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

16 October 2014

Sharing pointedly or broadly

Clark @ 8:06 am

In a (rare) fit of tidying, I was moving from one note-taking app to another, and found a diagram I’d jotted, and it rekindled my thinking. The point was characterizing social media in terms of their particular mechanisms of distribution. I can’t fully recall what prompted the attempt at characterization, but one result of revisiting was thinking about the media in terms of whether they’re part of a natural mechanism of ‘show your work’ (ala Bozarth)/’work out loud’ (ala Jarche).

whether person to person or one to manyThe question revolves around whether the media are point or broadcast, that is whether you specify particular recipients (even in a mailing or group list), or whether it’s ‘out there’ for anyone to access.  Now, there are distinctions, so you can have restricted access on the ‘broadcast’ mode, but in principle there’re two different mechanisms at work.

It should be noted that in the ‘broadcast’ model, not everyone may be aware that there’s a new message, if they’re not ‘following’ the poster of the message, but it should be findable by search if not directly.  Also, the broadcast may only be an organizational network, or it can be the entire internet.  Regardless, there are differences between the two mechanisms.

So, for example, a chat tool typically lets you ping a particular person, or a set list. On the other hand, a microblog lets anyone decide to ‘follow’ your quick posts.   Not everyone will necessarily be paying attention to the ‘broadcast’, but they could.  Typically, microblogs (and chat) are for short messages, such as requests for help or pointers to something interesting.  The limitations mean that more lengthy discussions typically are conveyed via…

Formats supporting unlimited text, including thoughtful reflections, updates on thinking, and more tend to be conveyed via email or blog posts. Again, email is addressed to a specific list of people, directly or via a mail list, openly or perhaps some folks receiving copies ‘blind’ (that is, not all know who all is receiving the message.  A blog post (like this), on the other hand, is open for anyone on the ‘system’.

The same holds true for other media files besides text.   Video and audio can be hidden in a particular place (e.g. a course) or sent directly to one person. On the other hand, such a message can be hosted on a portal (YouTube, iTunes) where anyone can see.  The dialog around a file provides a rich augmentation, just as such can be happening on a blog, or edited RTs of a microblog comment.

Finally, a slightly different twist is shown with documents.  Edited documents (e.g. papers, presentations, spreadsheets) can be created and sent, but there’s little opportunity for cooperative development.  Creating these in a richer way that allows for others to contribute requires a collaborative document (once known as a wiki).  One of my dreams is that we may have collaboratively developed interactives as well, though that still seems some way off.

The point for showing out loud is that point is only a way to get specific feedback, whereas a broadcast mechanism is really about the opportunity to get a more broad awareness and, potentially, feedback.  This leads to a broader shared understanding and continual improvement, two goals critical to organizational improvement.

Let me be the first to say that this isn’t necessarily an important, or even new, distinction, it’s just me practicing what I preach.  Also, I  recognize that the collaborative documents are fundamentally different, and I need to have a more differentiated way to look at these (pointers or ideas, anyone), but here’s my interim thinking.  What say you?


14 October 2014

Types of meaningful processing

Clark @ 8:21 am

In an previous post, I argued for different types and ratios for worthwhile learning activities. I’ve been thinking about this (and working on it) quite a bit lately. I know there are other resources that I should know about (pointers welcome), but I’m currently wrestling with several types of situations and wanted to share my thinking. This is aside from scenarios/simulations (e.g. games) that are the first, best, learning practice you can engage in, of course. What I’m looking for is ways to get learners to do processing in ways that will assist their ability to do.  This isn’t recitation, but application.

So one situation is where the learner has to execute the right procedure. This seems easy, but the problem is that they’re liable to get it right in practice.  The problem is that they still can get it wrong when in real situations. An idea I had heard of before, but was reiterated through Socratic Arts (Roger Schank & cohorts) was to have learners observe (e.g. video) of someone performing it and identifying whether it was right or not. This is a more challenging task than  just doing it right for many routine but important tasks (e.g. sanitation). It has learners monitor the process, and then they can turn that on themselves to become self-monitoring.  If the selection of mistakes is broad enough, they’ll have experience that will transfer to their whole performance.

