Today brings another bit of bad news for a distance education institution, with TELUQ's future looking uncertain, though it is good to see that its importance and contribution is also recognized, and it is a long way from dead yet. Though rumours of Athabasca University's own demise resulting mainly from our acting president's message that has widely been construed as a suicide note to the world are greatly exaggerated, and repudiated by the acting president himself, similar issues are reflected here and in the Open University, UK, that has lost a quarter of its students over the past five years. I have heard informal whispers from Europe that the OUNl is in similarly dire straits, though have no references to support that and it might just be hearsay - I'd welcome any news on that.
We are all institutions that were established within a very few years of one another (AU and OU-UK within months of each other) at a time that there were no viable higher education alternatives for students without formal qualifications, who were stuck in a location without a university, who were in full-time employment, or for whatever reason could not or would not attend a physical institution.
Moving on 40-50 years, times have changed dramatically but, fundamentally, we have not. Sure, we have mostly dropped the archaic technologies that we used when we were founded, but paper course packs and associated processes and pedagogies lurk deep within our organizational DNA even if the objects themselves are mostly a memory. Sure, we have, collectively, been leaders and prime movers in establishing the research, the pedagogies and the technologies of distance education that are now widespread in most physical universities, but it is notable that most of our innovative practices have been taken up more widely elsewhere than in our own institutions. And there are lots of alternatives elsewhere nowadays, from MOOCs to the massive growth of distance courses on face-to-face campuses, and much else besides.
Competition is only one of many reasons for the peril distance institutions are now in. It is odd, at first glance, that we have reached this point because we were first past the post for decades and, thanks to our relative independence of physical infrastructure and our research leadership, should have been more agile in adapting to what, from the early 90s, has clearly been a rapidly changing educational and technological landscape to which we should have been perfectly adapted. But there are some critical structural flaws in our design that have held us back. All of the open universities of this era originally adopted an industrial design model, based heavily on the work of people like Otto Peters and Charles Wedermeyer, who talked of independent learning but actually meant anything but when it came to teaching. This was essential in pre-Internet times, because communication was too slow and cumbersome to do anything else, both pedagogically and in business processes. But it had systemic consequences.
We have been and to a large extent remain driven by process in all that we do. We were designed primarily as machines for higher education, not as communities of scholars. Just as we structured our teaching, so we structured our organizations and, as transactional distance theory suggests, the result was less dialogue, especially in places like AU that had a distributed workforce. We have inherited a culture of process and structure, and consequent sluggish change. This has been improving in places thanks to things like the Landing at AU and similar initiatives elsewhere, but not fast enough and, certainly at AU and I gather also in our sister institutions, there have been steps backwards as well as forwards. At AU we have, of late, made some very poor ICT choices and retrograde organizational restructuring that actually increases, rather than reduces the amount of structure and process, and that reduces the potential for the spread of knowledge and dialogue. Meanwhile, thanks to our traditional course model, with its lack of feedback loops, we have till now mainly designed our teaching around quality assurance, not quality control: courses can take years to prepare and tend to be pretty well written but, for the majority, their success is measured by meaningless proxies that tell us little or nothing about their true impact and effectiveness. Though there are plenty of exceptions, too few courses use pedagogies, processes and other technologies that allow us to know our students and gain deep understanding of their concerns and interests.
Three things that could save open and distance universities from irrelevance
Given the imminent peril that open and distance universities appear to be finding themselves in, the solution is not to tweak what we have or to seek even more efficiencies in processes that are no longer relevant. Now is the time for a little bit of reinvention: not much. All of what is needed already exists in pockets. We have learned a lot - far more than our physical counterparts - about the challenges of distance learning and many of us have discovered ways of doing it that work. And, for all the path dependencies that claw at us, we do have innate organizational agility, so change is not impossible. More to the point, it is worth doing: distance education has innate advantages that physically co-present education (there must be a better term!) cannot hope to match.
At least part of the solution lies firstly in capitalizing on and enhancing the natural benefits that distance learning brings, notably in terms of freedom. Secondly, it lies in reducing as many of its disadvantages as we can.
