From The Science of Learning, Bradley Busch, Edward Watson, and Ludmila Bogatchek have kicked it up a notch in this fresh innovative presentation of Teaching & Learning Illuminated: the Big Ideas Illustrated.
While revamping my college course, I was given this book, and suddenly, prepping felt less like a chore and more like rediscovering the excitement of teaching—like stepping into a bookstore where every title holds the promise of a new perspective. But this isn’t a collection of gimmicks; it’s a book designed to challenge and refine your thinking, helping you sharpen your teaching practice with the most well-supported research. If The Science of Learning is the blueprint, this follow-up book is the user-friendly manual, packed with visuals that make big ideas click. Teaching is a constant process of adapting, and Teaching & Learning Illuminated acts as both a guide and a catalyst, helping you build on your knowledge while freeing your mind to think in new and innovative ways.
What makes this book unique is how it presents information. The graphics aren’t just illustrations; they are well-designed thinking tools that clarify teaching principles backed by decades of research. Each topic is covered in a two-page spread, pairing a full-page visual with a clear, concise explanation. This format simplifies complex ideas while easing the cognitive load, allowing us to imagine how these concepts playout in the classroom. The graphics encourage deeper thinking, serving as both inspiration and a framework for instructional design.
Every illustration invites reflection—from the key takeaways of retrieval, interleaving, and cognitive load theory to Rosenshine’s principles, thinking biases, and fostering motivation and resilience. These visuals do more than convey information; they prompt us to reconsider our approaches and apply insights in new ways.
One of the most practical aspects of this book is how versatile the visuals are. I’ve used them not just for lesson planning but also as quick reference points throughout the day. Even better, the book includes access to high-resolution downloadable posters, which I’ve printed and placed in my workspace. These serve as constant reminders of strategies I want to implement, keeping important ideas at the forefront of my practice.
One of the challenges of learning effective teaching practices is the sheer volume of ways to improve, which can easily lead to analysis paralysis. This book strikes the perfect balance, providing just enough challenge to keep you engaged while offering the right support to help you apply new strategies and explore with confidence.
This book doesn’t just present research-backed insights—it makes them actionable and memorable. The visuals don’t just explain concepts; they stick with you in a way that words alone often can’t. They leave a lasting impression, nudging your thinking in new directions and helping your mind wander constructively.
What stands out most about Teaching & Learning Illuminated is how it refreshes the way we think about teaching. It’s not just another book—it’s a resource that makes cognitive science visible, tangible, and usable. Whether you’re a seasoned educator or just starting to integrate research-based strategies, this book makes it easier to turn theory into practice. It’s insightful, engaging, and a must-have for any educator eager to turn research into real classroom impact and illuminate their practice.
I don’t often repost articles, but I think this one deserves another look — for at least two reasons:
First: The study described below has been cited FREQUENTLY in recent weeks, although it really does not merit the confidence that it inspires, and
Second: The study has in fact drawn a strong rebuttal from other researchers in the field.
For both these reasons, I think you’ll find this post worth another look. (I have, by the way, updated this post to reflect the newly published rebuttal.)
Imagine this conversation that you and I might have:
ANDREW: The fastest way to drive from here to the school is via South Street.
YOU: It is? That seems like a long detour. Why would I go that way?
ANDREW: I didn’t say it was the fastest; I said it was the best because it’s the prettiest.
YOU: You DID say it was fastest…wait, the prettiest? It’s basically junk yards and construction sites.
ANDREW: Yes, but because of all the bakeries, it smells really nice.
YOU: What does that have to do with fastest/prettiest?
ANDREW: Why are you being so unpleasant and difficult? South Street is the best route…
I suspect you would think: “this conversation is very frustrating and unhelpful because the goal posts keep moving.”
That is: I initially claimed that South Street is the fastest…but keep moving my claims as soon as you object. (And, oddly, I’m mad at you for being unreasonable.)
I routinely notice this pattern when I ask questions about the claim that “handwriting is better than laptops for note taking.”