Another task that I faced earlier was the situation where people had to interpret guidelines to make a decision. Typically, the extreme cases are obvious, and instructors argue that they all are, but in reality there are many ambiguous situations.  Here, as I’ve argued before, the thing to do is have folks work in groups and be presented with increasingly ambiguous situations. What emerges from the discussion is usually a rich unpacking of the elements.  This processing of the rules in context exposes the underlying issues in important ways.

Another type of task is helping people understand applying models to make decisions. Rather than present them with the models, I’m again looking for more meaningful processing.  Eventually I’ll expect learners to make decisions with them, but as a scaffolding step, I’m asking them to interpret the models in terms of their recommendations for use.  So before I have them engage in scenarios, I’ll ask them to use the models to create, say, a guide to how to use that information. To diagnose, to remedy, to put in place initial protections.  At other times, I’ll have them derive subsequent processes from the theoretical model.

One other example I recall came from a paper that Tom Reeves wrote (and I can’t find) where he had learners pick from a number of options that indicated problems or actions to take. The interesting difference was then there was a followup question about why. Every choice was two stages: decision and then rationale. This is a very clever way to see if they’re not just getting the right answer but can understand why it’s right.  I wonder if any of the authoring tools on the market right now include such a template!

I know there are more categories of learning and associated tasks that require useful processing (towards do, not know, mind you ;), but here are a couple that are ‘top of mind’ right now. Thoughts?



8 October 2014

The resurgence of games?

Clark @ 8:44 am

I talked yesterday about how some concepts may not resonate immediately, and need to continue to be raised until the context is right.  There I was talking about explorability and my own experience with service science, but it occurred to me that the same may be true of games.

Now, I’ve been pushing games as a vehicle for learning for a long time, well before my book came out on the topic.  I strongly believe that next to mentored live practice (which doesn’t scale well), (serious) games are the next best learning opportunity.  The reasons are strong:

  • safe practice: learners can make mistakes without real consequences (tho’ world-based ones can play out)
  • contextualized practice (and feedback): learning works better in context rather than on abstract problems
  • sufficient practice: a game engine can give essentially infinite replay
  • adaptive practice: the game can get more difficult to develop the learner to the necessary level
  • meaningful practice: we can choose the world and story to be relevant and interesting to learners

the list goes on.  Pretty much all the principles of the Serious eLearning Manifesto are addressed in games.

Now, I and others (Gee, Aldrich, Shaffer, again the list goes on) have touted this for years.  Yet we haven’t seen as much progress as we could and should.  It seemed like there was a resurgence around 2009-2010, but then it seemed to go quiet again. And now, with Karl Kapp’s Gamification book and the rise of interest in gamification, we have yet another wave of interest.

Now, I’m not a fan of the extrinsic  gamification, but it appears there’s a growing awareness of the difference between extrinsic and intrinsic. And I’m seeing more use of games to develop understanding in at least K12 circles.  Hopefully, the awareness will arise in higher ed and corp too.

As some fear, it’s too costly, but my response is twofold:

  • games aren’t as expensive as you fear; there are lots of opportunities for games in lower price ranges (e.g. $100K), don’t buy into the $1M and up mentality
  • they’re actually likely to be effective (as part of a complete learning experience), compared to many if not most of the things being done in learning

So I hope we might finally go beyond Clicky Clicky Bling Bling, (tarted quiz shows, cheesy videos and more) and get to interaction that actually leads to change.  Here’s hoping!

7 October 2014

Service Thinking and the Revolution?

Clark @ 8:36 am

A colleague I greatly respect, who has a track record of high impact in important positions, has been a proponent of service science.  And I confess that it hadn’t really penetrated.  Yet last week I heard about it in a way that resonated much more strongly and got me thinking, so let me share where it’s leading my thinking, and see what you say.

One time I heard something exciting, a concept called interface ‘explorability‘ when I was doing a summer internship at NASA while a grad student.  When I brought it back to the lab, my advisor didn’t really resonate.  Then, some time later (a year or two)  he was discussing a concept and I mentioned that it sounded a lot like that ‘explorability’, and he suddenly wanted to know more. The point being that there is a time when you’re ready to hear a message. And that’s me with service science.

