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In Defense of Science

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I previously wrote in defense of cog psych. Here, I want to go broader. Not my usual topic, but… I feel the need to rail in defense of science.

When I’m talking about science, I’m talking about the systematic exploration of how things work. It’s about theorizing, and testing, and refining. It’s about having rigor in that, being systematic and principled. And, importantly, it’s about sharing the results and building on the works of others. Interestingly, it’s not a human universal, but instead emerged relatively recently. However, there’s a reason it’s been so successful.

And, let’s be honest, science has flaws. There have been recent problems in replicability. Another problem is academic politics; new ideas can struggle to get recognized. Pressure to publish can lead to fake data. Folks with money can influence what research gets done, and the outcomes. Similarly, politics can play a role. Like democracy, it’s not perfect, but…and this is an important but…there’s nothing better.

The evidence for science are the things that we’ve come to count on: sanitation, transportation, medicine, the list goes on. Your ability to read this depends on science. Most of what we use all day every day has been improved by science. So, too, some bad things, like how successful marketing can be at snagging your attention. Yes, we need to use it wisely. Science can help there, too! And, importantly, most scientists are ethical, caring, diligent individuals. They do what they do for science, not for wealth, not for fame (except amongst their colleagues, which goes back to doing it for science), and most certainly not to support conspiracies.

So, trying to pick and choose what science to believe isn’t a great bet. Unless you have a deep background in a particular domain, trying to ascertain the validity is challenging. You may listen to disparate voices, but not if they’re flying the face of a concerted viewpoint of people who have spent the requisite time to be true experts. In my mind, you’re either for science, or not. Saying “well, I’m not for this science because someone said it’s controversial”, then, is just not on.

Yes, there are controversies around most science: that’s how it advances. But there’s also essential truths that most every reputable scientist in the field will agree to. And that’s how we build products, services, and ultimately societies. I was trained as a scientist (though I’m more of an engineer, tracking it and applying it to solve real problems). I know, in my field, what makes sense and what’s silly. And then, in other fields, I look to what the received wisdom is. And I know what sorts of people to listen to, and it’s not politicians, or pundits. Unless they listen to science. The best guidance comes from the folks who know the field in question. And that holds true for medicine as well as meteorology.

And sure, I too could wish I lived in a world where magic worked. But if you think about it, they, too, use systematic experimentation to find out what works. Whether Earthsea or Hogwarts, they go to schools to learn and there the professors are studying. But here, magical thinking doesn’t work. Science is what has let us knock back polio, generate electricity from sunlight, and walk on the moon.

So, if you do want to go against what the scientists or reliable interpreters tell you, don’t do it piecemeal. Abandon all the science, because you’re unlikely to get it right in a domain that’s not your expertise. If anyone is telling you contrary to what’s known, question their motives! People mislead for lots of reasons, from money to mischief. If you let them, they’ll hurt you in ways that may be stark or subtle.

If they’re steering you away from something that has been shown to be better than the alternative, you should be wary. Their tricks are myriad: lack of context, distortions, selected subsets, and outright lying. For instance, our brains are wired to see patterns. If we’re pointed to them, we’ll see them. We’re also biased to look for evidence that confirms our beliefs, and avoid what contradicts it. Thus, it’s easy to gin up potential conspiracies, despite the incredible challenge in actually pulling them off!

I’m putting it out there. I can say that, in defense of science, it’s better than any other approach. That’s my stance, what’s yours?

 

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’?

Personalized and adaptive learning

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For reasons that are unclear even to me, I was thinking about personalized versus adaptive learning. They’re similar in some ways, but also different. And a way to distinguish them occurred to me. It’s kinda simplistic, but I think it may help to differentiate personalized and adaptive learning.

As background, I led a project to build an intelligently adaptive learning platform. We were going to profile learners, but then also track their ongoing behavior. And, on this basis, we’d serve up something appropriate for learner X versus learner Y. (We’d actually recommend something, and they could make other choices.)

It was quite the research endeavor, actually, as the CEO had been inspired by Guilford’s learning model. I dug into that and all the learning styles literature, and cognitive factor analysis, and content models around learning objectives, and revisited my interest in intelligent tutoring, and more. I was able to hire a stellar team, and create an approach that was scientifically scrutable (e.g. no learning ‘styles’ :). We got it up and running before, well, 2001 happened and the Internet bubble burst and…

In some sense, the system was really both, in the way I’m thinking about it. I’ve seen different definitions, and one has adaptive as a subset of personalized, but I’m going a different way. I think of personalized as pre-planned alternatives for different groups, whereas adaptive reacts to the learner’s behavior.

Our use of initial profiling, if we only used that, would be personalized. The ongoing adaptation is what made it adaptive. We had rules that would prioritize preferences, but we’d also use behavior to update the learner model. It’s something they’re doing now, but we had it a couple of decades ago.

So, my simple way of thinking about personalized versus adaptive is that personalized is based upon who you are: your role, largely. We’d swap out examples on marketing for people selling services versus those selling products, for instance. Or if we’re talking negotiation, a vendor might get a different model than a lawyer.

Adaptive, on the other hand, is based upon what you do. So, for instance, if you did poorly on the last problem, we might not give you a more difficult one, but give you another at the same level. Twice in a row doing badly, we might bring you another example, or even revisit the concept. This is what intelligent tutoring systems do, they just tend to require a rigorous model of expertise.

Of course, you could get more complicated. Personalization might have a more and less supportive path, depending on your anxiety and confidence. Similarly, adaptive might throw in an encouraging remark while showing some remedial materials.

At any rate, that’s how I differentiate personalized and adaptive learning. Personalized is pre-set based upon some determined differences that suggest different learning paths. Adaptive calculates on the fly and changes what the learner sees next.  How do you see it?

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?