As someone with qualifications in both (incredibly broad) areas, and interests in many sub-areas of each,  I find the arbitrary separation between them to be ludicrous, leading to no end of idiocy at both extremes, and little opportunity for cross-fertilization in the middle. It bothers me greatly that technology subjects like computing or architecture should be bundled with sciences like biology or physics, but not with social sciences or arts, which are way more relevant and appropriate to the activities of most computer professionals. In fact, it bothers me that we feel the need to separate out large fields like this at all. Everyone plays lip service to cross-disciplinary work but, when we try to take that seriously and cross the big boundaries, there is so much polarization between the science and arts communities that they usually don’t even understand one another, let alone work in harmony. We don’t just need more men in the liberal arts – we need more scientists, engineers, and technologists to cross those boundaries, whatever their gender. And, vice versa, we need more liberal artists (that sounds odd, but I have no better term) and social scientists in the sciences and, especially, in technology.

But it’s also a problem of category errors in the other direction. This clumping together of the whole of STEM conceals the fact that in some subjects – computing, say – there actually is a massive gender imbalance (including in Ontario), no matter how you mess with the statistics. This is what happens when you try to use averages to talk about specifics: it conceals far more than it reveals.

I wish I knew how to change that imbalance in my own designated field of computing, an area that I deliberately chose precisely because it cuts across almost every other field and did not limit me to doing one kind of thing. I do arts, science, social science, humanities, and more, thanks to working with machines that cross virtually every boundary.

I suspect that fixing the problem has little to do with marketing our programs better, nor with any such surface efforts that focus on the symptoms rather than the cause. A better solution is to accept and to celebrate the fact that the field of computing is much broader and vastly more interesting than the tiny subset of it that can be described as computer science, and to build up from there. It’s especially annoying that the problem exists at Athabasca where a wise decision was made long ago not to offer a computer science program. We have computing and information systems programs, but not any programs in computer science. Unfortunately, thanks to a combination of lazy media and computing profs (suffering from science envy) that promulgate the nonsense, even good friends of mine that should know better sometimes describe me as a computer scientist (I am emphatically not), and even some of our own staff think of what we do as computer science. To change that perception means not just a change in nomenclature, but a change in how and what we, at least in Athabasca, teach. For example, we might mindfully adopt an approach that contextualizes computing around projects and applications, rather than its theory and mechanics. We might design a program that doesn’t just lump together a bunch of disconnected courses and call it a minor but that, in each course (if courses are even needed), actively crosses boundaries – to see how code relates to poetry, how art can inform and be informed by software, how understanding how people behave can be used in designing better systems, how learning is changed by the tools we create, and so on.

We don’t need disciplines any more, especially not in a technology field. We need connections. We don’t need to change our image. We need to change our reality. I’m finding that to be quite a difficult challenge right now.

 

Address of the bookmark: http://windsorstar.com/opinion/william-watson-turns-out-the-stem-gender-gap-isnt-a-gap-at-all/wcm/ee4217ec-be76-4b72-b056-38a7981348f2

Originally posted at: https://landing.athabascau.ca/bookmarks/view/2929581/turns-out-the-stem-%E2%80%98gender-gap%E2%80%99-isn%E2%80%99t-a-gap-at-all

Evidence mounts that laptops are terrible for students at lectures. So what?

The Verge reports on a variety of studies that show taking notes with laptops during lectures results in decreased learning when compared with notes taken using pen and paper. This tells me three things, none of which is what the article is aiming to tell me:

  1. That the institutions are teaching very badly. Countless decades of far better evidence than that provided in these studies shows that giving lectures with the intent of imparting information like this is close to being the worst way to teach. Don’t blame the students for poor note taking, blame the institutions for poor teaching. Students should not be put in such an awful situation (nor should teachers, for that matter). If students have to take notes in your lectures then you are doing it wrong.
  2. That the students are not skillful laptop notetakers. These studies do not imply that laptops are bad for notetaking, any more than giving students violins that they cannot play implies that violins are bad for making music. It ain’t what you do, it’s the way that you do it. If their classes depend on effective notetaking then teachers should be teaching students how to do it. But, of course, most of them probably never learned to do it well themselves (at least using laptops). It becomes a vicious circle.
  3. That laptop and, especially, software designers have a long way to go before their machines disappear into the background like a pencil and paper. This may be inherent in the medium, inasmuch as a) they are vastly more complex toolsets with much more to learn about, and b) interfaces and apps constantly evolve so, as soon as people have figured out one of them, everything changes under their feet. It becomes a vicious cycle.

