L&B Blog – Page 2 – Education & Teacher Conferences

Doubting My Doubts; The Case of Gesture and Embodied Cognition

Andrew Watson

March-02-2025

Doubting My Doubts; The Case of Gesture and Embodied Cognition

The more time I spend hearing “research-informed educational advice,” the more I worry about the enticing words “research-informed.”

Many MANY people toss around the phrase “research says…”; all too often, even a brief investigation suggests that research really doesn’t say that.

For this reason, I find myself slower to get excited about new “research-based” teaching ideas than many of my colleagues…even colleagues whom I admire, respect, and generally trust.

For instance: lots of scholars are investigating the field of embodied cognition and — more specifically — of using gestures to promote learning.

I’m certainly open to the idea that combining gestures with words and visuals will improve learning. And: I want to know A LOT more about the specifics of this idea:

Who is making these gestures? Teachers? Students? Actors in videos?
What kind of gestures are they? “Deictic” or”iconic”? Rehearsed or improvised?
Does the strategy work well in all disciplines/grades/cultures?

And so forth.

I’d also love to see some straightforwardly convincing research to support the answers to those questions.

So, for instance, I wrote a post about students using gestures to learn about Brownian motion. While the outline of the study made sense to me, it…

… didn’t have a control group,

… chose a topic easily translated into gestures, and

… measured “learning” 2 days later. (Does 2 days count as learning?)

While I’m glad I read the study, and appreciate some of its nuances, I don’t think it’s a slam dunk.

At the same time, I should turn some of my skeptical energy towards myself.

In other words: given all of my doubts, I should also be ready to doubt my own doubts. Maybe the wisdom of the crowd should outweigh my own habitual caution here. Maybe I’m so invested in my skeptic’s persona that I’m subconsciously unwilling to be persuaded…

Enter the Steelman

Because I doubt my doubts, I’m always on the lookout for EXCELLENT research contradicting my outlier point of view. I genuinely WANT to have my errors pointed out to me.

For that reason, I was delighted to find a highly touted study about teaching physics with embodied cognition.

My source here — published by the Educational Endowment Foundation — looks for the very best evidence supporting all sorts of cognitive science-based teaching advice: interleaving, retrieval practice, schemas, and so forth.

Of the 26 studies they found looking at embodied cognition, one stood out for its excellence. (In their rating system, it’s the only only one they rated “high priority.”) If the EEF, and all the wise scholars behind this report, find this study persuasive, it’s likely to be among the best research I can find.

In other words: I’m not analyzing a straw man here. This study is the “steelman.”

Playground Physics

The idea behind this study sounds both sensible and fun. Many of the abstract concepts studied in physics class are acted out quite concretely — that is, they are EMBODIED — when our children get to the playground.

If we could connect abstract classroom physics with embodied playground phyics, that approach could be really helpful.

This study begins with a good idea…and an ENORMOUS sample size. Over 3400 (!) students were in the initial sample; after (unusually high) attrition, that number dropped to about 1300 — roughly 800 in the “playground physics” group, and 500 in the control group.

The researchers wanted to see if the students in the playground group would a) learn more physics, b) feel more engaged, and c) feel more motivated — all compared to the control group.

The special “playground physics” program begins with a training session for the teachers, and includes curricular materials.

Crucially, playground physics also includes a phone app that students use to analyze their own motion:

“Using the app, users record videos of themselves and their friends engaging in physical play, and the app generates graphs of distance traveled, speed, direction, and kinetic and potential energy. As users watch the video, they see graphs of their movement unfolding. Users can pause to examine where they are moving fastest or slowest, where a force is pushing or pulling, and where their kinetic and potential energies are at their highest and lowest points. This is intended to support conversations grounded in the children’s physical experience”

Honestly, the whole experience sound really interesting!

Persistent Doubts

Although I tried to find a Steelman Study to support the case for Team Embodied Cognition, I’m still not persuaded.

