Clark Quinn's Learnings about Learning
(The Official Quinnovation blog)

23 November 2015

When (and not) to crowdsource?

Clark @ 8:14 am

Will Thalheimer commented on my ‘reconciliation‘ post, and pointed out that there are times when you would be better off going to an expert. His apt observation is that there are times when it makes sense to crowdsource and when not to, but it wasn’t clear to him or me when each was. Naturally that led to some reflection, and this is where I ended up.

As a framework, I thought of Dave Snowden’s Cynefin model.  Here, we break situations into one of four types: simple or obvious, where there are known answers; complicated, where it requires known expertise to solve; complex, where we’re dealing in new areas; and chaotic, where things are unstable.

With this model, it’s clear that we’ll know what to do in the simple cases, and we should bring in experts to deal with the complicated. For chaotic systems, the proposal is just to do something, to try to move it to one of the other three quadrants!  It’s the other where we might want to consider social approaches.

The interesting place is the complex.  Here, I suggest, is where innovation is needed. This is the domain of trouble-shooting unexpected problems, coming up with new products or services, researching new opportunities, etc.  Here is where you determine experiments to try, and formulate plans to test.  While when the stakes are low you might do it individually, when the stakes are high you bring together a group.  It may be more than one expert, but here’s where you want to use good processes such as brainstorming (done right), etc.

Here is where the elements of the learning organization come in.  Here is where you want to value diversity, be open to new ideas, make it safe to contribute, and provide time for reflection. Here is where you want to tap into collaboration and cooperation. Here is where you want to find ways to get people to work together effectively.

Will was insightful in pointing out that you don’t always want to tap into the wisdom of the crowd, not least for pragmatics, so we want to be clear about when you do.  My point is that we want to be able to when it makes sense, and facilitate this as part of the new role for L&D in the revolution. So, as this is new to me, let me tap into the power of the crowd here: does this  make sense to you?

18 November 2015

Facilitating Knowledge Work #wolweek

Clark @ 8:22 am

In the course of some work with a social business agency, was wondering how to represent the notion of facilitating continual innovation.  This representation emerged from my cogitations, and while it’s not quite right, I thought I’d share it as part of Work Out Loud week.

5RsThe core is the 5 R’s: Researching the opportunities, processing your explorations by either Representing them or putting them into practice (Reify) and Reflecting on those, and then Releasing them.  And of course it’s recursive: this is a release of my representation of some ideas I’ve been researching, right?  This is very much based on Harold Jarche’s Seek-Sense-Share model for Personal Knowledge Mastery (PKM). I’m trying to be concrete about different types of activities you might do in the Sense section as I think representations such as diagrams are valuable but very different than active application via prototyping and testing.  (And yes, I’m really stretching to keep the alliteration of the R’s.  I may have to abandon that. ;)

What was interesting to me was to think of the ways in which we can facilitate around those activities.  We shouldn’t assume good research skills, and assist individuals in doing understanding what qualifies as good searches for input and evaluating the hits, as well as establishing and filtering existing information streams.

We can and should also facilitate the representations of interpretations, whether informing properties of good diagrams,  prose, or other representation forms.  We can help make the processes of representation clear as well. Similarly, we can develop understanding of useful experimentation approaches, and how to evaluate the results.

Finally, we can communicate the outcomes of our reflections, and collaborate on all these activities whether research, representation, reification (that R is a real stretch), and reflection.  As I’m doing here, soliciting feedback.

I do believe there’s a role for L&D to look at these activities as well, and ‘training’ isn’t the solution. Here the role is very much facilitation.   It’s a different skill set, yet a fundamental contribution to the success of the organization. If you believe, like I do, that the increasing rate of change means innovation is the only sustainable differentiator for success, then this role is crucial and it’s one I think L&D has the opportunity to take on.  Ok, those are my thoughts, what are yours?

7 November 2015

Vale Jay Cross

Clark @ 1:10 am

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 (online?) 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

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.


3 November 2015

Gary Stager #LearnTech2015 Keynote Mindmap

Clark @ 1:39 am

Gary presented a passionate and compelling argument for the value of using the maker movement as a vehicle for education reform.

2 November 2015

Roger Schank #learntech2015 Keynote Mindmap

Clark @ 7:29 pm

Roger gave his passioned, opinionated, irreverent, and spot-on talk to kick off LearnTechAsia. He covered the promise (or not) of AI, learning, stories, and the implications for education.

27 October 2015

Showing the World

Clark @ 8:03 am

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?

15 October 2015

The Polymath Proposition

Clark @ 8:21 am

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 remain 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…

13 October 2015

Supporting our Brains

Clark @ 8:29 am

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?

7 October 2015

AI and Learning

Clark @ 8:10 am

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?

30 September 2015

Connie Yowell #DevLearn Keynote Mindmap

Clark @ 4:58 pm

Connie Yowell gave a passionate and informing presentation on the driving forces behind digital badges.

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