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Clark Quinn’s Learnings about Learning

Five trends for 2021

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As frequently happens, I get asked for my predictions. And, of course, I have reservations. Here’s a video that provides the qualifications, and five trends for 2021 that I’d expect, or like, to see.

And the script:

Hi, I‘m Clark Quinn, of Quinnovation (a boutique learning experience design strategy consultancy). I was recently asked about what trends I thought would be seen next year.  

Two relevant quotes to set the stage. For one, Alan Kay famously said “the best way to predict the future is to invent it.” So I tend to talk about trends we should see. The other is “never predict anything, particularly the future.” I heard an expert talk about having looked at predictions and outcomes, and the noticeable trend is that it went as expected, with one unforeseen twist. So, expecting I‘ll get it wrong, here are some trends I‘m either expecting or keen to see:

The first trend I‘m seeing and think will continue is an emphasis on learning science. And that‘s all to the good! Admittedly, I‘m part of this, what with running a course on learning science and having a forthcoming book on the topic. But I‘m seeing more and more people talking about it, and not all hype and even mostly right! There are more books, the Learning Guild‘s regular research reports are good, the launch of an event past summer and an associated new society focused on evidence-based learning (the Learning Development Accelerator) are all signs of growing momentum.

Second, when there‘s a lot of hype about something, it tends to be followed by a backlash.  This may be farther out than 2021, but with all the buzz about AI, I think we might see some more awareness of limitations. Yes, it can do some very useful things, but it also isn‘t a panacea. We‘re seeing a growing awareness of the problems with bias in data sets, the limitations of ungrounded knowledge, and concerns about the human costs.  

Three. On a related note, then, I expect more emphasis on the importance of meaningful practice. This comes from learning science, but also the focus on engagement. Thus, the push for Short Sims, and better written multiple choice questions, and in general a focus on ‘do‘, not know.   Hopefully, we‘ll see tool vendors aligning their content and assessment capabilities towards designing scenarios and contextualized practice, along with specific feedback for each wrong answer and support for reflection.

Fourth, I hope for a push towards content systems as well. This, too, may not be in the short term, but ultimately we have to realize that hardwiring experiences may make sense for formal systems, but not for adaptive learning.LXPs are a good move here, even if misnamed (really, they‘re smart portals, not learning experience platforms). Ultimately, we‘ll be better off if we can deliver content by description and rules, like recommendation system, rather than by having to handcraft content to create a ‘one-size fits all‘ solution.  

Finally, I think that our collaboration tools haven‘t lived up to the promise of technology. They‘re very much oriented towards particular modes, instead of supporting really rich interaction. This, too, is more long term, but we really should be able to talk together while working to create representations that capture our evolving thinking. Easily and elegantly! There‘s real opportunity here to engage multiple representations in an elegant suite.  

So there you have it, a wishful list of five trends for 2021. So what do you expect, or hope, to see?

Foundations of Learning Science

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Another video, this time (ok, again ;) about learning science.

They like me to do this to push the course, but I did hear the feedback on LinkedIn that the video format works. Nice to know. As always, also the script.

And, announcing one other thing…

I‘ve argued in an earlier post for the value of learning science, but I want to go a little deeper. I want to talk a little about the evolution, and a little bit about what‘s involved. It‘s about establishing the foundations of learning science.

And I‘ve mentioned in a previous post  that learning science is interdisciplinary, and relatively new. While education had been proceeding for a long time, the approaches were ad hoc. Experimental science itself didn‘t emerge ‘til the medieval ages at the earliest. My take is that most of school still replicated what had been done since the Prussians invented school, somewhat modeled on religious lectures. The notion of scientific education had yet to emerge.

The first real systematic study of learning came from the field of educational psychology. Here, the focus was on schooling, and included cultural and motivational factors.

A different approach came from behavioral psychology roots. During World War II, the military was faced with training many soldiers, and behavioral psychologists created the field of instructional design. Here, the focus was more on training, including the influence of media and elements of instruction.

Learning science as a field was arguably created when the International Society for the Learning Science was created in the 1990s, perhaps sparked by the creation of the Institute of the Learning Sciences at Northwestern University. This is an integrative approach that looks beyond schools and training to more forms of learning including informal learning and even machine learning.

Having been involved in one way or another with all of these, I tend to create a synthesis. I think the care is cognitive science: how we process information. While there are neural underpinnings, most of the results and prescriptions operate at the cognitive level, or above. Within the information processing cycle we cover core processes like attention, elaboration, and retrieval. This is our core mental architecture.

