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Clark Quinn’s Learnings about Learning

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 Given the rate of change, most of what you learn as the core of a degree may well be out of date in short order!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.

In Defense of Cognitive Psychology

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A recent Donald Clark post generated an extension from Stephen Downes. I respect both of these folks as people and as intellects, but while I largely agreed with one, I had a challenge with the other. So here’s a response, in defense of cognitive psychology. The caveat is that my Ph.D. is  in Cognitive Psychology, so I may be defensive and biased, but I’ll try to present scrutable evidence.

Donald Clark’s post unpacks the controversies that surround efforts to measure the complicated concept of ‘intelligence’.  He starts with the original Binet measure, and talks about how it’s been misused and has underlying problems. He goes through multiple intelligences, and emotional intelligence as well, similarly unpacking the problems and misuses. I’m reminded of Todd Rose’s  End of Average,  which did a nice job of pointing out the problems of trying to compress complex phenomena into single measures.

He goes on to talk about how it may be silly to talk about intelligence. His argument talks about all the different ways computers can do impressive computational tasks, under the rubric  artificial intelligence (AI). While I laud the advances, my focus still remains on IA (intelligence augmentation), that is, using computers in conjunction with our own capability rather than purely on AI.

Stephen Downes responded to Donald’s article with a short piece. In it, he takes up the story of intelligence and argues that education and cognitive psychology have put on layers of ‘cruft’ (“extraneous matter“) on top of the neural underpinnings. And I have a small problem with that. In short, I think that the theories that have arisen have provided useful guidance for designing systems and learnings that wouldn’t have emerged from strictly neural explanations.

Take, for example, cognitive load. John Sweller’s theory posits that there are limits to our mental resources. Thus, having extraneous material can interfere with the ability to process what’s necessary. And it’s led to some important results on things like the importance of worked examples, and making useful diagrams.

We can also look to principles like Bjork’s desirable difficulty. Here, the type of practice matters (as also embodied in Ericsson’s deliberate practice), needing to be at the right level.  This might be more easily derivable from neural net models, but still provided a useful basis for design.

I could go on: the value of mental models, what makes examples work, the value of creating a motivating introduction, and so on. I’d suggest that these aren’t obvious (yet) from neural models. And even if they are, they are likely more comprehensible from a cognitive perspective than a neural. Others have argued eloquently that neural is the wrong level of analysis for designing learning.

I will suggest, in defense of cognitive psychology, that the phenomena observed provide useful frameworks. These frameworks give us hooks for developing learning experiences that are more complicated to derive from neural models. As I’ve said, the human brain is arguably the most complex thing in the known universe.  Eventually, our neural models may well advance enough to provide more granular and accurate models, but right now there’s still a lot unknown.

So I’m not ready to abandon useful guidance, even if some of it is problematic. Separating out what’s useful from what’s been overhyped may be an ongoing need, but throwing it all away seems premature. That’s my take, what’s yours?

A heuristic approach to motivation

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I’ve been pondering more about curiosity and ‘making it meaningful’ and how we might work on motivation to make learning truly meaningful. I’v come up with a rough cut. So, here’s a proposal for a heuristic approach to motivation.

As I mentioned, the desired true intrinsic motivation may be a goal too far. When possible, perhaps in a deeply specialized field, I’d go for it. In fact, that’s my first recommendation:

1. If there‘s a surprising answer to a question that‘s directly relevant, use it

I’ve seen folks do this by asking questions that the audience is likely to choose one answer, and it’s counter-intuitively wrong. Here, it has to be directly relevant to the question! For instance, asking in a ‘how to do multiple choice right’ class what they think is the right number of choices (turns out: 3). This is close to true intrinsic motivation, because folks interested in the topic might be surprised about the result, and therefore inquisitive. Surprise is great if you can get it!

However, that’s not assured. My second step is a bit more complex, but still straightforward. Here, I’m shooting for the level below intrinsic motivation, and looking for a recognition that someone does need it. Thus, second step is:

2. If there‘s either of the following –

a. Stats demonstrating meaningful aggregate consequences of solving, or not

b. A vivid consequence of solving, or not

– go with it

That is, if you can find either data, or a very visceral personal response, you use that to help people  get that it’s important. It’s playing on the consequences of having, or not, the knowledge. (Which is something I talk about in my LXD workshops, and in my forthcoming one, stay tuned.)

Again, it  has to be meaningful to the domain. Which brings up my last suggestion:

3. If neither, maybe this isn‘t needed!

Reallly, if you can’t find some reason why this is intrinsically important, why are you doing it? Even for compliance training, there’s a reason. Tap into it! Or you’re likely to be wasting time and money. (Give me counter examples, I invite you!)

I’m not sure what order 2a and 2b should be in. Maybe that depends on the audience (individualist vs collectivist?). Still, this is my first stab a heuristic approach to motivation, and I invite your feedback. Make sense, or off track?

