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Reading Research?

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I was honored to have a colleague laud my Myths book (she was kind enough to also promote the newer learning science book), but it was something she said that I found intriguing. She suggested that one of the things in it includes “discussing how to read research”. And it occurs to me that it’s worth unpacking the situation a wee bit more. So here’s a discussion about how we (properly) develop learning science that informs us in reading research.

Caveat: I  haven’t been an active researcher for decades,  serving instead to interpret and apply the  research, but it’s easier to say ‘we’ than “scientists”, etc.  

Generally, theory drives research. You’ve created an explanation that accounts for observed phenomena better than previous approaches. What you do then is extend it to other predictions, and test them.  Occasionally, we do purely exploratory studies just to see what emerges, but mostly we generate hypotheses and test them.

We do this with some rigor. We try to ensure that the method we devise removes confounding variables, and then we use statistical analysis to remove the effects of other factors. For instance, I created a convoluted balancing approach to remove order effects in my Ph.D. research. (So complicated that I had to analyze a factor or two first, to ensure it wasn’t a factor, so I could remove it from the resulting analysis!). We also try to select relevant subjects, design uncontaminated materials, and carefully control our analysis. Understanding the ways in which we do this requires an ability to know about experiment design, which isn’t common knowledge.

Moreover, we then need to share this with our colleagues so that they can review what we’ve done. We need to do it in unambiguous language, using the specific vocabulary of our field. And we need to make it scrutable. Thus, we publish in peer-reviewed journals which mean others have looked at our work and deemed it acceptable. However, the language is deliberately passive, unemotional, and precise, as well as focused on a very narrow topic. Thus, it’s not a lot of fun to read unless you  really care about the topic!

There are problems with this. Increasingly, we’re finding that trying to isolate independent variables doesn’t reflect the inherent interactions. Our brains actually have a lot of complexity that hinder simple explanations. We’ve also found that it’s difficult to get representative subjects, when what’s easy to get are higher education students in the developed world. There are also politics involved, sad to say, so that it can be hard for new ideas to emerge if they challenge the entrenched views. Yet, it’s still the best approach we have. The scientific method has led to more advances in understanding than anything else!

There are things to worry about as a consumer of science. For one, there are people who fake results. They’re few, of course. There’s also research that’s kept proprietary, for financial reasons. Or is commissioned. As soon as there’s money involved, there’s the opportunity for corruption (think: tobacco, and sugar). Companies may have something that they tout as valid, but the research base isn’t publically available. Caveat emptor!

Thus, being able to successfully read research isn’t for everyone. You need to be able to comprehend the studies, and know when to be wary. The easy thing to do is to look for translations, and translators, who have demonstrated a trustworthy ability to help sort out the wheat from the chaff. They exist.

I hope this illustrates what reading research requires. You can take some preliminary steps: give it the ‘sniff’ test, see if it applies to you, and see who’s telling you this (and if anyone else is agreeing or saying to the contrary) and what their stake in the game is. If these steps don’t answer a question, however, maybe you want to look for good guidance. Make sense?

 

Bad research

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How do you know what’s dubious research? There are lots of signals, more than I can cover in one post. However, a recent discovery serves as an example to illustrate some useful signals. I was trying to recall a paper I recently read, which suggested that reading is better than video for comprehending issues. Whether that’s true or not isn’t the issue. What is the issue is that in my search, I came across an article that really violated a number of principles. As I am wont to do, let’s briefly talk about bad research.

The title of the article (paraphrasing) was “Research confirms that video is superior to text”. Sure, that could be the case! (Actually the results say, not surprisingly, that one media’s better for some things, and another’s better at other; BTW, one of our great translators of research to practice, Patti Shank, has a series of articles on video that’s worth paying attention to.) Still, this article claimed to have a definitive statement about at least one study. However, when I looked at it, there were several problems.