Distance learning is all about freedom, but we have inherited two things from our physical forebears that unnecessarily constrain that: fixed-length courses, and accreditation umbilically linked to teaching. We need to rid ourselves of fixed-length courses, and disaggregate learning from assessment, so that learners can choose to work on things that really matter to them and gain accreditation for what they know rather than what we choose to teach. Right now, a course is like one of those cable TV packages that contains one or two channels you actually want and a whole load that you do not. The tightly bound assessments force students to bow to our needs, not theirs, which is awful for motivation and retention. This means that those with prior knowledge are bored, those who find it difficult are over-pressured, and the point of learning becomes not skill acquisition but credit acquisition. This in turn reinforces an unhealthy power relationship that only ever had any point in the first place because of the constraints of teaching in physical classrooms, and that is ultimately demotivating (extrinsically motivating) for all concerned.
This is ridiculous when we do not have such constraints - lack of need for teacher control (unless students want it, of course - but that's the point, students can choose) is one of the key ways that distance learning is inherently better than classroom learning. Classroom teachers need control. Indeed, it is almost impossible to do it effectively without it, notwithstanding a lot of tricks and techniques that can somewhat limit the damage for those that hate sticks and carrots. At the very least they need to get people in one place at one time, and organize behaviour once everyone is there. We do not.
We need better tailored learning, and to support many different ways of doing it. Smaller chunks would help a lot - the equivalent of unbundling channels on a cable TV package - but, really, courses should be no bigger or smaller than they need to be for the purpose. Only rarely is that 15 weeks/100 hours, or whatever standard size universities choose to use. We do it for reasons that are solely related to organizational convenience and that emerged only because of the need to schedule students, teachers, and classrooms in physical spaces. Some students may need no tuition at all - all adult learners come with some knowledge, and some bring a lot. Some may need more than we currently give. We need to recognize and accommodate all that diversity. One of the most effective ways to handle our accreditation role under such circumstances is to have separate assessment of learning, unrelated to the course in any direct way. Our challenge and PLAR processes at AU are almost ready for that already, so it's not an impossible shift. The other effective way to handle accreditation when we no longer control the inputs and outputs is to negotiate learning outcomes with the students through personalized learning contracts. There are plenty of models for such competency-based, andragogic ways of doing things: we would not be the first, by any means, and already run quite a few courses and processes that allow for it.
The second part of the solution lies in reducing or even removing the relative disadvantages of distance education. The largest of these by far is social isolation and its side-effects, notably on motivation. We need to build a richer, more connected community, to employ pedagogies that take advantage of the fact that we actually have about 40,000 students passing through every year at AU (OU-UK has many more, despite its losses), and to better support our teachers and researchers in engaging with one another and/or learning from one another. In too many of our courses and programs, students may never even be aware of others, let alone benefit from learning with them. This does not imply that we should always force our students to collaborate (or force them to do anything) and it certainly doesn't mean we should do truly stupid things like give marks for discussion contributions, but it does mean creating ubiquitous opportunities to engage, and making others (and their learning) more visible in the process. This matters as much to staff as it does to students. The Landing is a partial technological solution (or support for a solution) to that problem but it does not go nearly far enough and is not deeply embedded as it should be. Such opportunities to engage and to be aware of others should be everywhere in our virtual space, not on a separate site that only about a quarter of staff and students visit. And, of course, it only really makes sense if we adapt the ways we support learning to match, not just in our deliberate teaching but in our attitudes to sharing, engaging and connecting.
There are lots of other things that could be done - whole books can be and have been written about that - but these three simple changes would be sufficient, I think, to bring about profound positive change throughout the entire system:
- valorizing and enabling the social,
- variable length courses and lessons, and
- disaggregating assessment from learning
Physical universities would equally benefit from all of these but, apart from in their social affordances (that are certainly great, if sometimes under-utilized), have far less innate ability to support them. I think that means that distance universities still have a place at the vanguard of change.