I have two substantial concerns:

First:

This study does not measure the benefits of embodied cognition for learning physics.

Instead, it measures the benefits of embodied cognition PLUS cool tech gadgetry for learning physics. In fact, the study is published in a journal that focuses on technology in education.

Yes, the students learned more — but the extra learning could have come from the app (so much fun with video!) or from the embodied cognition (moving is so cool!) or both. We just don’t know.

I am not the only person pointing out this concern. The study’s authors say several times that they don’t know what the “mechanism” is that created additional learning. In other words: they do not claim that the embodiment matter more than the tech — or that it mattered at all. They don’t know.

To be persuaded by research into the use of gestures, I want to see a study that singles out the gestures; it should — in the lingo of research — “isolate the variable.” This one doesn’t.

Second:

When we compare two groups, we want them to be close enough to each other to be good proxies for each other. I’m not sure we can say that for this study.

A) The teachers of Playground Physics received extra PD; the teachers in the control group didn’t. Did the PD itself make the difference? We don’t know.

B) The study used a “business-as-usual control group.” That is: control group teachers just did what they always did. Teachers and students in the Playground Physics group got a Shiny New Thing. Was it the novelty that made the difference? We don’t know.

C) The Playground Physics group spent 15.5 hours studying physics; the control group spent 13.2 hours. The study’s authors write that this difference isn’t “statistically significant.” But — as a classroom teacher — I’m thinking two hours and fifteen minutes of additional practice would be significant, even if it isn’t “significant.” *

Because the study doesn’t isolate the variable (that’s the first concern) and the two groups don’t sufficiently resemble each other (that’s the second concern), I’m still stuck thinking: “this study doesn’t persuade me that embodied cognition is a thing.”

And — as you recall — I looked at this study because a respected group said it’s the best one they found.

TL;DR

I’m still looking for the study that makes the Embodied Cognition approach to teaching persuasive enough for me to recommend it to others.

I haven’t found it yet…but I haven’t given up hope.

By the way: if you know of such a study, please send it my way!

* I spoke with a stats-whisperer friend, who agrees with me that this simply isn’t a reasonable claim.

Margolin, J., Ba, H., Friedman, L. B., Swanlund, A., Dhillon, S., & Liu, F. (2021). Examining the impact of a play-based middle school physics program. Journal of Research on Technology in Education, 53(2), 125-139.

Revisiting the “Handwriting vs. Laptops” Debate: More Moving Goalposts

Andrew Watson

February-23-2025

Revisiting the “Handwriting vs. Laptops” Debate: More Moving Goalposts

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.

Goals, Failure, and Emotions: a Conceptual Framework

Andrew Watson

February-16-2025

Goals, Failure, and Emotions: a Conceptual Framework

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 frameworks that help us think through a 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.

“Learning from Mistakes” vs. “Learning from Explanations”

Andrew Watson

February-09-2025

“Learning from Mistakes” vs. “Learning from Explanations”

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 almost all 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.

“All People Learn the Same Way”: Exploring a Debate

Andrew Watson

February-02-2025

“All People Learn the Same Way”: Exploring a Debate

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.

Difference Maker: Enacting Systems Theory in Biology Teaching, by Christian Moore-Anderson

Guest Post

January-26-2025

Difference Maker: Enacting Systems Theory in Biology Teaching, by Christian Moore-Anderson

Today’s book review is by Beth Hawks.

Teaching Science is a Challenge

Science classes cover a massive amount of content knowledge, and it can feel overwhelming finding the best approach to teaching it without feeling like students are merely acquiring a set of disjointed facts.

In the introduction to his book, Difference Maker: Enacting Systems Theory in Biology Teaching, Christian Moore-Anderson sums up the challenge well, when he says, “I’m sure you’ve felt – at some point – that to grasp biology was to master an encyclopedia.”

For some time, he had taught in most of the typical ways, but he felt he was tied to creating resources and activities for students and that students still weren’t seeing the deeper connecting threads of biology.