Interesting results for learning emerge from this architecture, including the role of models and examples, and the core importance of practice. A post-cognitive perspective reflects that our thinking isn‘t formally logical, but instead is emergent, distributed, and social.

Two other important areas are the emotional aspects of learning, and meta-learning. The former is more the conative area of intent to learn, e.g. motivation and anxiety, rather than the affective area of personality. The latter has to do with learning to learn, including looking at our own learning processes.

All these affect the elements that contribute to learning. Our introductions, concepts, examples, practice, and closings should reflect what‘s known about learning. And the components of science and engagement need to be elegantly integrated to yield the best outcomes.

Of course, these foundations of learning science are what I cover in the learning science courses I‘m offering through HR.Com and the Allen Academy. Stay tuned for more ;).

AI and Meaningful Practice

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Again, a video of an idea I want to talk about. This time about AI and Meaningful Practice (just around 2 minutes). I welcome your thoughts.

By the way, I’m experimenting with video as a blog mechanism. A colleague mentioned that no one remembers the author of an article or post, but they do remember the speaker in a video. And much as I hate to do it, I do want people to associate my ideas with me! I welcome, very much, your feedback on this too!

Script:

Hi, I‘m Clark Quinn, Executive Director of Quinnovation (my vehicle for learning experience design strategy).    Today I want to talk about an insight I had, sparked in a conversation I had with a colleague.  We were talking about learning (of course), and the difference between knowledge versus practice.

I was reminded that we‘re now seeing AI technologies that can parse content and then answer questions about it.  We even see ones that can ask you questions about the content!  Which is part of learning.  But not all.

My realization was that, increasingly, these systems will take over this form of content presentation.  That is, we‘ll write a white paper, and an AI will parse it, then present it, and drill it.  Which is, after all, way too much of corporate learning.  See, for instance, the Serious eLearning Manifesto as a response.

Now, I‘ve always maintained that such systems aren‘t sufficient for real learning.  Meaningful learning includes more: motivation and contextualized practice.  Content presentation and quiz questions may be necessary, but by no means are they sufficient.  And that for now and the foreseeable future, AI will not be able to create those elements.  

This is the job of LXD: integrating learning science and deep engagement into experiences that transform us.  Which means that what L&D needs to do is stop doing information dump and knowledge test,  and learn how to do real learning experience design!  That, I suggest, is a noble pursuit (and, to be fair, what we should have been doing all along).  

Of course, there‘s also the necessary new role, per my last post/video, of being a facilitator of informal learning.  Coupling the optimal execution with continual innovation.  But, for now, I‘m suggesting we truly have to master everything that makes learning work, in particular meaningful practice.

That‘s my take, I welcome hearing yours.  Thanks for watching!

 

The Future of L&D? A pitch

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I was talking with a colleague the other day, and got a wee bit dramatic. I also thought it was an important point. So here, for your dining enjoyment, I’ve roughly recreated the pitch (in 3 mins and 30 secs):

I hope this makes sense. I welcome your thoughts and feedback.

The Script:

Hi, I‘m Clark Quinn, of Quinnovation, and I‘ve been around the elearning for well-nigh forever, and around L&D for the past couple of decades.  So…I joke that:

L&D isn‘t doing near what it could and should, and what it is doing, it‘s doing badly.  Other than that, it‘s fine.  

Seriously, I think there‘s the obviously important role for L&D,  but also a really important opportunity.  

Things aren’t getting any simpler.    We‘re facing increasing complexity and uncertainty.  And, going forward, I suggest, optimal execution is only the cost of entry.  Continual innovation will be the necessary differentiator.  That is, we will have to do well what we know we have to do,  but we also have to become agile, nimble, and able to pivot in the face of change.  So that means doing courses right, when courses are the answer.  That‘s the optimizing role, going beyond being efficient to being effective.

And it also means that organizations will have to get good at problem-solving, research, design, and more.The thing is with those things, when you start you don‘t know the answer.  That is: They. Are. Learning!  And that is the important opportunity.

Going back to being effective, that means that when we design courses, we need to effectively integrate learning science with true engagement.  Deep LXD,  not tarted up quiz shows and ‘click to see more‘. And, we should only do that when it‘s the right answer!  It‘s not ‘we need a course on this‘, but instead  “we can identify that we have a skills gap and we need to improve our performance”.

And then, it‘s about facilitating social and informal learning:  tapping into the power of our people, creating a learning culture, assisting the organization in systematic in good practices.  

