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Clark Quinn’s Learnings about Learning

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?

Authentic Marketing

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I’m not a marketing expert, or even a marketer, so take the following with the proverbial boulder of salt. Still, I have to market Quinnovation, and I’ve advised orgs on marketing (learning) products, and I’ve taken down a lot of bogus marketing. So when something prompted me to reflect, I realized I had some thoughts on authentic marketing.

First, I’ve argued that good marketing is really good customer education. That is, you should be helping your customers understand why your product is the right thing for their needs. Of course, you should be first designing to ensure that it  is the answer. Or, perhaps,  an answer, and then helping your customers to understand if they’re the right customer for this solution.

And, when I’ve studied marketing for services, there are several steps that make sense. First, the clear thing is knowing your customer’s pain, and being able to articulate how you solve that pain. You want to help articulate clearly what the problem is, and what’s it’s costing, so that then you can suggest a solution and the benefits.

At core it’s about building up a solid, scrutable, case. Which is, in essence, building up trust that you know what you’re talking about, and that you can truly meet the need.

And that may not be the quick easy way. It appears to be the case that some folks would rather use clickbait-style advertising. Perhaps to cover up from not having a defensibly different product or solution? When there are hundreds of LMSs around, how do you differentiate yourself?   And I guess it works, because I keep finding new examples of marketing that goes for the cheap ploy rather than authentic education.

So I guess this is a plea for being an aware consumer. It’d be great if orgs started building products that really do make their customers awesome, and then use authentic marketing to sell them. In lieu of that, be wary. Look for unsubstantiated hype, buzzword bandwagon behavior, and style over substance. Know what you need, take your time to do due diligence, and spend wisely. Caveat emptor, after all.

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.

Top 10 Tools for Learning 2020

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It’s time, once again, for Jane Hart’s excellent Top 10 Tools for Learning survey. And, so, it’s time once again for my reflections. Here are my take on the top 10 tools that support my learning.

The first way I learn is to process what I’ve seen. That, toolwise, is largely about representing and communicating.

Processing Tools

1-2. Writing is arguably the top way I reflect. And, so that’d put Microsoft Word at the top of my list. That’s where I write books and articles first. And, of course WordPress is how I write my blog (e.g. here!).   Writing is a way to sort out how I think about things. As I say, things that end up in presentations and books tend to show up on blog first. Well, one of the main ways.

3-4. Besides writing, two ways I sort out my understandings are to diagram and to outline. I use OmniGraffle as a general purpose diagramming tool because, well, it largely works the way I want to think about it. Diagrams, mind maps, even recently as sort of posterboard. And I use OmniOutliner to do, well, outlines. Another way to map out structures. I’d use a less costly tool, but…the columns feature is really helpful for annotation. Both, unfortunately, are Mac only (and sadly quite dear).

5. Keynote is how I create presentations, another way I do, and then share, my thinking. Diagrams are a big part of my talks, punctuated with stock photos to represent concepts (from Pixabay and occasionally Unsplash). I believe (and don’t have evidence for) that using an image that relates to the concept but doesn’t exactly communicate it leaves open some curiosity that then gets connected. And that this leads to better comprehension (I avoid bullet points in live presos, and save them for handouts). Anyone got that data?

The second thing I do is see what other people are pointing to and have to say, and ask them questions   as well. So the second category is about interacting with others.

Social tools

6. Twitter is a regular feature of how I see what people are pointing to, as well as pointing to things I’ve found as well. Chats there are fun, too. Like Jane, Tweetdeck is my tool of course on my Mac. I have to use the Twitter client on iPad/iOS, since they’ve taken away Tweetdeck on the iPad (grr).

7. I like FeedBlitz as a way to sign up for blogs, as it brings them into my inbox, instead of me needing a separate app. Reading a select list of blogs is one of my tactics. That’s how people can sign up to get my blog in email, too.

8. Slack has also been a major component of getting things done, mostly with IBSTPI. It’s a handy way to get things done with others.

9-10. Social networks are a big part of my learning, which means that Facebook and LinkedIn also play big roles. Facebook’s more personal, ie less about work, but I learn about   many societal things there. And LinkedIn is a place for learning as well, professionally as opposed to personally.

And…

Honorable Mention: to round out the picture (10 is such an arbitrary number ;), sharing collaborative documents, e.g. Google Docs, is a major way to collaboratively process and learn together. Also socially, Zoom and BlueJeans (the latter’s almost the same, and what ISBTPI uses) are used a lot to discuss and negotiate understandings. And email, of course (using the Mac Mail client) is a major way I learn, e.g. blogs appear there, and it’s a major way I interact.

DuckDuckGo has become my goto search engine (and Brave as my browser,  at least on my Mac, awaiting cross-device sync), because I don’t need to spread my data any further than necessary. And searching is a big part of my learning.

As an aside, owing to the pandemic, like everyone else I’ve been doing much more with Zoom to interact with colleagues than I had in the past. And I find, interestingly, that the ways I reach out are more opportunistic: I’ll use FB Messenger, or a Twitter DM, or a LinkedIn message, or an email depending on who, why, and what tool I’m in at the time. There may be some method to the madness, but I’m not confident on that point ;).