Time for a Change

As with many things, the move to online teaching during the pandemic motivated him to make a change…because what he had been doing was no longer working.

This concern led him to the world of cybernetics and systems theory; and moved him from a sense of mass knowledge transfer to one of teaching biology from a set of unifying principles.

As he dug even more deeply, he found that he wasn’t just teaching about systems; he was enacting systems theory as a method of instruction. He co-created diagrams with students and engaged them in dialogue to reveal their understanding.

By doing so, he created an interactive feedback loop that allowed him to respond flexibly to student needs.

Model Found in Cybernetics

The book begins with a few chapters of explanation of cybernetics. (Don’t let the terminology of “cybernetics” frighten you. It is not necessary to have a deep understanding of all of these terms.)

After I set aside my mental images from Star Trek of Dr. Noonien Soong creating Data’s positronic brain (my first exposure to the word cybernetics), I was able to see his blending of two aspects of the discipline.

Conversation theory posits that – since meaning is made in the mind of the listener rather than being transmitted by the speaker – we can have a shared understanding of meaning only through dialogue. The teacher explains, but then he discovers what the student heard through conversation.

Moore-Anderson describes doing this through multiple choice questions or open-ended questions; he also acknowledges that it can be done with other methods (e.g. mini-whiteboards, written answers on paper).

The law of requisite variety – When a system is complex, it can only survive if its ability to adapt is equally complex. In other words, there must be a variety of responses to a variety of changes. If a teacher has only a small set of responses when something happens in her classroom, she won’t be able to adapt to the needs of students during a lesson.

He combines these theories into a model of instruction he calls “the recursive teaching model.”

The teacher explains, while the student interprets. Then the student explains what they understand while the teacher interprets. This cycle keeps looping back on itself until they agree on their understanding.

Moore-Anderson provides guidance by opening each section with a key idea and walking through the process of implementation in the classroom. He includes the conversations he has with his students as well as the diagrams he creates with them during those conversations.

Have Students Notice Differences by Predicting Outcomes

After setting up his foundational theory, Moore-Anderson gets to the heart of his new practice: having students perceive distinctions in the concept being taught.

He defines distinctions as “differences that make a difference to the observer.”

As teachers, we often begin with sameness – giving multiple examples of a new concept to solidify students’ recognition of the standard. This strategy, however, shows only the idea itself and not its interaction with a conceptual whole.

Having students repeat similarities in their own words might not give them a full grasp of the influence they have on the biological system overall.

Moore-Anderson argues that we should begin with variations of the concepts so that students can see what difference a change would make. He prompts students to notice these differences (and the difference they make) by posing “what if” questions.

What if someone drinks sea water rather than fresh water?
What if the predator in this ecosystem suddenly disappears?
What if this heart valve were missing?
What if the sugar concentration was increased in this solution?

When students first predict the outcome of a change, and then add those changes to diagrams they create together, they arrive at a shared understanding of each concept. This approach lets them understand in a deeper way than simply explaining how something works and having students paraphrase that explanation.

Moore-Anderson restricts the responses to keep things from getting out of hand by giving choices like, “Will a change in X make Y increase, decrease, or stay the same?” and having students defend their answers.

Practical Examples Inspire Teachers

The true strength of this book for me as a classroom teacher comes from his descriptions of using this method in his lessons.

When Moore-Anderson moves from summaries of cybernetic theories into examples of actual classroom conversations with students, he allows me to imagine implementing his method with my own students.

As a teacher, my favorite education books are those that inspire ideas outside of those mentioned in the writing, and Moore-Anderson does exactly that throughout each chapter. As I read his stories, I was able to picture myself having similar conversations with my students and thought of other topics to which I could apply his method.

Difference Maker gives me a way to think about content delivery rather than prescribing an exact method for me to copy.

Is It for Everybody?

The Difference Maker method might not be equally appropriate in all settings.

I imagined my middle schoolers might find this approach frustrating because they lack the foundational knowledge to make reasonable predictions. On the other hand, I thought my juniors and seniors would thrive with these sorts of classroom conversations.