Watch the goalposts move:
CLAIM: Handwriting is better than laptops for note taking. This study says so.
ANDREW: That study starts with the BIZARRE assumption that students can’t learn how to do new things — like, how to take notes correctly. And, research since then has routinely complicated or contradicted it.
CLAIM: I didn’t say handwriting is better because of this study. It’s because writing by hand changes neural networks. This research says so.
ANDREW: You DID make that claim because of that study…wait, that other research says that writing by hand helps students learn to write by hand. Of course it does.
But that doesn’t mean that writing by hand helps students learn other things — like, say, history or chemistry or German. Can you show me research supporting that claim?
CLAIM: I can’t, but when students write on laptops they distract students around them.
ANDREW: Yes, but that’s a completely different claim than the one you started with.
CLAIM: Why are you being so unpleasant and difficult? Writing by hand is better than taking notes on laptops!
Once again, I find this conversation frustrating and unhelpful. SO MANY MOVING GOALPOSTS.
I am entirely open to the idea that handwriting is better. But if someone makes that claim, and says it’s “research-based,” I’d like them to provide research that actually supports the claim.
So far, that turns out to be a big ask.
This idea that “handwriting is better than keyboarding” keeps popping (I suspect because of a recent study), so I want to re-investigate this claim — with a keen eye on those goalposts.
Reasonable Start
If you see a headline that says, “Why Writing by Hand Is Better for Memory and Learning,” you might interpret that claim roughly this way:
Students who take handwritten notes — in their 6th grade history class, say, or their 10th grade science class — remember more of that material after 2 weeks than students who took notes on laptops.
Yes, I conjured up some of those specifics: “6th grade history,” “two weeks later.” But those seem like reasonable extrapolations. What else could the claim substantively mean?
Briefly: plausible goalpost = “students remember more history 2 weeks later.”
So, let’s look at the recent research being used to support this claim.
Here’s a very basic question: “how did the researchers measure how much the students learned and remembered?”
Did the students take a quiz two weeks later? Did they undertake a “brain dump” the following day? How, precisely, do we know what they learned?
The answer is:
The researchers did not measure how much the students learned/remembered.
Honestly. No quiz. No brain dump. Nothing.
And yet, even though the study doesn’t measure memory or learning, it is being used to argue that handwriting enhances memory and learning.
I find this astonishing.
Imagine that I claimed “research shows that this drug will lower your blood pressure!” but I never actually measured anyone’s blood pressure. This study takes a similar logical shortcut.
That is: the study measures activity “in brain regions associated with memory and learning.”
Did you notice something?
Goalpost plausibly was: “students remember more history 2 weeks later.”
Goalpost now is: “more activity in important brain regions.”
Grrr.
Getting Specific
When evaluating “research-based” claims, it’s helpful to know exactly what the participants in the research did.
So, these 36 participants wrote the same fifteen words multiple times. Sometimes they wrote with a stylus on a tablet; sometimes they typed using only their right index finger. (BTW: all the participants were right handed.)
Now, this insistance on “right index finger” makes sense from a neuro-research perspective. If both “handwriters” and “keyboarders” are using one hand, then the researchers reduce lots of confounding variables.
At the same time, this emphasis also leads to highly artificial circumstances.
Presumably some people type with one finger. But, I’m guessing that most people who want to take laptop notes don’t. I suspect they want to take laptop notes because they have some degree of facility on a keyboard.
So:
Goalpost initially was: “students remember more history 2 weeks later.”
Goalpost then was: “more activity in important brain regions.”
Goalpost now is: “more activity in important brain regions when participants write as they usually do than when they type in a really, really unnatural way.”
Double grrr.
It is, of course, helpful to know about these differences in neural responses. But I don’t think they plausibly add up to “students remember more.” Because — remember — no one measured learning.
I Am Not Alone
Since I published the original version of this article almost a year ago, it has been sharply questioned by other scholars in the very same journal.