It has long annoyed me that distance education is seen by many as a poor cousin to face-to-face learning. In some cases and in some ways, sure, physical co-presence gives an edge. But, in others, especially in terms of freedom - pedagogical and personal freedom, not just in terms of space, pace and place - distance education can be notably superior. To achieve its potential, it just needs to throw off the final shackles it inherited from its ancestor.
I’ve been involved in educational technology since the late 1990′s when I was at Red River College and involved in deploying the first laptop program in Canada. Since that time, I’ve been involved in many technology deployments in learning and in researching those deployments. Some have been systems-level – like a learning management system. Others have been more decentralized and unstructured – like blogs, wikis, and social media.
But there is something different in the ed tech space today than what I have experienced in the past. Most of my career has involved using technology to help people get better access to learning resources and materials, to better connect with each other, to better access formal education, and to improve their teaching practices and pedagogies. I’ve been fortunate to journey with talented folks: Grainne Conole, Stephen Downes, Dave Cormier, Martin Weller, Dragan Gasevic, Shane Dawson, Carolyn Rose, David Wiley, Ryan Baker, and many many others. At some level we all shared a goal that fairness, justice, and equity underpin the role of education in society and that by enabling access to learning and improving the the quality of learning, we were helping to improve the lives of learners and of society more broadly. Sometimes this meant helping people to develop digital skills to find new jobs or transition into new roles. Sometimes it meant connecting people eager to collaborate with others from around the world. Sometimes it was about righting a wrong or injustice. Regardless of whether the goal was finding a job or developing new mindsets, my focus was always on the learner, on the human.
Emerging technology today departs from my previous vision of improving the human condition. Through AI/Machine Learning, we are constantly hearing that technology is becoming more human and becoming more capable of judgements that we once thought were our domain. In education though, the opposite is happening: educational technology is not becoming more human; it is making the human a technology. Instead of improving teaching and learning, today’s technology re-writes teaching and learning to function according to a very narrow spectrum of single, de-contextualized skills.
Two articles this past week crystallized my thinking. First, Sebastian Thrun, in an Economist article, states: “BECAUSE of the increased efficiency of machines, it is getting harder and harder for a human to make a productive contribution to society”. If that is true, why is his startup trying to teach humans? Why not drop the human teaching thing altogether and just develop algorithms for making the stated productive contribution to society? He also details nanodegrees which are essentially what we in academia have to date called “certificates”. Perhaps we can call them nano-robo-certificates. Making up words is fun when media attention is petitioned. Most discouraging about this is that I’ve met Sebastian and he is a friendly, caring, deeply motivated person. The Thrun-of-media doesn’t align with the thoughtful Thrun-in-person.
The second article focused on Knewton. Jose Fereirra states “this robot tutor can essentially read your mind”. I’ve met Jose on numerous occasions. He’s bright, charismatic, and appears to genuinely care about improving learning. His rhetoric doesn’t align with the real challenges of education where cognitive capability alone is a small factor in learner success. Robot tutors will not make personalized learning easy. Learning is contextual, social, and involves whole person dynamics. In the past, I’ve stated that Knewton is the only edtech company with Google like potential. That is likely still the case, but I’m no longer convinced that this is a good thing.
Both Udacity and Knewton require the human, the learner, to become a technology, to become a component within their well-architected software system. Sit and click. Sit and click. So much of learning involves decision making, developing meta-cognitive skills, exploring, finding passion, taking peripheral paths. Automation treats the person as an object to which things are done. There is no reason to think, no reason to go through the valuable confusion process of learning, no need to be a human. Simply consume. Simply consume. Click and be knowledgeable.
My framework for technologies in the edtech space now, those that I find empowering for learners and reflective of a human and creative-oriented future, includes five elements:
- Does the technology foster creativity and personal expression?
- Does the technology develop the learner and contribute to her formation as a person?
- Is the technology fun and engaging?
- Does the technology have the human teacher and/or peer learners at the centre?
- Does the technology consider the whole learner?
