A lot of progress has been made in medicine in recent years through the application of cocktails of drugs. Those used to combat AIDS are perhaps the most well-known, but there are many other applications of the technique to everything from lung cancer to Hodgkin's lymphoma. The logic is simple. Different drugs attack different vulnerabilities in the pathogens etc they seek to kill. Though evolution means that some bacteria, viruses or cancers are likely to be adapted to escape one attack, the more different attacks you make, the less likely it will be that any will survive.
Unfortunately, combinatorial complexity means this is not a simply a question of throwing a bunch of the best drugs of each type together and gaining their benefits additively. I have recently been reading John H. Miller's 'A crude look at the whole: the science of complex systems in business, life and society' which is, so far, excellent, and that addresses this and many other problems in complexity science. Miller uses the nice analogy of fashion to help explain the problem: if you simply choose the most fashionable belt, the trendiest shoes, the latest greatest shirt, the snappiest hat, etc, the chances of walking out with the most fashionable outfit by combining them together are virtually zero. In fact, there's a very strong chance that you will wind up looking pretty awful. It is not easily susceptible to reductive science because the variables all affect one another deeply. If your shirt doesn't go with your shoes, it doesn't matter how good either are separately. The same is true of drugs. You can't simply pick those that are best on their own without understanding how they all work together. Not only may they not additively combine, they may often have highly negative effects, or may prevent one another being effective, or may behave differently in a different sequence, or in different relative concentrations. To make matters worse, side effects multiply as well as therapeutic benefits so, at the very least, you want to aim for the smallest number of compounds in the cocktail that you can get away with. Even were the effects of combining drugs positive, it would be premature to believe that it is the best possible solution unless you have actually tried them all. And therein lies the rub, because there are really a great many ways to combine them.
Miller and colleagues have been using the ideas behind simulated annealing to create faster, better ways to discover working cocktails of drugs. They started with 19 drugs which, a small bit of math shows, could be combined in 2 to the power of 19 different ways - about half a million possible combinations (not counting sequencing or relative strength issues). As only 20 such combinations could be tested each week, the chances of finding an effective, let alone the best combination, were slim within any reasonable timeframe. Simplifying a bit, rather than attempting to cover the entire range of possibilities, their approach finds a local optimum within one locale by picking a point and iterating variations from there until the best combination is found for that patch of the fitness landscape. It then checks another locale and repeats the process, and iterates until they have covered a large enough portion of the fitness landscape to be confident of having found at least a good solution: they have at least several peaks to compare. This also lets them follow up on hunches and to use educated guesses to speed up the search. It seems pretty effective, at least when compared with alternatives that attempt a theory-driven intentional design (too many non-independent variables), and is certainly vastly superior to methodically trying every alternative, inasmuch as it is actually possible to do this within acceptable timescales.
The central trick is to deliberately go downhill on the fitness landscape, rather than following an uphill route of continuous improvement all the time, which may simply get you to the top of an anthill rather than the peak of Everest in the fitness landscape. Miller very effectively shows that this is the fundamental error committed by followers of the Six-Sigma approach to management, an iterative method of process improvement originally invented to reduce errors in the manufacturing process: it may work well in a manufacturing context with a small number of variables to play with in a fixed and well-known landscape, but it is much worse than useless when applied in a creative industry like, say, education, because the chances that we are climbing a mountain and not an anthill are slim to negligible. In fact, the same is true even in manufacturing: if you are just making something inherently weak as good as it can be, it is still weak. There are lessons here for those that work hard to make our educational systems work better. For instance, attempts to make examination processes more reliable are doomed to fail because it's exams that are the problem, not the processes used to run them. As I finish this while listening to a talk on learning analytics, I see dozens of such examples: most of the analytics tools described are designed to make the various parts of the educational machine work ' better', ie. (for the most part) to help ensure that students' behaviour complies with teachers' intent. Of course, the only reason such compliance was ever needed was for efficient use of teaching resources, not because it is good for learning. Anthills.
This way of thinking seems to me to have potentially interesting applications in educational research. We who work in the area are faced with an irreducibly large number of recombinable and mutually affective variables that make any ethical attempt to do experimental research on effectiveness (however we choose to measure that - so many anthills here) impossible. It doesn't stop a lot of people doing it, and telling us about p-values that prove their point in more or less scupulous studies, but they are - not to put too fine a point on it - almost always completely pointless. At best, they might be telling us something useful about a single, non-replicable anthill, from which we might draw a lesson or two for our own context. But even a single omitted word in a lecture, a small change in inflection, let alone an impossibly vast range of design, contextual, historical and human factors, can have a substantial effect on learning outcomes and effectiveness for any given individual at any given time. We are always dealing with a lot more than 2 to the power of 19 possible mutually interacting combinations in real educational contexts. For even the simplest of research designs in a realistic educational context, the number of possible combinations of relevant variables is more likely closer to 2 to the power of 100 (in base 10 that's 1,267,650,600,228,229,401,496,703,205,376). To make matters worse, the effects we are looking for may sometimes not be apparent for decades (having recombined and interacted with countless others along the way) and, for anything beyond trivial reductive experiments that would tell us nothing really useful, could seldom be done at a rate of more than a handful per semester, let alone 20 per week. This is a very good reason to do a lot more qualitative research, seeking meanings, connections, values and stories rather than trying to prove our approaches using experimental results. Education is more comparable to psychology than medicine and suffers the same central problem, that the general does not transfer to the specific, as well as a whole bunch of related problems that Smedslund recently coherently summarized. The article is paywalled, but Smedlund's abstract states his main points succinctly:
"The current empirical paradigm for psychological research is criticized because it ignores the irreversibility of psychological processes, the infinite number of influential factors, the pseudo-empirical nature of many hypotheses, and the methodological implications of social interactivity. An additional point is that the differences and correlations usually found are much too small to be useful in psychological practice and in daily life. Together, these criticisms imply that an objective, accumulative, empirical and theoretical science of psychology is an impossible project."
You could simply substitute 'education' for 'psychology' in this, and it would read the same. But it gets worse, because education is as much about technology and design as it is about states of mind and behaviour, so it is orders of magnitude more complex than psychology. The potential for invention of new ways of teaching and new states of learning is essentially infinite. Reductive science thus has a very limited role in educational research, at least as it has hitherto been done.
But what if we took the lessons of simulated annealing to heart? I recently bookmarked an approach to more reliable research suggested by the Christensen Institute that might provide a relevant methodology. The idea behind this is (again, simplifying a bit) to do the experimental stuff, then to sweep the normal results to one side and concentrate on the outliers, performing iterations of conjectures and experiments on an ever more diverse and precise range of samples until a richer, fuller picture results. Although it would be painstaking and longwinded, it is a good idea. But one cycle of this is a bit like a single iteration of Miller's simulated annealing approach, a means to reach the top of one peak in the fitness landscape, that may still be a low-lying peak. However if, having done that, we jumbled up the variables again and repeated it starting in a different place, we might stand a chance of climbing some higher anthills and, perhaps, over time we might even hit a mountain and begin to have something that looks like a true science of education, in which we might make some reasonable predictions that do not rely on vague generalizations. It would either take a terribly long time (which itself might preclude it because, by the time we had finished researching, the discipline will have moved somewhere else) or would hit some notable ethical boundaries (you can't deliberately mis-teach someone), but it seems more plausible than most existing techniques, if a reductive science of education is what we seek.
To be frank, I am not convinced it is worth the trouble. It seems to me that education is far closer as a discipline to art and design than it is to psychology, let alone to physics. Sure, there is a lot of important and useful stuff to be learned about how we learn: no doubt about that at all, and a simulated annealing approach might speed up that kind of research. Painters need to know what paints do too. But from there to prescribing how we should therefore teach spans a big chasm that reductive science cannot, in principle or practice, cross. This doesn't mean that we cannot know anything: it just means it's a different kind of knowledge than reductive science can provide. We are dealing with emergent phenomena in complex systems that are ontologically and epistemologically different from the parts of which they consist. So, yes, knowledge of the parts is valuable, but we can no more predict how best to teach or learn from those parts than we can predict the shape and function of the heart from knowledge of cellular organelles in its constituent cells. But knowledge of the cocktails that result - that might be useful.
England is a weird, sad, angry little country, where there is now unequivocal evidence that over half the population - mainly the older ones - believe that experts know nothing, and that foreigners (as well as milllions of people born there with darker than average skins) are evil. England is a place filled with drunkenness and random violence, where it's not safe to pass a crowd of teenagers - let alone a crowd of football supporters - on a street corner, where you cannot hang Xmas decorations outside for fear of losing them, where your class still defines you forever, where whinging is a way of life, where kindness is viewed with suspicion, where barbed wire fences protect schools from outsiders (or vice versa - hard to fathom), where fuckin' is a punctuation mark to underline what follows, not an independent word. It's a nation filled with fierce and inhospitable people, as Horace once said, and it always has been. For all the people and places that I love and miss there, for all its very many good people and slowly vanishing places that are not at all like that, for all its dark and delicious humour, its eccentricity, its diversity, its cheeky irreverance, its feistiness, its relentless creativity, its excellent beer, its pork pies and its pickled onions, all of which I miss, that's why I was glad to leave it.
It saddens and maddens me to see the country of my birth killing or, at least, seriously maiming itself in such a spectacularly and wilfully ignorant way, taking the United Kingdom, and possibly even the EU itself with it, as well as causing injury to much of the world, including Canada. England is a country-sized suicide bomber. Hopefully this mob insanity will eventually be a catalyst for positive change, if not in England or Wales then at least elsewhere. Until today I opposed Scottish independence, because nationalism is almost uniformly awful and the last thing we need in the world is more separatism, but it is far better to be part of something big and expansive like the EU than an unwilling partner in something small in soul and mind like the UK. Maybe Ireland will unify and come together in Europe. Perhaps Gibraltar too. Maybe Europe, largely freed of the burden of supporting and catering for the small-minded needs of my cantankerous homeland, will rise to new heights. I hope so, but it's a crying shame that England won't be a part of that.
I am proud, though, of my home city, Brighton, the place where English people who don't want to live in England live. About 70% of Brightonians voted to stay in the EU. Today I am proudly Brightonian, proudly European, but ashamed to be English.
So true. So recursive.
Address of the bookmark: http://thesciencepost.com/study-70-of-facebook-commenters-only-read-the-headline/
As it turns out, yes.
The good news is that we are intuitively altruistic. This doesn't necessarily mean we are born that way. This is probably learned behaviour that co-evolves with that of those around us. The hypothesis on which this research is based (with good grounding) is that we learn through repeated interactions to behave kindly to others. At least, by far the majority of us. A few jerks (as the researchers discovered) are not intuitively generous and everyone behaves selfishly or unkindly sometimes. This is mainly because there are such jerks around, though sometimes because the perceived rewards for being a jerk might outweigh the benefits. Indeed, in almost all moral decisions, we tend to weigh benefits against harm, and it is virtually impossible to do anything at all without at least some harm being caused in some way, so the nicest of us are jerks to at least some people. It might upset the person who gave you a beautiful scarf that you wrecked it while saving a drowning child, for instance. Donating to a charity might reduce the motivation of governments to intervene in humaniarian crises. Letting a car in front of you to change lanes in front of you slows everyone in the queue behind you. Very many acts of kindness have costs to others. But, on the whole, we tend towards kindness, if only as an attitude. There is plentiful empirical evidence that this is true, some of which is referred to in the article. The researchers sought an explanation at a systemic, evolutionary level.