These scholars describe the original study’s conclusions as “a logical shortcut.” They share my alarm that research which never measured any learning is being used to make strong claims about learning.
They also note we shouldn’t reach an emphatic verdict about grade-school learners based on college-age students:
“Drawing conclusions on learning processes in children in a classroom from a lab study carried out on a group of university students that did not include any type of learning seems slippery at best.” (Exasperated emphasis added)
This rebuttal also expresses technical concerns about the original study’s neuro-conclusions:
While theta and alpha oscillations have been functionally related to a variety of cognitive processes it has not been clearly established that increased theta/alpha connectivity creates appropriate conditions for learning.
I don’t know enough about theta and alpha oscillations to have a strong opinion here — but I think it’s helpful to know that other neuro-experts express reasons to doubt the original study’s confidence.
Lest I Be Misunderstood
In such conversations, I’m often misunderstood to be confident about the right answer. That is: I might seem to be saying “I’m confident that laptops are better than handwriting for learning.”
I am NOT saying that.
Instead, I’m asking for research that directly measures the claim being made.
If I say to you: “research shows that handwriting is better for learning than laptops,” I should be able to show you research that directly measures that claim.
If, instead, I have research showing that handwriting develops neural networks that might be beneficial for learning, I should say that.
My frustration about this point stems from a broader concern.
Over and over, I find that non-teachers cite research — especially neuroscience research — to boss teachers around. While I certainly do believe that teachers should know about pertinent research findings (that’s why I write this blog!), I also believe that we need to acknowledge the limits of our research-based knowledge.
I just don’t think that research (yet) demonstrates that handwritten notes generate more learning than laptop notes.
Overall, I’m inclined to believe:
Practicing fine motor skills (by, say, handwriting) is really important for young learners.
Practicing handwriting makes us better at handwriting — and other word-related skills.
As students get older and more facile with a keyboard, the benefits of handwriting vs. keyboarding will probably depend on the student, the subject, the kind of notes being taken, etc.
And if I see more than one study directly testing the claim that handwriting helps people learn better, I’m entirely open to that possibility.
But at least so far, that claim is not — by any definition that seems reasonable to me– “research-based.”
Van der Weel, F. R., & Van der Meer, A. L. (2024). Handwriting but not typewriting leads to widespread brain connectivity: a high-density EEG study with implications for the classroom. Frontiers in Psychology, 14, 1219945.
Pinet, S., & Longcamp, M. (2025). Commentary: Handwriting but not typewriting leads to widespread brain connectivity: a high-density EEG study with implications for the classroom. Frontiers in Psychology, 15, 1517235.
Researchers can provide guidance to teachers by looking at specific teaching practices.
In last week’s post, for instance, I looked at a study about learning from mistakes. TL;DR: students learned more from review sessions where they explored their own mistakes than those where teachers reviewed ideas.
Or,
Back in December, I looked at a study about using “pre-questions” to reduce mind-wandering. Sure enough, students who answered pre-questions about a topic spent less time mind-wandering than those who didn’t.
Obviously, these studies might provide us with lots of useful guidance.
At the same time, this “one-study-at-a-time” approach has its drawbacks. For instance:
What if my students (or class) don’t really resemble the students (or class) in the study?
What if THIS study says that pre-questions reduce mind-wandering, but THAT study says they don’t?
What if THIS study (again) says that pre-questions reduce mind wandering, but THAT study says that mindful meditation reduces mind-wandering? Which strategy should I use?
And so forth.
Because of these complexities, we can — and should — rely on researchers in another way. In addition to all that research, they might also provide conceptual frameworksthat help us think througha teaching situation.
These conceptual frameworks don’t necessarily say “do this.” Instead, they say “consider these factors as you decide what to do.”
Because such guidance is both less specific and more flexible, it might be either especially frustrating or especially useful.