The extra cognitive load involved in manipulating a laptop app (and stopping the distractions that manufacturers seem intent on providing even if you have the self-discipline to avoid proactively seeking them yourself) can be a hindrance unless you are proficient to the point that it becomes an unconscious behaviour. Few of us are. Tablets are a better bet, for now, though they too are becoming overburdened with unsought complexity and unwanted distractions. I have for a couple of years now been taking most of my notes at conferences etc with an Apple Pencil and an iPad Pro, because I like the notetaking flexibility, the simplicity, the lack of distraction (albeit that I have to actively manage that), and the tactile sensation of drawing and doodling. All of that likely contributes to making it easier to remember stuff that I want to remember. The main downside is that, though I still gain laptop-like benefits of everything being in one place, of digital permanence, and of it being distributed to all my devices, I have, in the process, lost a bit in terms of searchability and reusability. I may regret it in future, too, because graphic formats tend to be less persistent over decades than text. On the bright side, using a tablet, I am not stuck in one app. If I want to remember a paper or URL (which is most of what I normally want to remember other than my own ideas and connections that are sparked by the speaker) I tend to look it up immediately and save it to Pocket so that I can return to it later, and I do still make use of a simple notepad for things I know I will need later. Horses for courses, and you get a lot more of both with a tablet than you do with a pencil and paper. And, of course, I can still use pen and paper if I want a throwaway single-use record – conference programs can be useful for that.

 

 

 

 

Address of the bookmark: https://www.theverge.com/2017/11/27/16703904/laptop-learning-lecture

Originally posted at: https://landing.athabascau.ca/bookmarks/view/2871283/evidence-mounts-that-laptops-are-terrible-for-students-at-lectures-so-what

Small talk, big implications

fingerprint (public domain) An article from Quartz with some good links to studies showing the very many benefits of interacting with others, even at a very superficial level. I particularly like the report of a study showing the (quite strong) cognitive benefits of small talk.

It’s all solid stuff that supports much of what I and many others have written about the value of belongingness and social interaction in learning but, like much research in fields such as psychology, education, sociology, and so on, it makes some seemingly innocuous but fundamentally wrong assertions of fact. For instance:

“Those who were instructed to strike up a conversation with someone new on public transport or with their cab driver reported a more positive commute experience than those instructed to sit in silence.”

What, all of them? That seems either unbelievably improbable, or the result of a flawed methodology, or a sign of way too small a sample size. The paper itself is inaccessibly paywalled so I don’t know for sure, but I suspect this is actually just a sloppy description of the findings. It is not the result of bad reporting in the Quartz article, though: it is precisely what the abstract of the paper itself actually claims. The researchers make several similar claims like “Those who were instructed to strike up a hypothetical conversation with a stranger said they expected a negative experience as opposed to just sitting alone.” Again – all of them? If that were true, no one would ever talk to strangers (which anyone that has ever stood in a line-up in Canada knows to be not just false but Trumpishly false), so this is either a very atypical group or a very misleading statement about group members’ behaviours. The findings are likely, on average, correct for the groups studied, but that’s not the way it is written.

The article is filled with similarly dubious quotes from distinguished researchers and, worse, pronouncements about what we should do as a result. Often the error is subtly couched in (accurate but misleadingly ambiguous) phrasing like “The group that engaged in friendly small talk performed better in the tests.” I don’t think it is odd to carelessly read that as ‘all of the individuals in the group performed better than all of those in the other groups’, rather than that, ‘on average, the collective group entity performed better than another collective group entity’, which is what was actually meant (and that is far less interesting). From there it is an easy – but dangerously wrong – step to claim that ‘if you engage in small talk then you will experience cognitive gains.’ It’s natural to want to extrapolate a general law from averaged behaviours, and in some domains (where experimental anomalies can be compellingly explained) it makes sense, but it’s wrong in most cases, especially when applied to complex systems like, say, anything involving the behaviour of people.

It’s a problem because, like most in my profession, I regularly use such findings to guide my own teaching. On average, results are likely (but far from certain) to be better than if I did not use them, but definitely not for everyone, and certainly not every time.  Students do tend to benefit from engagement with other students, sure. It’s a fair heuristic, but there are exceptions, at least sometimes. And the exceptions aren’t just a statistical anomaly. These are real people we are talking about, not average people. When I do teaching well – nothing like enough of the time –  I try to make it possible for those that aren’t average to do their own thing without penalty. I try to be aware of differences and cater for them. I try to enable those that wish it to personalize their own learning. I do this because I’ve never in my entire life knowingly met an average person.