The researchers developed a simulation of a Prisoners' Dilemma scenario. Traditional variants on the game make use of rational agents that weigh up defection and cooperation over time in deciding whether or not to defect, using a variety of different rules (the most effective of which is usually the simplest 'tit-for-tat'). Their twist was to allow agents to behave 'intuitively' under some circumstances. Some agents were intuitively selfish, some not. In predominantly multiple round games, "the winning agents defaulted to cooperating but deliberated if the price was right and switched to betrayal if they found they were in a one-shot game." In predominantly one-shot games - not the norm in human societies - the always-cooperative agents died out completely. Selfish agents that deliberated did not do well in any scenario. As ever, ubiquitous selfish behaviour in a many-round game means that everyone loses, especially the selfish players. So, wary cooperation is a winning strategy when most other people are kind, and it benefits everyone so it is a winning strategy for societies and favoured by evolution. The explanation, they suggest is that:
"when your default is to betray, the benefits of deliberating—seeing a chance to cooperate—are uncertain, depending on what your partner does. With each partner questioning the other, and each partner factoring in the partner’s questioning of oneself, the suspicion compounds until there’s zero perceived benefit to deliberating. If your default is to cooperate, however, the benefits of deliberating—occasionally acting selfishly—accrue no matter what your partner does, and therefore deliberation makes more sense."
This accords with our natural inclinations. As Rand, one of the researchers, puts it: “It feels good to be nice—unless the other person is a jerk. And then it feels good to be mean.” If there are no rewards for being a jerk under any circumstances, or the rewards for being kind are greater, then perhaps we can all learn to be a bit nicer.
The really good news is that, because such behaviour is learned, selfish behaviour can be modified and intuitive responses can change. In experiments, the researchers have demonstrated that this can occur within less than half an hour, albeit in a very limited and artificial single context. The researchers suggest that, in situations that reward back-stabbing and ladder-climbing (the norm in corporate culture), all it should take is a little top-down intervention such as bonuses and recognition for helpful behaviour in order to set a cultural change in motion that will ultimately become self-sustaining. I'm not totally convinced by that - extrinsic reward does not make lessons stick and the learning is lost the moment the reward is taken away. However, because cooperation is inherently better for everyone than selfishness, perhaps those that are driven by such things might realize that those extrinsic rewards they crave are far better achieved through altruism than through selfishness as long as most people are acting that way most of the time, and this might be a way to help create such a culture. Getting rid of divisive and counter-productive extrinsic motivation, such as performance-related pay, might be a better (or at least complementary) long-term approach.
Address of the bookmark: http://nautil.us/issue/37/currents/selfishness-is-learned
Over the past year, I’ve been whining about how wearable technologies will have a bigger impact on how we learn, communicate, and function as a society than mobile devices have had to date. Fitness trackers, smart clothing, VR, heart rate monitors, and other devices hold promising potential in helping understand our learning and our health. They also hold potential for misuse (I don’t know the details behind this, but the connection between affective states with nudges for product purchases is troubling).
Over the past six months, we’ve been working on pulling together a conference to evaluate, highlight, explore, and engage with prominent trends in wearable technologies in the educational process. The http://awear.interlab.me“>aWEAR conference will be held Nov 14-15 at Stanford. The call for participation is now open. Short abstracts, 500 words, are due by July 31, 2016. We are soliciting conceptual, technological, research, and implementation papers. If you have questions or are interested in sponsoring or supporting the conference, please send me an email
From the site:
The rapid development of mobile phones has contributed to increasingly personal engagement with our technology. Building on the success of mobile, wearables (watches, smart clothing, clinical-grade bands, fitness trackers, VR) are the next generation of technologies offering not only new communication opportunities, but more importantly, new ways to understand ourselves, our health, our learning, and personal and organizational knowledge development.
Wearables hold promise to greatly improve personal learning and the performance of teams and collaborative knowledge building through advanced data collection. For example, predictive models and learner profiles currently use log and clickstream data. Wearables capture a range of physiological and contextual data that can increase the sophistication of those models and improve learner self-awareness, regulation, and performance.
When combined with existing data such as social media and learning management systems, sophisticated awareness of individual and collaborative activity can be obtained. Wearables are developing quickly, including hardware such as fitness trackers, clothing, earbuds, contact lens and software, notably for integration of data sets and analysis.
The 2016 aWEAR conference is the first international wearables in learning and education conference. It will be held at Stanford University and provide researchers and attendees with an overview of how these tools are being developed, deployed, and researched. Attendees will have opportunities to engage with different wearable technologies, explore various data collection practices, and evaluate case studies where wearables have been deployed.
So this is how job titles at our university are thought up! I knew there had to be a rational explanation. Wonderful.
Press the button for an endless supply of uncannily familiar job titles. I've not yet found one that precisely matches one of ours, but they are often very close indeed.
Address of the bookmark: http://universitytitlegenerator.com/
It has been about 30 months now since I took on the role to lead the LINK Research Lab at UTA. (I have retained a cross appointment with Athabasca University and continue to teach and supervise doctoral students there).
It has taken a few years to get fully up and running – hardly surprising. I’ve heard explanations that a lab takes at least three years to move from creation to research identification to data collection to analysis to publication. This post summarizes some of our current research and other activities in the lab.
We, as a lab, have had a busy few years in terms of events. We’ve hosted numerous conferences and workshops and engaged in (too) many research talks and conference presentations. We’ve also grown significantly – from an early staff base of four people to expected twenty three within a few months. Most of these are doctoral or post doctoral students and we have a terrific core of administrative and support staff.
Finding our Identity
In trying to find our identity and focus our efforts, we’ve engaged in numerous activities including book clubs, writing retreats, innovation planning meetings, long slack/email exchanges, and a few testy conversations. We’ve brought in well over 20 established academics and passionate advocates as speakers to help us shape our mission/vision/goals. Members of our team have attended conferences globally, on topics as far ranging as economics, psychology, neuroscience, data science, mindfulness, and education. We’ve engaged with state, national, and international agencies, corporations, as well as the leadership of grant funding agencies and major foundations. Overall, an incredible period of learning as well as deepening existing relationships and building new ones. I love the intersections of knowledge domains. It’s where all the fun stuff happens.
As with many things in life, the most important things aren’t taught. In the past, I’ve owned businesses that have had an employee base of 100+ personnel. There are some lessons that I learned as a business owner that translate well into running a research lab, but with numerous caveats. Running a lab is an entrepreneurial activity. It’s the equivalent of creating a startup. The intent is to identify a key opportunity and then, driven by personal values and passion, meaningfully enact that opportunity through publications, grants, research projects, and collaborative networks. Success, rather than being measured in profits and VC funds, is measured by impact with the proxies being research funds and artifacts (papers, presentations, conferences, workshops). I find it odd when I hear about the need for universities to be more entrepreneurial as the lab culture is essentially a startup environment.
Early stages of establishing a lab are chaotic. Who are we? What do we care about? How do we intersect with the university? With external partners? What are our values? What is the future that we are trying to create through research? Who can we partner with? It took us a long time to identify our key research areas and our over-arching research mandate. We settled on these four areas: new knowledge processes, success for all learners, the future of employment, and new knowledge institutions. While technologies are often touted as equalizers that change the existing power structure by giving everyone a voice, the reality is different. In our society today, a degree is needed to get a job. In the USA, degrees are prohibitively expensive to many learners and the result is a type of poverty lock-in that essentially guarantees growing inequality. While it’s painful to think about, I expect a future of greater racial violence, public protests, and radicalized politicians and religious leaders and institutions. Essentially the economic makeup of our society is one where higher education now prevents, rather than enables, improving one’s lot in life.
What does it mean to be human in a digital age?
Last year, we settled on a defining question: What does it mean to be human in a digital age? So much of the discussion in society today is founded in a fetish to talk about change. The narrative in media is one of “look what’s changing”. Rarely is the surface level assessment explored to begin looking at “what are we becoming?”. It’s clear that there is much that is changing today: technology, religious upheaval, radicalization, social/ethnic/gender tensions, climate, and emerging super powers. It is an exciting and a terrifying time. The greatest generation created the most selfish generation. Public debt, failing social and health systems, and an eroding social fabric suggest humanity is entering a conflicted era of both turmoil and promise.
We can better heal than any other generation. We can also better kill, now from the comfort of a console. Globally, less people live in poverty than ever before. But income inequality is also approaching historical levels. This inequality will explode as automated technologies provide the wealthiest with a means to use capital without needing to pay for human labour. Technology is becoming a destroyer, not enabler, of jobs. The consequences to society will be enormous, reflective of the “spine of the implicit social contract” being snapped due to economic upheaval. The effects of uncertainty, anxiety, and fear are now being felt politically as reasonably sane electorates turn to solutionism founded in desire rather than reality (Middle East, Austria, Trump in the US to highlight only a few).
In this milieu of social, technology, and economic transitions, I’m interested in understanding our humanity and what we are becoming. It is more than technology alone. While I often rant about this through the perspective of educational technology, the challenge has a scope that requires thinking integratively and across boundaries. It’s impossible to explore intractable problems meaningfully through many of the traditional research approaches where the emphasis is on reducing to variables and trying to identify interactions. Instead, a complex and connected view of both the problem space and the research space is required. Trying to explore phenomena through single variable relationships is not going to be effective in planning
Complex and connected explorations are often seen to be too grandiose. As a result, it takes time for individuals to see the value of integrative, connected, and complex answers to problems that also possess those attributes. Too many researchers are accustomed to working only within their lab or institutions. Coupled with the sound-bite narrative in media, sustained and nuanced exploration of complex social challenges seems almost unattainable. At LINK we’ve been actively trying to distribute research much like content and teaching has become distributed. For example, we have doctoral and post-doctoral students at Stanford, Columbia, and U of Edinburgh. Like teaching, learning, and living, knowledge is also networked and the walls of research need the same thinning that is happening to many classrooms. Learning to think in networks is critical and it takes time, especially for established academics and administrators. What I am most proud of with LINK is the progress we have made in modelling and enacting complex approaches to apprehending complex problems.
In the process of this work, we’ve had many successes, detailed below, but we’ve also encountered failures. I’m comfortable with that. Any attempt to innovate will produce failure. At LINK, we tried creating a grant writing network with faculty identified by deans. That bombed. We’ve put in hundreds of hours writing grants. Many of which were not funded. We were involved in a Texas state liberal arts consortium. That didn’t work so well. We’ve cancelled workshops because they didn’t find the resonance we were expecting. And hosted conferences that didn’t work out so well financially. Each failure though, produced valuable insight in sharpening our focus as a lab. While the first few years were primarily marked by exploration and expansion, we are now narrowing and focusing on those things that are most important to our central emphasis on understanding being human in a digital age.
Grants and Projects
It’s been hectic. And productive. And fun. It has required a growing team of exceptionally talented people – we’ll update bios and images on our site in the near future, but for now I want to emphasize the contributions of many members of LINK. It’s certainly not a solo task. Here’s what we’ve been doing:
1. Digital Learning Research Network. This $1.6m grant (Gates Foundation) best reflects my thinking on knowing at intersections and addressing complex problems through complex and nuanced solutions. Our goal here is to create research teams with R1 and state systems and to identify the most urgent research needs in helping under-represented students succeed.
2. Inspark Education. This $5.2m grant (Gates Foundation) involves multiple partners. LINK is researching the support system and adaptive feedback models required to help students become successful in studying science. The platform and model is the inspiration of the good people at Smart Sparrow (also the PIs) and the BEST Network (medical education) in Australia and the Habworlds project at ASU.
3. Intel Education. This grant ($120k annually) funds several post doctoral students and evaluates effectiveness of adaptive learning as well as the research evidence that supports algorithms that drive adaptive learning.
4. Language in conflict. This project is being conducted with several universities in Israel and looks at how legacy conflict is reflected in current discourse. The goal is to create a model for discourse that enables boundary crossing. Currently, the pilot involves dialogue in highly contentious settings (Israeli and Palestinian students) and builds dialogue models in order to reduce legacy dialogue on impacting current understanding. Sadly, I believe this work will have growing relevance in the US as race discourse continues to polarize rather than build shared spaces of understanding and respect.