How do we get there?  I argue there are two major steps.  First, we need to measure,  and here I mean more than just efficiency.  It‘s not how much it costs to have a bum in a seat for an hour,  but instead whether that bum in that seat for that hour does the organization any good.  Right now, we don‘t know whether our efforts are really moving any needles.  It‘s a matter of faith that if it look like school, it must be learning.

Second, it means we have to start practicing those principles within L&D: smart experimentation; collaborating; and learning continually and out loud.  We can‘t have credibility if we haven‘t walked the walk.It won‘t happen overnight.  We‘ll have to build back our reputation as scrutable practitioners.  We‘ll have to continually educate.  And likely have to do the ‘better to seek forgiveness than permission‘.

Here‘s the vision I see.  When we‘re not only ensuring good execution on what we know we have to do,  but are responsible for the ongoing success of the organization,  we‘ve moved to an indispensable position.  We‘re key to success in the toughest times!  As key as IT and Finance.  Other groups can and will take it on if we don‘t  but we‘re supposed to be the ones who understand how we learn.  And learning, going forward, is the key to not just surviving, but thriving.  Our orgs need it, the employees need it, and our professional standards demand it.  So let‘s do it.    Let‘s reengineer our status in, and value to, the organization.

Thanks for listening.

Flow, workflow, and learning

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On LinkedIn, a colleague asked “Why do people think that integrating content in the flow of work equals learning in the flow of work?” An apt question. My (flip) response was “because marketing”. And I think there’s a lot to that. But, a comment prompted me to think a little bit deeper, because ‘flow’ is its own meaningful concept and we need to be careful about meaning. So here are some reflections on flow, workflow, and learning.

The response that triggered my reflection was:

I can’t recall the last time I told someone that I was in the “flow” of work today and learned so much!!

Flow state(Which is pretty funny!) The comment was a bit pointed, but it made me think about being in the ‘flow’ state, and the relationship with learning. I’ve previously pointed out how Csikszentmihalyi’s Flow and Vygotsky’s Zone of Proximal Development  (ZoPD) are essentially the same. If the difficulty is too far above your skill level, the experience is frustrating. If it’s too easy, it’s boring. And in between is the flow state, and where learning happens.

Now, when we’re in the ‘flow’ at work (which is different than being in the workflow), we’re performing optimally. And I’m not sure learning happens there. Similarly with the ZoPD. You’re working and I’m not sure learning happens  then. When I state that learning is action and reflection, I think reflection is a necessary component.

Now, the original complaint talked about learning in the workflow, and opined that content in the workflow won’t necessarily equal learning. Another comment pointed out what I believe is often conflated with “workflow learning”, and that’s performance support. There are lots of reasons that we might want content in the workflow to help us succeed, but it may have nothing to do with learning. If, indeed, learning is to happen, it might need some content, and feedback, and so actually break the flow!

Now, I also recognize that many times we’re in the flow of work, but not in the ‘flow’  zone. So, we could definitely be learning in the workflow. And it happens by deciding to look up the answer to some contextually relevant question. Or from a comment from a person. But it’s a bit different than being in the zone, and we’d like to be there in our work too!

And, I wonder whether Vygostky’s ZoPD really aligns with the Flow Zone, or if it needs to be coupled with some offline reflection. It’s certainly possible. Maybe the flow zone is a superset of the ZoPD. More to ponder.

There isn’t a real revelation here about flow, workflow, and learning, other than we have to keep our concepts straight. We need to recognize when we’re supporting performance, and when we’re learning. And we need to be clear about workflow, and being in the flow zone. And there may be more here to unpack. Thoughts?

 

Ritual

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I’ve talked before about the power of ritual, but while powerful, it also seemed piecemeal. That is, there were lots of hints, but not a coherent theory. That has now changed. I recently found a paper by Nicholas Hobson & colleagues (Schroeder, Risen, Xylagatas, & Inzlicht; warning, PDF) titled  The Psychology of Rituals  that creates an integrated framework. And while my take simplifies it down, I found it interesting.

At core, what the model suggests is that there are two components that are linked together. The first element are things that involve the senses. The second element are the semantics we’re looking to create allegiance and adherence too. And there are important elements about this relationship.

There are a number of elements that are on tap for involving the senses. Certain movements, sounds, and words said or to be spoken can be used. There can also be food, drink, smells, and more. Objects also. Timing is an element; at the micro level of things in order, and at the macro level of the triggers for the ritual.

Semantics come, of course, from your needs. It can be about things you want people to believe, or a set of values you want people to subscribe to. Or, of course, both. From the design purpose, I’d suggest it’s about agreeing to be a member of a community of practice; to undertake certain actions when appropriate, and to uphold certain values.