I go through five year cycles. My early interest was in blogs and wikis in learning. Then my attention turned to connectivism and networked learning. Then to MOOCs. And then to learning analytics. These have all been terrific experiences and I’m proud to have been able to work with leading researchers and exceptional students. But it’s time for change. A curious disconnect has been emerging in my thinking, one that has been made clear with the hype-oriented buzzwords of today’s ed tech companies. I no longer want to be affiliated with the tool-fetish of edtech. It’s time to say adios to technosolutionism that recreates people as agents within a programmed infrastructure.
Over the last several years, my grants and research interests have turned to something…else. I’m not sure what the unifying thread is a this stage. Partly it’s a focus on the whole person. On empowered states of learning. On mindfulness, complexity, integrative learning, contemplative practices, formative learning, creativity, making. The dLRN grant focuses on connecting researchers with state systems to improve learning opportunities for under represented learners. (btw, you really should join us at our conference at Stanford in October). Our grant with Smart Sparrow focuses on multiple dimensions of learning success where the teacher remains central in the learning experience. Our project with Intel involves several post docs exploring how personalization can be improved in the learning process by developing a graph model of the learner that considers contextual, cognitive, social, and metacognitive factors. Two of our NSF grants are focused on language and discourse analysis and using big data to explore roles that learners adopt in variously configured knowledge spaces (Wikipedia, Stack Overflow, and MOOCs). Our MRI grant produced a report on digital learning – an evaluation of how technologies foster learning, rather than foster routine clicking. These are promising narratives to the de-humanizing edtech narratives. Others, such as Lumen Learning, Domain of One’s Own, and Candace Thille’s research on adaptive learning are similarly advancing humanizing technologies.
These transitions in research are part of a broader agenda that will help, at least in LINK lab, to create tools, technologies, and pedagogies that enable creation, personal formation, engagement, fun, and joy. I’m still fleshing out exactly what this will look like over the next several years. Obviously technology will be central in this process, but it will be one where mindful and appropriate learning practices are promoted. Where technology humanizes rather than reduces people to algorithmic and mechanical practices. Whatever this research agenda becomes, I’m more excited for the future of technology enabled learning than I have been in many years.
A few weeks ago, I received an invitation to the White House. The invitation was somewhat cryptic, but basically stated that the focus on the meeting was on quality and innovation. This invite was then followed a week later with a link to a post by Ted Mitchell, Undersecretary of Education, on Innovation and Quality in Higher Education, to help prepare for the conversation.
The event organizers made it clear that no media or social media was allowed during the event in order to have an open brainstorming session. My thoughts below are suitably vague so as to not identify who else was there and the specifics of the meeting. Instead, my comments are more about my personal reactions to the conversation without going into details about who said what specifically. (I was worried that the trip would have to be cancelled as I managed to get food poisoning a few days prior to the event, but fortunately, things worked out).
1. The White House is secure. As a “foreign national” it took me over two hours to clear three layers of security, was provided a special pink badge to identify me as a foreign national and was required to navigate only with an escort (including restroom visits and ultimately WH departure). I’m baffled how people manage to jump the White House fence. I felt watched over with lovingkindness.
2. Higher education generally has no clue about what’s brewing in the marketplace as a whole. The change pressures that exist now are not ones that the existing higher education model can ignore. The trends – competency-based learning, unbundling, startups & capital inflow, new pedagogical models, technology, etc – will change higher education dramatically.
3. No one knows what HE is becoming. Forget the think tanks and the consultants and the keynote speakers. No one knows how these trends will track or what the university will look like in the future. This unknowability stems from HE being a complex systems with many interacting elements. We can’t yet see how these will connect and inter-relate going forward. The best strategy in a time of uncertainty is not to seek or force the way forward, but to enter a cycle of experimentation. The Cynefin Framework provides the best guidance that I’ve seen on how to function in our current context.