5. Educational Discourse Research. This NSF grant ($254k) is conducted together with University of Michigan. The project is concerned with evaluating the current state of discourse research and to determine where this research is trending and what is needed to support this community.
6. Big Data: Collaborative Research. This NSF grant ($1.6m), together with CMU, evaluates the impact of how different architectures of knowledge spaces impacts how individuals interact with one another and build knowledge. We are looking at spaces like wikipedia, moocs, and stack overflow. Space drives knowledge production, even (or especially) when that space is digital.
7. aWEAR Project. This project will evaluate the use of wearables and technologies that collect physiological data as learners learn and live life. We’ll provide more information on this soon, in particular a conference that we are organizing at Stanford on this in November.
8. Predictive models for anticipating K-12 challenges. We are working with several school systems in Texas to share data and model challenges related to school violence, drop out, failure, and related emotional and social challenges. This project is still early stages, but holds promise in moving the mindset from one of addressing problems after they have occurred to one of creating positive, developmental, and supportive skillsets with learners and teachers.
9. A large initiative at University of Texas Arlington is the formation of a new department called University Analytics (UA). This department is lead by Prof Pete Smith and is a sister organization to LINK. UA will be the central data and learning analytics department at UTA. SIS, LMS, graduate attributes, employment, etc. will be analyzed by UA. The integration between UA and LINK is one of improving the practice-research-back to practice pipeline. Collaborations with SAS, Civitas, and other vendors are ongoing and will provide important research opportunities for LINK.
10. Personal Learning/Knowledge Graphs and Learner profiles. PLeG is about understanding learners and giving them control over their profiles and their learning history. We’ve made progress on this over the past year, but are still not at a point to release a “prototype” of PLeG for others to test/engage with.
11. Additional projects:
- InterLab – a distributed research lab, we’ll announce more about this in a few weeks.
- CIRTL – teaching in STEM disciplines
- Coh-Metrix – improving usability of the language analysis tool
I know I’ve missed several projects, but at least the above list provides an overview of what we’ve been doing. Our focus going forward is very much on the social and affective attributes of being human in our technological age.
Human history is marked by periods of explosive growth in knowledge. Alexandria, the Academy, the printing press, the scientific method, industrial revolution, knowledge classification systems, and so on. The rumoured robotics era seems to be at our doorstep. We are the last generation that will be smarter than our technology. Work will be very different in the future. The prospect of mass unemployment due to automation is real. Technology is changing faster than we can evolve individually and faster than we can re-organize socially. Our future lies not in our intelligence but in our being.
Sometimes when I let myself get a bit optimistic, I’m encouraged by the prospect of what can become of humanity when our lives aren’t defined by work. Perhaps this generation of technology will have the interesting effect of making us more human. Perhaps the next explosion of innovation will be a return to art, culture, music. Perhaps a more compassionate, kinder, and peaceful human being will emerge. At minimum, what it means to be human in a digital age has not been set in stone. The stunning scope of change before us provides a rare window to remake what it means to be human. The only approach that I can envision that will help us to understand our humanness in a technological age is one that recognizes nuance, complexity, and connectedness and that attempts to match solution to problem based on the intractability of the phenomena before us.
Gardner Campbell looms large in educational technology. People who have met him in person know what I mean. He is brilliant. Compassionate. Passionate. And a rare visionary. He gives more than he takes in interactions with people. And he is years ahead of where technology deployment current exists in classrooms and universities.
He is also a quiet innovator. Typically, his ideas are adopted by other brash, attention seeking, or self-serving individuals. Go behind the bravado and you’ll clearly see the Godfather: Gardner Campbell.
Gardner was an originator of what eventually became the DIY/edupunk movement. Unfortunately, his influence is rarely acknowledged.
He is also the vision behind personal domains for learners. I recall a presentation that Gardner did about 6 or 7 years ago where he talked about the idea of a cpanel for each student. Again, his vision has been appropriated by others with greater self-promotion instincts. Behind the scenes, however, you’ll see him as the intellectual originator.
Several years ago, when Gardner took on a new role at VCU, he was rightly applauded in a press release:
Gardner’s exceptional background in innovative teaching and learning strategies will ensure that the critical work of University College in preparing VCU students to succeed in their academic endeavors will continue and advance…Gardner has also been an acknowledged leader in the theory and practice of online teaching and education innovation in the digital age
And small wonder that VCU holds him in such high regard. Have a look at this talk:
Recently I heard some unsettling news about position changes at VCU relating to Gardner’s work. In true higher education fashion, very little information is forthcoming. If anyone has updates to share, anonymous comments are accepted on this post.
There are not many true innovators in our field. There are many who adopt ideas of others and popularize them. But there are only a few genuinely original people doing important and critically consequential work: Ben Werdmuller, Audrey Watters, Stephen Downes, and Mike Caulfield. Gardner is part of this small group of true innovators. It is upsetting that the people who do the most important work – rather than those with the loudest and greatest self-promotional voice – are often not acknowledged. Does a system like VCU lack awareness of the depth and scope of change in the higher education sector? Is their appetite for change and innovation mainly a surface level media narrative?
Leadership in universities has a responsibility to research and explore innovation. If we don’t do it, we lose the narrative to consulting and VC firms. If we don’t treat the university as an object of research, an increasingly unknown phenomena that requires structured exploration, we essentially give up our ability to contribute to and control our fate. Instead of the best and brightest shaping our identity, the best marketers and most colourful personalities will shape it. We need to ensure that the true originators are recognized and promoted so that when narrow and short-sighted leaders make decisions, we can at least point them to those who are capable of lighting a path.
Thanks for your work and for being who you are Gardner.
The unsurprising fact that Facebook selectively suppresses and promotes different things has been getting a lot of press lately. I am not totally convinced yet that this particular claim of political bias itself is 100% credible: selectively chosen evidence that fits a clearly partisan narrative from aggrieved ex-employees should at least be viewed with caution, especially given the fact that it flies in the face of what we know about Facebook. Facebook is a deliberate maker of filter bubbles, echo chambers and narcissism amplifiers and it thrives on giving people what it thinks they want. It has little or no interest in the public good, however that may be perceived, unless that drives growth. It just wants to increase the number and persistence of eyes on its pages, period. Engagement is everything. Zuckerberg's one question that drives the whole business is "Does it make us grow?" So, it makes little sense that it should selectively ostracize a fair segment of its used/users.
This claim reminds me of those that attack the BBC for both its right wing and its left wing bias. There are probably those that critique it for being too centrist too. Actually, in the news today, NewsThump, noting exactly that point, sums it up well. The parallels are interesting. The BBC is a deliberately created institution, backed by a government, with an aggressively neutral mission, so it is imperative that it does not show bias. Facebook has also become a de facto institution, likely with higher penetration than the BBC. In terms of direct users it is twenty times the size of the entire UK population, albeit that BBC programs likely reach a similar number of people. But it has very little in the way of ethical checks and balances beyond legislation and popular opinion, is autocratically run, and is beholden to no one but its shareholders. Any good that it does (and, to be fair, it has been used for some good) is entirely down to the whims of its founder or incidental affordances. For the most part, what is good for Facebook is not good for its used/users. This is a very dangerous way to run an institution.
Whether or not this particular bias is accurately portrayed, it does remain highly problematic that what has become a significant source of news, opinion and value setting for about a sixth of the world's population is clearly susceptible to systematic bias, even if its political stance remains, at least in intent and for purely commercial reasons, somewhat neutral. For a site in such a position of power, though, almost every decision becomes a political decision. For instance, though I approve of its intent to ban gun sales on the site, it is hard not to see this as a politically relevant act, albeit one that is likely more driven by commercial/legal concerns than morality (it is quite happy to point you to a commercial gun seller instead). It is the same kind of thing as its reluctant concessions to support basic privacy control, or its banning of drug sales: though ignoring such issues might drive more engagement from some people, it would draw too much flak and ostracize too many people to make economic sense. It would thwart growth.
The fact that Facebook algorithmically removes 95% or more of potentially interesting content, and then uses humans to edit what else it shows, makes it far more of a publisher than a social networking system. People are farmed to provide stories, rather than paid to produce them, and everyone gets a different set of stories chosen to suit their perceived interests, but the effect is much the same. As it continues with its unrelenting and morally dubious efforts to suck in more people and keep them for more of the time, with ever more-refined and more 'personalized' (not personal) content, its editorial role will become ever greater. People will continue to use it because it is extremely good at doing what it is supposed to do: getting and keeping people engaged. The filtering is designed to get and keep more eyes on the page and the vast bulk of effort in the company is focused wholly and exclusively on better ways of doing that. If Facebook is the digital equivalent of a drug pusher (and, in many ways, it is) what it does to massage its feed is much the same as refining drugs to increase their effects and their addictive qualities. And, like actual drug pushing that follows the same principles, the human consequences matter far less than Facebook's profits. This is bad.
There's a simple solution: don't use Facebook. If you must be a Facebook user, for whatever reason, don't let it use you. Go in quickly and get out (log out, clear your cookies) right away, ideally using a different browser and even a different machine than the one you would normally use. Use it to tell people you care about where to find you, then leave. There are hundreds of millions of far better alternatives - small-scale vertical social media like the Landing, special purpose social networks like LinkedIn (which has its own issues but a less destructive agenda) or GitHub, less evil competitors like Google+, junctions and intermediaries like Pinterest or Twitter, or hundreds of millions of blogs or similar sites that retain loose connections and bottom-up organization. If people really matter to you, contact them directly, or connect through an intermediary that doesn't have a vested interest in farming you.
Address of the bookmark: http://gizmodo.com/former-facebook-workers-we-routinely-suppressed-conser-1775461006
Yesterday as I was traveling (with free wifi from the good folks at Norwegian Air, I might add), I caught this tweet from Jim Groom:
— Jim Groom (@jimgroom) May 11, 2016
The comment was in response to my previous post where I detailed my interest in understanding how learning analytics were progressing in Chinese education. My first internal response was going to be something snarky and generally defensive. We all build in different ways and toward different visions. It was upsetting to have an area of research interest be ridiculed. Cause I’m a baby like that. But I am more interested in learning than in defending myself and my interests. And I’m always willing to listen to the critique and insight that smart people have to offer. This comment stayed with me as I finalized my talk in Trondheim.
What is our obligation as educators and as researchers to explore research interests and knowledge spaces? What is our obligation to pursue questions about unsavoury topics that we disagree with or even find unethical?
Years ago, I had a long chat with Gardner Campbell, one of the smartest people in the edtech space, about the role of data and analytics. We both felt that analytics has a significant downside, one that can strip human agency and mechanize the learning experience. Where we differed was in my willingness to engage with the dark side. I’ve had similar conversations with Stephen Downes about change in education.
My view is that change happens on multiple strands. Some change from the outside. Some change from the inside. Some try to redirect movement of a system, others try to create a new system altogether. My accommodating, Canadian, middle child sentiment drives my belief that I can contribute by being involved in and helping to direct change by being a researcher. As such, I feel learning analytics can play a role in education and that regardless of what the naysayers say, analytics will continue to grow in influence. I can contribute by not ignoring the data-centric aspects in education and engage them instead and then attempting to influence analytics use and adoption so that it reflects the values that are important for learners and society.
Then, during the conference today, I heard numerous mentions of people like Ken Robinson and the narrative of creativity. Other speaking-circuit voices like Sugata Mitra were frequently raised as well. This lead to reflection about how change happens and why many of the best ideas don’t gain traction and don’t make a systemic level impact. We know the names: Vygostky, Freire, Illich, Papert, and so on. We know the ideas. We know the vision of networks, of openness, of equity, and of a restructured system of learning that begins with learning and the learner rather than content and testing.
But why doesn’t the positive change happen?