Unfortunately, our educational systems really don’t help me in my mission because they are pretty much geared to cater for someone that probably doesn’t exist. That said, the good news is that there is a general trend towards personalized learning that figures largely in most institutional plans. The bad news is that (as Alfie Kohn brilliantly observes) what is normally meant by ‘personalized’ in such plans is not its traditional definition at all, but instead ‘learning that is customized (normally by machines) for students in order that they should more effectively meet our requirements.’  In case we might have forgotten, personalization is something done by people, not to people. 

Further reading: Todd Rose’s ‘End of Average‘ is a great primer on how to avoid the average-to-the-particular trap and many other errors, including why learning styles, personality types, and a lot of other things many people believe to be true are utterly ungrounded, along with some really interesting discussion of how to improve our educational systems (amongst other things). I was gripped from start to finish and keep referring back to it a year or two on.

Address of the bookmark: https://qz.com/1134958/small-talks-positive-benefits-outweigh-your-fear-of-being-awkward/

Originally posted at: https://landing.athabascau.ca/bookmarks/view/2849927/small-talk-big-implications

Addicted to learning or addicted to grades?

Skinner teaching machine 08

Figure 1: Skinner’s teaching machine

It is not much of a surprise that many apps are designed to be addictive, nor that there is a whole discipline behind making them so, but I was particularly interested in the delightfully named Dopamine Labs‘ use of behaviourist techniques (operant conditioning with variable ratio scheduling, I think), and the reasoning behind it. As the article puts it:

One of the most popular techniques … is called variable reinforcement or variable rewards. 
It involves three steps: a trigger, an action and a reward.
A push notification, such as a message that someone has commented on your Facebook photo, is a trigger; opening the app is the action; and the reward could be a “like” or a “share” of a message you posted.
These rewards trigger the release of dopamine in the brain, making the user feel happy, possibly even euphoric, Brown says.
“Just by controlling when and how you give people that little burst of dopamine, you can get them to go from using [the app] a couple times a week to using it dozens of times a week.”

For well-designed social media and games, the reward is intrinsic to the activity, and perfectly aligned with its function. If the intent is to create addicts – which, in both kinds of system, it probably is – the trick is to design an environment that builds rewards into the algorithms (the rules) of the system, and to keep them coming, ideally making it possible for the rewards to increase in intensity as the user gains greater expertise or experience, but varying ratios or intervals between rewards to keep things interesting. Though this particular example falls out from behaviourist theory, it is also well supported by cognitivist and brain-based understandings of how we think. Drug dealers know this too, as it happens. If you want to keep people using your product, this is how to make your product particularly addictive.

Learning addicts

Lovers of learning experience addiction too. The more we learn, the more there is to learn, the greater the depth and pleasure there is to be found in doing so, and the sporadic ups and downs, especially when faced with challenges we eventually solve, are part of the joy of it. Increasing mastery of anything is a reward in itself that seems quite intrinsic to our make-up, and to that of many other animals. Doing it in a social context is even better, as we share in the learning of others and gain value (social capital, different perspectives, help overcoming problems, etc) in the process. We gain greater control, greater autonomy, greater capability to live our lives as we want to live them, which is very motivating. As long as the reward comes from the activity itself, and the activity is not harmful, this is good news. It makes sense from an evolutionary perspective. We are innately motivated to learn, because learning is an extremely valuable survival characteristic. Learning generally makes dopamine positively drip from our eyeballs.

So what’s the problem with applying the principle in education?

None at all, until you hit something that you do not wish to learn, that is too difficult to master right now, that is too boring, that has no obvious rewards in and of itself. The correct response to this problem is, ideally, to find what there is to love in it. Good teachers can help with that a lot, inspiring, revealing, supporting, demonstrating, and discussing. Other learners can make a huge difference too, supporting, modelling behaviours, filling gaps, and so on. We very often learn things for other people, with other people, or because of other people. Educational systems offer a good substrate for that.

