The Sluice

There’s a thing I’ve found that a lot of people want in their lives but don’t have. Today I’m calling it the information sluice. Other times I’ve called it an epistemological entry vector and other, even sillier, names.

The idea is that in an age of change you need lots of data about your environment and your options, and these data have to be a kind of stream or flow rich in nutrients that is both constantly regenerating but also getting processed, evaluated, the good stuff noted, and pulled out, and built upon. Like an oyster filtering specks of food out of the ocean or a classic newspaper clipping service on a massive scale. Or the baleen of all the whales together, or some kind of moisture collector system perched on outcrops of rock in a romantic desert on the planet Dune, or, in my new way of looking at it, as if it were a sluice.

You can pan for gold painstakingly in the stream alone with your hole-y overalls and your one little pan that doubles as your complete set of table china, and you can might pick up a little gold dust. That’s the analog grammarian’s way of prospecting, maybe.

But you can also build a living channel to direct a big onrush of water to slowly wash the hillside away and you can create some filters in that sluice to net the fish, as it were. Put a weir in your sluice. And you can have some people watching and tending and regulating the flow and adjusting the filters, or the stakes in the weir, learning which size mesh to use, etc. That’s the Corpus Linguistics gold mining method. That’s gold prospecting at volume.

The bad part of this sluice metaphor is of course that in the real world this kind of mining destroys the earth. The good part of the metaphor, though, is that there’s a flow and it’s constant and refreshing and it generates a lot of dirt, but wondrous good stuff, if you tend it, and you’re attentive in your tending, comes out of that dirt. And you wouldn’t get that wondrous goodness by just sitting around camping or watching TV or panning in the old way, staying on the surface, that is. And of course this is not real earth we’re talking about but rather the hillside is of ideas, an inexhaustible mound, and the gold is not gold but the invaluable, discomfitting idea, the game changer, the second idea that adheres to a first and makes a connection, etc.

A workplace with a sluice has a group–or everyone–involved in the process of gathering and sorting and sharing info. This gathering could be conducting primary research, it could be reading other people’s research, it could be reading blogs, it could be site visits and talking to people, it could be taking notes at community meetings, it could be listening to feedback when you give a talk. It’s probably a smorgasbord that combines formal and informal kinds of knowing across disciplines, mixing the sublime and the ridiculous, and mixing now and then, because the good ideas are not going to be in the places you’d expect. You have to look where you don’t want to look. The ideas that change the way you think about things aren’t going to pop up comfortably pre-categorized within an existing system. They’ll misbelong, like jokers in the card deck, and they’ll have been discarded or ignored by people playing according to Hoyle.

A key part of all this is the conversation between the sluice-tenders. For one, no one person can filter as much as three or four or five, so more learn faster over all than their individual parts, if they share. For two, the other people serve as the necessary feedback on your own filtering: confirming whether your mesh is set correctly, etc. For three, it’s more fun when you learn with other people. This conversation and sharing requirement is important to talk about, because it’s hard. It’s relatively easy to have a one-person sluice. But it’s hard to build it up between several people, and it requires more investment in communication and willingness-to-be-affected-by-others than I think most people expect to make except in their personal relationships, if even there.

Which may explain why it it seems most people don’t experience work as a sluice-tending, weir-adjusting, gold-gathering process. Some people seem to want anything but a flow of new, possibly discomfiting data (although they probably wouldn’t mind if someone else managed the data and delivered them in safely wrapped packages like a lamb chop from the butcher’s). They are happy to simply camp by the creek (and maybe not even prospect at all). But many people do want the sluice, and often they feel alone in the wilderness, intuiting that there’s a limit to their pan-prospecting, but not knowing where to find the partners to aid in the construction of the torrent (and maybe even a little afraid of that torrent themselves).

But I suspect that sluices are on the way. I talk too much about what age it is. I’ve said it’s the Age of the Gums, the Age of the System. I’ll do it again and predict that this will be the Age of the Sluice. In a recent post I noted the trend in the business community to see people’s ideas as a thing to cultivate and grow and tend and respect, as a forester loves a forest of pine–that’s a pro-sluice mentality. At an IT Governance meeting on campus the other day I was delighted to hear a broad-based outcry for a kind of “marketplace of ideas,” through which everyone could know what everyone else was doing–that’s a pro-sluice idea, too (I’ll blog on this particular event later).

Before I leave you, three additional thoughts.

1. It’s Recursive. A weird thing about this sluice — when it really works, what comes out of it changes the people using it, and changes how it works itself. Or you might say, the person-sluice hybrid evolves. On a simple level you can see that happening when people adjust the filter mesh for better results. But this kind of double-loop learning has infinite possibilities for spiraling evolution into unknowable complexities. So we have to see the sluice as a thing to some degree turned back upon itself and always in the process of becoming something else. What would that something else be? A sluice that evolves into a sluice of sluices, a meta-sluice? A sluice that fills the mound of ideas back up, that discovers, evaluates and creates? A sluice that takes away its need to be there, like self-absorbing stitches? I am not sure. Let’s find out.

2. This is what all those smart people do. You know those Ted talkers and Steve Jobses, people who are always popping up with wisdom and new ideas and opening your mind to something–they have found a way to have a flow of ideas pouring through, they are looking for good ones, and when they find them they hold them and start to layer others on as they come in. Doing it makes you better at doing it. This is how they are able to keep generating their Ted talks.