The reason, I believe, is due to the lack of systems/network-level and integrative thinking that reflects the passion of advocates AND the reality of how systems and networks function. It’s not enough to stand and yell “creativity!” or “why don’t we have five hours of dance each week like we have five ours of math”. Ideas that change things require an integrative awareness of systems, of multiple players, and of the motivations of different agents. It is also required that we are involved in the power-shaping networks that influence how education systems are structured, even when we don’t like all of the players in the network.
I’m worried that those who have the greatest passion for an equitable world and a just society are not involved in the conversations that are shaping the future of learning. I continue to hear about the great unbundling of education. My fear is the re-bundling where new power brokers enter the education system with a mandate of profit, not quality of life.
We must be integrative thinkers, integrative doers. I’m interested in working and thinking with people who share my values, even when we have different visions of how to realize those values.
Slides from my talk today are below:
This is a nicely crafted, deeply humanist, gentle and thought-provoking sermon, given by Terry Anderson to members of his Unitarian church on atheistic thinking and values.
I have a lot of sympathy with the Unitarians. A church that does not expect belief in any gods or higher powers; that welcomes members with almost any theistic, deistic, agnostic or atheistic persuasions; that mostly eschews hierarchies and power structures; that focuses on the value of community; that is open to exploring the mysteries of being, wherever they may be found; that is doing good things for and with others, and that is promoting tolerance and understanding of all people and all ideas is OK with me. It's kind of a club for the soul (as in 'soul music', not as in 'immaterial soul'). As Terry observes, though, it does have some oddness at its heart. It's a bit like Christianity, without the Christ and without the mumbo jumbo, but it still retains some relics of its predominantly Christian ancestry. Terry focuses (amongst other things) on the word 'faith' as being a particularly problematic term in at least one of its meanings.
For all their manifest failings and evils they are used to justify or permit, religious teachings can often provide a range of useful perspectives on the universe, as long as we don't take them any more seriously than fairy tales or poetry: which is to say, very seriously at some levels, not at all seriously in what they tell us of how to act, what to believe, or what they claim to have happened. And, while the whole 'god' idea is, at the very best, metaphorical, I think the metaphor has potential value. Whether or not you believe in, disbelieve in or dismiss deities as nonsense (to be clear, depending on the variant, I veer between disbelief and outright dismissal), it is extremely important to retain a notion of the sacred - a sense of wonder, humbleness, awe, majesty etc - and a strong reflective awareness of the deeply connected, meaning-filled lives of ourselves and others, and of our place in the universe. For similar reasons I am happy to use an equally fuzzy word like 'soul' for something lacking existential import, but meaningful as a placeholder for something that the word 'mind' fails to address. It can be helpful in reflection, discussion and meditation, as well as poetry. There are beautiful souls, tortured souls, and more: few other words will do. I also think that there is great importance in rituals and shared, examined values, in things that give us common grounding to explore the mysteries and wonders of what is involved in being a human being, living with other human beings, on a fragile and beautiful planet, itself a speck in a staggeringly vast cosmos. This sermon, then, offers useful insights into a way of quasi-religious thinking that does not rely on a nonsensical belief system but that still retains much of the value of religions. I'm not tempted to join the Unitarians (like Groucho, I am suspicious of any club that would accept me as a member), but I respect their beliefs (and lack of beliefs), and respect even more their acknowledgement of their own uncertainties and their willingness to explore them.
Address of the bookmark: http://virtualcanuck.ca/2016/04/27/whats-so-new-about-the-new-atheists/
The Learning Analytics and Knowledge conference (LAK16) is happening this week in Edinburgh. I unfortunately, due to existing travel and other commitments, am not in attendance.
I have great hope for the learning analytics field as one that will provide significant research for learning and help us move past naive quantitative and qualitative assessments of research and knowledge. I see LA as a bricolage of skills, techniques, and academic/practitioner domains. It is a multi-faceted approach of learning exploration and one where anyone with a stake in the future of learning can find an amenable conversation and place to research.
Since I am missing LAK16, and feeling nostalgic, I want to share my reflections of how LAK and the Society for Learning Analytics Research (SoLAR) became the influential agencies that they now are in learning research. Any movement has multiple voices and narratives so my account here is narrow at best. I am candid in some of my comments below, detailing a few failed relationships and initiatives. If anyone reading this feels I have not been fair, please comment. Alternatively, if you have views to share that broaden my attempt to capture this particular history, please add them below.
How we got started
On March 14, 2010, I sent the following email to a few folks in my network (Alec Couros, Stephen Downes, Dave Cormier, Grainne Conole, David Wiley, Phil Long, Clarence Fisher, Tony Hirst, and Martin Weller. A few didn’t respond and those that joined didn’t stay involved, with the exception of Phil):
As more learning activities occur online, learners produce growing amounts of data. All that data cries out to be parsed, analyzed, interrogated, tortured, and visualized. The data being generated could provide valuable insight into teaching and learning practices. Over the last few years, I’ve been promoting data visualization as an important trend in understanding learners, the learning process, and as an indicator of possible interventions.
Would you be interested in participating in a discussion on educational analytics (process, methods, technologies)? I imagine we could start this online with a few elluminate meetings, but I think a f2f gathering later this year (Edmonton is lovely, you know) would be useful. (Clarence, Alec, and I tackled this topic about three years ago, but we didn’t manage to push it much beyond a concept and a blog ).
At the same time, I sent an email to colleagues in TEKRI (Rory McGreal, Kinshuk, and Dragan Gasevic) asking if this could be supported by Athabasca University. Dragan promptly replied stating that “I can say that most of the things we are doing with semantic technologies are pretty much related to analytics and I would be quite interest in such an event”. Then he told me that my plan for a conference in fall 2010 were completely unrealistic asking “[who] would be a potential participant? How we can get any audience in December?”.
Dragan and Shane Dawson, who I connected with through a comment on this blog, are two critical connections and eventually friends. Except Shane. He is mean and has relationship issues. SoLAR would not exist without their involvement. Another important connect was Ryan Baker. Ryan started the International Educational Datamining Society a few years earlier. The fact that Ryan was willing to assist in the formation of a possibly competing organization speaks volumes about his desire to have rich scientific discourse. We ended up publishing an article in LAK12 about collaboration and engagement between our fields.
Organization was slow plodding for the first LAK conference. We built out our steering committee (defined by anyone who agreed to join) to include Erik Duval, Simon Buckingham Shum, and Caroline Haythornthwaite). We set up a Google group at the end of March on Education Analytics. The bulk of the planning for the first conference happened in that Google Group. By the end of June, I had seen the light of Dragan’s wisdom and agreed to move the conference to 2011. The LAK11 conference was held in Banff, Alberta in March. Important to note that we paid $500 for that logo. It should have come with a hit of acid.
The financials of any first event are critical. There is always risk. I’ve had events fail that cost a fair bit of money – a social media conference that I ran in Edmonton was a pleasant financial failure. For LAK11, we received financial support from Athabasca University, CEIT (University of Queensland), Kaplan, D2L, and the Gates Foundation. We generated a profit of ~$10k and that was forwarded to the organizers of LAK12 (Shane Dawson) to help seed the next conference. We didn’t have a formal organization to share in the expenses so each organizer for the first several years had to bear the financial risk. Paying past success forward made things easier for the next event. Leading up to LAK14, we were legally organized as SoLAR and took on the financial risk for local organizers.
Finding a publisher
In order to improve the scholarly profile of the conference, we pursued formal affiliation with a publisher. For many academics in Europe and Latin America, this was important in order to receive funding for travel. Dragan made numerous attempts to get Springer’s LNCS volume affiliation for the conference. The LNAI affiliation ended up being the avenue that we were suggested to pursue. Dragan put in the application on September 11, 2010. Springer stonewalled us at great length. We finally received confirmation that they would publish on July 17, 2011. Needless to say, as a professional organization, we did not want to work with a partner where that type of delay was considered acceptable. We were fortunate to connect with ACM and our first proceedings were published with them. Simon Buckingham Shum and Dragan were critical in securing this relationship, and in many ways for the academic rigour now found in LAK. I have been appropriately criticized by top researchers like Ryan Baker that the conference proceedings aren’t open. It was a decision that we made to broaden, oddly enough, access to travel funds to researchers from other countries.
My momma don’t like you
Not everyone was a fan of the idea of learning analytics. As this discussion thread on Martin Weller’s blog post reveals, there were voices of doubt around the idea of learning analytics:
Wish you luck in pursuing this Next Greatest Thing. Maybe next year’s can include the words “Mobile” “Emergent” and “Open” to broaden its hipness even further…really, really, really have been trying very hard not to make any comments since I first saw this announced early in 2010. I mean REALLY hard, because that comment above doesn’t even start to capture the amount of bullshit this smells like to me. But I am sure it will be a smashing success, a new field will have been invented, and my suspicions that there is no ‘there there’ even more unfounded. History will surely side with you George, of that I have little doubt.
Some of these doubts have become reality due to a techno-centric view of analytics, as is often captured by Audrey Watters. Interestingly, one of my first interviews on LA was with Audrey when she was writing for O’Reilly. The field has sometimes moved distressingly close to solutionism and Audrey has rightly turned toward criticism. We need more criticism of the field – both from researchers and practitioners and I find people like Audrey who are bluntly honest are essential to progressing as a research domain.
Leading up to LAK11, I organized a LA MOOC (haha, MOOCs were so cool back then). This served as an opportunity to get people onto the same page regarding LA and to broaden possible attendance to the conference. LAK11 was fairly small with about 100+ people in attendance.
About two days before LAK11, I sent out an email stating:
We are expecting a week of nice weather – beautiful for strolling around Banff and enjoying the amazing scenery. Weather in the Canadian Rockies can be a bit temperamental, so it’s advised to pack clothing for the possibility of some chilly days.
Well, I lied. We were expecting -2C. We got -35C. Freaking cold for those of you that haven’t experienced it before. Also, it generated exceptionally high attendance rates as few people wanted to be outside.
The conference agenda (here) reveals the significant contributions of early attendees. While my first email to colleagues included my blogging network (Stephen, Alec, Dave, Martin) the LAK conference itself resulted in me engaging with a largely new social network disconnected from much of what I had been doing with connectivism and MOOCs, though there were points of overlap. In many ways, I see both MOOCs and LA as an extension of my thinking on connectivism as my more recent focus on the social, affective, and whole person aspects of learning.
Expanding and Growing
Following LAK, we spent some time organizing and getting our act together about what we had created. Over time it became clear that we needed an umbrella organization – one that was research centric – to guide and develop the field. On Oct 2, I sent the following email to our education analytics Google Group. I include the bulk of it as it reflects our transition to SoLAR – the Society for Learning Analytics Research.
With interest continuing to grow in learning analytics – at institutional, government, and now entrepreneurial levels – some type of organization of our shared activities might be helpful.
Based on the sentiment expressed at the post-LAK11 meeting on developing a group or governing body for learning analytics, a few of us have been working on forming such an organization. In the process, I’ve had the opportunity to meet and chat with several SC members (Erik Duval, Dragan Gasevic, Simon Buckingham-Shum) on different organizational structures that might serve as a model. We’ve done enough organizing work, we think, to open the discussion to a broader audience…namely the LAK SC (that’s you).
We’ve decided on Society for Learning Analytics Research (SoLAR) as a name for our organization. The term was coined by Simon Buckingham-Shum (program co-chair, LAK12). Obviously, we would like to invite existing LAK conference steering committee members to be a part of it. Are you interested in transferring your SC role to SoLAR? If so, please provide an image of your lovely head as well as a preferred link to your site/blog/work and a few sentences about how awesome you are.
We have also reserved the domain name: solaresearch.org for our society.