The concept is considering a mutual value generation process between provider and customer, and engineering it across the necessary system components and modular integrations to yield a successful solution.  As organizations need to be more customer-centric, this perspective yields processes to do that in a very manageable, measurable way.  And that’s the perspective I’d been missing when I’d previously heard about it, but Hastings & Saperstein presented it last week at the Future of Talent event in the form of Service Thinking, which brought the concept home.

I wondered how it compared to Design Thinking, another concept sweeping instructional design and related fields, and it appears to be synergistic but perhaps a superset. While nothing precludes Design Thinking from producing the type of outcome Service Thinking is advocating, I’m inferring that Service Thinking is a bit more systematic and higher level.

The interesting idea for me was to think of bringing Service Thinking to the role of L&D in the organization. If we’re looking systematically at how we can bring value to the customer, in this case the organization, systematically, we have a chance to look at the bigger picture, the Performance & Development view instead of the training view.  If we take the perspective of an integrated approach to meeting organizational execution and innovation needs, we may naturally develop the performance ecosystem.

We need to take a more comprehensive approach, where we integrate technology capabilities, resources, and people into an integrated whole. I’m looking at service thinking, as perhaps an integration of the rigor of systems thinking with the creative customer focus of design thinking, as at least another way to get us there.  Thoughts?

1 October 2014

Constructive vs instructive

Clark @ 8:11 am

A commenter on last week’s post asked an implicit question that caused me to think. The issue was whether the solutions I was proposing are having the learners be self directed or whether it was ‘push’ learning.  And I reckon there’s a bit of both, but I’m fighting for more of a constuctivist approach  than the instructivist model.

I’ve argued in the past for a more active learning, and I think the argument for pure instructivism sets up a straw man (Feuerzeig argued for guided discovery back in ’85!).  Obviously, I think that pure exploration is doomed to failure, as we know that learners can stay in one small corner of a search space without support (hence the coaching in Quest).  However, a completely guided experience doesn’t ‘stick’ as well, either.

Another factor is our target learners.  In my experience, more constructivist approaches can be disturbing to learners who have had more instructivist approaches.  And the learners we are dealing with haven’t been that successful in school, and typically need a lot of scaffolding.

Yet our goals are fairly pragmatic overall (and in general we should be looking for ways to pragmatic in more of our learning). We’re focused on meaningful skills, so we should leverage this.

In this case, I’m moving the design to more and more “here’s a goal, here’re some resources” type of approach where the goal is to generate a work-related integration (requiring relevant cognitive processing).  Even if it’s conceptual material, I want learners to be doing this, and of course the main focus is on real contextualized practice.

I’m pushing a very activity-based pedagogy (and curriculum). Yes, the tasks are designed, but they’re expected to take some responsibility for processing the information to produce outputs. The longer term goal is to increase the challenge and variety as we go through the curriculum, developing learner’s ability to  learn to learn and ability to adapt as well. Make sense?

30 September 2014

Types and proportions of learning activities?

Clark @ 8:49 am

I’ve been on quite the roll of late, calling out some bad practices and calling for learning science. And it occurs to me that there could be some pushback.  So let me be clear, I strongly suggest that the types of learning that are needed are not info dump and knowledge test, by and large.  What does that mean? Let’s break it down.

First, let me suggest that what’s going to make a difference to organizations is not better fact-remembering. There are times when fact remembering is needed, such as medical vocabulary (my go-to example). When that needs to happen, tarted up drill-and-kill (e.g .quiz show templates, etc) are the way to do it.   Getting people to remember rote facts or arbitrary things (like part names) is very difficult. And largely unnecessary if people can look it up, e.g. the information is in the world (or can be).  There are some things that need to be known cold, e.g. emergency procedures, hence the tremendous emphasis on drills in aviation and the military. Other than that, put it in the world, not the head.  Look up tables, info sheets, etc are the solution.  And I’ll argue that the need for this is less than 5-10% of the time.

So what is useful?  I’ll argue that what is useful is making better decisions.  That is, the ability to explain what’s happened and react, or predict what will happen and make the right choice as as consequence.  This comes from model-based reasoning.  What sort of learning helps model-based reasoning? Two types, in a simple framework. You need to process the models to help them be comprehended, and use them in context to make decisions with the consequences providing feedback.  Yes, there likely will be some content presentation, but it’s not everything, and instead is the core model with examples of how it plays out in context. That is, annotated diagrams or narrated animations for the models; comic books, cartoons, or videos for the examples.  Media, not bullet points.