Interestingly, according to their model, the relationship between the two is effectively arbitrary. That is, there is no intrinsic relationship between what you’re signifying, and how you do so. Rituals are about the practices. Which means you could in theory do just about  anything to make the relationship.

The other thing is that the ritual has to be invariable in its aspects. You define it, and so do it. Note that the execution can vary considerably; from several times a day to upon certain triggering conditions. So, for instance, having completed a course, or before engaging in certain activities.

While such a definition gives us lots of freedom, it also doesn’t necessarily serve as a guide for design. Still, thinking about it in this way does suggest the utility in developing deeply held beliefs and appropriately practiced behaviors. At least, that’s how I see it. You?

What is wrong with (higher) education?

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I was having a conversation with a colleague, sparked by dropping enrollments in unis. Not surprisingly, we ended up talking about flaws in higher education. He suggested that they don’t get it, and I agreed. He was thinking that they get the tech, but not the learning. I think it’s more complex. There are those that get some parts of the learning right. Just not enough, and not all of it right. Thinking further, post-convo, it occurs to me that there is a layer beneath the surface that matters. So I want to consider what is wrong with higher education.

And, let’s be clear, I’m  not talking about the problems with tuition and administration. Yes, tuition’s risen faster than the cost of living. And yes, there’s little commercial pressure to keep universities free from the persistent creep of increasing administration. I saw an interesting article talking about how universities without a solid financial foundation,  and ones without a good value proposition, will perish. It’s the latter I’m talking about.

I previously mentioned the three pillars I think create a valid learning offer:

  • a  killer learning experience,
  • being a partner in your success
  • and developing you as an individual.

I suggest that all three are doable, but it occurred to me that there’s a bit more to unpack.

The ‘being a partner in your success’ bit is most frequently seen. Here, it’s about looking for signs of trouble and being proactive about reaching out and assisting. It’s not ‘sink or swim’, but recognizing there can be troubles and helping learners cope. The Predictive Analytics work that Ellen Wagner did is the type of opportunity we have here.

The ‘developing you as an individual’ is really building your more general skills: communicating, working with others, a positive attitude, knowing how to search, etc. And, of course, knowing how to continue to learn. Given the rate of change, most of what you learn as the core of a degree may well be out of date in short order!  But you can’t address these skills on their own, they’re specifically about how you do domain things.  And that’s a layer I’ve yet to see.

And the ‘killer learning experience’ is a second area where I think folks still aren’t doing well. My short (and admittedly cheeky) statement about education is that they’re wrong on two things, the curriculum and pedagogy, other than that they’re fine. Most universities aren’t doing a good job of curriculum, focusing on knowledge instead of skills. And some are moving in a good direction. Startups are addressing this area as well.

The other problem is the pedagogy. There’re two elements here: the learning design, and engagement. Too often, it’s still the ‘information dump and knowledge test’. But even when that’s right, making it truly meaningful for the learners is sadly neglected. Even professors who care often forget to put the ‘why’ into the syllabus.

In short, what is wrong with higher education is the ability to successfully execute on  all these points. (It’s true for other education, too, but…) I’ve seen efforts that address one, or two (and plenty that get none right). However, as of yet, I have not seen anyone doing it the way it could be done.  It’s doable, but not without some serious attention to not only the elements, but their successful integration. And it’s important enough that we should be doing it. At least, that’s what I think. So, what do  you think?

Learner-centered, or…

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I saw a post the other day that talked about ’empathy’, and I’m strongly supportive. But along the way they cited another topic that I’ve had mixed feelings about. So I thought it was time to address it. I’m wondering about ‘learner-centered’, and it may seem churlish to suggest otherwise. However, let me make the case for an alternative.

First, ‘learner-centered’ (apparently also known as ‘student centered‘) is used to take the focus away from the teacher. And I approve. It’s too easy, without awareness, to put the emphasis on ‘teaching’, and you’re on a slippery slope to lectures and knowledge tests. I’m all for that. However, I’m worried about a down-side.

My worry, with learner-centered learning, is that we may become too accommodating. It could be too easy to cater to learners. For instance, one belief that persists is that learning should be ‘fun’. Which is wrong. We know that we need ‘desirable difficulty’ (ala Bjork). That’s why I’ve lobbied for ‘hard fun‘. We could also use learner-centered to make the case for adapting to preferred learning styles. Which, too, would be wrong.

Obviously, you can also argue that learners need meaningful learning, so a learner-centered approach would be appropriate. But I want to suggest another candidate. One that, I argue, leads to good outcomes without carrying any opportunity for baggage.