We envision SoLAR as an umbrella group that runs the LAK conference, engages in collaborative research, work with research students, scholar exchange, applies for grants, provides access for researchers to broader skill sets than they might have on their team, produces publications, etc. SoLAR is expected to be an international society/network where learning analytics researchers can connect, collaborate, and amplify their work. It is possible that SoLAR may occasionally provide feedback on policy details as states and provinces adopt LA. Maybe that’s a bit too blue sky…
Over the next few months, various documents will be drafted, including a charter, mission, and decision making process for SoLAR. For example, how do we elect officials? How do we decide where the conference will be held next year? etc. We (currently: Shane, Simon, Dragan, Caroline, John (Campbell), and myself) recommend that an interim SoLAR leadership board – the group just listed – be tasked with developing those documents and sharing with the SoLAR steering committee for comment and approval. Once this interim leadership has completed its organizing work, we will then open the process to democratic elections based on SC and society membership. We haven’t yet determined the criteria for being a SoLAR member (fees? attend a conference? invite only?) or how long SC members serve. Currently we are a self-organized group. Everyone is here either by an invite or expressing interest. Laying a clear, democratic, foundation now will help to position SoLAR as a strong advocate for learning analytics in education.
LAK12 was a tremendous success. Shane was a spectacular host. It became clear to us that interest was high in LA as a research activity and practice space. We arranged a meeting following the conference where we brought in ~50 representatives from funding agencies, corporations, and government officials. The intent was to discuss how LA might evolve as a field, what was needed to broaden impact, and how grant and foundation funding might assist in improving the impact of work.
Following LAK12, SoLAR engaged in a series of initiatives to improve the sharing of research and increase support for faculty entering the field. We had spent time in late 2011 discussing a journal, but didn’t get much traction on this until 2012. In early April, Dragan and Simon had put together an overview of the journal theme and it was approved by SoLAR executive and announced at LAK12. Dragan, Simon, and Phil were the first editors. Simon stepped down shortly after it started and Shane stepped in. Shane and Dragan have been the main drivers of the Journal of Learning Analytics.
A mess of other activities were started during this time including workshops at HICCS (organized by Dan Suthers, Caroline Haythornthwaite, and Alyssa Wise), Storms – local workshops, Flares – regional conferences, events affiliated with other academic organizations such as learning sciences. Basically, we were putting out many shoots to connect with as many academics and practitioners as possible.
One activity that continues to be highly successful is the Learning Analytics Summer Institute (LASI). In August of 2012, I sent Roy Pea from Stanford an email asking if he’d be interested in joining SoLAR in organizing a summer institute. We felt the Stanford affiliation signalled a good opportunity for SoLAR. Roy agreed and we started organizing the first event.
Roy and I didn’t connect well. Roy felt I was too impatient. I was pushing too hard to get things organized. Academic timelines always give me a rash. We managed to secure significant funding from the Gates Foundation and the first LASI was a success, in no small part do to Roy’s organizing efforts. After LASI, we decided to move the institute to different locations annually – a perspective that I strongly pushed as I didn’t want LASI to be affiliated with only one school. Due to my head bumping with Roy and suggestions to host the next LASI elsewhere (Harvard it turned out), I was written out of the final learning analytics report that he produced for the Gates Foundation on LASI. Academics are complex people!
A list of LASI, Flare, and LAK events can be found here.
Getting the finances right
Follow LAK11, we started exploring university subscriptions to SoLAR. This was informed by Shane’s thinking on paying an annual fee to be involved in groups such as NMC or EDUCAUSE. We set up a series of “Founding Universities”, each committing about $10k to be founding members. This served to be a prudent decision as it gave us a base of funds to use for growing our membership and hosting outreach events. Our doctoral seminars, for example, are funded and supported by these subscriptions.
We had strong corporate support as well with organizations like D2L, Oracle, Intel, Instructure, McGraw-Hill, and others providing support for the conferences and summer institutes. Corporate support has proven to be valuable in running successful conferences and enabling student opportunities. We decided to stay away from sponsored keynotes so as to ensure academic integrity of our conferences. I continue to be disappointed that we have been largely unable to get support from pure LA companies such as Civitas and education research arms of companies such as SAS. The students that we graduate grow the field. LA companies benefit from field growth. Or at least that’s my logic.
The founding members and current institutional partners are listed here. Each one has been central to our success.
Grace Lynch joined SoLAR work in 2012. During LASI at Stanford, she pitched the idea of hiring someone to do administrative and organizing work with SoLAR. Up to that point, we were run by academics devoting their time. The work load was increasing. And those who know me also know my attention for detail is somewhat, um, varied. Hiring Grace was the best decision that I made in SoLAR. She was able to get us organized, financially and administratively. The success of SoLAR and LAK and LASI events is due to her effort. I frequently hear from others who first attend a SoLAR event about how impressed they are with the professionalism and organization. That’s Grace’s doing.
Engaging with with big ideas
During LAK11, we expressed our goals as an association:
Advances in knowledge modeling and representation, the semantic web, data mining, analytics, and open data form a foundation for new models of knowledge development and analysis. The technical complexity of this nascent field is paralleled by a transition within the full spectrum of learning (education, work place learning, informal learning) to social, networked learning. These technical, pedagogical, and social domains must be brought into dialogue with each other to ensure that interventions and organizational systems serve the needs of all stakeholders.
In order to serve multiple stakeholders, beyond LAK/LASI/Journal, we also held leadership summits and produced reports such as Improving the Quality and Productivity of the Higher Education Sector: Policy and Strategy for Systems-Level Deployment of Learning Analytics.
We have also been active in helping to shape the direction of the field by advocating for open learning analytics – a project that is still ongoing.
Losing Erik Duval
When one’s personal and professional worlds come together, as they often due in long term deep collaborative relationships, individual pain becomes community pain. Erik Duval, a keynote speaker at our first LAK conference, passed away earlier this year. He shared his courageous struggle on his blog. Reading the Twitter stream from LAK16, I am encouraged to see that SoLAR leadership has set up a scholarship in his honour. His contributions to LA as a discipline are tremendous. But as a friend and human being, his contributions to people and students are even more substantive. You are missed Erik. Thank you for modelling what it means to be an academic and a person of passion and integrity.
What I am most proud of
LAK is a unique conference and SoLAR is a special organization. I have never worked with such open, non-ego, “we’re in it because we care”, people in my life. I wish that future leadership also has the pleasure of experiencing this collegial and collaborative spirit. Our strengths as a community are in the diversity of our membership. This diversity is reflected in global representation and academic disciplines. As a society, we have better gender diversity than what is found in many technical fields. It is not where it should be yet. And the progress that we have made is due to the advocacy of Caroline Haythornthwaite and Stephanie Teasley. The current executive is a reflection of that diversity.
At LAK15, I stepped down as founding president of SoLAR. I felt like it was time to go – I’ve seen too many fields where a personality becomes too large for the health of the field. We’ve always emphasized that SoLAR should be a welcoming space where individuals from different disciplines and research interests can find a place to play, to work, to connect. In order for this to happen, fluid processes for getting opinionated people out and new ideas in is important!
My attention is now primarily focused on two areas: developing LA as a field in China and increasing the sophistication of data collection. Recent visits to China, Tsinghua University and Beijing Normal University as well as an Intel LA event in Hangzhou in fall, have made it clear to me that LA is robust, active, and sophisticated in China. In many of the projects and products that I’ve seen, they’re well ahead of where the current state of publishing in English suggests that we are. In conversations with colleagues at Tsinghua, we have agreed to make the development of a research network and academic community in China a key priority.
Secondly, at LINK Research Lab, we have turned our research attention to wearables and ambient computing. As I stated in my keynote at LAK12, increasing and improving the scope and quality of data collection is needed in order to improve the sophistication of our work as a field. Physiological and contextual data will assist in advancing the field, as will a greater focus on social and affective aspects of learning. Cognition is only one aspect of learning. As a consequence, focus on affective, social, meta-cognitive, and process and strategy is required. To get there, we need better, broader data.
Well, that’s my reflection how we got here with LA and SoLAR. What have I missed?
Damn it, I didn't bring my big camera. The camera in my phone does not do this justice...
There is something genuinely awesome - in the original sense of the word - about being out on the water in a boat that is smaller than the creature swimming next to you. The humpback whale swam around us for about 40 minutes before moving on. Somewhere between 10 and 20 seals hung around nearby hoping for some left-overs, as did a small flock of seagulls. We tried to keep our distance (unlike a couple of boats) but the whale was quite happy to swim around us.
As I am preparing for a talk next week on the future of online learning and writing a bit in a paper about the same kind of thing, I am pleased to see another timely publication in a long line of excellent Pew reports on American life, this time focusing on lifelong learning, which is hugely relevant to what I will be speaking and writing about about. As I need to think a bit more on this topic anyway, this seems like a good opportunity for reflection.
Findings of the report
Before moving on to my reflections, there are a few things that particularly stand out for me. For instance:
- 74% of Americans have engaged in some deliberate personal learning (as measured by the researchers) over the past year, though only 16% have taken an online course.
- 73% consider themselves to be lifelong learners.
This makes me worry greatly about over a quarter of Americans that have done no such thing and that do not consider themselves to be lifelong learners. It is hard to understand how one could be human and not consider oneself a learner but the study's design likely shaped the kind of answers it received. I will have more to say on that. It is also interesting that courses play such a small role. More on that later too.
I am fascinated by the motivations of the subjects of the study:
- 80% of personal learners say they pursued knowledge in an area of personal interest because they wanted to learn something that would help them make their life more interesting and full.
- 64% say they wanted to learn something that would allow them to help others more effectively.
- 60% say they had some extra time on their hands to pursue their interests.
- 36% say they wanted to turn a hobby into something that generates income.
- 33% say they wanted to learn things that would help them keep up with the schoolwork of their children, grandchildren or other kids in their lives.
This accords better with my understanding of human beings. People love to learn, and learning has huge social value in both process and product. It is notable that far fewer of the study's subjects have extrinsic than intrinsic motivation, and it appears that, for the vast majority, the extrinsic driver is at most a catalyst for them to do something that is intrinsically fulfilling. This is reinforced in the following graphs, that are a terrific confirmation of the predictions of self-determination theory (SDT):
As we already know from SDT, the value of learning is fundamentally about achieving competence as a good thing in itself, deeply social in purpose and value, and highly concerned with being in (or gaining) control: in brief, competence, relatedness and autonomy support. This is exactly what we see here. It is noteworthy that, though advancement in occupations matters to professional learners, there is no mention of money nor of qualifications in any of this. This accords with the fact that only 16% of those in the study took courses, given that courses tend to lead to formal or less formal credentials. It is very unfortunate that institutional learning has become so much concerned with courses and credentialing that all of these very good reasons for learning are incredibly crowded out. Much of the time, people in institutions learn in order to get the qualification, not for the pleasure that is so profoundly obvious in these findings. The luckiest ones get both. Most are not so lucky. More than a few get neither fulfillment nor credentials.
Matthew Effects: the rich get richer
The survey finds very strong links between existing education, prosperity and culture, and lifelong learning. Furthermore, the digital divide is, at least by some measures, widening:
As a rule, those adults with more education, household incomes and internet-connecting technologies are more likely to be participants in today’s educational ecosystem and to use information technology to navigate the world.
This is not too surprising - it's pretty much there in the definition - but the Matthew Effect is in full swing here:
For personal learning, 87% of those with college degrees or more (throughout this report adults with college degrees or more refers to anyone who has at least a bachelor’s degree) have done such an activity in the past year, compared with 60% for among those with high school degrees or less. For professional learning, about three quarters (72%) of employed adults with at least college degrees have engaged in some sort of job-related training in the past year, while half (49%) of employed adults with high school degrees or less have done this.
Those that have learned to learn, and to see the value in it, learn more. They probably have more time and resources for it:
Among those with a smartphone and a home broadband connection (just over half the population), 82% have done some personal learning activity in the past year. For the remaining adults (those with just one of these connection devices or neither of them), 64% have done personal learning in the past year.