First, the study was a survey asking instructors what they thought of video. That’s not the same as an experimental study! A good study would choose some appropriate content, and then have equivalent versions in text and video, and then have a comprehension test. (BTW, these experiments have been done.) Asking opinions, even of experts, isn’t quite as good. And these weren’t experts, they were just a collection of instructors. They might have valid opinions, but their expertise wasn’t a basis for deciding.

Worse, the folks conducting the study had. a. video. platform.  Sorry, that’s not an unbiased observer. They have a vested interest in the outcome. What we want is an impartial evaluation. This simply couldn’t be it. Not least, the author was the CEO of the platform.

It got worse. There was also a citation of the unjustified claim that images are processed 60K times better than text, yet the source of that claim hasn’t been found! They also cited learning styles! Citing unjustified claims isn’t a good practice in sound research. (For instance, when reviewing articles, I used to recommend rejecting them if they talked learning styles.) Yes, it wasn’t a research article on it’s own, but…I think misleading folks isn’t justified in any article (unless it’s illustrative and you then correct the situation).

Look, you can find valuable insights in lots of unexpected places, and in lots of unexpected ways. (I talk about ‘business significance’ can be as useful as statistical significance.) However, an author with a vested interest, using an inappropriate method, to make claims that are supported by debunked data, isn’t it. Please, be careful out there!

New recommended readings

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My Near Book ShelfOf late, I‘ve been reading quite a lot, and I‘m finding some very interesting books. Not all have immediate take homes, but I want to introduce a few to you with some notes. Not all will be relevant, but all are interesting and even important. I‘ll also update my list of recommended readings. So here are my new recommended readings. (With Amazon Associates links: support your friendly neighborhood consultants.)

First, of course, I have to point out my own Learning Science for Instructional Designers. A self-serving pitch confounded with an overload of self-importance? Let me explain. I am perhaps overly confident that it does what it says, but others have said nice things. I really did design it to be the absolute minimum reading that you need to have a scrutable foundation for your choices. Whether it succeeds is an open question, so check out some of what others are saying. As to self-serving, unless you write an absolute mass best-seller, the money you make off books is trivial. In my experience, you make more money giving it away to potential clients as a better business card than you do on sales. The typically few hundred dollars I get a year for each book aren‘t going to solve my financial woes! Instead, it‘s just part of my campaign to improve our practices.

So, the first book I want to recommend is Annie Murphy Paul‘s The Extended Mind. She writes about new facets of cognition that open up a whole area for our understanding. Written by a journalist, it is compelling reading. Backed in science, it’s valuable as well. In the areas I know and have talked about, e.g. emergent and distributed cognition, she gets it right, which leads me to believe the rest is similarly spot on. (Also her previous track record; I mind-mapped her talk on learning myths at a Learning Solutions conference). Well-illustrated with examples and research, she covers embodied cognition, situated cognition, and socially distributed cognition, all important. Moreover, there‘re solid implications for the redesign of instruction. I‘ll be writing a full review later, but here‘s an initial recommendation on an important and interesting read.  

I‘ll also alert you to Tania Luna‘s and LeeAnn Renninger‘s Surprise. This is an interesting and fun book that instead of focusing on learning effectiveness, looks at the engagement side. As their subtitle suggests, it‘s about how to Embrace the Unpredictable and Engineer the Unexpected. While the first bit of that is useful personally, it‘s the latter that provides lots of guidance about how to take our learning from events to experiences. Using solid research on what makes experiences memorable (hint: surprise!) and illustrative anecdotes, they point out systematic steps that can be used to improve outcomes. It‘s going to affect my Make It Meaningful  work!

Then, without too many direct implications, but intrinsically interesting is Lisa Feldman Barrett‘s How Emotions Are Made. Recommended to me, this book is more for the cog sci groupie, but it does a couple of interesting things. First, it creates a more detailed yet still accessible explanation of the implications of Karl Friston‘s Free Energy Theory. Barrett talks about how those predictions are working constantly and at many levels in a way that provides some insights. Second, she then uses that framework to debunk the existing models of emotions. The experiments with people recognizing facial expressions of emotion get explained in a way that makes clear that emotions are not the fundamental elements we think they are. Instead, emotions social constructs! Which undermines, BTW, all the facial recognition of emotion work.