3. Having ideas is an artistic skill. Alan Kay says learning to have great ideas is a mastery skill like any other, like playing an instrument, say, and if you put in 4 – 5K hours, you’ll get there (this from a NITLE talk I summarized in a recent post). As he said, “A good idea is really improbable, but you won’t have any if you filter too early.” The trick is learning to adjust the filter and increasing the probability by accelerating the flow. The fine arts reference is meaningful–artists know all about this sluice idea. What does a painter do, sit around waiting for an idea to pop up and only then get out her paints (the gold-panning method)? Or does she paint a lot and consistently and every day, and discover in her flow and volume the nuggets that become the elemental matter of her personal periodical table? Ask Stephen King or Anthony Trollope: it’s the second option.

4. In another way the sluice is a replacement of school. Your formal education is kind of like a sluice that someone else filters, pointed at you. You wake up every day and have ideas dumped on you; isn’t that the general experience? That’s bad in ways–as in it’s a kind of teacher-centric focus on content that the progressive pedagogy movement has decried for a long time–but in others it’s not bad. Having the intuition or habit of what a flow of ideas is, learning to feel a passionate need for that flow, sense that that flow is related to your personal growth, that’s all good. For many these feelings are lost when they shift to work, and they desperately want to replace them, and I think that’s a salutary impulse. The trick is, of course, to see also that you need to be the sluice-tender, not just the passive recipient, because the thing you’re changing is your way of knowing, not the cumulative amount of knowing you do.

Whither Higher Education? 16 Ideas.

Whither higher education in the global, digital, flat world of today and tomorrow? It’s the cocktail party conversation topic du jour. My pick of 16 thoughts on the subject:

1. We’ll Pay to Be Members: Education will be seen as something you pay for regularly, before and after you draw on it, like life insurance or a membership to a benevolent society or tithes to a church; although there won’t be an “after”–in the future we’ll never stop learning;
2. Disaggregated Learning Bits: The “feel” of participating in higher education will be disaggregated, with much more involvement of crowd-sourced-like components and entrepreneurial thinking (and perhaps funding), in which people in all walks of life will play equal parts (as in Jim Groom’s “proto-MOOC” which is both in and outside of a university);
3. Control to the Students: Students will have a greater role in shaping and selecting the components of their education; course catalogs will take on the dynamic feel of stock markets or some other wide-scale selection and value-confirming interface; students will be allowed to drop and add components as they feel they should; students will write components that other students use; students may even sometimes teach teachers; and that’s OK because of number 4, below;
4. More Sophisticated Learners: Students will be much more sophisticated about how learning works and more aware of their own learning (we’ll encourage this with “how to learn” structures of all kinds), so they’ll be much more thoughtful in the selection and creation of their educational components, more conscious of whether they’re learning or not, and much more demanding; they’ll move away quickly from things they don’t like; also they’ll be of every age and culture and life experience;
5. End of Bankers Hours: Hours of synchronous instruction, where it remains, will spread across the clock and will include times 16 – 32 year olds are mentally active (midnight to 4 am) as well as times the rest of us are; the work day for staff and faculty will be replaced by widely distributed work-chunks popping up throughout the calendar and clock;
6. Faculty and Staff Will Phone It In: Faculty and staff will increasingly work from home and spend minimal time on campus, and that’s good, because we’ll be able to draw on a greater variety of people, and have access to wider skills, and people will be able to live where they want (like among beautiful grasslands) and still work for schools elsewhere (like in the city); where I talk about the end of the four-year student residency below, I also mean the end of the life-long residency for many faculty and staff;
7. Work and Learning will be Similar: It will be less easy to distinguish education from work and vice-versa (and that’s good, in that we’re retraining the entire workforce to be effective in the digital, flat, global age, even as we’re training students to be similarly effective); and there’s a lot both work and formal learning can learn from each other; and people will be shifting in between each mode constantly;
8. On-sites are Brief and Intense: Residential experiences will only happen at key points–bookends, or for particular parts of a sequence, but won’t be constant throughout the learning cycle, which will let us move many more people through the campus, as through a hotel or a resort and give more access to a campus experience to more people; it’s the end of the four-year residency. But don’t worry: you can still get that community feeling from brief stints: remember summer camp?;
9. It’s About the Culture: More emphasis will be placed on creating and assessing the “culture” that supports and surrounds learning (this will complement our focus heretofore–on learning as a thing that happens in the head of the student); this means more investment in (and assessment of) faculty and staff learning and more attention to community-enriching things like faculty-student interaction studies or assessments of workplace dynamics; we’ll consciously try to craft a “learning organization” (or Argyris “Model 2″) culture in our schools and workplaces;
10. Roles Will Be Fluid: There will be less differentiation between what have been seen as fixed roles: most staff will have some greater hand in instruction; students will increasingly teach each other (through tutoring, etc); and faculty may even play student-like roles more happily; instruction will be seen as a collaborative partnership of multiple people;
11. Massive Retraining Will be the Norm: We’ll be constantly ready to retrain all staff and faculty at a moment’s notice in the various new processes and forms dictated by shifting market conditions and incessant innovation;
12. We’ll Cultivate Ideas: We’ll see our own internal creativity and ideas as perhaps the key component of long-term institutional success and we’ll build systems and cultures to support, generate, and encourage ideas, the testing of new models, entrepreneurial thinking, innovation laboratories, etc.;
13. We’ll Share with Other Schools: We always said we would, but now we really will–collaborate with other schools. In shared infrastructure (LMS, Information Systems, shared skill positions, shared risky innovation environments) and in shared academics (you offer French and we’ll offer Greek), but we’ll try to keep a wrapper of core institutional identity around the things we offer and do;
14. Feelings Will Guide Us: We’ll describe a certain kind of institutional “feeling” that should exist in the learning that happens under our auspices, and this will be the thing that we’ll use to vet new structures and courses, which are likely to be formally radical;
15. We’ll Analyze Stuff: We’ll make much more use of Learning Analytics and Corpus Linguistics sorts of real-time analyses and dashboards to better understand (in meaningful ways) how our students learn and to adjust our pedagogy in response (and we’ll share these analyses with the students themselves);
16. We’ll Archive Everything: We’ll invest significantly in the infrastructure that archives and retains (and makes analyzable) the intellectual record of the institution–and we’ll interpret this “record” broadly, to include conversations, written work, emails, course syllabi.