It is interesting that technology appears to have quite a large effect on learning. This is causal, not just a correlation. It's not the tools, per se, but the adjacent possible that the tools bring. Basically, the tools can support learning or not but, if you don't have the tools, the opportunity never arises. Those that claim technology has no effect on learning are simply wrong, but what is significant here is that it is not the teachers, but the learners, that make this so. There may be some very faint and equivocal glimmer of truth in the belief that technology does not normally do much to improve teaching, but it sure does a lot to improve learning.
Being America, a land of conspicuous inequality, the report shows that there are also strong divisions along ethnic lines, with African Americans and Hispanics considerably less likely to have engaged in personal learning, and somewhat less likely to have engaged in professional learning. The report is less clear whether this is a socio-economic issue or a more broadly cultural concern. I'm guessing a bit of both. When a social system separates particular groups, for whatever reason (and ethnicity is a deeply stupid reason), then patterns of behaviour are likely to cluster. As always, diversity (and the celebration of diversity) is much to be wished for here. We are wisest when we are exposed to and open to diverse views, values and opinions.
Finally, an opportunity for distance institutions like Athabasca University. Some of the notable preference for face to face learning (81% to 54%) is almost certainly down to lack of awareness of digital learning methods:
Noteworthy majorities of Americans say they are “not too” or “not at all” aware of these things:
- Distance learning – 61% of adults have little or no awareness of this concept.
- The Khan Academy, which provides video lessons for students on key concepts in things such as math, science, the humanities and languages – 79% of adults do not have much awareness of it
- Massive open online courses (MOOCs) that are now being offered by universities and companies – 80% of adults do not have much awareness of these.
- Digital badges that can certify if someone has mastered an idea or a skill – 83% of adults do not have much awareness of these.
It seems we have not been particularly smart about getting the message out! That's a huge and untapped population of people who do not even know our methods of teaching exist, let alone of our own existence. At least some of those appear to be educated people with a thirst for knowledge.
Learning and the Kardashians
A lot of the inequalities demonstrated in the Pew report are deeply worrying and endemic. It seems to me that, as well as trying to address that imbalance directly, we in education should give a bit more thought to how we might embed productive learning more deeply into all our interactions, rather than just concentrating on making courses and tutorials in educational systems. While some of this embedding can be addressed with deliberate intent - popular channels, celebrity scientists and artists, accessible and appealing museums and galleries, subsidies for Internet access, libraries, etc - a lot of this is about system design. It's about building tools and environments where critical and reflective engagement is part of the fabric of the system.
With that in mind, I think it is important to note a strong methodological bias in these findings. Significantly, they rely on self-reporting of deliberate learning activities that are largely defined by the researchers. There's a strong bias towards things like courses, tutorials, guides, workshops, conferences and clubs that are explicitly designed to support learning. It is worth observing that most learning is not designed and not intentional (including in formal education). Almost every act of communication involves at least a hint of learning and, especially for interactive media such as Internet or Mobile technologies, the percentage of time spent learning in the process is normally significant. Almost all reading, watching and dialogue involves learning. We might not recognize it as such, but every time we learn of Bieber's latest exploits, or Trump's latest vileness, or our friend's new puppy, we are deeply engaged in acts of learning. It is not just (and rarely most importantly) about the content of what is learned, but the ways of being that such learning engenders. Our values, beliefs and attitudes are deeply dependent on our interactions with others, mediated or not, and what we perceive of the world around us (especially the people and their creations within it). What we choose to observe or communicate changes us. Often, we engage critically with what we read or watch or talk about. Even simple learning from observation is not just about copying but about interpreting and constructing. Internet technologies, in particular, have massively increased the quantity and breadth of such observation and communication. Most of what we know is not learned deliberately but emerges through our interactions with other people and the world around us. Most of what even traditional teachers teach is not the content of what they teach but the ways of being and thinking that go along with it.
To suggest or imply, therefore, that lack of deliberate learning through conventional channels means that no learning is happening is deeply mistaken, and somewhat dangerous, because it ignores all but the visible tip of the iceberg. By far the biggest opportunities for education lie not in the stuff that we educators currently do for a job, but in embedding learning in the everyday; in designing pedagogies that are not pedagogies; in creating architectures where learning can thrive rather than in deliberately leading people in directions we think they should go. It is possibly sad but definitely true that the Kardashians are far better teachers with far greater reach than most professional teachers, apart from (maybe) celebrities like David Attenborough, Randall Munroe, David Suzuki or Neil Degrasse Tyson. What the Kardashians teach might seem to have little value and, arguably, might have negative value, but it should not be discounted as irrelevant learning. Nor, for that matter, should what we learn (directly and indirectly) from politicians, musicians and sports stars. The shapers of our emerging global society are many and varied, and I would be hesitant to suggest, snobbishly, that the reflective, critical, synthetic, analytic and creative skills that professional teachers try to support should have a monopoly over the emotional, social, value-forming ways of thinking that other contributors to society provide in greater measure.
Boundaries and education
Personally, I think the things we try to formally teach (not so much the content as the reflective, critical, synthetic, analytic and creative skills) matter a great deal. Taught well, they directly and demonstrably lead to better, healthier, richer, more creative, more caring, more productive societies, where people can look more critically on the likes of Trump and the Kardashians, with greater perspicacity, with greater creativity, and with more kindness to and understanding of those that think differently. But they also lead to a lot of things that are not so healthy, especially in their institutionalized control-freakery and cataleptic attitudes to change. Educational institutions have done and continue to do a lot of good but, if we really want to bring about a better, more educated world, there is a very good chance that they are no longer the ideal platform for it, and definitely far from the only one.
In my talk next week I will be exploring the ways that physical boundaries, notably of time and place, have deeply influenced how we go about the process of education. Almost all of our pedagogies are predicated on the assumption that a number of people need to gather in a particular place at a particular time, with associated structures, rules and processes to support that. Teachers are a scarce resource, classrooms are rival goods, and schedules matter. So we invented classes, courses, timetables, and methods of managing them. This in turn inevitably demands that people learn things they don't need to learn, that they may be unable or unwilling to learn, at times that may not suit them, under conditions that greatly restrict their autonomy. All in all, despite good support for relatedness, this is terrible for motivation, and it crowds out almost all the great benefits that are reported on in the Pew study. One-to-one learning works much better because it largely avoids those constraints but is, for all but a few, economically unviable. Voluntary attendance to learning activities when needed (much of what is reported on in the study) is also good, but not well catered for in our educational systems that need to adopt tight schedules and lack much flexibility. Thus, much of our pedagogical practice and almost all of our educational system is designed to overcome or reduce the demotivating side-effects of simple physics. All too often, and all too often institutionalized, the solution is to fall back on primitive behaviourist models of motivation that do a great deal more harm than good. Such physics seldom if ever applies online, where boundaries are inherently fuzzy, metaphorical, fluid and malleable. However, most of us still adopt substantially the same pedagogies and we pointlessly (or worse) attempt to fit our teaching into systems that were designed for and with different boundaries. We even build tools like learning management systems that embody them, saving them from exinction and perhaps even magnifying them (it's often easier to see what is going on in a live classroom than within the confines of an LMS). And, having done so, we cement the demotivational effects by controlling learners through grades and certificates, rewarding and punishing with Skinnerian efficiency. It's no surprise that, when you take such things away, MOOC completion rates, though improving thanks mainly to better self-selection and increasing use of real reward and punishment through more recognized credentials (often becoming significantly less open in the process), average no more than 15%
Shifting boundaries and open spaces
Though online boundaries are different, there are lessons to be drawn from the built environment. I am incredibly lucky to live in Vancouver, where public art, information and hey-wow architecture and design is everywhere to be seen. It is hard to look anywhere without being informed, delighted or provoked in useful ways, from the shapes of leaves immortalized on the sidewalks to street art and poetry on the walls. Our cognition is fundamentally distributed, and the richness of the spaces around us, virtual or physical, contributes considerably to how and what we know, as well as our values and behaviours. Even simple separation of space can make a huge difference. It took a while after coming here to realize what was the main difference between schools here and in the UK: fences. In the UK, a school is normally enclosed by tall fences that both keep people out and keep children in. Around the school along the sea wall from me there are no such barriers, and children play at break-time in the parks and playgrounds outside. It's still very safe - many eyes see to that, as well as a culture of trust - but it makes all the difference in the world to the meaning of the space, especially to the children but also to the community around them. Such little things make big differences. Part of the value of that is, again, diversity: being exposed to different stimuli and people is always a good thing, and another of Vancouver's immense strengths. The area around the school is a wonderful mix of expensive luxury waterfront property and cheap but attractive and well cared-for community housing: unless you happen to know that red roofs signify community housing, you would be very unlikely to spot the difference. Messing with boundaries and celebrating diversity is, of course, a big part of the thinking behind the Landing. It's a space where boundaries are deliberately softened, where learning can be visible and shared, but which is still safe and where everyone is accountable. Simply opening up the space is enough to bring about greater and different learning, and a different attitude towards it.
Openness alone is not enough, though. Far too many public forums and comment areas (e.g. most newspaper sites) that are quite open are filled with vitriol, inanity and stupidity. Sure, a lot of learning happens, but mostly not in a productive or useful way, at least from my biased perspective and that of a lot of people that are turned off by it. I am guessing that this might well be what would happen if fences around UK schools were torn down without considering the surrounding community and environment. Community makes a huge difference: though I am sure they have to indulge in a bit of judicious pruning and moderation, when I read blogs by people like, say, Stephen Downes, George Siemens , Terry Anderson, or David Wiley, I see almost nothing but intelligent dialogue from those that comment, because those with an interest in the area have shared concerns and contested but concordant values. Well, perhaps the dialogue is not always intelligent, but at least it is always a learning dialogue. The downside of that is, of course, a relative lack of diversity in the communities that read their work.
So, environment matters too, and often helps to shape the community. For instance, I am still much smitten, after nearly two decades, by the model of SlashDot, which shapes learning dialogues through a combination of smart algorithms and, most importantly, the actions and interactions of people using the system. The best of these dialogues is more than a match for any textbook or classroom, and the worst are not too bad: anything else evolves away. The algorithms are complex and it takes skill to get the most out of them, so it is way too geeky to be of general use, but it shows the general methods and principles that might underlie a system that makes knowledge grow and learning happen simply by shaping the space of interaction, giving individuals the tools to filter and form the space, and providing a space to gather. Less sophisticated/effective but more generally usable tools of this nature include Reddit and StackExchange, which combine ratings and karma information to allow the community to shape what the community sees. While both are flawed and neither is infallible, the combination of human organization and machine filtering generally makes both quite useful for a wide range of topics. I am also much encouraged by how Wikipedia has evolved: its more deliberate structuring and guidance of the flow means it involves higher maintenance than more obviously collectively guided tools but it is incredibly successful at supporting and spreading useful knowledge (including about the Kardashians). The approach of each of these systems to diversity is a little like that of the Vancouver City planners: to design for it. There are places where communities meet and interact but there is also parcellation, with signals of their boundaries but no significant barriers, that supports the growth of a supportive culture (at least in places - there are, of course, some areas that thrive on dischord), and that makes trust visible.
There are potential opportunities for analytics tools, collaborative filters, and similar forms of data-driven algorithmic approaches here too. Such methods come with enormous risks, mostly due to the insatiable desire of programmers to control what other people do: to erect new boundaries. Even when done with good intentions, they can have harmful effects. Almost the last thing we need in such spaces is filter bubbles and echo chambers, but such approaches can embed and reinforce patterns and attitudes simply by doing their job, building boundaries that are all the more dangerous because they are invisible and unmentioned. The absolute last thing we need is machines to make decisions for us based on what a programmer has decided is best for us or, just as bad, using criteria over which we have no say. There are huge risks of designing new boundaries that are just as controlling and just as demotivating as the ones they replace. I don't resent Amazon's recommendations of what I might like to read next at all, for example, especially when it tells me why it is making those recommendations, because it does nothing to enforce those recommendations and learns when I disagree. I do resent Netflix limiting what it shows me that I might want to watch, though: this reduces my autonomy. I greatly dislike learning analytics tools that tell me how well I am meeting someone else's goals, but I approve of those that help me to define and reach my own. I am happy for Google Search to suggest relevant sites I might want to visit, as long as it continues to show me those it is less impressed with, but I am deeply unhappy that Facebook shows me a tiny percentage of posts I might like to see. I love that clicking a word or phrase in an e-book will give me a definition and a link to Wikipedia or Google Search. I hate that clicking a help link will tell me what someone else thinks I need to know (especially when the nugget I need is hidden in a lengthy video that gives me no clues about where to find it). What all of this boils down to is support for the fundamental drivers found in the Pew report: autonomy, relatedness and competence. Take away any one of those, and you take away the love of learning. But, with care, scrutability, and attention to supporting human needs, such systems can be expansive and liberating.