If intrinsic motivation fails to move us, then at least the motivation should be self-determined. Figure 2 shows a very successful and well-validated model of motivation (from Ryan and Deci) that, amongst other things, usefully describes differing degrees of extrinsic motivation (external, introjected, identified, and integrated) that, as they approach the right of the diagram, increasingly approach intrinsic motivation in value, though ‘external regulation’ is rather different, of which more soon. When intrinsic motivation fails, what we need is some kind of internal regulation to push us onwards. It is not a bad idea to find some internally regulated reason that aligns with your beliefs about yourself and your goals, or that at least fits with some purpose or goal that you find valuable. It’s sometimes useful to develop a bit of ‘grit‘ – to be able to do something that you don’t love doing in order to be able to do things that you do love doing, to find reasons for learning stuff that are meaningful and fit with your personal values, even if the immediately presenting activity is not fun in itself. Again, teachers and other people can help a lot with that, by showing ways that they are doing so themselves, by providing support, by engaging, or by being the reason that we do something in the first place. It’s all very social, at its heart.

Amp-55-1-68-fig1a

Figure 2: Forms of motivation

That social element is important, and not clearly represented in the diagram, despite being a critical aspect of intrinsic motivation and mattering a lot for the ‘higher’ identified forms of extrinsic motivation. From an evolutionary perspective, I suspect this ability to learn because of the presence of others accounts for our species’ apparent dominance in our ecosystems. We are not particularly clever as independent individuals but, collectively, we are mighty smart. This could not be the case without having an innate inclination to value, and to gain value from, other people, and for this to have the consequence that others very materially contribute towards our motivation to do something. I guess I should mention that ‘innate’ does not mean ‘pre-programmed’ – this is almost certainly an emergent phenomenon. But it is a big part of who we are.

Grade addicts

So far so good. Educational systems are, at least in principle, very effective ways of bringing people together. It all goes horribly wrong, however, when the educators’ response to amotivation (or worse, to motivation to avoid) is to change the rules by throwing in extrinsic rewards and punishments, like grades, say, or applying other controls to the process like forced attendance. Externally regulated extrinsic motivation is extremely dangerous.

Extrinsic rewards and punishments do work, in the sense that they coerce people and other animals into behaving as the giver of the rewards or punishments wishes them to behave. And yes, dopamine is implicated. This immediate effectiveness is what makes them so alluring. But it’s like giving an athlete performance-enhancing but ultimately harmful drugs. Rewards and punishments are also highly addictive and, like other addictions, you need more and more to sustain your addiction because you become inured to the effects, and withdrawal gets more painful the longer you are addicted. This works two ways. Those that get the rewards (the good grades, gold stars, praise, whatever) go on to want more of them, and will do what they need to get them, whether or not there are any further benefits (like, say, learning). Cheating is one popular way to do this. Tactical study, where the student tries to do what will get good grades rather than learn for the love of it, is another. But grading, though extrinsically motivating for the most part, is not always effective: bad grades can achieve the opposite effect, like drugs spiked with something horrible. Those that get grades as punishments often try to avoid them by whatever means they can: dropping out and cheating (a way to bypass the system to get hold of the good stuff) are popular solutions.

The biggest problems, however, come when you take the rewards/punishments away. As a vast body of research has shown and continues to show, this diminishes intrinsic motivation and often eliminates it altogether. If people are not very inclined to do something then you can temporarily boost interest by adding extrinsic rewards or punishments but, when you take them away, people are considerably less inclined to do the thing than they were before your started even when they originally liked to do it. At a high level this can be explained by the fact that, in giving a reward or punishment, you are drawing attention away from (crowding out) the thing itself and, at the same time, sending a strong signal that the activity itself is not rewarding enough in itself to be worth doing. But I am not sure that this fully explains the very strong negative effects on motivation that we actually see when rewards or punishments are withdrawn. I idly speculate that part of the reason for this effect might be the dopamine crash. We come to associate an activity with a dopamine boost and, when that boost is no longer forthcoming, it can be very disappointing, like smoking a nicotine-free cigarette (trust me – that’s awful). Cold turkey is not the best state to be in, especially when you associate it with an activity like learning something. It could really put you off a subject. This is just a thought: I know of no evidence that it is true, but it seems a plausible hypothesis that would be worth testing.

Whatever the cause, the effects are terrible. By extrinsically driving our students, we kill the love of the activity itself for those that might have loved it, and permanently prevent those that might have later found it valuable from ever wanting to do it again. Remarkably few survive unscathed, and a disproportionate number of those that do go on to become teachers, and so the cycle continues. I don’t think this is how education should be, and I don’t think it is what most of us in the system intend from it.