So, there’re my Top 10 Tools for Learning. I hope you’ll post or send your list to Jane too, so we can continue to see what emerges.

 

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!

Curious about Curiosity

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Looking into motivation, particularly for learning, certain elements appear again and again.   So I’ve heard ‘relevance’, ‘meaningfulness‘, consequences, and more. Friston suggests that we learn to minimize surprise. One I’ve heard, and wrestled with, is curiosity. It’s certainly aligned with surprise. So I’ve been curious about curiosity.

Tom Malone, in his Ph.D. thesis, talked about intrinsically motivating instruction, and had curiosity along with fantasy and challenge. Here he was talking about helping learners see that their understanding is incomplete. This is in line with the Free Energy Principle suggesting that we learn to do better at matching our expectations to real outcomes.

Yet, to me, curiosity doesn’t seem enough. Ok, for education, particularly young kids, I see it. You may want to set up some mismatch of expectations to drive them to want to learn something. But I believe we need more.

Matt Richter, in his well-done L&D conference presentation on motivation, discussed self-determination theory. He had a nice diagram (my revision here) that distinguished various forms of motivation. From amotivated, that is, not, there were levels of external motivation and then internal motivation. The ultimate is what he termed intrinsic motivation, but that’s someone wanting it of their own interest. Short of that, of course, you have incentive-driven behavior (gamification), and then what you’re guilted into (technically termed Introjection), to where you see value in it for yourself (e.g. WIIFM).

While intrinsic motivation, passion, sounds good, I think having someone be passionate about something is a goal too far. Instead, I see our goal as helping people realize that they need it, even if not ‘want’ it. That, to me, is where consequences kick in. If we can show them the consequence of having, or not, the skills, and do this for the right audience and skills, we can at least ensure that they’re in the ‘value’ dimension.

So, my take is that while we should value curiosity, we may not be able to ensure it. And we can ensure that, with good analysis and design, we can at least get them to see the value. That’s my current take after being curious about curiosity. I’d like to hear yours!

Practicing the Preach

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I’m working on my next plan for global domination. And as I do, I’ve been developing my thinking, and there are some interesting outcomes. Including a realization that I wasn’t doing what I usually recommend. And I also believe that you should ‘show your work‘. So here I’m practicing the preach.

First, I’m developing my understanding, getting concrete about it. I usually use Omnigraffle as a diagramming tool, to represent my conceptual understandings. And I started doing that as part of the ‘developing thinking’ part. But I started with a diagram, and took the elements out and mindmapped them, and threw in other bits. In short, the ‘diagram’ has become a visual place to store bits and pieces of different diagrams, representations, mindmap, prose, or more. As well as outlining elsewhere. But it’s working out for me, so I thought I’d share.

The overall visualization gives me a place, like a business canvas, to drop stuff on and rearrange. It’s a ‘thinking tool’. I’m also copying part of the the activity map and linking things together to capture the actual flow between content and activities. Etc. A virtual whiteboard, I guess.

Second, one of the things to represent was how this would be communicated. Whether a course, or interactive ebook, or whatever, I want to create a flow. And I realized an activity map might make sense. I haven’t done this before (I’ve used storyboards and diagrams), but I find it interesting. Here’s the current status.

Across the top are the various stages (Introduction, the Principles, the resulting learning Elements, the associated Process, and the Closing). Your stages may vary.  Along the side are the different components (the Content topics, the associated practice Activities, the Emotions I to be evoked, the Stories to tell, and the Tools). I think putting in ’emotion’ is an important step! And then I can drop text bits into the intersections.

Finally, as I started developing the associated content, I realized one thing I advocate is backwards design. That is, envision the performance and how it’s distributed across tools and brains. Then, I realized I hadn’t designed the tools first! I’m going back and doing that. So it’s now in the activity map as well ;).

Just thought I’d share this, practicing the preach, and hope that you find it interesting, if not useful. Feedback welcome!

 

Thinking about reframing

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I found something interesting, and wanted to share, but…I realize this is supposed to be about my learnings about  learning. So, I’m framing it as thinking about reframing ;). Seriously, it’s about extant models and opportunities to rethink.

So, to begin with, I’ve been somewhat frustrated with the traditional model of capitalism. No, not as a plea for communism or something, but because it doesn’t align with our brains. When I champion that we should align with how we think, work, and learn, that’s true at the individual, team, organizational, and societal levels.

The problem is, capitalism assumes that we’re optimizing buyers. That is, we will search out and buy the best products, so there’ll be consistent pressure for quality, and this drives improvement. A lovely theory. With only one small flaw…

We’re not optimizing buyers. Herb Simon was part winner of a Nobel prize (kinda before he went on to be a leader in the cognitive science field) on the fact that we’re satisficing buyers, not optimizing. That is, we’ll buy ‘good enough’. I’ve used the fish shop story to document this. We know how to make light, crispy, non-greasy fish’n’chips. So, the capitalist model would posit that every shop should have beautiful fish. Er, no. You’re just as (more?) likely to find greasy sodden fish. Because we’re not likely to drive one borough/neighborhood/town over to get perfect when what’s close is ‘good enough’.