I trust Moore-Anderson when he says he applies the method in class with eleven year old students, but I’m not sure I would. As with all techniques, success relies on adapting them to your context.

As the title makes clear, this book is intended for biology teachers. Since all biological processes have noticeable cause and effect relationships within systems, that makes sense.

I had a bit harder time recognizing topics in which I might apply it to chemistry and physics. So, I will definitely recommend this book to my biology teacher friend and suggest that he loan it to the environmental science teacher across the hall.

As a chemistry and physics teacher, I might want to have it in the back of my mind as I planned some lessons, because it would provide a way of thinking about how to explain cause and effect. However, I wouldn’t make it a regular practice as Moore-Anderson does with biology. (Did I mention earlier that it is good to adapt to context?)

Can I Be in This Class?

My biggest takeaway from reading Difference Maker is that I would have loved to be in this biology class when I was a student. I would have absorbed more, seen deeper threads, and remembered more. I would have walked away with a better understanding of myself and my relationship with my environment.

Beth Hawks taught middle and high school science for 25 years, serving as the science department chair at GRACE Christian School in Raleigh, North Carolina for 17 years. A graduate of Oral Roberts University, Beth has taught 8th grade Physical Science, Physics, Chemistry, Algebra IB, Health, Photography, and Yearbook. She frequently provided professional development to colleagues in her role as resident brain enthusiast and has now moved into consulting full time under the name The Learning Hawk.

You can hear Beth speak at our Science of Learning conference in NYC in April.

Andrew Watson

January-19-2025

“AHA!”: A Working Memory Story…

Teachers, students, people: we spend lots of our time figuring stuff out.

Sometimes, we do that figuring out by sorting through options, considering similar situations in the past, trying out logical possibilities, and so forth.

And other times, the figuring out just happens: “AHA!”

If we’re going to think about these different mental experiences in a scientific way, we need technical terminology; so, let’s go ahead and call that first process “analysis” and the second one “insight.”

Analysis (I’m paraphrasing from this study here)

involves searching long-term memory for potential algorithms, schemas, or factual knowledge,
feels effortful, and
happens consciously;

Insight, on the other hand,

happens more-or-less automatically,
feels effortless, and
happens unconsciously.

The two questions I’ll explore below are:

how does working memory load influence the Aha! experience? and
how does the answer to that question shape the way we plan teaching?

Brace yourself for a radical answer to question #2.

AHA + Working Memory

Obviously, analysis loads working memory. All that comparing options and combing through long-term memory takes up scarce working memory resources.

But what about insight? Do those Aha! moments require working memory?

To answer this question, a group of Dutch researchers asked 100+ college students to solve fun mental puzzles.

Here’s the game:

I’m going to list 3 words, and you’re going to tell me another word that “goes with” all three.

So, if I say “artist, hatch, route,” you might come up with the word “______.”

Perhaps you came up with a solution by working your way through various familiar phrases: “con artist? makeup artist?” That would be an analysis solution.

Or perhaps the answer — “escape” — just came to you without any deliberate thought process. That would be an insight solution.

These problems have a splendidly cumbersome name: “compound remote association tests.” Happily, they allow for a handy acronym: CRA.

In their study, the Dutch researchers had students solve CRA problems.

One group of students had no additional working memory load.

A second group had a small WM load; they had to remember a two-digit string while solving problems.

A third group had a larger WM load; they had to remember a 4 digit string.

So, here’s the research question: did the WM load have an effect on analysis solutions or insight solutions as students undertook CRA tests?

Answers, Plus

“Yes, and no.”

In other words:

“Yes”: as WM load increased, the number of correct analysis solutions decreased.

“No”: as WM load increased, the number of correct insight solutions stayed the same.

Now, the first half of that answer was easy to predict. When researchers increased the WM load, the students’ WM “headroom” decreased. Because analysis requires WM capacity, students’ reduced headroom made CRA solutions harder.

