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Clark Quinn’s Learnings about Learning

Vale Jay Cross

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It’s too soon, so it’s hard to write this. My friend and colleague, Jay Cross, passed away suddenly and unexpectedly. He’s had a big impact on the field of elearning, and his insight and enthusiasm were a great contribution.

Version 2I had the pleasure to meet him at a lunch arranged by a colleague to introduce learning tech colleagues in the SF East Bay area.  Several of us discovered we shared an interest in meta-learning, or learning to learn, and we decided to campaign together on it, forming the Meta-Learning Lab. While not a successful endeavor in impact, Jay and I discovered a shared enjoyment in good food and drink, travel, and learning. We hobnobbed in the usual places, and he got me invited to some exotic locales including Abu Dhabi, Berlin, and India.

Jay was great to travel with; he’d read up on wherever it was and would then be a veritable  tour guide. It amazed me how he could remember all that information and point out things as we walked.  He had a phenomenal memory; he read more than anyone I know, and synthesized the information to create an impressive intellect.

After Princeton he’d gone on for an MBA at Harvard, and amongst his subsequent endeavors included creating the first MBA for the University of Phoenix.  He was great to listen to doing business, and served as a role model; I often tapped into my ‘inner Jay’ when dealing with clients.  He always found ways to add more value to whatever was being discussed.

He was influential. While others may have quibbled about whether he created the term ‘elearning’, he definitely had strong opinions about what should be happening, and was typically right.  His book  Informal Learning  had a major impact on the field.

He was also a raconteur, with great stories and a love of humor. He had little tolerance for stupidity, and could eviscerate silly arguments with a clear insight and incisive wit. As such,  he could be a bit of a rogue.  He ruffled some feathers here and there, and some could be put off by his energy and enthusiasm, but his intentions were always in the right place.

Overall, he was a really good person. He happily shared with others his enthusiasm and energy.  He mentored many, including me, and was always working to make things better for individuals, organizations, the field, and society as a whole. He had a great heart to match his great intellect, and was happiest in the midst of exuberant exploration.

He will be missed. Rest in peace.

Some other recollections of  Jay:

Harold Jarche

Jane Hart

Charles Jennings

Kevin Wheeler

Laura Overton

Inge de Waard

Alan Levine

Curt Bonk

David Kelly

Brent Schlenker

Dave Ferguson

George Siemens

Mark Oehlert

Gina Minks

John Sener

Sahana Chattopadhyay

Christy Tucker

Adam Salkeld

Learning Solutions  from the eLearning Guild

CLO Magazine

A twitter collection (courtesy of Jane Hart)

Bio from his graduating class.

#itashare

Showing the World

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One of the positive results of investigations into making work more effective has been the notion of transparency, which manifests as either working and learning ‘out loud‘, or in calls to Show Your Work.  In these  cases, it’s so people can know what you’re doing, and either provide useful feedback or learn from you.  However, a recent chat in the L&D Revolution group on LinkedIn on Augmented Reality (AR) surfaced another idea.

We were talking about how AR could be used to show how to do things, providing information for instance on how to repair a machine. This has already been seen in examples by BMW, for instance. But I started thinking about how it could be used to support education, and took it a bit further.

So many years ago, Jim Spohrer proposed  WorldBoard, a way to annotate the world. It was like the WWW, but it was location specific, so you could have specific information about a place  at the place.  And it was a good idea that got some initial traction but obviously didn’t continue.

The point, however, would be to ‘expose’ the world. In particular, given my emphasis on the value of models, I’d love to have models exposed. Imagine what we could display:

  • the physiology of an animal we’re looking at to flows of energy in an ecosystem
  • the architectural or engineering features of a building or structure
  • the flows of materials through a manufacturing  system
  • the operation of complex devices

The list goes on. I’ve argued before that we should expose our learning designs as a way to hand over learning control to learners, developing their meta-learning skills. I think if we could expose how things work and  the thinking behind them, we’d be boosting STEM in a big way.

We could go further, annotating exhibits and performances as well.  And it could be auditory as well, so you might not need to have glasses, or you could just hold up the camera and see the annotations on the screen. You could of course turn them on or off, and choose which filters you want.