4. I was struck by how antagonistic some for-profits are toward public higher education. I sat in one session where a startup spent much of the time expressing intense dislike for higher education in today’s form “my tax dollars are going to bad actors”, ironically to be followed up with “I loved my time in university. It shaped me and made me”. It reminds me of Peter Thiel’s drop out of school and start a company. But what does Thiel expect when his money and his life is at stake? He expects, for his hedge fund: “High GPA from top-tier university; preferably in computer science, mathematics, statistics, econometrics, physics, engineering or other highly quantitative”. I’m worried that the future will have an education system where the wealthy continue to receive high quality education on campuses, but the poor receive some second-tier alternative system that prepares them mainly to work but not to be change agents in the world. This gets at the heart of a challenge in higher education. HE is a system that is deeply embedded in societal realities, including equity and justice. It’s not an ROI equation. It’s a quality of life equation. A startup or corporate entity has a primary purpose of doing what makes sense economically. It’s their job. But it conflicts with the most dominant needs of our society today: how to educate individuals from low socio-economic status. The bottom income quartile of society has seen zero increase in degree completion over the past 50 years. Any meaningful redesign of higher education, for the benefit of individuals and society broadly, has to be primarily focused on helping to move this population toward success.
5. Title IV is the kingmaker. This is the alpha agent in change. Title IV drives federal student aid in the US. Systems that are included have access to students aid funding. Those that are not included (say a bootcamp startup) do not have access. As Title IV funding changes, so will US education. I heard several pushes for voucher systems (i.e. fund the student directly and they decide what to do with the dollars). This is the main space to watch in identifying which innovations will have legs and which ones will fail to get traction.
6. Expect a future of universities being more things to more people. A future of broadening scope regionally and of greater engagement in the lives of individuals. I addressed this toward the end of this presentation, starting slide 28. Higher Education is moving from a 4 year relationship to students to a 40 year relationship
7. Expect a future of far greater corporate involvement in HE. VC funds are flowing aggressively and these funders are also targeting policy change at local, state, and national levels. We aren’t used to this level of lobbying and faculty is unprepared to respond to this. Expect it. Your next faculty meeting will involve a new student success system, a personalized learning system, an analytics system, a new integrated bootcamp model, new competency software, new cloud-based computing systems, and so on. Expect it. It’s coming.
8. Expect M & A activities in higher education. I fully anticipate some combination of partnering with companies like General Assembly, creation of in-house bootcamps, or outright acquisitions by innovative universities.
9. The scope of change is starting to settle somewhat in HE. It’s a more comprehensible landscape than it was a few years ago. We’ve had our MOOC hype moment. The system of universities globally withstood the assault (remember when this was a legitimate conference topic??). Not only that, it was discovered that MOOCs are exceptional for those on campus. Similarly, some solidification of innovative teaching and learning practices is happening and it’s making it a bit easier for leaders to respond. As stated previously, this doesn’t mean that we know what HE will look like in the future, but it does provide a firmer foundation for planning for leaders. Any university that doesn’t yet have some department or committee focused on “responding systemically to innovations and change pressures” is missing an important opportunity.
10. Higher education is a great integrator and subsumer. I fully expect a future of more, not less universities globally. They play too significant a regional and local economic and identity role for regions to not expect a university in their backyard. Look how hard it has been to kill Sweet Briar. The clock is ticking on the nonsense of Drucker and Christensen’s statements about 50% campus closures. We are entering the golden age of learning. Why would we kill our universities?
11. I was stunned and disappointed at the lack of focus on data, analytics, and evidence. In spite of the data available, decision making is still happening on rhetoric. We don’t understand the higher education market analytically – i.e. scope, fund flows, student flows, policy directives, long term impact, – well nationally and internationally. I want to hold both universities and corporate sectors to accountability in their claims of impact. We can’t do that without a far better data infrastructure and greater analytics focus.
12. I’m getting exceptionally irritated with the narrative of higher education is broken and universities haven’t changed. This is one of the most inaccurate pieces of @#%$ floating around in the “disrupt and transform” learning crowd. Universities are exceptional at innovating and changing. Explore any campus today. It’s a new world on most campuses, never mind the online, competency, and related systems. And if your slide deck includes an image of desks and argues that nothing has changed, you’re being dishonest and disingenuous. Repent. Healing is possible for you, but first you must see the falseness of your words.