Here’s a recent example…
Setting Goals, and Failing…
We spend a lot of time — I mean, a LOT of time — talking about the benefits of short-term failure. Whether the focus is “desirable difficulty” or “productive struggle” or “a culture of error,” we talk as if failure were the best idea since banning smoking on airplanes.
Of course, ask any student about “failure” and you’ll get a different answer. Heck: they might prefer smoking on airplanes.
After all: failure feels really unpleasent — neither desirable nor productive, nor cultured.
In a recent paper, scholars Ryan Carlson and Ayelet Fishbach explore the complexity of “learning from failure”: specifically, how failure interefers with students’ goals.
To create a conceptual framework around this question, Carlson and Fishbach create two concept pairs.
First: they consider the important distinction between goal setting and goal striving.
Happily, those terms mean just what they say.
When I decide that I want to learn Spanish, or strengthen my friendships, or stop drinking caffein, I am setting a goal.
When I decide to enroll in a Spanish class, schedule more frequent dinners with pals, or purge my kitchen of all my coffee clutter, now I’m goal striving.
This pair helps us think through the big category “goals” in smaller steps.
Second: Carlson and Fishbach consider that both emotional barriers and cognitive barriers can interfere with goal setting and goal striving.
The resulting conceptual possibilities look like this:
The grid created by these conceptual pairs allows us to THINK differently about failure: both about the problems that students face, and the solutions that we might use to address them.
Troubling Examples
Having proposed this grid, Carlson and Fishbach explore research into its four quadrants. I’ll be honest, resulting research and insights frequently alarmed me.
For instance, let’s look at the top-left quadrant: “emotional barriers during goal setting.”
Imagine that one of my students contemplates an upcoming capstone project. She wants to set an ambitious goal, but fears that this ambitious target will lead to failure.
Her emotional response during goal setting might prompt her to settle for an easier project instead.
In this case, her emotional response shuts down her thinking before it even started. As Carlson and Fishbach pithily summarize this situation: “people do not need to fail for failure to undermine learning.”
YIKES. (Suddenly, the whole “desirable difficulties” project sounds much less plausible…)
Or, top right (emotional barriers/goal striving): it turns out that “information avoidance” is a thing.
People often don’t want to learn results of medical tests — their emotions keep them from getting to work solving a potential health problem.
So, too, I can tell you from painful experience that students often don’t read the comments on their papers. When they’re disappointed with a grade, they don’t consistently react by considering the very feedback that would help them improve — that is, “strive to meet the goal of higher grades.”
Or, lower right (cognitive barriers/goal striving). Carlson and Fishbach describe a study — intriguingly called “The Mystery Box Game.”
Long-story short: in this game, learning how to fail is more beneficial than learning about one path to success. Yet about 1/3 of participants regularly choose the less beneficial path — presumably because “learning how to fail” feels too alarming.
Problems Beget Solutions?
So far, this blog post might feel rather glum: so much focus on failure!
Yet Carlson and Fishbach conclude their essay by contemplating solutions. Specifically, they use a version of that grid above to consider solutions to the cognitive and emotional barriers during goal setting and goal striving.
For example:
“Vicarious learning”: people learn more from negative feedback when it’s directed at someone else.
“Giving advice”: counter-intuitively, people who give advice benefit from it at least as much as those who receive it. So, students struggling with the phases above (say: cognitive barriers during goal striving) might be asked for advice on how to help another student in a similar situation. The advice they give will help them.
“Counter-factual thinking”: students who ask “what if” questions (“what if I had studied with a partner? what if I had done more practice problems”) bounce back from negative feedback more quickly and process it more productively.
Because I’ve only recently come across this article, I’m still pondering its helpfulness in thinking about all these questions.
Given the optimism of “desirable difficulty/productive struggle” in our Learning and the Brain conversations, I think it offers a helpful balance to understand and manage these extra levels of realism.
Carlson, R. W., & Fishbach, A. (2024). Learning from failure. Motivation Science.
As I wrote last week, thinkers in edu-world often make strong claims at the expense of nuanced ones.