Alan Kay, Systems, and Textbooks

Alan Kay give a talk called “Is Computing a Liberal Art?” yesterday at the 2012 NITLE Summit. Here I discuss his key idea: that systemic thinking is a liberal art, and I explain a corollary idea, that textbooks suck.

Kay is attuned to how ideas evolve and are instantiated in the culture and the mind. For him a key piece in this process is the relationship between ideas and the categories we have for them; the relationship is this: if you don’t have a category for an idea, it’s very difficult to receive that idea.

Kay says we’re born with 300 or so preexisting categories that the species has evolved to know it needs to think about to survive, and we’re wired to be looking around for thoughts in those categories (food, shelter, pleasure, etc.). But the story of the last few hundred years is that we’ve quickly developed important ideas, which society needs to have to improve and perhaps even to continue to exist, and for which there are no pre-existing, genetically created categories. So there’s an idea-receiving capacity gap.

Education’s job should be, says Kay, to bridge this gap. To help, that is, people form these necessary new idea-receiving categories–teaching them the capacity for ideas–early on in their lives, so that as they grow they are ready to embrace the things we need them to know. Let me say that in a better way: so that as they grow they are ready to know in the ways we need them to know.

Said he, “If you have a new idea come in and education won’t teach people it from birth, you get a pop culture.” Pop culture! A harsh but fair critique of our society. More on that pop culture below.

For now, what are the ideas or categories, or what capacity for ideas should we now be teaching? Kay has one major thought in mind. He wants us to cultivate the ability to conceive of, work with, create, understand, manipulate, tinker with, disrupt, and, generally, appreciate the beauty of systems. This he hails as perhaps the most important of all the liberal arts.

It is the zeitgeist of the last 100 years that everything now appears as a system that was but a piece of a system before–or everything is now multi-dimensional that was linear before—thinking of the body as a system, the environment as a system, economics as systems, computers as systems. It’s why we talk about gamification so much–because a game, or a simulation, thought of as a thing we might create (rather than a thing we only act within), is a visceral example of systems thinking. (If this sounds familiar to readers of this blog, it’s because I’ve written about seeing systems before, in The Age of the Gums, or in Errol Morris and Spirals of Learning, or in Pieces of an Ecology of Workplace Learning, or even in The Conduit Metaphor, for instance. It might be all I write about.)

Seeing systems is an epistemology, a way of knowing, a mindset. As Kay said, “the important stuff I’m talking about is epistemological . . . about looking at systems.” It’s the Flatland story–that we need to train our 2D minds to see in a kind of 3D–and Kay’s genius is that he recognizes we have to bake this ability into the species, through education, as close to birth as possible.

One main point implied here is that we’re not talking about learning to see systems as an end point. Systems thinking is to be conceived of as a platform skill or an increased capacity on top of which we will be able to construct new sorts of ideas and ways of knowing, of more complex natures still. The step beyond seeing a single system is of course the ability to see interacting systems – a kind of meta-systemic thinking – and this is what I think Kay is really interested in, because it’s what he does. At one point he showed a slide of multiple systems–the human body, the environment, the internet, and he said in a kind of aside, “they’re all one system . . .” Compare that to the advanced stages in Bob Kegan’s constructive developmental psychology: “At Kegan’s sixth and final stage . . . there is a dawning awareness of an underlying unity that transcends human and environmental complexity.” (That from Philip Lewis’ work on Kegan, The Discerning Heart: Just happened to read that on the Metro on the way back to the hotel, as I was passing through Arlington National Cemetery).

Kay’s complaint is that higher education does not cultivate the particular epistemology of systemic thinking. We don’t teach integrative ways of knowing; we instead dwell within our disciplines, which dwelling you can see as being trapped within an arbitrarily chosen system. The point is to be able to see connections between the silos. Says Kay, the liberal arts have done a bad job at “adding in epistemology” among the “smokestacks” (i.e. disciplines).

Ok, so we’re not teaching systemic thinking. So what? What happens if you don’t teach people systemic thinking?