The second half of that answer is really interesting.

Students were equally good at insight solutions no matter the WM load. The logical implication: insight solutions do not require WM. (At least, not in a way that is detected in this research paradigm.)

Now that we know the answer to that question, what do we teachers do with that information? How does it help us plan our teaching?

Thinking Aloud

I should say at this moment that I’m switching from research to speculation. That is: the blog post up to know has been a summary of a research study. I’m now leaving that study to consider what we might do with this information.

First off, I suspect that a very large percentage of the school work students do requires analysis, not insight (as defined in this study).

That is: my students have to think their way through grammar solutions. They have to ponder the meaning of that symbol — or that sentence — right there.

They rarely say: “it just came to me — that’s a participle!”

If I’m right that MOST school work relies on analysis, then MOST of the time we teachers must focus on working memory load.

If we place too much stress on working memory, we will hamper our students’ ability to accomplish those analytical tasks.

But…drum roll please…I can imagine niche-y circumstances where we WANT students to prefer insight to analysis. In those circumstances, I hope my students say, “Aha!” rather than “let me think about that.”

For instance: improv theater.

When actors try improv, we want them to “get out of their heads” and let instincts take over. (For the record: I’m bad at improv my self, but I founded and coached an improv troupe at the high school where I taught.)

This thought process leads to an even more surprising idea…

There’s a First Time for Everything

I spend much of my professional life explaining working memory to teachers and coaching them to avoid working memory overload. After all: “no academic information gets into long-term memory except through working memory.”

If, however, WM load hampers analysis, it might thereby indirectly promote insight.

Perhaps then I should deliberately ramp up WM load during improv rehearsals. This approach would make analytical solutions less likely, and in that way make insight solutions more likely.

This improv-coaching idea leads to other, equally radical possibilities. Are there other times during a students’ academic career where we prefer insight to analysis? Should we, during those lesson plans, keep working memory demands unusually high?

I can hardly believe that I’m seriously talking about deliberately stressing working memory. My professional identity is wobbling.

TL;DR

A recent study by Dutch researchers suggests that analytical problem-solving requires WM, but insight problem solving doesn’t.

This finding has prompted me to wonder if we should — in rare circumstances — increase WM load to make students’ insight solutions likelier.

That possibility is entirely new to me — but quite fun to ponder. I hope that my WM friends — and my improv friends — will join the conversation.

Stuyck, H., Cleeremans, A., & Van den Bussche, E. (2022). Aha! under pressure: The Aha! experience is not constrained by cognitive load. Cognition, 219, 104946.

Andrew Watson

January-12-2025

Nerd Alert: Focusing on Definitions

You come to Learning and the Brain conferences — and to this blog — because you want research-based insight into teaching and learning.

We sincerely hope that you get lots of those insights, and feel inspired by them.

At the same time, all sorts of work has to go on behind the scenes to make sure such advice has merit. Much of that work seems tedious, but all of it is important.

For instance: definitions.

When researchers explore a particular topic — say, “learning” — they have to measure something — say, “how much someone learned.”

To undertake that measurement, they rely on a definition of the thing to be measured — for example: “getting correct answers on a subsequent test = learning.”

Of course, skeptics might reject that definition: “tests don’t reveal learning. Only real world application reveals learning.”

Because these skeptics have a different definition, they need to measure in a different way. And, of course, they might come to a different conclusion about the value of the teaching practice being measured.

In other words:

If I define learning as “getting answers right on a test,” I might conclude that the Great Watson Teaching Method works.

If you define learning as “using new concepts spontaneously in the real world,” you might conclude that the Great Watson Teaching method is a bust.

The DEFINITION tells researchers what to MEASURE; it thereby guides our ultimate CONCLUSIONS.

A Case in Point

I recently read an article, by Hambrick, Macnamara, and Oswald, about deliberate practice.