I also was pointed to Tim Harford‘s The Data Detective, and I do think it‘s a well done work about how to interpret statistical claims. It didn‘t grip me quite as viscerally as the afore-mentioned books, but I think that‘s because I (over-)trust my background in data and statistics. It is a really well done read about some simple but useful rules for how to be a more careful reviewer of statistical claims. While focused on parsing the broader picture of societal claims (and social media hype), it is relevant to evaluating learning science as well.  

I hope you find my new recommended readings of interest and value. Now, what are you recommending to me? (He says, with great trepidation. ;)

Theory or Research?

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There’s a lot of call for evidence-based methods (as mentioned yesterday): L&D, learning design, and more. And this is a good thing. But…do you want to be basing your steps on a particular empirical study, or the framework within which that study emerged? Let me make the case for one approach. My answer to theory or research is theory. Here’s why.

Most research experiments are done in the context of a theoretical framework. For instance, the work on worked examples comes from John Sweller’s Cognitive Load theory. Ann Brown & Ann-Marie Palincsar’s experiments on reading were framed within Reciprocal Teaching, etc. Theory generates experiments which refine theory.

The individual experiments illuminate aspects of the broader perspective. Researchers tend to run experiments driven by a theory. The theory leads to a hypothesis, and then that hypothesis is testable. There  are some exploratory studies done, but typically a theoretical explanation is generated to explain the results. That explanation is then subject to further testing.

Some theories are even meta-theories! Collins & Brown’s Cognitive Apprenticeship  (a favorite) is based upon integrating several different theories, including the Reciprocal Teaching, Alan Schoenfeld’s work on examples in math, and the work of Scardemalia & Bereiter on scaffolding writing. And, of course, most theories have to account for others’ results from other frameworks if they’re empirically sound.

The approach I discuss in things like my Learning Experience Design workshops is a synthesis of theories as well. It’s an eclectic mix including the above mentioned, Cognitive Flexibility, Elaboration, ARCS, and more. If I were in a research setting, I’d be conducting experiments on engagement (pushing beyond ARCS) to test my own theories of what makes experiences as engaging and effective. Which, not coincidentally, was the research I was doing when I  was  an academic (and led to  Engaging Learning). (As well as integration of systems for a ubiquitous coaching environment, which generates many related topics.)

While individual results, such as the benefits of relearning, are valuable and easy to point to, it’s the extended body of work on topics that provides for longevity and applicability. Any one study may or may not be directly applicable to your work, but the theoretical implications give you a basis to make decisions even in situations that don’t directly map. There’s the possibility to extend to far, but it’s better than having no guidance at all.

Having theories to hand that complement each other is a principled way to design individual solutions  and design processes. Similarly for strategic work as well (Revolutionize L&D) is a similar integration of diverse elements to make a coherent whole. Knowing, and mastering, the valid and useful theories is a good basis for making organizational learning decisions. And avoiding myths!  Being able to apply them, of course, is also critical ;).

So, while they’re complementary, in the choice between theory or research I’ll point to one having more utility. Here’s to theories and those who develop and advance them!

Deeper Learning Reading List

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So, for my last post, I had the Revolution Reading List, and it occurred to me that I’ve been reading a bit about deeper learning design, too, so I thought I’d offer some pointers here too.

The starting point would be Julie Dirksen’s Design For How People Learn (already in it’s 2nd edition). It’s a very good interpretation of learning research applied to design, and very readable.

A new book that’s very good is Make It Stick, by Peter Brown, Henry Roediger III, and Mark McDaniel, the former being a writer who’s worked with two scientists to take learning research into 10 principles.