Addressing fear in learning

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One of my mantras in ‘make it meaningful‘ is that there’re three things to do. And one of those  was kind of a toss away, until a comment in a conversation with a colleague brought it home. So here’s a first take at addressing fear in learning.

The mantra, to be clear, is that you have 3 major hurdles to overcome in getting someone to ‘buy in’ to learning:

  • I  do need this
  • I don’t know it already
  • and I trust this experience will address that

I’ve focused mostly on the first, to date. The second is for the case where someone’s overconfident in their own abilities. However, the latter one was a toss-away, until…

My colleague mentioned how in trying to train data analysis, you could be coming up against decades of a belief such as ‘I don’t do well at math’. And I saw how you could have anxiety or a lack of confidence that this learning could address it.

Which makes it clear that you need to know the audience, and anticipate barriers. How can you address such a situation? I think you have to make sure that you make it steady and slow enough, or that it’s misperceived. So here, I could see either suggesting “we’ll take it slow enough and make it simple enough that you’ll find it easy” or “you may think data analytics is about math, but that’s the least part of it, it’s really about asking and answering questions”.

The point being, you need that trust, and that means addressing any barriers. It’s addressable, but you need to be aware. I also wonder if the typical elearning experience might have undermined trust such that there would have to be a series of successes to reestablish the trust that a learning unit  should have. However, if we start regularly addressing all three, we have a start. That includes establishing the need, removing false conceptions, and addressing fear in learning. Those are my thoughts, what are yours?

The plusses and minuses of learning science research

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A person who I find quite insightful (and occasionally inciteful ;) is Donald Clark. He built and sold Epic, an elearning company, and now he leads a learning AI company, Wildfire. He’s knowledgeable (for instance, having read up and summarized centuries of learning theorists), willing to call out bad learning, and he’s funny. And so, when he reported on a new study, I of course looked into it. And I find that it points out the plusses  and  minuses of learning science research.

To be clear, this is about his product, so there’s a vested interest. However, he’s got integrity; he’s not going to sully his reputation with a bad study. And, it’s a good study. It rightly demonstrates an important point. It’s just that it stops short of what we need for full  learning.

So, his product does something pretty amazing. You give it content, and it can not only answer questions about the content (as, for instance, some chat tools do), it can turn the tables and ask  you questions about the content. That is, it can serve as a sort of tutor. Which is all to the good.

What it can’t do, of course, is design meaningful practice. As Van Merriënboer’s Four Component Instructional Design (4C/ID) points out, you need to know the information, and you also need practice applying it. And I reckon we’re still far from that. So, while this is part of a whole solution (and Donald knows this), it’s not the full solution. He’s subsequently let me know it can do language tasks, which is impressive. I’m thinking more of contextualized scenarios, however.

The study demonstrates, as you might expect, that breaking up a video into reasonable chunks, and having system-generated questions asked in-between, led to 61% better retrieval, going from getting 8 to 14 questions right. That is a big improvement. it’s also impressive, since it’s generating those questions from video! That is, it parses the video, establishes a transcript, and then uses that to generate a knowledge base. Very cool.

And it’s a well-designed study. It’s got a control group, and a  reasonable number of subjects. It uses the same test material, for an AB comparison. Presumably, the video chunking was done by hand, into four pieces. The chunking and break might account for the difference, which wasn’t controlled for, but it’s still a big improvement. Granted, we know that watching a video alone isn’t necessarily going to improve retention (except, perhaps, over some other non-interactive way of dumping content). But still, this is good as it’s an improvement and a lot of work was saved.

What I quibble about, however, is the nature of the retrieval. The types of questions liable to be asked (and it’s not indicated), are knowledge questions. As suggested above, knowledge is a necessary component. But using that knowledge to make decisions in context is typically what our goals are. And to achieve such goals, you basically have to practice making decisions in context. (Interestingly, the topic here was equality and diversity, a topic he has complained about!)

Knowledge about a topic isn’t likely to impact your ability to apply it. What will  make a difference are actually doing things about it, like calling it out, having consequences, and actively working to remedy imbalances. And that requires separate practice. Which he’s acknowledged in the past, and rightly points out that his solution means you can devote more resources to that end.

Thus, the plusses of learning science research are we nibble away at the questions we need to answer, and find answers about the questions we ask. The minus, of course, is not necessarily asking the most important questions. It’d be easy to see this and say: “we’ve improved retention, and we’re done”. However, it won’t necessarily lead to reducing the behaviors being learned about, or building ability to deal with it.  There are plusses and minuses of learning science research, and we need to know the strengths, and limitations, of it when we hear it.

The case for learning science

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In a perfect world, we’d spend all the time we want on learning. However, we don’t live in that world, we live in the real world. Which means our decisions are about tradeoffs. Which means we have to evaluate the case for paying attention to research. So here’s a stab at the case for learning science.

Learning is a probabilistic game. That is, there’s a probability that anything we’ve invested in learning will arise at the appropriate time. I’ll suggest that our brains have some randomness built into them, so there’s always a chance we’ll do things differently. Thus, in a sense, learning design is about increasing the probability that the right thing will occur.