Now, if you’ve spent time at a Learning and the Brain conference in the last decade, you’ve heard researcher K. Anders Ericsson and others present on this topic. It means, basically, “practicing with the specific intention of getting better.”

According to Ericsson and others, deliberate practice is THE key to developing expertise in almost any field: sports, music, chess, academics, professional life.

Notice, however, that I included the slippery word ‘basically’ in my definition two sentences ago. I wrote: “it means, basically, ‘practicing with the specific intention of getting better.’ ”

That “basically” means I’m giving a rough definition, not precise one.

But, for the reasons explained above, we shouldn’t use research to give advice without precise definitions.

As Hambrick, Macnamara, and Oswald detail, deliberate practice has a frustratingly flexible definition. For instance:

Can students create their own deliberate practice regimens? Or do they need professionals/teachers to create them and give feedback?
Does group/team practice count, or must deliberate practice be individual?

As the authors detail, the answers to those questions change over time.

Even more alarmingly, they seem to change depending on the context. In some cases, Ericsson and his research partners hold up studies as examples of deliberate practice, but say that Hambrick’s team should not include them in meta-analyses evaluating the effectiveness of deliberate practice.

(The back-n-forth here gets very technical.)

Although the specifics of this debate quickly turn mind-numbing, the debate itself points to a troubling conclusion: because we can’t define deliberate practice with much confidence, we should hesitate to make strong research claims about the benefits of deliberate practice.

Because — again — research depends on precise definitions.

Curiouser and Curiouser

The argument above reminded me of another study that I read several years ago. Because that study uses lots of niche-y technical language, I’m going to simplify it a fair bit. But its headlines were clear:

Project-based learning helps students learn; direct instruction does not.

Because the “constructivist” vs. “direct instruction” debate rages so passionately, I was intrigued to find a study making such a strong claim.

One of my first questions will sound familiar: “how, precisely, did the researchers define ‘project-based learning’ and ‘direct instruction.’ ”

This study started with these definitions:

Direct instruction: “lecturing with passive listening.”

Constructivism: “problem-solving opportunities … that provide meaning. Students learn by collaboratively solving authentic, real-life problems, developing explanations and communicating ideas.”

To confirm their hypothesis, the reseachers had one group of biology students (the “constructivism” group) do an experiment where they soaked chicken bones in vinegar to see how flexible the bones became.

The “direct instruction” students copied the names of 206 bones from the chalkboard into their notebooks.

After even this brief description, you might have some strong reactions to this study.

First: OF COURSE students don’t learn much from copying the names of 206 bones. Who seriously thinks that they do? No cognitive scientist I’ve ever met.

Second: no one — and I mean NO ONE — who champions direct instruction would accept the definition as “lecturing with passive listening.”

In other words: we might be excited (or alarmed) to discover research championing PBL over direct instruction. But we shouldn’t use this reseach to make decisions about that choice because it relies on obviously inaccurate definitions.

(If you’re interested in this example — or this study — I’ve written about it extensively in my book, The Goldilocks Map.)

In Brief:

It might seem nerdy to focus so stubbornly on research definitions. If we’re serious about following research-informed guidance for our teaching, we really must.

Hambrick, D. Z., Macnamara, B. N., & Oswald, F. L. (2020). Is the deliberate practice view defensible? A review of evidence and discussion of issues. Frontiers in Psychology, 11, 1134.

Andrew Watson

January-05-2025

Finding a Framework for Trauma

Although education itself encourages detailed and nuanced understandings of complex ideas, the field of education often rushes to extremes.

According to the loudest voices:

Artificial intelligence will either transform education for the better, or make us all dumber.
Memorization is either an essential foundation for all learning, or “drill and kill.”
A growth mindset will either motivate students to new successes, or delude teachers into this out-dated fad (“yet” schmet).

And so forth.

This tendency to extremes seems especially powerful at the intersection of education and trauma.

Depending on your source and your decade, trauma is

Either a problem so rare that it doesn’t merit discussion, or
a problem so pervasive and debilitating that we need to redesign education.