I’m arguing for ‘learning-centered’, not learner-centered. That is, the focus is on the learning needed, not on the learner. Which isn’t to say we leave the learner out of the equation, but the question then becomes: what does this mean?

I’m suggesting that the key is learning focused on:

  • meaningful outcomes
  • aligned design
  • addressing learners’ prior knowledge
  • addressing learners’ emotions: motivation, trust, anxiety, confidence

And, look, I get that folks talking about ‘learner-centered’ will argue that they’re talking about the same things. I just see it also carrying a greater potential for focusing on the learner  at the expense of learning. And, in general, I would expect to be wrong. That is, most folks aren’t going to go awry. But is there an alternative without the problems?

So, the question is whether ‘learning-centered’ has similar pitfalls, or is it more likely to lead to better outcomes? And I don’t know the answer. It’s just a concern that I’ve felt, and thought I’d raise. Now it’s your turn!  What are your thoughts on the phrase ‘learner-centered’?

Learning science again

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In an earlier post, I made a defense  of cognitive psychology (really, to me, cognitive science, a bigger umbrella). And, previously, the case for learning science. And I’m coming at learning science again, with a personal interest.

Learning science is an interdisciplinary field, including cognitive science, educational psychology, and more. Having emerged relatively late, it’s now finding a solid footing with a unified approach to looking at how we learn, and how to facilitate it.

Most importantly, having this knowledge is critical for those who practice learning. In fact, I’ve railed against learning malpractice, and that’s a legitimate concern. We, should, as professionals, have a solid basis for our decisions. Just as you wouldn’t want your doctor not to know biochemistry and biophysics, and your electrician not to understand voltage and current, you similarly should want your instructional designers to understand how learning proceeds.

Yet, sad to say, it’s not the case that what we see in practice is well-grounded in what learning science tells us. Such that several of us banded together to prescribe what  should be done!

It goes beyond courses, of course. We shouldn’t be using courses when job aids will suffice, as cognitive science tells us. (Our brains are bad at remembering rote, abstract, arbitrary, and voluminous information.) We should be facilitating informal learning as well.

All of this, done right, depends on understanding learning science, again. Seriously, everything that L&D does largely boils down to knowing how our brains work. And the better we know it, the better we can make decisions. This includes avoiding myths, buying platforms and services, designing experiences, facilitating learning, and more.

So what can you do? There are a fair bit of resources out there already. I’ve created a reading list. I’ll have more to announce soon. I can also announce that I’ll be running a learning science (er, effective learning strategies) workshop, through HR.com. It’s a five week session, starting Oct 21. Cog Sci 101, learning artifacts, social/emotional/cultural, I’ve tried to give a good coverage.  I believe, as the first one, it has a ‘pilot’ pricing!  Whether I see you there or not, I hope you do ensure a good basis for your practice.

Skills, competencies, and moving forward

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I was asked, recently, about skills versus competencies. The context was an individual who saw orgs having competency frameworks, but only focusing on skill development. The question was where the focus should be. And I admit I had to look up the difference first! But then I could see where the emphasis should be on skills, competencies, and moving forward.

Now, the reason I joined with IBSTPI (the International Board for Standards in Training, Performance, and Instruction) was to learn more about competencies. So I didn’t feel inadequate looking it up (and probably should’ve asked my colleagues), but my search revealed a consistent viewpoint that kept me from having to bother them. The story was that there are individual skills, but that it takes more to do a job.

Competencies are suites of skills, knowledge, and attitudes* that create the ability to apply them in context to accomplish goals.  So you may be able to address customer objections, but there’s more to closing a sale than that. Competencies are aggregates of skills; they’re not just focused on what, but how. They’re a richer picture, based upon performance.

Should you care? It seems to me that you should. The clear implication is that if you only focus on skills, you may be missing other elements. You could develop skills and still not develop the ability to succeed. Thus, organizations are increasingly needing to focus on contextualized abilities to perform.

I’ll go further. In the days of optimizing performance, skills could potentially be sufficient. You knew what you had to do, and you had to do it. However, increasingly optimal execution is only the cost of entry, and continual innovation is the only sustainable differentiator. And that, I suggest, comes from competencies beyond skills.

Increasingly, you see orgs moving to competency-based hiring as well as development. Performance management likewise benefits from focusing on competencies.

Overall, my take is that when you’re looking at skills, competencies, and moving forward, competencies offer more power.

*”attitude” added based upon sound critique from Paul Kirschner.