Then, Kay says, you’re allowing them to be stuck in whatever system they happen to be in, without thinking of it as a system. What happens when you’re stuck in a system? You don’t understand the world and yourself and others as existing in constant development, as being in process; you think you are a fixed essence or part within a system (instead of a system influencing systems) and you inadvertently trap yourself in a kind of tautological loop where you can only think about things you’re thinking about and do the things you do and you thus limit yourself to a kind of non-nutritive regurgitation of factoids, or the robotic meaningless actions of an automaton, or what Kay calls living in a pop culture. He sees this problem in higher education, where even faculty, experts in their own fields, are uneducated, in the sense that they can make no meta-connections among the fields, such that (as he said) hardly anyone exists who can understand the breadth of thought in a magnum cross-functional opus like the Principia Mathematica. And yet our future will be built on such integrative meta-connections as Newton’s.

By way of conclusion, I’ll now tell you why textbooks suck, according to Kay. A downside of being epistemologically limited to thinking within a system is that you overemphasize the importance of the content and facts as that system orders them. If you’re a teacher, you limit your students to processing bits according to a pre-ordained structure, to being a program, if you will, instead of learning to write a program. It would be better to use the system itself as the information students act upon when they construct their knowledge, and to find a way to get students to build new systems and even systems of systems. We teach students vocabulary within one set of grammatical rules, with the rules as the endpoint, say, but if we were disciples of Kay we would allow students to make grammars of grammars and languages of languages, with spirals of increasing complexity of thought looping into infinity and no endpoint in sight. That’s the order of consciousness Kay is after. Most textbooks, however, are on the stuck-within-the-system and vocab-and-grammar level. Which is why they draw Kay’s ire.

Pieces of an Ecology of Workplace Learning

Lately I’ve been saying that you should cultivate learning in your organization as you might manage an ecological resource, like a forest, or any other complex system of high priority (like your computer network or your budget). As if learning were a “cognitive enterprise infrastructure” or worked like a kind of water cycle. But how would you do that, and what would it be like, and how would it be different than what you do when you think of your workplace as a kind of machine that consistently produces material stuff? I am not totally sure, but here I take a guess at nine possible pieces of an ecology of workplace learning.

1. Cultivate Development, Rather Than Manage Performance. The point is not to manage people’s performances, but rather to get them to develop as much as they can, on the assumption that more highly evolved people do better things. The annual performance review that tracks behaviors against rather limited metrics and has a kind of binary output (wrong or OK) here evolves into something more like a coaching relationship in an experiential context: growth is the focus, not proscription. You look for activities that are motivating to the individual, that are a bit out of their comfort zone, yes, and you expect to support them in iterative cycles of trying things out, reflecting, adapting, and trying them out again. And you might add a variety of unheard-of supports and activities to help people think and reflect and be aware of themselves in a variety of dimensions, drawing on things from personality styles assessments to mentoring relationships to counseling-like activities, such as item 2, below. The trick is that these things, that we kinda do now in a knee-jerk way, away from work, would be more like the work.
2. Support Cognitive Development. According to the work of Bob Kegan and Lisa Lahey (whom I talk about a lot), we evolve through a series of increasingly sophisticated ways of seeing ourselves and the world around us. That is, we can so develop, if given the right environment. And with this increased epistemological sophistication comes a better ability to deal with and thrive in complex environments. One such complex environment is the increasingly global, flat, multi-cultural, resource-starved, post-ideological, environmentally-challenged, a-traditional, scary world of today. To help people be effective in this kind of world requires activities that help us know differently; Kegan and Lahey’s Immunity to Change coaching process is one such structure. Having done it myself, I am amazed by its ability to make you reinvent the way you think about yourself and the world in which you engage; I fairly salivate to try it with a team of colleagues in a workplace. The downside? It’s an intensive, six-month process of bi-weekly meetings, invoking much deep personal questioning; that’s a huge investment. But in an ecology of workplace learning, invest in people is what you do: no rain means no rivers means no seas means no evaporation means no rain, etc.
3. Assess Development in New Ways. In Higher Education we try to assess student learning, and it’s a challenge. But we don’t even try to assess faculty and staff learning; and the generic workplace doesn’t generally assess staff learning, either. But we should.  To promote development over production, we have see where this development is happening, individually and in teams. Of course it gets tricky: it’s easy to see your behaviors, but how do you see what’s going on behind the behaviors? Fortunately there are new kinds of tools that have potential in this regard: things like the Developmental Testing Service’s reflective judgment assessments, such as their test of managerial decision-making, which asks you to analyze complex, asymmetrical workplace problems, assesses you according to a complex scale of cognitive development rooted in Kurt Fischer’s work, and gives you (you yourself, the test taker!) rich feedback about your strengths and ways in which you can improve, data which feeds right back into the coaching relationship I mention above.
4. Represent the Learning Ecosystem. If you’re going to try to manage an ecosystem, you need some kind of a representation of it. As the water cycle has its famous circular chart with arrows and the budget has its classic representations in profit/loss statements and balance sheets, so does the learning system have something. I don’t know what it will look like, exactly; but I imagine it will be something like the famous Kellogg Logic Model, which the well-known foundation suggests you use to understand your various high-stakes interventions, and which helps you see programmatic inputs, outputs, assessments, changes. With a key difference: the effect of your ecology isn’t an output external to you, it’s an evolution of your ecology. So a learning logic model would show as its characteristic feature a looping back upon its constantly changing self.
5. Analyze How We Work; Analyze Our Culture. Part of learning is seeing yourself learn. That may be the single biggest difference between a learning organization and a producing organization: the learning organization sees itself and not just the things it makes. We will need to learn to pay attention not just to the products of our culture but to our culture, not just to the deliverable of the project, but to the way we work together on the project. For that a lot of tools exist already, like various kinds of post-activity group reflection encouraged in psychologically safe spaces, that let anyone share their experiences along the way. But new tools will help: the same sort of analytics thinking that has been transforming everything around us can help transform how we work together: social and network analyses to show us how we engage, corpus-linguistics analyses on the big data of our communications and cultural artifacts; these will help us, too, to see the patterns that make up our togetherness.
6. Assign Staff to Cultivate Learning. Of course you can’t really have a garden without a gardener. And all the network analysis and group reflection exercises you might want to use won’t be that helpful unless it’s somebody’s job to watch learning in the organization at a meta level: to gather relevant data, assess its meaning, and help the group understand where it’s going. The teacher, if you will, of the organization. This would be a new thing: we’re used to thinking of Chief Information Officers, Chief Information Security Officers, Chief Executive Officers: this would be a Chief Learning Officer. Although of course it needs to be more than one person. And of course everyone has to be involved. But still the CLO might help organize it all. How much of your people resources should you put into learning, CLO and everything else thrown in? I propose 20% as a start. But I suspect it should be more, maybe up to 50%. Maybe 63%.
7. Find New Ways to Gather and Share Ideas. Which Means Liking Them. One of the most important things in your organization are the ideas in people’s minds. The business world is just beginning to learn that to be relentlessly innovative, they have to gather and tend ideas in new ways, because ideas are the seed of innovation, be these ideas from their staff, their customers, their partners, their competitors. (See my last post for more on this). Part of this idea-tending requires a real cultural change–towards the acceptance and collective cultivation of ideas–and away from the general distrust of all things new that naturally grows up in an organization designed to perform consistently. Let me say that again: we will have to learn to like each other’s ideas.  And treat them, as it were, like a community resource, like, as it were, children. Because without them growing and maturing, we’ll fail. Businesses are starting to do this by building open, inclusive, idea-participation systems called Ideation Engines or Idea Stock Markets that aim to make the ideas in the group transparent and collectively developed. But I suspect you can go a long way without a particularly unique tool (a shared spreadsheet might work as well).
8. Create Loops and Groups. In my perhaps over-simplified way of thinking, learning comes down to loops (in that feedback and reflection are crucial) and groups (in that learning is social; and in that your co-learners are as important for your learning as your own mind). So I think much of the key work of the Chief Learning Officer and her team will boil down to finding or building, and supporting, new sorts of groups in which people are desirous of learning together, and in adding “loops” to existing processes, to work reflection into the fabric of the organization.
9. Do Some Old-School Ethnography. I am continually amazed by the complexity and mystery of people and of organizations. And by the fact that all you need to do to begin seeing and unravelling (or ravelling) the mystery is to observe people and ask questions (of course taking notes and writing down the answers). This is the way anthropologists settled on coming to know things as complex and mysterious as entire alien (to them) cultures. Libraries and IT departments have recently begun seeing that ethnography helps them understand the mysterious complexities of cultures alien to them, too (their customers). And it will work for you. On a certain level you can see an ethnographically-inclined research project as a kind of mirror to the people (if its results are shared with the people it studies), a loop at a high level, that both honors people and lets them see what’s going on. I think a lot about the emphasis in the popular Reggio Emilia model on the artful documentation of what the learners are doing; an ethnographic approach to your own organization is like that.