For example:
“A growth mindset undergirds all learning” vs. “growth mindset is an obvious boondoggle.”
“AI will transform education for the better” vs. “AI will make people dumber and schools worse.”
“Be the sage on that stage!” vs “get off the stage to guide from the side!”
The list goes on (and gets angrier).
When researchers start digging into specifics, however, the daily experience of teaching and learning gets mightily complicated, and mighty fast.
All those strong claims start to look…well…too strong for their own good.
One extraordinary example of “digging into the specifics” can be found in Graham Nuthall’s The Hidden Lives of Learners. Nuthall put cameras and mics on students in New Zealand classrooms, and arrived at all sorts of astonishing conclusions.
Another recent study looks quite specifically — no, really specifically — at 4 teachers. The goal: to understand what part of their work helped students learn.
Here’s the story.
Time to Review
A group of scholars, led by Dr. Janet Metcalfe, wondered if students learned more from teachers’ responses to their mistakes than from teachers’ direct instruction. (You can learn more about the study here.)
A few important points merit attention right away.
First: the classroom sessions I’m about to describe are REVIEW sessions. The students have ALREADY learned the math covered in these lessons; the teachers are helping them review in preparation for a high stakes exam.
In other words: this study does not focus on initial instruction. It focuses on subsequent review.
Second: the students involved VOLUNTEERED to take part. They are, presumably, atypically motivated to learn math.
Keep these points in mind as you think about applying the ideas described below.
In this study, 4 teachers helped 175 8th grade students prepare for upcoming state math exams.
For half of the students, the teachers taught 8 lessons (“explicit instruction”) reviewing core math concepts that would be on that exam.
For the other half, the teachers responded to the misakes that students made on practice tests. That is: during 4 sessions, students took 45 minute math tests. And after each of those sessions, the teachers
“were instructed […] to focus on the students’ errors and to do whatever they deemed appropriate to ensure that the issues underlying the errors would not reoccur and that the students would learn from their errors.”
So, which review approach proved more helpful — the explicit instruction, or the learn-from-mistakes instruction? And, why?
An Envelope, and LOTS of Questions…
The answer to that first question — which kind of review proved most helpful? — is easy to answer.
Students in both groups learned math; they did better on the post-test than the pre-test.
The students in the “learn-from-mistakes” group learned more.
This straightforward finding leads to obvious questions. And — alas — those obvious questions are VERY tricky to answer.
For instance, “how much more did the students in the learn-from-mistakes group learn?” That’s a reasonable question. The answer takes some careful parsing.
Roughly speaking, students in the explicit instruction group increased their scores about 2% per hour of instruction.
For those in the learn-from-mistakes group, the answer depended on the teacher.
The least successful teacher helped students in this group improve 2% per hour of instruction. The most successful teacher helped students improve 5% per hour of instruction.
Of course, that last paragraph prompts another reasonable question: what was different about those two teachers? Why did one teacher benefit his/her students more than twice as much as their colleague?
Let the Sleuthing Commence…
The study’s authors spend a great deal of time — and crunch a great many equations — to answer that question.
For instance:
Maybe the teacher whose students learned more (let’s call her Teacher M) is just a better teacher than the one whose students learned less (Teacher L).
As the researchers point out, that explanation doesn’t make much sense. After all, in their explicit instruction sessions, both Teacher M and Teacher L helped their students equally.
(By the way: to simplify this blog post, I’m leaving out the two other teachers for now.)
Okay, maybe Teacher M did a better job of focusing on students’ mistakes, whereas Teacher L spent too much time focusing on questions that students got right.
Nope. This study includes quite an eye-watering graph to show that they both focused about the same on students’ mistakes.
As the researchers write: “all of the teachers taught to the errors of their students, and … the extent to which they did so did not predict student learning.”
So, what was the secret sauce?
The Perfect Combination
After a few more false leads, the study focuses on two moment-by-moment variables: the teachers’ focus, and the kind of interaction with the student.