And let me mention two Ruth Clark books. One with Dick Mayer from UCSB, e-Learning and the Science of Instruction, that focuses on the use of media.  A second with Frank Nguyen and the wise John Sweller, Efficiency in Learning, focuses on cognitive load (which has many implications, including some overlap with the first).

Patti Schank has come out with a concise compilation of research called The Science of Learning that’s available to ATD members. Short and focused with her usual rigor.  If you’re not an ATD member, you can read her  blog posts that contributed (click ‘View All’).

Dorian Peters book on Interface Design for Learning also has some good learning principles as well as interface design guidance.  It’s not the same for learning as for doing.

Of course, a classic is a compilation of research by a blue-ribbon team lead by John Bransford,  How People Learn, (online or downloadable).  Voluminous, but pretty much state of the art.

Another classic is  the Cognitive Apprenticeship  model of Allen Collins & John Seely Brown. A holistic model abstracted across some seminal work, and quite readable.

The Science of Learning Center has an academic integration of research to instruction theory by Ken Koedinger, et al,  The Knowledge-Learning-Instruction Framework, that’s freely available as a PDF.

I’d be remiss if I don’t point out the Serious eLearning Manifesto, which has 22 research principles underneath the 8 values that differentiate serious elearning from typical versions.  If you buy in, please sign on!

And, of course, I can point you to my own series for Learnnovators on Deeper ID.

So there you go with some good material to get you going. We need to do better at elearning, treating it with the importance it deserves.  These don’t necessarily tell you how to redevelop your learning design processes, but you know who can help you with that.  What’s on your list?

Design Readings

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Another book on design crossed my radar when  I was at a retreat and in the stack of one of the other guests was Julie Dirksen’s book Design for How People Learn  and  Susan Weinschenk’s  100 Things Every Designer Needs to Know About People.  This book provides a nice complement to Julie’s, focusing on straight facts about how we process the world.

Dr. Weinschenk’s book systematically goes through categories of important design considerations:

  • How People See
  • How People Read
  • How People Remember
  • How People Think
  • How People Focus Their Attention
  • What Motivates People
  • People Are Social Animals
  • How People Feel
  • People Make Mistakes
  • How People Decide

Under each category are important points, described, buttressed by research, and boiled down into useful guidelines. This includes much of the research I talk about when I discuss deeper Instructional Design, and more.  While it’s written for UI designers mostly, it’s extremely relevant to learning design as well.  And it’s easy reading and reference, illustrated and to-the-point.

There are some really definitive books that people who design for people need to have read or have to hand. This fits into the latter category as does Dirksen’s book, while  Don Norman’s books, e.g.  Design of Everyday Things  fit into the former.  Must knows and must haves.

Is “Workflow Learning” a myth?

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There’s been a lot of talk, of late, about workflow learning. To be fair, Jay Cross was talking about learning in the flow of work way back in the late 1990s, but the idea has been recently suborned and become current. Yet, the question remains whether it’s real or a mislabeling (something I’m kind of  anal about, see microlearning). So, I think it’s worth unpacking the concept to see what’s there (and what may not be). Is workflow learning a myth?

To start, the notion is that it’s learning at the moment of need. Which sounds good. Yet, do we really need learning? The idea Jay pointed to in his book Informal Learning, was talking about Gloria Gery’s work on helping people in the moment. Which is good! But is it learning? Gloria was really talking about performance support, where we’re looking to overcome our cognitive limitations. In particular, memory, and putting the information into the world instead of in the head. Which isn’t learning! It’s valuable, and we don’t do it enough, but it’s not learning.

Why? Well, because learning requires action and reflection. The latter can just be thinking about the implications, or in Harold Jarche’s Personal Knowledge Mastery model, it’s about experimenting and representing. In formal learning, of course, it’s feedback. I’ve argued we could do that, by providing just a thin layer on top of our performance support. However, I’ve never seen same!  So,  you’re going to do, and then not learn. Okay, if it’s biologically primary (something we’re wired to learn, like speaking), you’re liable to pick it up over time, but if it’s biologically secondary (something we’ve created and aren’t tuned for, e.g. reading) I’d suggest it’s less likely. Again, performance is the goal. Though learning can be useful to support comprehending context and  making complex decisions, what we’re good at.