You can get backup from behavioral economics or the work of Daniel Kahneman about how we aren’t logical beings. The point being, we don’t behave in rational ways. For instance, we’re vulnerable to marketing that affects our perceptions. And economics is linked to politics about whether all the real costs are included. Thus, the fundamental foundation of capitalism is flawed.

As an aside, it’s also predicated on unlimited growth. That is, we’ll continually advance in our ability to meet needs. Yet we live on a finite planet…and yes, I know that there are also technological advances. It’s just that I reckon there are limits to growth.

A serious problem is that the alternatives are also flawed. Capitalism proposes that it passes back by the creator of the superior end product purchasing the components and that cascades backwards. However, to change it, e.g. to track based upon the value of a person’s contribution to the greater good, we’d need bookkeeping to track it.

What I hit a wall against was working within the assumptions. And yet, maybe there’s another way, that is thinking about reframing the problem. Just as I previously talked about replacing happiness with contentment, maybe we can rethink economics. If we think about it differently, can we come up with a different model.

Something pointed me to doughnut economics. And it’s not a full solution, but it does have some interesting properties. The reason it’s called the doughnut model is that there’s a hole in the center, then the body, and then the external limits. The hole consists of the basic capabilities humans need: clean water, reliable and healthy food, etc. This, to me, is kind of the ‘aligning with us’. Then the outside are the practical limits: finite planet, limits on water, energy, air, etc. Between these two are where humans can (and should) live.

It’s a different way of looking at things. I’m not an economist by any means (I find it aversive ;), but I do like looking at society in ways that might make it better. And this model, as far as I know, doesn’t have a clear path to replace our current economic system (e.g. prices on goods). But it’s way of rethinking what matters that’s somehow closer to how we really exist.

The take-home for learning, of course, is being willing to step back and reframe what we think we know. Different perspectives enable different insights. It’s part of the creative process to diverge before you converge. So here’s hoping we can find ways to be thinking about reframing. What ways do you use to think afresh?

Losing our collective minds?

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microscopeSo, after that mess on Twitter, I next see on LinkedIn a recognized personage who proceeds to claim that learning styles are legit, and promises a post (see tomorrow’s review). And, the basis for this claim is fundamentally wrong. So I’m beginning to fear that we’re losing our collective minds!   Let me be clear about the claim, the problem, and a healthy approach.

The claim started like this:

I know there is a huge camp of folks who say no one has learning styles and they provide all types of links of others who concur. Then there are folks who say they do exist, and change of a period of time (as you age). And you may have more than one.

I admit I am in the latter group, because I have seen it first-hand as a Director of Training, and when I taught at the HS and University levels.

And, this is a problem, because it misrepresents what’s going on. My response was:

Sure, learners differ, no one who’s taught can say otherwise. But, identifying how they differ, reliably? Er, no. And that we should adapt to learning styles? Again, not what research says. And, to be clear about the ‘huge camp’ (why would that be?), we don’t post links to others who concur, we post links to the science that shows that the instruments to measure styles aren’t psychometrically valid and that the evidence shows no benefit to adapting to learning styles. A waste of time and money.

When called out, the response was similar:

You have perspective, I respectfully disagree.

What this response did was suggest that it’s about opinion. Which is not just irritating, but it’s  dangerously wrong. I’ve argued before about why myths matter. And, here, specifically, learning styles can cause you to waste money, but more importantly it may have people prematurely limit themselves. To their detriment.

But it’s also the refusal to acknowledge that it’s science, not opinion. Saying, basically, that the folks against learning styles support each other is very different, and wrong. We don’t point to each other, we point to the research!

It gets worse. The commentary on the post went sideways. Despite some apt questions about the legitimacy, there were counter opinions. One comment brought in neuro-linguistic programming! (Debunked, by the way.) It’d be funny if it weren’t so scary!

And, then, the followup, I have ‘perspective’. Sorry, but it’s not about your opinion versus mine. That may work for fashion, art, cinema. Not what we do in medicine, hazardous material, construction, flight, and the like. Even traffic! We follow what’s been demonstrated to save lives (or we should). When we get into the absurd situation of saying your anecdotal evidence is better than the weight of scientific evidence, we’re on a slippery slope to losing our collective minds.

Look, you can prefer vanilla to chocolate. You can like pineapple on your pizza. Or even put ketchup on your hotdog. (Quelle horreur!) I can differ. No one’s hurt. But if you yell ‘fire’ in a crowded theatre, or advise consuming disinfectant as a virus cure, or using nuclear waste as a skin lotion, you’re violating what’s known. And it’s wrong, if not outright illegal.

Please, be skeptical. Even of what I say! (The scientific method does have its flaws, but it’s better than everything else.) But please value controlled studies over anecdotes. There are lots of ways we can be misled by the latter. We don’t want to be losing our collective minds, we should be leveraging them. Please help!