Focus: did the teachers
“[dwell] upon how to solve the problem correctly,” or
“[delve] into the nature of the errors – why the students had made them, what the difficulty in the logic was, and/or how to recognize and circumvent such mistakes in the future”?
Kind of interaction: did the teachers explain/lecture, or did they discuss/interact?
With this pair of questions, at last, the study struck gold.
Teacher L — whose students learned relatively little — focused almostall her time on “how to solve the problem correctly.” While pursuing that goal, she divided her time equally between lecture and discussion.
Teacher M — whose students improved more quickly — spent almost all her time in discussion, with almost no time in lecture. While in this interactive mode, she divided her time more-or-less equally between solving problems and understanding the nature of the mistake.
This final insight allows us to make this claim:
Highly motivated 8th grade math students,
reviewing in preparation for a high-stakes exam,
learn less from explicit instruction and more from making and reviewing their mistakes,
as long as the teacher keeps those review sessions interactive,
and equally focused on “getting the answer right” and “understanding the nature of the mistake.’
Notice, by the way, all the nuance in this statement.
To emphasize just one point here: this study does NOT argue that “learning from mistakes” is better than “direct instruction” in all circumstances.
It argues that students learn more from mistakes when reviewing, as long as the teacher follows a very particular formula.
A Final Note
Heated battles in this field often get hung up on specific labels.
As I’ve written before, we do a LOT of arguing about benefits of “desirable difficulty” vs. “productive struggle” — an odd set of arguments, given that both phrases seem to mean the same thing.
This study was co-authored by (among other scholars) Robert Bjork — who helped coin the phrase “desirable difficulty.” For that reason, you might be surprised to learn that this study touts the benefits of “productive struggle.”
That is: the students took a test, they made mistakes, they wrestled with those mistakes, and they learned more. Their struggle (trying to understand what they did wrong) was productive (they improved on their test scores — and probably their understanding of math).
Of course, I could just as easily describe that process as “desirable difficulty.” The difficulties these students faced here — the test, the mistakes, the analysis — turned out to be beneficial — that is, “desirable.”
My own view is: don’t get hung up on the label. The question is: are the students both thinking harder and ultimately succeeding? If “yes” and “yes,” then this teaching approach will benefit students.
Metcalfe, J., Xu, J., Vuorre, M., Siegler, R., Wiliam, D., & Bjork, R. A. (2024). Learning from errors versus explicit instruction in preparation for a test that counts. British Journal of Educational Psychology.
Over on eX/Twitter, a debate has been raging — with all the subtlety and nuance of your typical Twitter debate. The opening salvo was something like:
“Despite what you’ve heard, all people learn the same way.”
You can imagine what happened next. (Free advice: look away.)
Despite all the Twitter mishegas, the underlying question is useful and important — so I’ll do my best to find the greys among the black-vs-white thinking.
Here goes.
Useful…
I suspect that this claim — “all people learn the same way” — got started as a rebuttal to various myths about “meaningful sub-categories of learners.” Alas, most of those proposed sub-categories turn out not to be true or useful.
No, learning styles theory has not held up well.
No, the theory of “multiple intelligences” has no useful teaching implications. (And Howard Gardner didn’t claim that it did.)
No, “left-brain, right-brain” dichotomies don’t give us insights into teaching and learning.
No, the Myers-Briggs Type Indicator doesn’t tell us how to manage classrooms or lesson plans. *
My British friends tell me about some system to sort students according to different colored hats. (I do not think I’m making this up.)
(I’ve written about these claims so many times that I’m not going to rehash the evidence here.)
Whenever anyone says “we can usefully divide students into THIS kind of learner and THAT kind of learner,” we should be highly suspicious and ask to see lots of research. (If you want to evaluate that research critically, I can recommend a good book.)
Well, the shortest rebuttal to this sort of claim is: “Those sub-categories don’t exist. ALL PEOPLE LEARN THE SAME WAY.”