What is problematic is the notion of workflow and reflection in conjunction. Simply, if you’re reflecting, you’re by definition out of the workflow! You’re not performing, you’re stopping and thinking. Which is valuable, but not ‘flow’. Sure, I may be overly focused on workflow being in the ‘zone’, acting instead of thinking, but that, to me, is really the notion. Learning happens when you stop and contemplate and/or collaborate.

So, if you want to define workflow to include the reflection and thoughtful work, then there is such a thing. But I wonder if it’s more useful to separate out the reflection as things to value, facilitate, and develop. It’s not like we’re born with good reflection practices, or we wouldn’t need to do research on the value of concept mapping and sketch noting and how it’s better than highlighting. So being clear about the phases of work and how to do them best seems to me to be worthwhile.

Look, we should use performance support where we can. It’s typically cheaper and more effective than trying to put information into the head. We should also consider adding some learning content on top of performance support in times where people knowing why we’re doing it as much as what we should do is helpful. Learning should be used when it’s the best solution, of course. But we should be clear about what we’re doing.

I can see arguments why talking about workflow learning is good. It may be a way to get those not in our field to think about performance support. I can also see why it’s bad, leading us into the mistaken belief that we can learn while we do without breaking up our actions. I don’t have a definitive answer to “is workflow learning a myth” (so this would be an addition to the ‘misconceptions’ section of my myths book ;). What I think is important, however, is to unpack the concepts, so at least we’re clear about what learning is, about what workflow is, and when we should do either. Thoughts?

Sleep & Walking

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We interrupt our regularly scheduled blog for this public service announcement. We will resume normal broadcasting after this brief message.

My late friend, Jay Cross, once wrote a post that said something to the effect of: “if you want to have better health, lose weight…<and a litany of other health benefits>…start walking.”  My reasons are in addition to that, actually. I also believe strongly in sleep. (Let me be clear, not sleep walking, of which I have no knowledge.) So here’re some thoughts on sleep & walking.

First, let’s talk sleep. I don’t know why (self-justification?), but I’ve regularly tracked the research on sleep. And, I find some robust results:

  • Most of us really are best off with 8 hours of sleep
  • Reading in the same place you sleep means you don’t read nor sleep as well
  • Keeping a regular sleep schedule helps
  • Naps are good

Also, of course, most people don’t do this. Personally, I try. It used to be about optimizing performance, but these days it’s more about maintaining performance! I can nap, though I usually don’t need to because of the first three. Also, I do try to get my eight hours (and am generally successful). I definitely don’t read in bed (tho’ occasionally I’ll get up to write something down so it’s off my brain and I can go back to sleep). And I try to be pretty regular in my sleep. I’m just following what’s recommended, and it seems to work. There’s more I’m not necessarily so good at, of course.

When it comes to walking, I don’t get it every day. That’s ok, because I try to exercise 5 days a week, and 3 of those are to use my torture device, er, exercise machine. Which I now do for 30 minutes 3 times a week, per the doc who asked for that much time at >100 beats per minute. As well as two strength things and some physio things to counteract my sedentary work life. I was doing 20+ minutes, with High Intensity Interval Training (10 of those mins are 30 secs intense, 30 secs not), and that’s still the case. I just extended the cool down.

The other two days a week I walk (sometimes more if we do it on our weekend). I have a set route, so my mind can be free. Annie Murphy Paul, whose book The Extended Mind I cited in my recent ‘post cognitive’ presentation (requires free membership) for the LDA, talks about the benefits of being out in nature. Of course, my walk is through my neighborhood, but it’s a bit wild (no sidewalks; wild animals can be spotted such as turkeys, hawks, quail, the occasional coyote).