The processing that helps make models stick includes having learners generate products: giving them data or outcomes and having them develop explanatory models. They can produce summary charts and tables that serve as decision aids. They can create syntheses and recommendations.  This really leads to internalization and ownership, but it may be more time-consuming than worthwhile. The other approach is to have learners make predictions using the models, explaining things.  Worst case, they can answer questions about what this model implies in particular contexts.  So this is a knowledge question, but not a “is this an X or a Y”, but rather “you have to achieve Z, would you use approach X, or approach Y”.

Most importantly, you need people to use the models to make decisions like they’ll be making in the workplace.  That means scenarios and simulations.  Yes, a mini-scenario of one question is essentially a multiple choice (though better written with a context and a decision), but really things tend to be bundled up, and you at least need branching scenarios. A series of these might be enough if the task isn’t too complex, but if it’s somewhat complex, it might be worth creating a model-based simulation and giving the learners lots of goals with it (read: serious game).

And, don’t forget, if it matters (and why are you bothering if it doesn’t), you need to practice until they can’t get it wrong.  And you need to be facilitating reflection.  The alternatives to the right answer should reflect ways learners often go wrong, and address them individually. “No, that’s not correct, try again” is a really rude way to respond to learner actions.  Connect their actions to the model!

What this also implies is that learning is much more practice than content presentation.  Presenting content and drilling knowledge (particularly in about an 80/20 ratio), is essentially a waste of time.  Meaningful practice should be more than half the time.  And you should consider putting the practice up front and driving them to the content, as opposed to presenting the content first.  Make the task make the content meaningful.

Yes, I’m making these numbers up, but they’re a framework for thinking. You should be having lots of meaningful practice.  There’s essentially no role for bullet points or prose and simplistic quizzes, very little role for tarted up quizzes, and lots of role for media on the content side and  branching scenarios and model-driven interactions on the interaction side.  This kind of is an inverse of the tools and outputs I see.  Hence my continuing campaign for better learning.  Make sense?

24 September 2014

Better Learning in the Real World

Clark @ 8:25 am

I tout the value of learning science and good design.  And yet, I also recognize that to do it to the full extent is beyond most people’s abilities.  In my own work, I’m not resourced to do it the way I would and should do it. So how can we strike a balance?  I believe that we need to use smart heuristics instead of the full process.

I have been talking to a few different people recently who basically are resourced to do it the right way.  They talk about getting the right SMEs (e.g. with sufficient depth to develop models), using a cognitive task analysis process to get the objectives, align the processing activities to the type of learning objective, developing appropriate materials and rich simulations, testing the learning and using  feedback to refine the product, all before final release.  That’s great, and I laud them.  Unfortunately, the cost to get a team capable of doing this, and the time schedule to do it right, doesn’t fit in the situation I’m usually in (nor most of  you).  To be fair, if it really matters (e.g. lives depend on it or you’re going to sell it), you really do need to do this (as medical, aviation, military training usually do).

But what if you’ve a team that’s not composed of PhDs in the learning sciences, your development resources are tied to the usual tools, your budgets far more stringent, and schedules are likewise constrained? Do you have to abandon hope?  My claim is no.

Law of diminishing returns curveI believe that a smart, heuristic approach is plausible.  Using the typical ‘law of diminishing returns’ curve (and the shape of this curve is open to debate), I  suggest that it’s plausible that there is a sweet spot of design processes that gives you an high amount of value for a pragmatic investment of time and resources.  Conceptually, I believe you can get good outcomes with some steps that tap into the core of learning science without following the letter.  Learning is a probabilistic game, overall, so we’re taking a small tradeoff in probability to meet real world constraints.

What are these steps? Instead of doing a full cognitive task analysis, we’ll do our best guess of meaningful activities before getting feedback from the SME.  We’ll switch the emphasis from knowledge test to mini- and branching-scenarios for practice tasks, or we’ll have them take information resources and use them to generate work products (charts, tables, analyses) as processing.  We’ll try to anticipate the models,  and ask for misconceptions & stories to build in.  And we’ll align pre-, in-, and post-class activities in a pragmatic way.  Finally, we’ll do a learning equivalent of heuristic evaluation, not do a full scientifically valid test, but we’ll run it by the SMEs and fix their (legitimate) complaints, then run it with some students and fix the observed flaws.