Now, any time someone makes an absolute claim about teaching and learning in six words and seven syllables, you know that claim is oversimplified.
But you can understand the temptation to cut off all those untrue claims with a brusque rejoinder. That temptation pulses all the stronger because those untrue claims persist so stubbornly. (In 2025, schools of education are STILL teaching learning styles.)
…and (substantially) True
This claim (“all people…”) isn’t simply useful; it’s also largely accurate.
For example:
At the neuro-biological level — neurons, neurotransmitters, synapses, myelin, etc. — long-term memories form the same way for everyone.
As far as we know…
men and women
tall people and short people
introverts and extroverts
people who think cilantro tastes like soap, and the rest of us
… everyone forms new neural networks (that is: “learns”) the same way. (I should emphasize that our understanding of this neural process is still VERY basic. We’ve still got SO MUCH to learn.)
When we switch our analysis from neuroscience to psychology, the claim still holds up well.
For instance:
Everyone uses working memory to combine new information from the environment with concepts and facts stored in long-term memory.
Everyone depends on a complex of systems that we call “attention” to control the flow of all that information.
Everyone responds simultaneously with emotion and cognition to any given set of circumstances. (These two systems overlap so much that distinguishing between them creates lots o’ challenges.)
And so forth.
Given all these similarities, cognitive science research really can offer up advice that applies to almost everyone in almost all circumstances.
Yes: we really must manage working memory load so that students can build concepts effectively.
Yes: retrieval practice helps almost all learners consolidate and transfer almost all school learning. (Yes, “retrieval-induced forgetting” is a concern, but can be managed if we strategize effecively.)
Yes: spacing and interleaving enhance learning in most circumstances.
And so on…
Given the broad usefulness and truth of the “we-all-learn-the-same” claim, I certainly understand why it’s tempting to make it — and to defend it.
Exceptions Matter
I’ve written that the claim is “broadly” useful and true; but I don’t think it’s ALWAYS true.
For example:
Students with diagnoseable learning differences really might learn differently.
For instance: dyslexic readers combine distinctive neural networks to get their reading done. Those readers almost certainly benefit from distinct teaching strategies. In other words: by any reasonable definition, they “learn differently.”
Another example:
All learning depends on prior knowledge.
That claim — which sounds like “all people learn the same way” — also suggests that people learn differently.
Let’s imagine that you know A LOT more about opera than I do. (This assumption is almost certainly true.) If you and I both attend an advanced lecture about an obscure opera — “Der Häusliche Krieg” — your learning will function quite differently from mine. Because you’re an expert and I’m a novice, we will learn differently.
Lots of individual differences will bring teachers to this same point.
Because I teach English, I teach grammar — and MANY of my students simply hate grammar. Their prior experience tells them it’s boring, useless, and impossible to understand.
On the one hand, those enduring cognitive principles listed above (working memory, retrieval practice, etc.) do apply to them. But their emotional response to the content will in fact shape the way they go about learning it.
Core principles of learning apply, and my students’ prior experience means that their learning process might well be different.
Beyond Twitter Rage
Twitter generates lots of extreme debates because complex ideas can’t be boiled down into its trivializing format.
So it’s not surprising that a nuanced understanding of “individual differences within important, broad, and meaningful similarities” doesn’t work in Twitter-ville.
At the same time, I do think our discussions of learning should be able to manage — and to focus on — that nuance.
Our students will learn more when we recognize BOTH the broad cognitive principles that shape instruction, AND the individual variation that will be essential within those principles.
Back in 2019, Paul Kirschner wrote a blog post on this same point. His “digestive system” analogy is VERY helpful.
* A few years back, I emailed the MBTI people to ask for research supporting their claims. They did not send me any. They did, however, sign me up for their newsletter.
From The Science of Learning, Bradley Busch, Edward Watson, and Ludmila Bogatchek have kicked it up a notch in this fresh innovative presentation of Teaching & Learning Illuminated: the Big Ideas Illustrated.