How can we find a steady, helpful, realistic path without rushing to extremes?

A Useful Start

If we’re going to think about trauma, we should start with a definition of it.

A thousand-word blog post can’t get into the subtleties, but here’s a useful starting place:

“Trauma is a response to an event or series of events that overwhelms an individual’s capacity to cope.”

In that sentence, “overwhelmed” means a serious and ongoing response — not short-term unhappiness (even if intense).

Symptoms of being “overwhelmed” might include dissociation, flashbacks, night terrors, drug addiction, or major depression.

Note: unlike trauma, stress puts pressure on — but does not inherently overwhelm — coping capacity.

Thoughtful people might not agree with the sentences above, but I think most people will agree that they’re an honest attempt to describe a complex mental state.

The First Pendulum

Discussions of trauma — especially the extreme versions — begin with its sources.

When I started teaching, in the 1980s, our school — quite literally — NEVER discussed trauma. (To be fair, I should say: “I don’t remember ever discussing trauma.”)

The implied message: “trauma probably happens somewhere to some people. But it’s so rare, and so unlikely to be a part of our students’ lives, we’re not going to use precious faculty time to focus on it.”

In brief: “the causes of trauma aren’t relevant to teachers.”

Since those days, our profession has rightly recognized that trauma DOES happen. It does happen to our students and in their families and communities. The causes of trauma are absolutely relevant to teachers.

And yet, because our profession tends to extremes, I now hear the flipside of that earlier casual dismissal. Instead of being rare and almost irrelevant, trauma is common and pervasive.

One sign of this trend: a lengthening list of common occurances that cause trauma. Perfectly typical stressors — being cut from a sports team, getting a bad grade — are reframed as traumatic.

I’ve even seen the claim that “things that we don’t get to experience can be traumatic.” While missed chances can be disappointing, even stressful, it’s just hard to see how they fit the definition of trauma.

The list of symptoms has also grown. E.g.: “procrastination is a sign of trauma.”

Now, I don’t doubt that some people who have experienced trauma procrastinate; I also don’t doubt that almost everyone procrastinates. Traumatized people might procrastinate, but not all people who procrastinate have experienced trauma.

To avoid being caught up in this race to the extremes, I think it helps to keep the definition in mind: a response to an event or series of events that overwhelms an individual’s capacity to cope.

Such events do happen to our students — but not frequently, and not to all of them.

The Second Pendulum

While we negotiate this first pendulum (“trauma doesn’t happen/is universal”), we also watch a second one swing back and forth.

Old school: “least said, soonest mended. On those infrequent occasions when trauma really happens, we should all just keep going and not make a big deal about it.”

Pendulum swing: “a traumatized student is literally incapable of paying attention or learning. Schooling as we know it should come to a halt.”

This second statement is usually accompanied by neuroscience terminology, starting with “amygdala.”

I was reminded of this pendulum swing at the most recent Learning and the Brain conference in Boston — specifically in a keynote address by George A. Bonanno.

Dr. Bonanno has been studying trauma for decades; in his talk, he focused on the symptoms that follow trauma.

He and his team have been running studies and aggregating data, and he showed graphs representing conclusions based on more than 60 trajectory analyses.

To present his complex findings as simply as possible:

Roughly 10% of people who experience trauma have enduring symptoms;
Less than 10% start without symptoms, but symptoms develop over time and persist;
Roughly 20% initially experience symptoms, but recover over two years;
The rest never repond with serious symptoms.

In other words: in Bonanno’s research, two years after trauma, roughly 80% of people do not experience troubling symptoms.

For this reason, by the way, Bonanno does not speak of “traumatic events” but of “potentially traumatic events.”

That is: an event has the potential to create trauma symptoms in a person. But something like two-thirds of people do not experience trauma in response to that potentially traumatic event. (And another 10% recover from those symptoms in a year or two.)

Towards a Balanced Framework

How then should teachers think about trauma in schools.

First: we can avoid the extremes.