The systems exist: Layar commercially, ARIS in the open source space (with different capabilities).  The hard part is the common frameworks, agreeing what and how, etc.   However,  the possibilities to really raise understanding is very much an opportunity.  Making the workings of the world visible seems to me to be a very intriguing possibility to leverage the power we now hold in our hand. Ok, so this is ‘out there’, but I hope we might see this flourishing quickly.  What am I missing?

The Polymath Proposition

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At the recent DevLearn conference, one of the keynotes was Adam Savage.  And he said something that gave me a sense of validation. He was talking about being a polymath, and I think that’s worth understanding.

His point was that his broad knowledge of a lot of things was valuable.  While he wasn’t the world’s expert in any particular thing, he knew a lot about a lot of things.  Now if you don’t know him, it helps to understand that he’s one of the two hosts of Mythbusters, a show that takes urban myths and puts them to the test.  This requires designing experiments that fit within pragmatic constraints of cost and safety, and will answer the question. Good experiment design is an art as well as a science, and given the broad range of what the myths cover, this ends up requiring a large amount of ingenuity.

The reason I like this is that my interests vary broadly (ok, I’m coming to terms with a wee bit of ADD ;).  The large picture is how technology can be designed  to help us think, work, and learn.  This ends up meaning I have to understand things like cognition and learning (my Ph.D. is in cognitive psychology), computers (I’ve programmed and designed architectures at many levels), design (I’ve looked at usability, software engineering, industrial design, architectural design, and more), and organizational issues (social, innovation…). It’s led to explorations covering things like games, mobile, and strategy (e.g. the topics of my books).  And  more; I’ve led development of adaptive learning systems, content models, learning content, performance support, social environments, and so on.  It’s led me further, too, exploring  org change and culture, myth and ritual, engagement and fun, aesthetics and media, and other things I can’t even recall right now.

And I draw upon  models from as many fields as I can.  My Ph.D. research was related to the power of models as a basis for solving new problems in uncertain domains, and so I continue to collect them like others collect autographs or music.  I look for commonalities, and  try to make my understanding explicit by continuing to diagram and write about my reflections.  I immodestly think I draw upon a broad swath of areas. And  I particularly push learning to learn and meta-cognition to others because it’s been so core to my own success.

What I thrive on is finding situations where the automatic solutions don’t apply. It’s not just a clear case for ID, or performance support, or…  Where technology can be used (or used better) in systemic ways to create new opportunities. Where I really contribute is where it’s clear that change is needed, but what, how, and where to start aren’t obvious.  I’ve a reliable track record of finding unique, and yet pragmatic solutions to such situations, including the above named areas I’ve innovated  in.  And it is a commitment of mine to do so in ways that pass on that knowledge, to work in collaboration to co-develop the approach and share the concepts driving it, to hand off ownership to the client. I’m not looking for a sinecure; I want to help while I’m adding value and move on when I’m not.  And many folks have been happy to have my assistance.

It’s hard for me to talk about myself in this way, but I reckon I bring that  polymath ability of a broad background to organizations trying to advance.   It’s been  in assisting their ability to develop design processes that yield better learning outcomes, through mobile strategies and solutions that meet their situation, to overarching organizational strategies that map from concepts to system.  There’s a pretty fair track record to back up what I say.

I am  deep in a lot of areas, and have the ability to synthesize solutions  across  these areas in integrated ways. I may not be the deepest in any one, but when you need to look across them and integrate a systemic solution, I like to think and try to ensure that I’m your guy. I help organizations envision a future state, identify the benefits and costs, and prioritize the opportunities to define a strategy.  I  have operated independently or with partners, but I adamantly retain my freedom  to say what I truly think so that you get an unbiased response from the broad suite of  principles I have to hand.  That’s my commitment to integrity.

I didn’t intend this to be a commercial, but I did like his perspective and it made me reflect on what my own value proposition is.  I welcome your thoughts.  We now return you to your regularly scheduled blog already in progress…

Supporting our Brains

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One of the ways I’ve been thinking about the role mobile can play in design is thinking about how our brains work, and don’t.  It came out of both mobile and the recent cognitive science for learning workshop I gave at the recent DevLearn.  This applies more broadly to performance support in general, so I though I’d share where my thinking is going.