My rationale for walking, however, in addition to health, is time to think! I come up with blog post topics, resolve questions, and more. Further, I don’t have headphones on, deliberately, so I’m aware but also allow what comes to mind. I also walk on the left side of the road, to face oncoming traffic, both a good idea and the law. (Too often I see folks walking with earphones, on the wrong side of the road, sometimes even with animals on a leash or a kid in a stroller! Yikes!)

We know that having time to reflect works. Being outside is also a boon. Together, it’s valuable time to think, as well as a healthy activity. I encourage you to follow good sleep practices and get in some walking (or equivalent, if there’re reasons that’s not possible). I’ve heard that walking conversations are also productive, but I work from home, so…

We now return you to your regularly scheduled blog, already in progress.

About my books

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My booksSo, I’ve written about writing books, what makes a good book, and updated on mine (now a bit out of date). I thought it was maybe time to lay out their gestation and raison d’être. (I was also interviewed for a podcast, vidcast really, recently on the four newest, which brought back memories.) So here’re some brief thoughts on my books.

My first book, Engaging Learning came from the fact that a) I’d designed and developed a lot of learning games, and b) had been an academic and reflected and written on the principles and process. Thus, it made sense to write it. Plus, a) I was an independent and it seemed like a good idea, and b) the publisher wanted one (the time was right). In it, I laid out some principles for learning, engagement, and the intersection. Then I laid out a systematic process, and closed with some thoughts on the future. Like all my books, I tried to focus on the cognitive principles and not the technology (which was then and continues to change rapidly). It went out of print, but I got the rights back and have rereleased it (with a new cover) for cheap on Amazon.

I wanted to write what became my fourth book as the next screed. However, my publisher wanted a book on mobile (market timing). Basically, they said I could do the next one if I did this first. I had been involved in mlearning courtesy of Judy Brown and David Metcalfe, but I thought they should write it. Judy declined, and David reminded me that he had written one. Still I and my publisher thought there was room for a different perspective, and I wrote Designing mLearning. I recognized that the way we use mobile doesn’t mesh well with ‘courses on a phone’, and instead framed several categories of how we could use them. I reckon those categories are still relevant as ways to think about technology!  Again, republished by me.

Before I could get to the next book, I was asked by one of their other brands if I could write a mobile book for higher education. The original promise was that it’d be just a rewrite of the previous, and we allocated a month. Hah! I did deliver a manuscript, but asked them not to publish it. We agreed to try again, and The Mobile Academy was the result. It looks at different ways mobile can augment university actions, with supporting the classroom as only one facet. This too was out of print but I’ve republished.

Finally, I could write the book I thought the industry needed, Revolutionize Learning & Development. Inspired by Marc Rosenberg’s Beyond eLearning and Jay Cross’s Informal Learning, this book synthesizes a performance and technology-enabled push for an ecosystem perspective. It may have been ahead of its time, but it’s still in print. More importantly, I believe it’s still relevant and even more pressing! Other books have complemented the message, but I still think it’s worth a read. Ok, so I’m biased, but I still hear good feedback ;). My editor suggested ATD as a co-publisher, and I was impressed with their work on marketing (long story).

Based upon the successes of those books (I like to believe), and an obvious need in our field, ATD asked for a book on the myths that plague our industry. Here I thought Will Thalheimer, having started the Debunkers Club, would be a better choice. He, however, declined, thinking it probably wasn’t a good business decision (which is likely true; not much call for keynotes or consulting on myths). So, I researched and wrote Millennials, Goldfish & Other Training Misconceptions. In it, I talked about 16 myths (disproved beliefs), 5 superstitions (things folks won’t admit to but emerge anyways) and 16 misconceptions (love/hate things). For each, I tried to lay out the appeal and the reality. I suggest what to do instead, for the bad practices. For the misconceptions, I try to identify when they make sense.  In all cases I didn’t put down exhaustive references, but instead the most indicative. ATD did a great job with the book design, having an artist take my intro comic ideas for each and illustrating them, and making a memorable cover. (They even submitted it to a design competition, where it came close to winning!)