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.
Stephen Downes is a prolific writer. If you follow his work at OLDaily or on Half an Hour, you’re well aware of this. He covers an extremely broad territory: technology, learning, society, politics (sometimes a bit veiled, but generally not far below the surface), and philosophy.
Late last week, he posted an ebook on Connectivism and Connective Knowledge: Essays on meaning and learning networks (.pdf). It weighs in at an impressive 600+ pages. The work is basically a curation of his writings and presentations over the past decade. From the introduction:
Learning is the creation and removal of connections between the entities, or the adjustment of the strengths of those connections. A learning theory is, literally, a theory describing how these connections are created or adjusted. In this book I describe four major mechanisms: similarity, contiguity, feedback, and harmony. There may be other mechanisms, these and others may work together, and the precise mechanism for any given person may be irreducibly complex.
Stephen doesn’t make any apologies for the length of the ebook in stating that a formally structured book “would be sterile, however, and it [the ebook he has posted] feels more true to the actual enquiry to stay true to the original blog posts, essays and presentations that constitute this work”
I personally would like to see Stephen produce a succinct text. Until he does so, students (and others) have a valuable resource in tracking and citing his work in networks, MOOCs, meaning, groups & networks, semantics, and more. Simply being able to point to and cite a particular page will be helpful for students…Thanks Stephen!
The Change MOOC has been running since September of 2011. We’ve had the pleasure, in the past 30+ weeks, of many outstanding discussions. The archives of activity/readings/weeks are available on the main MOOc site.
Each week, different facilitators share readings and resources that they deem to be most reflective of their work and their passion.
My week is on sensmaking and analytics.
At first glance, sensemaking and analytics seem antagonistic. Sensemaking involves social processes…whereas analytics are algorithmically-driven. MOOCs are distributed systems of interaction and content. The traditional approach to courses – pre-packaged before learners arrive – is upended in a MOOC. The hyper-fragmentation of content and interaction presents problems for educators and learners: How do we make sense of what’s happening? How do we develop a coherent view of the many, many topics that comprise a MOOC? How do we re-create a centre that shares the bounding elements of a course, but is based on the networked centre-less structure of the internet?
Sensemaking is an activity that individuals engage in daily in response to uncertainty, complex topics, or in changing settings. Much like with the earlier discussion of the term “information”, sensemaking is a term in common use but with limited agreement on what it precisely means. Researchers argue that “[n]o single, unambiguous answer can be given…for sense-making theory has several meanings depending on the disciplinary or paradigmatic position of the speaker” (Kari 1998: 1).
In contrast to decision-making models in crisis situations, Weick, Sutcliffe, and Obstfeld (2005: 415) promote a narrative model of sensemaking. They argue that sensemaking is “not about truth and getting it right. Instead it is about continued redrafting of an emerging story so that it becomes comprehensible.” Weick’s sensemaking model emphasizes non-linearity, and pattern recognition. The importance of pattern recognition is consequential in that it integrates the expertise of individuals with narratives of coherence. Sensemaking is an effort “to create order and make retrospective sense of what occurs” (Weick 1993: 635).
Nowhere is the emphasis on dialogue more precise than in the work of Brenda Dervin (2003). The Dervin Sense-Making Methodology, dating back to the early 1970s, “is proposed as an alternative to approaches based on traditional transmission models of communication” (Dervin 2003: 6). Dervin (2003: 238) uses the metaphors of “situation” “outcomes”, and “gaps”, “moving across time and space, facing a gap, building a bridge across the gap, and then constructing and evaluating the uses of the bridge.”
Sensemaking and the process of learning are related, but each has distinct constructs (Schwandt 2005). Learning emphasizes time for consideration, reflection, and integration, whereas sensemaking is “swift and hasty as opposed to reflective” (Schwandt 2005: 189). In sensemaking, individuals understand a problem that “they face only after they have faced it and only after their actions have become inextricably wound into it” (Weick 1988: 306). In contrast, formal learning often occurs within a construct of increasing the capacity of an individual to act, instead of situation-specific sensemaking activities.