To begin with, our cognitive architecture is demonstrably awesome; just look at your surroundings and recognize your clothing, housing, technology, and more are the product of human ingenuity.  We have formidable capabilities to predict, plan, and work together to accomplish significant goals.  On the flip side, there’s no one all-singing, all-dancing architecture out there (yet) and every such approach also has weak points. Technology, for instance, is bad at pattern-matching and meaning-making, two things we’re really pretty good at.  On the flip side, we have some flaws too. So what I’ve done here is to outline the flaws, and how we’ve created tools to get around those limitations.  And to me, these are principles for design:

table of cognitive limitations and support toolsSo, for instance, our senses capture incoming signals in a sensory store.  Which has interesting properties that it has almost an unlimited capacity, but for only a very short time. And there is no way all of it can get into our working memory, so what happens is that what we attend to is what we have access to.  So we can’t recall what we perceive accurately.  However, technology (camera, microphone, sensors) can recall it all perfectly. So making capture capabilities available is a powerful support.

Similar, our attention is limited, and so if we’re focused in one place, we may forget or miss something else.  However, we can program reminders or notifications that help us recall important events that we don’t want to miss, or draw our attention where needed.

The limits on working memory (you may have heard of the famous 7 ±2, which really is <5) mean we can’t hold too much in our brains at once, such as interim results of complex calculations.  However, we can have calculators that can do such processing for us. We also have limited ability to carry information around for the same reasons, but we can create external representations (such as notes or  scribbles) that can hold those thoughts for us.  Spreadsheets, outlines, and diagramming tools allow us to take our interim thoughts and record them for further processing.

We also have trouble remembering things accurately. Our long term memory tends to remember meaning, not particular details. However, technology can remember arbitrary and abstract information completely. What we need are ways to look up that information, or search for it. Portals and lookup tables trump trying to put that information into our heads.

We also have a tendency to skip steps. We have some randomness in our architecture (a benefit: if we sometimes do it differently, and occasionally that’s better, we have a learning opportunity), but this means that we don’t execute perfectly.  However, we can use process supports like checklists.  Atul Gawande wrote a fabulous book on the topic that I can recommend.

Other phenomena include that previous experience can bias us in particular directions, but we can put in place supports to provide lateral prompts. We can also prematurely evaluate a solution rather than checking to verify it’s the best. Data can be used to help us be aware.  And we can trust our intuition too much and we can wear down, so we don’t always make the best decisions.  Templates, for example are a tool that can help us focus on the important elements.

This is just the result of several iterations, and I think more is needed (e.g. about data to prevent premature convergence), but to me it’s an interesting alternate approach to consider where and how we might support people, particularly in situations that are new and as yet untested.  So what do you think?

AI and Learning

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At the recent DevLearn, Donald Clark talked about AI in learning, and while I largely agreed with what he said, I had some thoughts and some quibbles. I discussed them with him, but I thought I’d record them here, not least as a basis for a further discussion.

Donald’s an interesting guy, very sharp and a voracious learner, and his posts are both insightful and inciteful (he doesn’t mince words ;). Having built and sold an elearning company, he’s now free to pursue what he believes and it’s currently in the power of technology to teach us.

As background, I was an AI groupie out of college, and have stayed current with most of what’s happened.  And you should know a bit of the history of the rise of Intelligent Tutoring Systems, the problems with developing expert models, and current approaches like Knewton and Smart Sparrow. I haven’t been free to follow the latest developments as much as I’d like, but Donald gave a great overview.

He pointed to systems being on the verge of auto parsing content and developing learning around it.  He showed an example, and it created questions from dropping in a page about Las Vegas.  He also showed how systems can adapt individually to the learner, and discussed how this would be able to provide individual tutoring without many limitations of teachers (cognitive bias, fatigue), and can not only personalize but self-improve and scale!