After the success of that tome, ATD came back and wanted a book on learning science. They’d previously asked me to edit the definitive tome, and while it was appealing, I didn’t want to herd cats. Despite their assurances, I declined. This, however, could be my own simple digest, so I agreed. Thus, Learning Science for Instructional Designers emerged. There are other books with different approaches that are good, but I do think I’ve managed to make salient the critical points from learning science that impact our designs. Frankly, I think it goes beyond instructional designers (really, parents, teachers, relatives, mentors and coaches, even yourself are designing instruction), but they convinced me to stick with the title.

Now, I view Learning Experience Design as the elegant integration of learning science with engagement. My learning science book, along with others, does a good job of laying out the first part. But I felt that, other than game design books (including mine!), there wasn’t enough on the engagement side. So, I wanted a complement to that last book (though it can augment others). I wrote Make It Meaningful as that complement. In it, I resurrected the framework from my first book, but use it to go across learning design. (Really, games are just good practice, but there are other elements). I also updated my thinking since then, talking about both the initial hook and maintaining engagement through to the end. I present both principles and practical tips, and talk about the impact on your standard learning elements. In an addition I think is important, I also talk about how to take your usual design process, and incorporate the necessary steps to create experiences, not just instruction. I do want you to create transformational experiences!

So, that’s where I’m at. You can see my recommended readings here (which likely needs an update.) Some times people ask “what’s your next book”, and my true answer at this point is “I don’t know.”  Suggestions? Something that I’m qualified to write about, that there’s not already enough out about, and it’s a pressing need? I welcome your thoughts!

Lazy thinking?

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Now, I’m the last who should throw stones. I can be quite guilty of lazy thinking, particularly when there’re commercial decisions to be made. (Providers have done a fabulous job of making sure you can’t compare apples to apples, and when there’re so many such situations…) Yet, there’s one place where I struggle with the consequences. That’s in our professional field, and it seems like there’re too many opportunities to yield.

A trigger was a recent conversation where an individual was talking about generations. Grouping folks by when they’re born is problematic at best. For one, the boundaries used seem to vary by who’s doing the categorizing. Not a solid basis. Moreover, the research suggests that there really aren’t meaningful differences. What do exist are explainable by age differences (which isn’t the same thing, for one it’s a continuum, not discrete chunks). Really, it’s a mild form of age discrimination, differentiating people by when they’re born, not who they are or how they behave. (Also problematic is the notion that events affect certain segments of the population, but that’s a longer conversation).  It’s one of the myths in my book on same.

Other examples include learning styles, hemispheres, gender differences, and more. First, they’re categorizations on things that people can’t control. Second, they don’t get backing from data. I just read that medical science has been excluding women from research based upon an assumption about temperature variability that was exposed as being irrelevant!

Sure, it’s much easier if we can reliably group people into segments that mean we have a reliable basis to do different things. Marketers do this with psychographics, for instance. Demographics can also matter. The problem here is that we’re using unreliable metrics. First, there are assumptions that turn out to be flawed. They frequently use self-report, also problematic. Some also have a flawed theoretical foundation.

Yes, it’s hard to keep on top of all of this. Ideally, you’d have time to investigate them all. In practice, there are other things to do. We all need ways to simplify our lives. Plus, vendors are telling you that they, at least, are immune to the complaints (with self-interest at stake).  On the other hand, there are good sources of insight from reliable translators of research. There are also practices we can follow to make it manageable. More help is on the way (at the LDA we’re working on it; stay tuned).

While lazy thinking is understandable, it’s not acceptable, at least not in our professional field. While we may not be sued for malpractice, we certainly should be responsible. So let’s avoid taking the easy path, at least when it matters. In our professional capacity, it matters when we’re designing for our learners. Let’s do so on evidence, not assumptions.