Errol Morris and Spirals of Learning

Errol Morris, the famous documentary filmmaker, says the purpose of a documentary is not to document things as they are, but rather to find and animate a compelling mystery. Not a mirror walking down the road, but a magnifying glass stopping on the road and probably even leaving the road. The point is not to reinforce a stable model of the world but rather to add new data to that model. Maybe to add so much data or data so strange that the model itself has to be remodeled.

That seems to be the particular genius of Errol Morris: to discover wonderfully inexplicable complexities right where everyone is fast and desperately trying to demystify and settle things and close down, rather than rev up, curiosity, as we once sprayed dioxin on dust to beat it down. After the trial, after the tabloid furor ends, decades after the war is over, he brings his questioning gaze.

His mysteries seem to re-ravel, if you will, a sleeve of care. To start with a single fiber that the following of attracts more substance to itself, like a grain in a supersaturated solution, and forms loops and lattices, working itself back into a crystal, or a sweater, or a shroud.

Finding simple things that don’t fit the model, and unpacking them until they are so complex and beautiful the mind strains to encompass them might be the very inductive, Deleuze-like, hallmark epistemology of the age. Everywhere we see ecosystems where we used to see simple causes and effects. Maybe civilization evolves by a constant epistemological pendulum, from reduction to production, from resemblance to representation (as Foucault said), from induction to deduction, from E-Pluribus to unum, like music coming out of an accordion, and so on.

In any event, I wanted to point out that Morris’ re-raveling is how we learn important things. If you imagine that learning is improvement with a self-consciousness about it, such that learning includes the experience of seeing yourself learn, then it’s easy to understand that your improvement, since it feeds on itself, grows sort of like money in the bank, where the interest adds to the principle which adds to the interest, and the graph of growth gets steeper and steeper. Or to put it another way the learning gets increasingly complicated and the rate of the increase in complexity gets increased. Or to put it another way, the thread becomes a row of loops becomes a flap of fabric becomes a 3-dimensional sweater. Or to put it another way, the line becomes a kind of spiral of Archimedes, slouching towards complexity shuffling step by shuffling step, and looking with every lunge more like a chapter title page out of the Book of Kells. As if you are always moving from a certain kind of Flatland into a world of plus-one dimensions.