With the breadth of the topic of sensemaking, and its intuitive feel and common use, it is unsurprising that numerous definitions exist. A sampling of definitions include:
- “Sensemaking is finding a representation that organizes information to reduce the cost of an operation in an information task” (Russell et al. 1993: 272).
- “[S]ensemaking is a motivated, continuous effort to understand connections . . . in order to anticipate their trajectories and act effectively” (Klein et al. 2006: 71).
- “Sensemaking is about labeling and categorizing to stabilize the streaming of experience” (Weick et al. 2005: 411) and differs from decision making in its focus on “contextual rationality” (Weick 1993: 636).
- Sensemaking involves individual’s attempting to “negotiate strangeness” (Weick 1993: 645). Failures in these settings occurs when “[f]rameworks and meanings [destroy] rather than [construct] one another” (Weick 1993: 645).
Sensemaking, then, is essentially the creation of an architecture of concept relatedness, such as placing “items into frameworks” (Weick 1995:6) and continually seeking “to understand connections” (Klein et al. 2006: 71). Sensemaking occurs in many facets of personal and organizational life, including crisis situations, routine information seeking, research, and learning. Individuals engage in nebulous problem solving without a clear path daily: a parent raising a child, an employee starting a new job, a doctor without a clear diagnosis for a patient, a master’s research student, and so on.
My interest in analytics is driven by my views on learning as a connection-making process. Through analytics we are able to trace connections, understand how they are formed, the nature of exchanges between people, and the impact of those connections. The data-trails that are created in our daily interactions online and with others form the basis of analytics in learning. The field, however, is still developing and new approaches to analysis, algorithms, and tools are quickly emerging.
Readings for the week:
- Howard Rheingold Interview w/ (me)
- Learning analytics as a research and practitioner domain
Slideshare presentation:Eli 2012 Sensemaking Analytics
Dervin, B. (2003) ed. by Foreman-Wernet, L., & Lauterbach, E. Sense-making methodology reader: selected writings of Brenda Dervin. New York: Hampton Press
Kari, J. (1998) Making sense of sense-making: from metatheory to substantive theory in the context of paranormal information seeking. Paper presented at Nordis-Net workshop (Meta)theoretical stands in studying library and information institutions: individual, organizational and societal aspects, November 12-15 1998, Oslo, Norway
Klein, G., Moon, B., and Hoffman, R. R. (2006) ‘Making sense of sensemaking 1: Alternative perspectives.’ IEEE Intelligent Systems 21, (4) 70–73. doi:10.1109/MIS.2006.75
Russell, D. M., Stefik, M. J., Pirolli, P., and Card, S. K. (1993) ‘The cost structure of sensemaking.’ In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York: Association for Computer Machinery: 269−276. doi:10.1145/169059.169209
Schwandt, D. R. (2005) ‘When managers become philosophers: Integrating learning with sensemaking.’ Academy of Management Learning & Education [online] 4, (2) 176–192. Available from
Weick, K. E. (1988) ‘Enacted sensemaking in crisis situations.’ Journal of Management Studies [online] 25, (4) 305-317. Available from
Weick, K. E. (1993) ‘The collapse of sensemaking in organizations: The Mann Gulch disaster.’ Administrative Science Quarterly 38, (4) 628-652
Weick, K. E., Sutcliffe, K. M., and Obstfeld, D. (2005) ‘Organizing and the process of sensemaking.’ Organization Science 16, (4) 409-421
TEKRI researcher blogs etc
- The Virtual Canuck has moved – note to subscribers!!!!
- Teaching Practices Inventory – Fast and easy!
- Three ways to save distance universities
- Where are the Women?
- Adios Ed Tech. Hola something else.
- White House: Innovation in Higher Education
- McDonald's as a learning technology
- Collective values
- Downes on Connectivism and Connective Knowledge
- Change MOOC: Sensemaking and Analytics