In short, what we’re doing here are  approximations to the full process that includes some smart guesses instead of full validation.  There’s not the expectation that the outcome will be as good as we’d like, but it’s going to be a lot better than throwing quizzes on content. And we can do it with a smart team that aren’t learning scientists but are informed, in a longer but still reasonable schedule.

I believe we can create transformative learning under real world constraints.  At least, I’ll claim this approach is far more justifiable than the too oft-seen approach of info dump and knowledge test. What say you?

23 September 2014

Design like a pro

Clark @ 8:20 am

In other fields of endeavors, there is a science behind the approaches.  In civil engineering, it’s the properties of materials.  In aviation, it’s aeronautical engineering.  In medicine, it’s medical science.  If you’re going to be a professional in your field, you have to know the science.  So, two questions: is there a science of learning, and is it used.  The answers appear to be yes and no.  And yet, if you’re going to be a learning designer or engineer, you should know the science and be using it.

There is a science of learning, and it’s increasingly easy to find.  That’s the premise behind the Serious eLearning Manifesto, for instance (read it, sign it, use it!).  You could read Julie Dirksen’s Design for How People Learn as a very good interpretation of the science.  The Pittsburgh Science of Learning Center is compiling research to provide guidance about learning if you want a fuller scientific treatment.  Or read Bransford, et al’s summary of the science of How People Learna very rich overview.  And Hess & Saxberg’s recent Breakthrough Leadership in the Digital Age: Using Learning Science to Reboot Schooling is both a call for why and some guidance on how.

Among the things we know are that rote and abstract information isn’t retained, knowledge test doesn’t mean ability to do, getting it right once doesn’t mean it’s known, the list goes on.  Yet, somehow, we see elearning tools like ‘click to learn more’ (er, less), tarted up quiz show templates to drill knowledge, easy ways to take content and add quizzes to them, and more.  We see elearning that’s arbitrary info dump and simplistic knowledge test.  Which will have a negligible impact on anything meaningful.

We’re focused on speed and cost efficiencies, not on learning outcomes, and that’s not professional.  Look, if you’re going to do design, do it right.   Anything less is really malpractice!

17 September 2014

Learning in 2024 #LRN2024

Clark @ 8:14 am

The eLearning Guild is celebrating it’s 10th year, and is using the opportunity to reflect on what learning will look like 10 years from now.  While I couldn’t participate in the twitter chat they held, I optimistically weighed in: “learning in 2024 will look like individualized personal mentoring via augmented reality, AI, and the network”.  However, I thought I would elaborate in line with a series of followup posts leveraging the #lrn2024 hashtag.  The twitter chat had a series of questions, so I’ll address them here (with a caveat that our learning really hasn’t changed, our wetware hasn’t evolved in the past decade and won’t again in the next; our support of learning is what I’m referring to here):

1. How has learning changed in the last 10 years (from the perspective of the learner)?

I reckon the learner has seen a significant move to more elearning instead of an almost complete dependence on face-to-face events.  And I reckon most learners have begun to use technology in their own ways to get answers, whether via the Google, or social networks like FaceBook and LinkedIn.  And I expect they’re seeing more media such as videos and animations, and may even be creating their own. I also expect that the elearning they’re seeing is not particularly good, nor improving, if not actually decreasing in quality.  I expect they’re seeing more info dump/knowledge test, more and more ‘click to learn more‘, more tarted-up drill-and-kill.  For which we should apologize!

2. What is the most significant change technology has made to organizational learning in the past decade?

I reckon there are two significant changes that have happened. One is rather subtle as yet, but will be profound, and that is the ability to track more activity, mine more data, and gain more insights. The ExperienceAPI coupled with analytics is a huge opportunity.  The other is the rise of social networks.  The ability to stay more tightly coupled with colleagues, sharing information and collaborating, has really become mainstream in our lives, and is going to have a big impact on our organizations.  Working ‘out loud’, showing our work, and working together is a critical inflection point in bringing learning back into the workflow in a natural way and away from the ‘event’ model.