Kurt Fischer, a cognitive scientist at Harvard, developed a scale of universal cognitive development that models this kind of growth—showing learning progressing from simple ideas to relationships of ideas to relationships of relationships and so forth. Importantly, key steps include the whole of the previous level as the first building block. I will insert a pic if I can find one.

Robert Kegan’s work on adult development is similar. Adult minds, if they’re in the right environments, says he, go through a series of epistemological changes—from the “socialized mind” to the “self-authoring mind” to the “self-transforming mind,” where the key starting point characteristic of every level is that you “see” the previous epistemology. You see as an object the thing through which you previously saw the world, or your subject—you form, that is, a relationship with the thing that was previously you—you are two ideas now linked, instead of one, etc.

We could look, too, at the double-loop learning of Argyris: which is characterized by not just reflecting on the performance per the established goals, but which includes reassessment of the goals themselves (!). Or the collaborative learning praised by Lee Shulman, which is distinct from cooperative learning, and in which you and the people you’re learning with figure out why you’re there, what your product will be, how you’ll go about producing it, and what the individual roles will be—all simultaneously, as in a Jazz improvisation: you have to improve to even know why you’re there.

The core experience in all these is the excruciating or exhilarating feeling of stretching your perspective to fit a torrent of nonconforming data, then looking around for new data (including data about yourself looking at data) and doing it again. What’s perhaps unusual about Morris and people like him is a compulsion to inundate himself and us with this nonconforming data. Most people don’t seem as inclined to jump out of the pond at any opportunity to make themselves evolve legs; he is, though. Driven by a kind of faith or fanaticism that there will be a there there as the line grows into a complex spiral. Many theres are probably there simultaneously.

This mystery-as-epistemology is a neat thing on a couple of levels. For one, it’s a humanism. The belief that there are in you, me, and every aspect of the world unfathomable multitudes of complexity and wonder—and that that’s ok–not just ok, but, even, that that’s how we ought to be, and that the highest evolved action might just be to go digging for this stuff—this is deeply reassuring. Much of life seems to involve the opposite: sweeping things under the covers, assuming veneers of normalcy, and dealing with the inevitable neurosis that arises from the conflict between your inner complexities and your epistemologically circumscribed outer self. To do the opposite, for once—to honor the complexity—is nice.

It’s healing, in fact. These mysteries repair the workaday world. Justice system, war, politics, religion–things that are supposed to order the cosmos, answer questions, and regulate–also seem to leave destroyed people and confusion in their wake. A restoration of ambiguity after these kinds of simplicities is a wonderful thing. And if it ends up you need ambiguity to learn, well then so much the better.

The Vygotsky Challenge

I was reading an article on Lev Vygotsky, the influential Soviet psychologist, and I was struck (again) by his emphasis on the social context of learning, and by what that implies for the way we organize ourselves in education.

In contrast to the individual orientation that permeates just about all organized learning, Vygotsky stresses the importance of focusing on the supporting structure, the social context, the scaffolding around the student.

According to Wertsch and Tulviste’s “L.S. Vygotsky and Contemporary Developmental Psychology” (in An Introduction to Vygotsky, 2nd Ed, Routledge, 2005), “mental functioning in the individual can be understood only by examining the social and cultural processes from which it derives” (60). Vygotsky’s Zone of Proximal Development, for example, which famously describes the area between what the person has learned and can learn (with help from teachers and adults and friends and culture) is not so much about improving individual learning, but rather about improving the social and cultural context in which that learning happens. In other words–you can improve the individual’s learning by focusing on the larger group (63).

This is about as revolutionary a thought as can be imagined for schools.

Generally speaking, institutions of education are designed with the goal of improving students as individuals. Everything we do is organized around individual students, from enrollment, to advising, to assessment, to grades. If we think about culture at all, it’s also student focused; how faculty interact with students. How students interact with students–in the classroom, in the dorm, in student clubs.

Nowhere do we really think to a similar degree about the larger culture of the institution. How faculty and staff and students as a collective whole, say, talk to each other, help each other, learn together, share ideas. Whether we trust each other. Whether we all get to have input, say, to identify institutional problems together. Whether we solve our problems together, etc.. In other words, we think a lot about assessing individual student learning, but we don’t really think about assessing the context around that learning.

Just as a simple little example, consider the amount of energy–resources, planning, assessment, time, space, books, chairs, etc.–that goes into just one regular college course. Then think of the corresponding amount of investment we make in the development of a given faculty or staff member. A faculty or staff member might get a few workshops and a retreat or conference in a given year, but there is no commensurate institutional investment in planning and guiding and supporting and assessing such learning. A student gets a teacher, a curriculum, an advisor, expectations, a dean, a dorm life supervisor, and on and on. Faculty and staff might get part of a manager or a chair and someone to review an activity report; but they of course get so much less support in their own development that I feel silly making the comparison. And that’s just thinking of faculty and staff themselves as individuals, and not taking into account their social context, which gets even less attention still, and which, after all, is the real point.

If we take Vygotsky to heart, we should be thinking precisely about how our faculty and students and staff–all of us–work together, share, think, learn, develop–as a community.  Observing and assessing and understanding and improving the bigger culture should be a priority, and doing it well should translate into vast gains for students. For a better culture will make a bigger zone of proximal development.  (I should note that many have developed Vygotsky’s idea here further, but it has not significantly penetrated into the DNA of our organizations. Yet.)