One of my short-term problems was that the questions auto-generated were about knowledge, not skills. While I do agree that knowledge is needed (ala VanMerriënboer’s 4CID) as well as applying it, I think focusing on the latter first is the way to go.

This goes along with what Donald has rightly criticized as problems with multiple-choice questions. He points out how they’re largely used as knowledge test, and  I agree that’s wrong, but  while there are better practice situations (read: simulations/scenarios/serious games), you can write multiple choice as mini-scenarios and get good practice.  However, it’s as yet an interesting research problem, to me, to try to get good scenario questions out of auto-parsing content.

I naturally argued for a hybrid system, where we divvy up roles between computer and human based upon what we each do well, and he said that is what he  is seeing in the companies he tracks (and funds, at least in some cases).  A great principle.

The last bit that interested me was whether and how such systems could develop not only learning skills, but meta-learning or learning to learn skills. Real teachers can develop this and modify it (while admittedly rare), and yet it’s likely to be the best investment. In my activity-based learning, I suggested that gradually learners should take over choosing their activities, to develop their ability to become self-learners.  I’ve also suggested how it could be layered on top of regular learning experiences. I think this will be an interesting area for developing learning experiences that are scalable but truly develop learners for the coming times.

There’s more: pedagogical rules, content models, learner models, etc, but we’re finally getting close to be able to build these sorts of systems, and we should be  aware of what the possibilities are, understanding what’s required, and on the lookout for both the good and bad on tap.  So, what say you?

Modelling

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So, I found an interesting inconsistency.  I had to submit my deck for my DevLearn workshop on Cognitive Science for Learning Design last week, but oddly, for every thing I was recommending I had a diagram, except for the notion of using models.  This is ironic, since diagrams can  be used to convey  models.  It bugged me, so I pondered.

And then I remembered that I gave a presentation years ago specifically on diagrams.  Moreover, in that presentation I had a diagram for a process for creating a diagram (Department of Redundancy Department).  So, I finally got around to trying to apply my own process to my lack of a model.  And voilà:

ModelReasoningThe process is to identify the elements, and the relationships, and then additional dimensions.  Then you  represent each, place them (elements first, relationships second, dimensions last), and tune.

Here the notion is that you have a mental model of a concept, capturing elements and causal relationships.  When you see a situation, you select a model where you can map the elements in the model to elements in the context.  Then you can use the model to predict what will happen or explain what happened. Which gives you a basis for making decisions, and adapting decisions to different contexts in principled ways.

Models  are a powerful concept I’ve harped on before, but now I’ve an associated diagram.  And I  like diagrams. I find mapping the conceptual dimensions to spatial dimensions both helps me get concrete about the models and then gives a framework to share with others.  Does this make sense to you, both the concept behind it, and the diagram to represent it?

I’ll be presenting this in the workshop, amongst many other implications from how our brains work (and learn) to the design of learning experiences.  Would love to see you there.

Concrete and Contextual

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I’m working on the learning science workshop I’m going to present at DevLearn  next month, and in thinking about how to represent the implications of designing to account for how we work better when the learning context is concrete and sufficient contexts are used, I came up with this, which I wanted to share.

Concrete deliverables and multiple contextsThe empirical data is that we learn better when our learning practice is contextualized.  And if we want transfer, we should have practice in a spread of contexts that will facilitate abstraction and application to all appropriate settings, not just the ones seen in the learning experience.  If the space between our learning applications is too narrow, so too will our transfer be. So our activities need to be spread about in a variety of contexts (and we should be having sufficient practice).

Then, for each activity, we should have a concrete outcome we’re looking for. Ideally, the learner is given a concrete deliverable as an outcome that they must produce (that mimics the type of outcome we’re expecting them to be able to create as an outcome of the learning, whether decision, work product, or..).  Ideally we’re in a social situation and they’re working as a team (or not) and the work can be circulated for peer review.  Regardless, then there should be expert oversight on feedback.

With a focus on sufficient and meaningful practice, we’re more likely to design learning that will actually have an impact.  The goal  is to have practice that is aligned with how our learning works (my current theme: aligning with how we think, work, and learn). Make sense?