3. What are the most significant challenges facing organizational learning today?

The most significant change is the status quo: the belief that an information oriented event model has any relationship to meaningful outcomes.  This plays out in so many ways: order-taking for courses, equating information with skills, being concerned with speed and quantity instead of quality of outcomes, not measuring the impact, the list goes on.   We’ve become self-deluded that an LMS and a rapid elearning tool means you’re doing something worthwhile, when it’s profoundly wrong.  L&D needs a revolution.

4. What technologies will have the greatest impact on learning in the next decade? Why?

The short answer is mobile.  Mobile is the catalyst for change. So many other technologies go through the hype cycle: initial over-excitement, crash, and then a gradual resurgence (c.f. virtual worlds), but mobile has been resistant for the simple reason that there’s so much value proposition.  The cognitive augmentation that digital technology provides, available whenever and wherever you are clearly has benefits, and it’s not courses!  It will naturally incorporate augmented reality with the variety of new devices we’re seeing, and be contextualized as well.  We’re seeing a richer picture of how technology can support us in being effective, and L&D can facilitate these other activities as a way to move to a more strategic and valuable role in the organization.  As above, also new tracking and analysis tools, and social networks.  I’ll add that simulations/serious games are an opportunity that is yet to really be capitalized on.  (There are reasons I wrote those books :)

5. What new skills will professionals need to develop to support learning in the future?

As I wrote (PDF), the new skills that are necessary fall into two major categories: performance consulting and interaction facilitation.  We need to not design courses until we’ve ascertained that no other approach will work, so we need to get down to the real problems. We should hope that the answer comes from the network when it can, and we should want to design performance support solutions if it can’t, and reserve courses for only when it absolutely has to be in the head. To get good outcomes from the network, it takes facilitation, and I think facilitation is a good model for promoting innovation, supporting coaching and mentoring, and helping individuals develop self-learning skills.  So the ability to get those root causes of problems, choose between solutions, and measure the impact are key for the first part, and understanding what skills are needed by the individuals (whether performers or mentors/coaches/leaders) and how to develop them are the key new additions.

6. What will learning look like in the year 2024?

Ideally, it would look like an ‘always on’ mentoring solution, so the experience is that of someone always with you to watch your performance and provide just the right guidance to help you perform in the moment and develop you over time. Learning will be layered on to your activities, and only occasionally will require some special events but mostly will be wrapped around your life in a supportive way.  Some of this will be system-delivered, and some will come from the network, but it should feel like you’re being cared for in the most efficacious way.

In closing, I note that, unfortunately,my Revolution book and the Manifesto were both driven by a sense of frustration around the lack of meaningful change in L&D. Hopefully, they’re riding or catalyzing the needed change, but in a cynical mood I might believe that things won’t change near as much as I’d hope. I also remember a talk (cleverly titled: Predict Anything but the Future :) that said that the future does tend to come as an informed basis would predict with an unexpected twist, so it’ll be interesting to discover what that twist will be.

16 September 2014

On the Road Fall 2014

Clark @ 8:05 am

Fall always seems to be a busy time, and I reckon it’s worthwhile to let you know where I’ll be in case you might be there too! Coming up are a couple of different events that you might be interested in:

September 28-30 I’ll be at the Future of Talent retreat  at the Marconi Center up the coast from San Francisco. It’s a lovely spot with a limited number of participants who will go deep on what’s coming in the Talent world. I’ll be talking up the Revolution, of course.

October 28-31 I’ll be at the eLearning Guild’s DevLearn in Las Vegas (always a great event; if you’re into elearning you should be there).  I’ll be running a Revolution workshop (I believe there are still a few spots), part of  a mobile panel, and talking about how we are going about addressing the challenges of learning design at the Wadhwani Foundation.

November 12-13 I’ll be part of the mLearnNow event in New Orleans (well, that’s what I call it, they call it LearnNow mobile blah blah blah ;).  Again, there are some slots still available.  I’m honored to be co-presenting with Sarah Gilbert and Nick Floro (with Justin Brusino pulling strings in the background), and we’re working hard to make sure it should be a really great deep dive into mlearning.  (And, New Orleans!)

There may be one more opportunity, so if anyone in Sydney wants to talk, consider Nov 21.

Hope to cross paths with you at one or more of these places!

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