So that’s what I call the Vygotsky challenge: as we’re thinking about redesigning our educational institutions to better help students learn in this the rambunctious digital age, we should also think about how we assess and improve the culture around them, which means focusing on the “other” people hovering around the school’s halls, and on how we all talk to and treat each other. Such a focus will be a wonderful boon for those mysterious non-student people (who will feel that it’s finally OK for them to learn and develop, too) and, ultimately, help the students. Maybe as well as or better than anything else we might do from within the traditional, individually-focused paradigm?

We might one day even go so far as to no longer distinguish between students and staff and faculty, who are, after all, just learners at different points on the continuum, but I’m perhaps getting carried away.

Soliloquy on Learning Analytics

Learning Analytics is a hot topic these days, but it also seems at the moment to some to be a kind of Google Wave: a neat concept, but with few concrete examples. I think it has a bright future, so I thought I would write about it.

Learning Analytics for me is simply analytics applied to learning: analysis of any data generated in the course of learning that can shed light on how people learn. Imagine those great data visualizations you see in the New York Times–only representing, not red states and blue states, but heretofore invisible patterns in the behaviors of learners.

It’s distinct for me from Business Analytics–the real-time analysis of data generated by organizations, designed to inform decision-making, also known as the famous “dashboard” to which all good leaders aspire. And it’s distinct from what my friend Ganesan Ravishanker and others call Academic Analytics (see his ECAR Research Bulletin on the topic): the analysis of data from the business operations of academic activities like enrollment, majors, tuition payments, faculty counts, and so on. These are wonderful and important activities; the difference in Learning Analytics is that it aims to focus on learning. Or, to make that slightly less esoteric, it aims to analyze the records of behavior occurring, and the artifacts produced, during learning.

What am I talking about?  Imagine all the things that happen when you are taking a class. Reading the syllabus. Listening and talking in the class. Reading the homework assignments. Taking notes, in class, and during reading those homework assignments. Writing paper drafts. Using particular sorts of software. Engaging in online discussions. Texting classmates. Contributing to a course backchannel in Twitter. Giving feedback and engaging in peer review. Taking surveys. Filling out evaluation forms. Posting at Rate my Professor.com. And so on. Increasingly this stuff is electronic or happens in an electronic medium, so it’s in theory collectable. If we can collect it, we can analyze it. Suddenly things that were invisible are no longer invisible. As Johann Larusson and Brandon White say in Detecting the “Point of Originality” in Student Writing:

The continuing migration of more and more teaching
materials to digital venues, and thus a machine-readable
form, has the supplementary benefit of making a student’s
day-to-day learning activities more transparent in each
stage of the teaching process.

Okay, if we collected all this stuff, what sorts of things would we see? Basically we’d see better what people thought during the evolution of the course. About the content in the course, about the ideas in the course, about conversation in the course, about assignments. We’d see their work evolve–through drafts, through feedback. We’d see their feedback evolve. We’d see how people responded to evolved feedback. We’d see the conversation evolve. In other words, we would see the way people developed over the course of the semester. And we’d be able to understand a bit better what learning is and what parts of our learning environments were most beneficial. The main way we see at all into this area now is basically through the intuition of the teachers and learners engaged in the course–their intuition informed by their own sometimes semi-conscious collection and analysis of data–which is of course a wonderfully valuable and reliable tool. Learning Analytics would just supplement it.

How about an example? Here’s one: In their “Point of Originality” work (see link above), Johann Larusson and Brandon White created a tool that uses established linguistic theories to measure the originality of student writing, and uses as its data source student blogs. In a course where students regularly blog, their tool can give a sense of when students are engaged and thoughtful about the topics discussed, in the aggregate and in the individual. A professor can use this new tool to complement his or her intuition or other assessments–and s/he can start to make connections that can help improve the learning environment. Are there patterns to the engagement? Does a particular topic excite more originality? Did the teaching method have an effect? Does originality correlate with performance on exams? Do I sense an engagement in class that corresponds to the activity in the blogs? And so on.

The above is what you might call the human perspective of Learning Analytics. There’s also a structural perspective of Learning Analytics, and it has potential too. I’m indebted to Greg Crane for this key insight (see my post from last Spring). We have collections of data from systems–be they computer systems or systems of activity–that can shed light–from a large-scale perspective–on how people define and shape learning containers (if you will) and pathways or sequences. What systems do I mean? Collections of syllabi, course descriptions, definitions of course learning objectives, reserves reading lists, records of textbooks used by courses, course catalogs, departmental or program descriptions, course prerequisites, transcripts, credit transfer agreements, and on and on: every school could generate a vast amount. It’s relatively easy to imagine these kinds of materials collected and organized, even on a national scale. Analyzing these data would start to give us a kind of linguistic understanding of the grammar of learning sequences. We could start to see how people shape learning, what subjects are considered learn-worthy (if you will), what sequences of subjects are considered appropriate, even what information resources are associated with what subjects. You could see how these structures changed over time or were different in different cultures. You might even start to conceive of a dynamic learning advising tool that could give a self-directed learner lots of options based on analysis of these large scale patterns of learning constructions.  ”Welcome, David. You’re interested in learning Economics? Based on our analysis of all world Economics courses, the first step generally consists of activities like these . . . and uses information sources like these . . . and assessments like these . . . Given your personality and past successes, we recommend activities with a strong experiential flavor . . . we’re now generating a syllabus for you. Also, based on your projected learning path, three professors have made a bid to serve as your personal guide at reasonable rates. We think you’ll like Dr. Jones. Etc.”