For now, most of the new systems we use to replace the formal process of teaching show promise but most have numerous weaknesses, most of which formal teaching overcomes: concerns about reliability, trust, safety, efficiency, and the effects of deliberate malice are well founded, and there are big issues of control and autonomy to overcome. But it seems to me that, as we start to dismantle the boundaries of traditional educational practice, the opportunities to extend and improve learning through reinvention of our learning spaces online are (virtually!) limitless, while we reached a state of near stasis in physically located learning many hundreds of years ago. Sure, there have been incremental improvements here and there but they have been uneven at best, and it is possible to see examples of great pedagogies being used thousands of years ago that are barely, if at all, improved today. It's all down to physics.Footnote
I wouldn't know a Kardashian if one kicked me in the face and, until just now, I had little idea about what they were apart from being a family that is known across the Internet for nothing more substantial than their own celebrity. For quite a long time I actually thought the headlines and post titles about them were about a fictitious race from Star Trek. What's quite interesting about that is that I had learned what little I knew on the subject without, until just now, any intention of doing so. I found out a bit more just now by way of fact checking, through Wikipedia, but it seems that what I already knew was pretty much accurate. Education happens whether we seek it or not. It would be good if that education were more valuable more of the time.
An article from The Atlantic describing a study that reveals autonomy is, almost entirely, the reason people like to have power. This accords very well with the predictions of self-determination theory.
Power (in the most meaningful sense of the word) is pretty much the same thing as autonomy, I think: it's about feeling that you are in control of your life, regardless of whether that feeling is justified. This suggests that some forms of what we generally recognize as power (ie. positions of authority, with control over what others do) might not be so great, inasmuch as the accompanying responsibilities can considerably reduce autonomy. Those in middle management, myself incuded, are in a great many ways less autonomous than those over whom they have purported power, in part because of their responsibility to those they lead, and in part due to their accountability to those with greater power. I'm guessing that the same is true right up to leaders of institutions, who are accountable to governments and other funding bodies in much the same way as those lower in the pecking order are accountable to them.
For optimal happiness, organizational hierarchies (not those that occur in natural systems but that are designed by humans) are an inherently weak idea, most notably because they must always be antagonistic to autonomy. They survive as a reasonably effective compromise made to make organizations and societies function like machines: indeed, they are one of our most fundamental enabling technologies. They are the main way that large groups of people can efficiently live in peace and prosperity together. Hierarchies are responsible for many good things, a foundational technology on which much of human society, culture and technology is based, without which we would likely still be in the trees. But it is important to remember that they are just technologies: they are inventions that can be improved upon and that could easily be superceded by better inventions. Democratic governance was likely the last major successful innovation in the technology, but it doesn't solve many of the inherent weaknesses. For the most part, the inevitable inefficiencies, filtering of information and, above all, diminution of intrinsic motivation make organizational hierarchies a deeply flawed solution to the problems of large scale human coordination that they are designed to solve.
With modern technologies, especially those involved in and emerging from ubiquitous communication and availability of knowledge, we can and should do better than hierarchies. I am increasingly intrigued by and drawn to the model of The Morning Star Company, that thrives without hierarchies, where everyone, from temporary tomato pickers to the CEO, is a manager, and where power is not given but taken as a natural right. What's remarkable about it is not so much the pattern (which is not unlike that of traditional academia and many other organizations and social forms) but the fact that the pattern works really well.
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Several years ago, a group of us wrote a concept paper on Open Learning Analytics (.pdf). Our goal was to create openness as a foundation for the use of data and analytics in education. We have, it appears, largely failed to have our vision take root.
Few things are more important in education today than the development of an open platform for analytics of learning data. It’s a data-centric world. Data, and the analysis of those data, are a rapidly emerging economic value layer. Most educators and students are unaware of how much algorithmic sorting happens in the educational process. Even before students apply to a university, the sorting has started (postal/ZIP codes can indicate chances of success). Recommender systems suggest next courses. Engagement with course content produces predictive models. Suggested help resources are generated for students identified to be at risk. And this all happens behind the scenes as the Wizard of Algorithms spins dials and outputs intimidating results (often with more smoke and noise than actual usefulness) that are starting to drive learning practices that cover the full range of a student’s engagement with higher education.
We are, as a field, facing an interesting time. The decisions that we make now will cast a long shadow into the future. And the best decision, in uncertain times, is the one that allows the greatest range of decisions in the future. It is here, in analytics and data use in education, that far more attention and awareness is needed than is currently evident. Algorithms will subsume most of our educational practices as they will embody certain pedagogies, support roles, and even faculty practices. Quite simply, the shape of tomorrow’s university is now actively being coded into analytics models. I’m generally fine with this as a concept, but quite nervous about this as an action. The future needs to be open. And yet, the exact opposite is happening.
The article in the Chronicle today on Big Data and Education is timely reminder of the importance of the work and the challenges of a closed learning analytics future. The work is rather urgent. And we as academics have been sleeping.
Whenever I visit a new country, region or city I visit McDonald's as soon as I can to have a Bic Mac and an orange juice. Actually, in Delhi that turns into a Big Raj (no beef on the menu) and in some places I substitute a wine or a beer for the orange juice, but the food is not really important. There are local differences but it's pretty much as horrible wherever you go.
I inflict this on myself because The McDonald's Experience should, on the whole, be a pretty consistent thing the world over: that's how it is designed. Except that it isn't the same. The differences, however, compared with the differences between one whole country or city and another, are relatively slight and that's precisely the point. The small differences make it much easier to spot them, and to focus on them, to understand their context and meaning. Differences in attitudes to cleaning, attitudes to serving, washroom etiquette, behaviour of customers, decor, menu, ambiance, care taken preparing or keeping the food etc are much easier to absorb and reflect upon than out on the street or in more culturally diverse cafes because they are more firmly anchored in what I already know. Tatty decor in McDonald's restaurants in otherwise shiny cities speak worlds about expectations and attitudes, open smiles or polite nods help to clarify social expectations and communication norms. Whether people clear their own tables, whether the dominant clientele are fat, or families, or writers, whether it's a proletarian crowd or full of intelligentsia or a place that youth hang out. Whether people smoke, whether they drink. How loud the music (if any) is playing. The layout of the seating. How people greet their friends, how customers are greeted, how staff interact. How parents treat their children. There's a wide range of different more or less subtle clues that tell me more about the culture in 20 minutes than days spent engaging more directly with the culture of a new place. Like the use of the Big Mac Index to compare economies, the research McDonald's puts into making sure it fits in also provides a useful barometer to compare cultures.
McDonald's thus serves as a tool to make it easier to learn. This is about distributed cognition. McDonald's channels my learning, organises an otherwise disorganised world for me. It provides me with learning that is within my zone of proximal development. It helps me to make connections and comparisons that would otherwise be far more complex. It provides an abstract, simplified model of a complex subject.
It's a learning technology.
Of course, if it were the only technology I used then there would be huge risks of drawing biased conclusions based on an outlier, or of misconstruing something as a cultural feature when it is simply the result of a policy that is misguidedly handed down from a different culture. However, it's a good start, a bit of scaffolding that lets me begin to make sense of confusion, that makes it easier to approach the maelstrom outside more easily, with a framework to understand it.
There are many lessons to be drawn from this when we turn our attention to intentionally designed learning technologies like schools, classrooms, playgrounds, university websites, learning management systems, or this site, the Landing. Viewed as a learning technology about foreign culture, McDonald's is extraordinarily fit for purpose. It naturally simplifies and abstracts salient features of a culture, letting me connect my own conceptions and beliefs with something new, allowing me to concentrate on the unfamiliar in the context of the familiar. Something similar happens when we move from one familiar learning setting to the next. When we create a course space in, say, Moodle or Blackboard, we are using the same building blocks (in Blackboard's case, quite literally) as others using the same system, but we are infusing it with our own differences, our own beliefs, our own expectations. Done right, these can channel learners to think and behave differently, providing cues, expectations, implied beliefs, implied norms, to ease them from one familiar way of thinking into another. It can encourage ways of thinking that are useful, metacognitive strategies that are embedded in the space. Unfortunately, like McDonald's, the cognitive embodiment of the designed space is seldom what learning designers think about. Their focus tends to be on content and activities or, for more enlightened designers, on bending the tools to fit a predetermined pedagogy. Like McDonald's, the end result can be rather different from the intended message. I don't think that McDonald's is trying to teach me the wealth of lessons that I gain from visiting their outlets and, likewise, I don't think most learning designers are trying to tell me:
- that learning discussions should be done in private places between consenting adults;
- that it is such a social norm to cheat that it's worth highlighting on the first page of the study guide;
- that teachers are not important enough to warrant an image or even an email link on the front page;
- that students are expected to have so little control that, instead of informative links to study guide sections, they are simply provided with a unit number to guide their progress;
- that the prescribed learning outcomes are more important than how they will be learned, the growth, and the change in understanding that will occur along the way.
And yet, too many times, that's what the environment is saying: in fact, it is often a result of the implied pedagogies of the technology itself that many such messages are sent and reinforced. The segregation of discussion into a separate space from content is among the worst offenders in this respect as that blocks one of the few escape routes for careful designers. Unless multi-way communication is embedded deeply into everything, as it is here on the Landing, then there is not even the saving grace of being able to see emergent cultural behaviours to soften and refine the hegemonies of a teacher-dominated system.
Like McDonald's, all of this makes it far more likely that you'll get a bland salty burger than haute cuisine or healthy food.
Terry Anderson and I have written a fair bit about the different social forms that apply in (at least) an educational context. We reckon that they fall fairly neatly into physically overlapping but conceptually distinct categories of groups, nets and sets. In the past, we used the term 'collectives' instead of 'sets' but we have come to realise that collectives are something else entirely.This post starts with an overview of the distinctions and then drifts into vaguer territory in an attempt to uncover what it might be like for something to have meaning for a social entity. That's a rather bizarre concept at first glance: is there any sense at all in which a collection of people, not the people within that collection but the collection itself, can feel or think anything and, if not, how can anything be said to have meaning to it? And yet, oddly, we do ascribe human attributes to collections of people all the time in our everyday speech - 'Apple is a creative company', 'Canada got another gold medal', 'We came top of the league', 'the crowd is angry', 'this is the most enthusiastic class I've ever taught', 'Google beat Oracle in the court case', 'Athabasca University is committed to open learning' and so on. While this is often just a shorthand notation for something else or a poetic metaphor, the ubiquity of such language makes it worth examining further.
Groups, nets, sets and collectives
Groups are the stuff of conventional teaching and learning: they are distinct and intentional entities that people join and know that they are members. You are in a group or out of it: you might be more or less engaged, but there is no real in-between state. Groups are generally characterised by things like purposes, collaboration, hierarchies, roles, exclusion. We know a lot about groups and their effects on learning, and the whole field of social constructivist models of teaching and learning is based on them.