Getting out of the loop

The only really effective way to ensure lifelong interest and ongoing love of learning is to find the reward in the activity itself, not in an extrinsic reward. The games and social applications described in this article do that very well but it is important to remember that the intent of the designers of the applications is to increase addiction to them in order to sell or promote the product, and that there is perfect alignment between the reward and the activity itself. This is built into the rule system. In an education system that is driven by marks, we are making grades (not learning) the product, and making those the source of the addiction. This is very different. It has nothing to do with the activity of learning itself: it is extrinsic to the process. It might be even more effective give our students addictive drugs (higher concentrations equate to higher grades) to increase the incentive. I’m surprised no one has tried this.

But, seriously, what we really need to be doing is to make learning the addiction.

We can reduce the harm to an extent by removing grades from the teaching process and focusing on useful feedback and encouragement instead. If forced to judge, we can use pass/fail grades that are still harmful but not quite as controlling. If we are inexplicably drawn to grading, then we can build systems similar to those of ‘likes’ and badges of social media where, instead of rewards we give awards – in other words, we remove the expectation of a grade but, where merit is found, sometimes show our approval – and we can make that a social process, so that it is not dominated by a teacher and therefore does not involve exercise of arbitrary power. We can use pedagogies that give teachers and students the chance to model and demonstrate their passion and interest. We can encourage students to reflect on why they are doing it, ideally shared so they can gain inspiration from others. We can help students to integrate work with other things that matter to them. We can help them personalize their own learning so that it is appropriately challenging, not too dull, not to hard, and so that it matches the goals they set for themselves. We can help them to set those goals, and help them to figure out how to attain them. We can make them participants in the grading process, picking outcomes and assessments that match their interests and needs. We can build communities that support and nourish learning through sharing and mutual support. This is just a small sample of ways – there are really quite a few things that we can do, even within a broken system, to make learning addictive, to find ways to make it rewarding in and of itself, even when there is little initial interest to build upon. But we are still stuck in a system that treats grades as rewards, so we are still faced with a furious current pushing against all of our efforts.

Really, we need to change the system, but just  a bit: our current educational systems have evolved for pragmatic reasons, mainly because alternatives are too expensive or inconvenient for teachers to manage, not because they are any good for learners. One of the consequences of that is that it is almost impossible to run an institutional course or program without at least some form of grading, even if only at pass/fail level, even if only at the end.

An obvious big part of the solution is to decouple learning and grading. Some more advanced competency-based approaches already do that, as do things like challenge assessments and assessment of prior experience and learning, to some extent project/essay/thesis paths, outcomes-based programs, and even some kinds of professional exams (the latter not in a good way, for the most part, because they tend to drive the process). However, there are risks that universities might turn into an up-market version of driving schools, teaching how to pass the tests and doing just as they are doing now, rather than enabling more expansive learning as they should. To avoid that, it is critical that learners are involved in helping to determine their own personalized outcomes, and very much not to have those learning outcomes ‘personalized’ for them – personal, not personalized, as Alfie Kohn puts it and as Stephen Downes agrees. Grades that learners control, for activities that they choose to undertake, are many times better than grades that someone else imposes. It would also be a good idea either to split teaching activities into assemblable chunks, or into open narratives, without alignment with specific awards or qualifications. Students might build competences from smaller pieces – often from different sources – in order to seek a specific award, or might gain more than one award from a single learning narrative (or perhaps from a couple that overlap). It would be a very good idea to provide ways to mentor and help learners to seek appropriate paths, perhaps through personal tuition, and/or through automated help, and/or through membership of supportive communities (I am a fan of action learning sets for this kind of thing). Such mechanisms might also assist in the preparation of portfolios of evidence that would be an obvious way to manage the formal assessment process. I’m not in any way suggesting that we educators (especially for adult learners) should get rid of our accreditation role, merely that we should stop using it to drive our teaching and to enforce compliance in our students.