I’m perhaps getting a little carried away here with this Greg-Crane-inspired advising machine, which reminds me of what I think we should do and not do with knowledge derived from Learning Analytics. I don’t think the point of analyzing learning is to create a machine to replace teachers and automate assessment, or that the data collected would be used in secret to make some vast nefarious decisions about people and programs (these are two of the initial worries I think people feel when they start to think about Learning Analytics). I think the point of Learning Analytics is instead simply to give back to learners and teachers more information about how they are doing and about what is working. The ultimate arbiters for me are still the learner and the teacher–who, with Learning Analytics, will have more tools at their disposal as they try to understand how to help themselves or others learn. Learning Analytics should contribute to the kind of ongoing, imbedded, formative, transparent assessment hoped for by people everywhere.

And so ends my soliloquy. If you’re interested in learning more, look for an upcoming webinar co-hosted by the New Media Consortium and the NorthEast Regional Learning Analytics group (NERLA) to appear this Fall, take a look at my friend Malcolm Brown’s excellent ELI Brief on the topic, or review the proceedings of the Banff conference from last Spring, which had some wonderful presentations. And if you have an idea or a project in the works, consider presenting on it at the first ever NERLA Learning Analytics Symposium, January 2012 in Norwood, Massachusetts: the call for papers is open through September 26, 2011.

Whither Academic Support?

I’ve been thinking about academic support professionals. Library and IT folks who spend their time helping teachers and students learn and do research. Mostly, it seems, by giving them stuff, or helping them use stuff, to wit, Learning Management Systems, online full-text search engines, overhead projectors, hand-held audio recorders, HTML, PowerPoint, citation guides, chalkboards, research guides, plagiarism detection software, the Library of Congress subject headings, the WordPerfect 3.0 reveal codes function, books, the Readers’ Guide to Periodical Literature, etc.

Helping people get and use stuff is the kind of task that works if everyone knows what the big picture is. If, that is, you know enough about the overarching idea of teaching and learning that you can divide all the work to produce learning into chunks and you can focus on your chunk (the support chunk) because you know that other people will focus on the other chunks (the teaching part, say) and you know what it is they will be doing, and they you. But if you don’t know what teaching and learning is, say, or what teachers are doing and needing, or how learners are learning, how can you get them the right stuff? You can’t. You can only randomly throw some things at the wall to see if they stick.

We in the academic support profession can probably be accused of desperately trying not to have to think about the overarching idea of teaching and learning. “That’s for faculty to figure out,” we might say. “Not our business.” Maybe that worked for a while.

Now, however, we find ourselves in an age where all members of the learning apparatus are rethinking what it means to teach and learn. And nobody is sure what’s going on.

We can continue picking random stuff to offer and advise on, and organize workshops around, without really knowing whether it’s what our community needs, or we can join the community and help figure out what it needs. I say random stuff because there are at this point WAY too many possible tools and information sources and new formats and new software burgeoning up for us to be able to sift and evaluate and prioritize. Even if we had the criteria of selection and evaluation agreed upon with our community, which we don’t.

How does the community figure out what it needs? By learning, I suggest. You create a learning group, a team, a research collective, a conversation space, to figure it out. As a learning group doing something meaningful and challenging, it will obey some rules, and those rules are wonderfully humane. In exchange for learning together, you will be vulnerable. But everyone else will be, too. You will also have to read and observe and structure and reflect and talk and test things out and adapt endlessly and adjust and listen, and all of this in a group. You will have a voice, and what you say will be evaluated. You’ll be challenged and provoked and reinforced and confused and enlightened; you won’t know where you’re headed exactly when you set out; you’ll get more clarity about the world around you; and you’ll build relationships with everyone else in the group that will last. Your community will learn its way forward.

It’s people stuff, it’s faith, it’s risk, it’s scary, it’s trust, it’s vulnerability, it’s Negative Capability, it’s relationship-building, it’s engagement on an ideas plane, it’s meaningful personal and community development. It’s perhaps the opposite of everything we’ve ever done. It’s perhaps everything we’ve consciously and subconsciously veered away from and protected ourselves from and eschewed and avoided and bemoaned.

But it’s the best way we, the big we, the institution, can figure out what’s next and what our own niche role in that next will be. If we do it right, we’ll know afterwards what stuff to provide. Though I note that after we participate in the thing I’m talking about here, I’m not sure we’ll want to return to a bit role, providing necessary if kind of uninspiring coal, if you will, to the furnace of learning and becoming. I think we’ll instead want to continue to learn and become, being a part of the fire that consumes the coal. That would be good, too. That would be development.

So I enjoin us to drop our stuff and take up our learning teams.

You might say, but if we recreate ourselves as peers to users in learning about things and defining needs and testing solutions and adapting together we lose all control, and with loss of control we have to deal with anxieties we’ve successfully suppressed heretofore. Yes. And you might say, but if we stop helping people use stuff they might get mad. Maybe. But they might also figure out they don’t really need our help with the stuff, which would be good for everyone. I predict they’ll like the new us.