Networks are more tenuous entities. To join a network you connect with one or more of its nodes. You might intentionally wish to make connections with particular people or kinds of people, but a network has no formal constitution, no innate roles and hierarchies, no innate exclusion: it's about individuals and their connections with one another. It is composed of nothing but connections and ties and has no formal boundaries. Networks are traversable and offer ways of linking and connecting to others and their knowledge. Learning in networks tends to be informal, connected and undirected by any individual. Networks are great for on-demand and serendipitous learning, combining social ties with unbounded knowledge.
Sets are about categories and topics. Set-based learning is about finding people and knowledge based on shared characteristics, typically a topic about one wishes to learn. Wikipedia, YouTube, and Google Search epitomise the nature and value of sets in learning, with ascending social interest sites like Pinterest or Quora beginning to enter the fray. However, libraries and bookshops are also primarily set-oriented, so this is nothing new. Unlike networks, there may be no direct connection with others and certainly no expectation of sustained interaction (though it may occur and develop into other social forms). Unlike groups, there is no formal constitution of a collection of individuals. It is just a bunch of people joined (in a set-theory sense) by a shared interest.
When social forms act together as a single entity, they become collectives - not a social form, as such, but the result of social forms and the interactions of individuals within them. A collective may be the result of direct or indirect interactions of individual autonomous agents, such as may be found in natural social forms like ant or termite nests, herds, flocks or shoals or, in human systems, in the operations of money markets, mobs, stock exchanges, group-think and forest path formation. The 'invisible hand' is a collective in action, the result of myriad local interactions rather than a deliberate global plan. The environment plays a strong role in this: things like the availability of resources, sight-lines, weather patterns, topology and more play a role in determining how such dynamics play out.
In computer-based systems, the combination that leads to a collective is not just a result of the emergent results of individual agents but may be effected and consequently notably affected by a machine: Amazon recommendations, Google Search, PayPal reputations and so on are all combining intelligent and independent actions of humans using algorithms in a machine in order to affect human action. The computer system extends what is possible through direct/indirect interaction alone, but it is still powered by individual intelligent beings making intelligent choices. It leads to a cyborg entity where collective emergence is part-human, part-machine. This makes such systems very powerful and flexible as a means to create collective intelligence that is directed to some end, rather than being simply an emergent feature of a complex system that happens to have value. Not only does the environment itself play a role in shaping behaviour, as in 'natural' systems, but it actually creates some of the rules of interaction. In effect, it bends and sometimes creates the rules of social physics.
Values in collections of people
In some sense, groups, sets, nets are all identifiable entities in the world that achieve some kind of action or purpose that is distinct from the individual actions or purposes of the people of which they are comprised. Clay Shirky talked of them as first class objects - things in themselves. But are these entities, these first class objects, anything like people? Are there values we can ascribe to them? Do they have intentions and purposes that are analogous to those of individuals? Do they have attitudes that are separable or different from the attitudes of those that comprise them? This is a problem that my student Eric Von Stackelberg has been exploring in his masters thesis and he has made some very interesting progress on this by using categories, that are used in psychology to describe individual values, as a means of describing group values ('group' used here in the generic sense of a collection of people of some identifiable sort). I've been challenging him to clarify what it would mean for that to be true. Can a bunch of people (not the individuals, the bunch itself) be kind, or hedonistic, or happy, or avaricious, or whatever in a manner that is meaningfully different from saying that the individuals themselves, or even a majority of them, have those attitudes? It seems that a corollary of that implies we might ascribe to them something akin to emotion. Could a bunch of people (the bunch, not the people in the bunch) feel happiness, amusement, tiredness, anger, pain, hate or love? I find this a difficult concept to get my head around. And yet...
It seems intuitively obvious that there is something organism-like in a social cluster. It is certainly normal to speak of organizational values, national values, group beliefs, group norms and so on. Athabasca University, for example treats itself as a unified entity in its mission statement that talks of values, purposes and intentions as though it were (almost) a human being. Corporations are treated in the law of some countries almost exactly like people (albeit odd ones, given that all would be diagnosed as having, on analysis, serious psychopathic disorders). Nations are very similar - we can talk of America invading Afghanistan without batting an eyelid, even though it is very clearly not something that is literally or physically the case in the way it would be were, say, a bully to pick on someone in a playground. A similar but far more worrisome phrase like 'the French have always despised the English' sounds like it plays on a similar notion but suggests something rather different. When we say that a country has invaded another we are talking about a group activity, something organized and intentional, whereas when we suggest that a whole population of people thinks in a certain way we are talking about a set: people with the shared attribute of nationality (the same applies to race, or gender, or physical attribute, etc - that way bigotry lies). There are interesting hybrids: it is normal to say 'we won' when a hockey team wins even though 'we' had negligible input or nothing to do with it at all. We identify at a set level (we, the supporters of the team) in a manner that encompasses the team (a distinct group). It is harder to find examples of networks being treated in quite the same way, though the flow of memes that is so easily facilitated through social networking sites may be an example of values of a sort being a feature of networks. However, the innately diffuse nature of a network means it is significantly less likely to have values of its own. It may be predicated on individuals' values (e.g a network of religious believers) but a network itself does not seem to have any, at least at first glance. Networks are primarily about individuals and their connections to other individuals, each seeing their part of the network from their own unique perspective. This is not promising territory to find anything apart from emergent patterns of value.
There are natural parallels though, that suggest an alternative view. It makes no sense to think of an ant colony as just a load of autonomous ants - the colony itself is undoubtedly a super-organism and an ant from such a colony is, on its own, not a meaningful entity: it is constituted only in its relation to others, as part of a single network. We can use telological language about the colony, and even ascribe to it wants, desires and intentions. It is also absolutely reasonable to think of an organism like a human being as a group/network/set of tightly coupled cells that are behaving, together, as a single unified entity that is not dissimilar to an ant colony in its complexity and interdependence. An individual cell may live on its own, but its meaning only becomes apparent in the presence of others. Even at a cellular level, our cells are a community of different symbiotic organisms. The vast majority of the cells in our bodies don't even have human DNA (that still staggers me - what are we?) but we still cannot think of ourselves as anything other than individuals that have values, intentions, meaning and - well - an autonomous life of their own. Are social forms so very different? It seems that at least one contained network that constitutes an entity may well have values because, well, we have values and we can be viewed as networks. In fact, we can also be thought of as sets and, in some senses, as groups.
While chatting about this kind of thing, a friend recently remarked that perhaps the most crucial value that we can ascribe to an individual is the value of survival: the will to survive. An arbitrary collection of entities does not have this. If we are thinking in terms of organisms, then I guess we might more properly think of it in evolutionary terms as a bunch of genes seeking to survive, but that's a layer of abstraction higher than needed here.
At the individual organism level it is the organism that tries to survive. This is one obvious reason that it is logical to think of an ant, termite or bee colony as a single organism: individuals will readily sacrifice themselves for the colony exactly as the cells in our own bodies continuously sacrifice themselves in order to protect and sustain the entity that we recognise as a person. We can easily see this survival imperative in intentionally created groups, from small departments to sewing circles, from gangs and teams to companies to countries (groups). If a group exists, it will typically try to preserve itself, and individual members may often be seen as expendable in meeting that need: thing of countries at war, political parties, hockey teams and so on. We can also see it in less rigidly defined entities such as cultures (sets/nets) and institutions (sets/groups). Even though individuals may have no formal connections with one another with, at most, tenuous networks and no unifying constitution, the simple fact of observable similarities and shared features leads to a self-reinforcing crowd effect that leads to survival. Often, intentional groups will be formed to support these but the interesting thing is that they are not groups defending their own 'lives' but a kind of collective antibody formed to protect the broader, sometimes barely tangible, set. People who form organizations to defend society against some challenge to what they see as being its central cultural, aesthetic, ethical or social values are doing just that. The set of which they feel a part is somehow greater than the group that they form to protect it.
It is harder to see this in human networks. Although there do appear to be emergent and dynamically stable features in many networks, that's just it: they are emergent features like a solonic wave in a river, the rhythmic dripping of a tap, or a whorl of clouds in a storm. It makes far less sense to talk of a cloud formation as trying to survive than it does of an ant colony. We do, however, see moods and trends spread through networks - if you know people who are getting fatter then you are far more likely to become fat yourself, for instance, and depression is contagious. It is reasonable to surmise that values spread in much the same way: indeed, if we look at extremes such as the spread of Naziism or the growth of fundamental religions, there is a very strong sense in which networks act as conduits for value. But I think that's it: they are conduits, not containers of value. Whatever has values may consist of networks that facilitate the spread or even the formation of those values, but it is the thing, not the network, that is what we care about here.
All of this leads me to suspect that the social forms that Terry and I identified as different in their pedagogical uses and affordances have some fundamental characteristics that go quite a way beyond that and relate to and intersect with one another in quite distinctively different ways. When we picture them as a Venn diagram it homogenizes these differences and makes it seem as though there are simply overlaps between vaguely similar entities, but there is more to it. Networks provide conduits for the spread of value between and within sets and groups. They are not the only conduits by any means: for example, if the human race were attacked by an alien civilization then I think it unlikely that a network would be needed to spread a range of values that would surface fairly ubiquitously (as a set characteristic), though it might help spread attitudes to how we should respond to such a threat. The same is true of many things in the more mundane realms of broadcast media, city planning and publication, not to mention the effects of natural features of the environment. Part of the reason for the distinctive culture and values in Canada, for example, is surely related to its dangerously cold climate that makes assistance to and from others a very strong necessity, plus a million other things like the opportunities afforded by its abundant natural resources and its proximity to other places. Prairie people are not quite the same as mountain people for reasons that go beyond historical happenstance and path dependencies. This is all about sets: shared characteristics and features. Sets can help to generate values: the fact that shorter-than-average people have to interact differently with the environment than taller-than-average people in many different ways leads to (at least) greater tendencies to share some values. The fact that people are collocated in a region, quite apart from network and group facets that emerge, means they are likely to share some attitudes and tendencies. It's simple evolutionary theory. It's why the finches in the Galapagos Islands have evolved differently: they have to interact with their different environments, and those environments have varied constraints and affordances. Other factors like path dependencies play an enormous role. Networks have a crucial part here too as co-evolution occurs not only in response to the environment but in response to the interconnections between agents in the system. In human systems, groups are both containers of networks and are themselves nodes in networks, so there are layers of scale that make this quite a complex thing.
The complexity becomes much more manageable if, instead of focusing on the social forms of aggregation, we think of values as being attached not to the aggregations themselves but to the collectives that emerge from them. Collectives are, by definition, behaviours that emerge from multiple interactions and are different from those interactions. A human can be viewed as a net, a set or even a group (there are hierarchies of organisation in which the brain might be seen as a controller) but it is the collective, the emergent entity that arises out of sets, nets and groups that is recognisably an individual, that has values. In the development of nationalist or religious values, it is the operation of algorithms that makes the set, net or group of which it is comprised into something distinct and potentially able to embody values, typically resulting from a mix of interactions combined with intentional categorisation by individuals - a collective.
I don't see any of this as suggesting even a glimmer of consciousness but it does seem at least possible that collectives can, at least sometimes, be described as having tropisms and to talk, perhaps loosely, in terms of intentionality. Whether this is enough to ascribe values to them is another matter, but it is not entirely absurd. We sometimes talk of plants as 'liking the sun' or 'liking the shade' in ways that probably have more to do with metaphor than beliefs about plant feelings, but there is a sense that it is true. It is even more obviously true in animals: even single-celled organisms are slightly more than just billiard balls bouncing round in reaction to their surroundings. They have purposes, aversions, likes and dislikes. Some exhibit fascinatingly complex behaviours - slime moulds, for example. It is not a great stretch from there to talking about human collectives in similar terms. Financial markets, for instance, are archetypal examples of human collectives that in principle need little or no machine mediation, yet move in complex ways that are not simply the sum of their parts. And, interestingly, we talk blithely of bull and bear markets as though they were in some way alive and, in some sense, imbued with feelings and even emotions. And maybe, in some sense, they are.
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