I think that such relatively small tweaks to how we teach and assess could have massive benefits further upstream. In one fell swoop it would change the focus of educational systems from grades to learning, and change the reward structure from extrinsic to intrinsic. Instead of building fixed-length courses with measurable outcomes that we the teachers control, we could create ecosystems for learning, where cooperation and collaboration would have greater value than competition, where learners are really part of a club, not a cohort, where teachers are perceived as enablers of learning, not as causes, and certainly not as judges. The words ‘learner-centred’ have been much over-used, often being a shorthand for ‘a friendlier way of making students comply with our demands’ or ‘helping students to get better grades’, but I think they fairly accurately denote what this sort of system would entail when taken seriously. Some of my friends and colleagues prefer ‘learning-centred’ and that works for me too. But really this is about being more human and more humane. It’s about breaking the machines that determine what we do and how we do it, and focusing instead on what we – collectively and individually – want to be. We can do this by thinking carefully about what motivates people, as opposed to attempting to motivate them. As soon as our attitude is one of ‘how can we make our students to this?’ rather than ‘how can we help our students to do this?’ we have failed. It’s easy to create addicts of extrinsic motivation. It is hard to make addicts of learning. But, sometimes, the hard way is the right way.

 

Address of the bookmark: http://www.cbc.ca/news/technology/marketplace-phones-1.4384876

Originally posted at: https://landing.athabascau.ca/bookmarks/view/2816324/addicted-to-learning-or-addicted-to-grades

Study links student cheating to whether a course is popular or disliked

examWe already know that extrinsically motivated students (mainly those driven by grades and testing) are far more likely to cheat than those who are more intrinsically motivated. I bookmarked yet another example of this effect just the other day but there are hundreds if not thousands of research papers that confirm this in many different ways. And, as this article reaffirms, we already know that mastery learning approaches (that focus on supporting control, appropriate levels of challenge, and, ideally, social engagement) tend to make cheating far less likely, because they tend to better support intrinsic motivation. Hardly anyone cheats if they are doing stuff they love to do, unless some strong extrinsic force overrides it (like grades, rewards, punishments, hard-to-meet deadlines, etc). 

This research reveals another interesting facet of the problem that exactly accords with what self-determination theory would predict: that, whether or not the pedagogy is sensible (supportive of intrinsic motivation) or dumb (extrinsically driven), a student’s dislike of a course appears to predict an increased likelihood of cheating. This is pretty obvious when you think about it. If someone does not like a course then, by definition, they are not intrinsically motivated and, if they are still taking it despite that, the only motivation they can possibly have left is extrinsic.

The increased chances of cheating on disliked courses, whether or not mastery learning techniques are used, is completely unsurprising because it ain’t what you do, it’s the way that you do it. If mastery learning techniques are not working then it probably means that we are simply not using them very well. Most likely there is not enough support, or not enough learner control, or insufficient social engagement, or not enough/too much challenge, or there’s too much pressure, or something along those lines. It is actually much more difficult and usually far more time consuming to teach well using techniques that respect learner autonomy and individual needs than it is to follow the objectivist instructivist path, at least in an institutional environment that deeply embeds extrinsic motivation at its very core, so it is not surprising that it quite often fails.  It is also very possible that the problem is almost entirely due to the surrounding educational ecosystem. For instance if it is one that forces students down institutionally-determined paths whether or not they are ready, whether or not it matters to them, or if not enough time is allowed for it, or if the stakes for failure are high, then even well-designed courses with enthusiastic, supportive, skilled, well-informed, compassionate, unpressured teachers are not likely to help that much.

Some people will take a pragmatic lesson from this to look more carefully for cheating on courses that they know to be disliked. That’s not the solution. Others will look at those courses and try to find ways to make them more likeable. That’s much better. But really, once we have done that, we need to be wondering about why anyone would be taking a course that they dislike in the first place. And that points to a central problem with our educational systems and the tightly coupled teaching and accreditation that they embed deep in their bones. Given enough time, support, and skilled tuition, almost anyone can learn almost anything, and love doing so. We live in a time of plenty, where there are usually countless resources, people, and methods to learn almost anything, in almost any practical way, so it makes no sense that people should still be forced to learn in ways that they dislike, at inappropriate times, and at an inappropriate pace. If they do, it is because (one way or another) we make them do so, and that’s the root of the problem. We – the educators and, above all, the educational system – are the cause of cheating, as much as we are the victims of it. And we are the ones that should fix it.

The original paywalled paper can be found here.

Address of the bookmark: https://www.insidehighered.com/news/2017/10/06/study-links-student-cheating-whether-course-popular-or-disliked

Originally posted at: https://landing.athabascau.ca/bookmarks/view/2762299/study-links-student-cheating-to-whether-a-course-is-popular-or-disliked