And there are consequences. Say, for instance, that we want people to wash hands sufficiently. Then we might rightly work to increase awareness (along with making sure there’s soap, water, sinks, towels, etc) and the proper procedure. To do that sufficiently, say, takes X minutes of instruction.

Yet we may not have X minutes. Given the drain on resources, allocating that much time means the cost of the washing may be more than the cost of not. People not on the job for that time are an expensive resource. What’s a manager to do?

What we do all the time is make a probabilistic decision. We provide the rules in places where people might get their hands dirty, and we provide support materials (e.g. signs on the walls) in the places you wash your hands.

Most importantly, however, we make a determination of what’s a level of time that is going to likely do ‘good enough’. We’ll spend X-Y minutes, and make essentially a gamble that we’ll get 80% there, and the support materials will do the rest.

What this means is that since we’re not allocating sufficient time, we should be optimizing the quality of the learning design we apply. If they’re only getting X-Y minutes, that time should be as effective as possible. Which means we practice serious learning design, reflecting the best practices.

Quite simply, if you do less than use the best learning design principles derived from research, you’re decreasing the value of your investment in design time and learner time. And there are lots of ways we go wrong, whether it’s myths or just underinformed design. It’s a matter of professionalism as well. So let’s be smart and design smart. That’s the case for learning science. We owe it to our learners and our organizations.

Tips to Avoid Millennials Marketing Hype

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I received, in my email, a solicitation for a webinar titled 5 Tips to Engage Gen Z and Millennial eLearners in 2020 and Beyond.  And, as you might imagine, it tweaked my sensibilities for the worse. My initial reaction is to provide, as a palliative, tips to avoid millennials marketing hype.

The content starts off with this scintillating line: “if you‘re searching for current, new ways to engage people online and keep your business thriving, look to your youngest learners.” What? Why do you want current and new ways to engage people? How about the evidence-based ways instead? Tested and validated ones. And why your youngest learners? Organizations need to be continually learning across all employees. Why not just your  newest employees (regardless of age)?

So, your first tip is to look for phrases like ‘new’ as warnings, and look for “research-based” or “evidence-based” instead. “Science-based” is likely okay, as long as it’s not neuroscience-based (wrong level) or brain-based (which is like saying ‘leg-based walking’ as someone aptly put it.)

Second tip: don’t be ageist. Why focus on their age at all? Deal with people by their knowledge and background. It’s discriminatory, really.

The ad goes on: “To future-proof your learning program, make sure your content is designed with these young professional learners in mind.”   What’s different for these learners? Their cognitive architecture isn’t fundamentally different; evolution doesn’t work that fast. So why would you do something just for them (and discriminate against others) instead of doing what’s right for the topic?

Next tip: avoid any easy and inappropriate categorizations. Don’t try to divide content or experiences in trendy ways instead of meaningful ways.

You should already be leery. But wait, there’s more! “On one hand, they can be distracted, overwhelmed, and impatient. On the other, they are highly collaborative, technically-savvy, and driven by fairness and storytelling.”This is like a horoscope; it fits most everyone, not just young people. We all have distractions and increasingly feel overwhelmed. And our brains are wired for storytelling.   These describe human nature! And that ‘tech savvy’ bit is a clear pointer to the digital native myth. Doh!

They then go on. “With this in mind, how can you effectively engage this digitally dependent group to attract, train, and retain them?” Um, with what attracts, trains, and retains humans in general?  That would be helpful!

Thus, another tip: let’s not make facile attributions that falsely try to portray a meaningful difference. Let’s focus on design that addresses capturing and maintaining attention and motivation, and communicating in clear and compelling ways. And skip mashing up myths, ok?

We’re not  quite done with the pitch: “how to level up your existing learning strategy to meaningfully engage your Millennial and Gen Z learners.” This is just a rehash of the tips above. Meaningfully engage  all your learners!

There’s also this bullet list of attractions:

  • What motivates Millennials and Gen Z and how to tailor your learning strategy to keep them engaged
  • Ways in which traditional learning programs fail younger learners and how you can prevent these common mistakes
  • A step-by-step process for evaluating your instructional content, providing you with an actionable blueprint on transforming your content

This could easily be rewritten as:

  • What motivates Millennials and Gen Z learners and how to tailor your learning strategy to keep them engaged
  • Ways in which traditional learning programs fail younger learners and how you can prevent these common mistakes
  • A step-by-step process for evaluating your instructional content, providing you with an actionable blueprint on transforming your content

And, for all I know, that’s what they’re really doing. That would be actually useful, if they avoid perpetuating the myths about generational differences. But, as you can tell, they’re certainly trying to hit buzzword bingo in drawing you in with trendy and empty concepts. Whether they actually deliver is another issue.

Please, avoid the marketing maelstrom. Follow these tips to avoid millennials marketing hype, and focus on real outcomes. Thanks!