Yes, trauma does happen.

No, it isn’t common. (Bad grades aren’t traumatic.)

Yes, schools and teachers should respond appropriately to the trauma that students experience.

No, not everyone responds to trauma the same way. Most people react to potentially traumatic events without trauma symptoms (or recover over time).

Second: within this nuanced perspective, we should acknowledge the importance of responding to trauma appropriately.

That is: events that potentially create trauma might be rare; most people might not respond to them with trauma symptoms.

And: our students who do experience trauma symtoms deserve informed and sympathetic response.

By way of analogy: something like 3% of K-12 students are on the autism spectrum. That’s a relatively small number. And: those students deserve the best education we can provide.

If 3% of our students experience trauma symptoms (I have no idea what the actual percentage is), they too deserve our professional best.

Attempting a Summary

In our profession, we have all too frequently overlooked and downplayed the trauma that some of our students experience. As we try to correct that serious error, we should not commit another error by seeing trauma everywhere, and by assuming it debilitates everyone.

A Final Note:

To keep this post a readable length, I have not discussed ACES scores. Depending on the response this post gets, I may return to that topic in a future post.

How to Reduce Mind-Wandering During Class

Andrew Watson

December-29-2024

How to Reduce Mind-Wandering During Class

I recently wrote a series of posts about research into asking questions. As noted in the first part of that series, we have lots of research that points to a surprising conclusion.

Let’s say I begin class by asking students questions about the material they’re about to learn. More specifically: because the students haven’t learned this material yet, they almost certainly get the answers wrong.

Even more specifically — and more strangely — I’m actually trying to ask them questions that they won’t answer correctly.

In most circumstances, this way of starting class would sound…well…mean. Why start class by making students feel foolish?

Here’s why: we’ve got a good chunk of research showing that these questions — questions that students will almost certainly get wrong — ultimately help them learn the correct answers during class.

(To distinguish this particular category of introductory-questions-that-students-will-get-wrong, I’m going to call them “prequestions.”)

Now, from one perspective, it doesn’t really matter why prequestions help. If asking prequestions promotes learning, we should probably ask them!

From another perspective, we’d really like to know why these questions benefit students.

Here’s one possibility: maybe they help students focus. That is: if students realize that they don’t know the answer to a question, they’ll be alert to the relevant upcoming information.

Let’s check it out!

Strike That, Reverse That, Thank You

I started by exploring prequestions; but we could think about the research I’m about to describe from the perspective of mind-wandering.

If you’ve ever taught, and ESPECIALLY if you’ve ever taught online, you know that students’ thoughts often drift away from the teacher’s topic to…well…cat memes, or a recent sports upset, or some romantic turmoil.

For obvious reasons, we teachers would LOVE to be able to reduce mind-wandering. (Check out this blog post for one approach.)

Here’s one idea: perhaps prequestions could reduce mind-wandering. That is: students might have their curiosity piqued — or their sense of duty highlighted — if they see how much stuff they don’t know.

Worth investigating, no?

Questions Answered

A research team — including some real heavy hitters! — explored these questions in a recent study.

Across two experiments, they had students watch a 26-minute video on a psychology topic (“signal detection theory”).

Some students answered “prequestions” at the beginning of the video.
Others answered those questions sprinkled throughout the video.
And some (the control group) solved unrelated algebra problems.

Once the researchers crunched all the numbers, they arrived at some helpful findings.

First: yes, prequestions reduced mind-wandering. More precisely, students who answered prequestions reported that they had given more of their attention to the video than those who solved the algebra problems.

Second: yes, prequestions promoted learning. Students who answered prequestions were likelier to get the answer correct on a final test after the lecture than those who didn’t.

Important note: this benefit applied ONLY to the questions that students had seen before. The researchers also asked students new questions — ones that hadn’t appeared as prequestions. The prequestion group didn’t score any higher on those new questions than the control group did.

Third: no, the timing of the questions didn’t matter. Students benefitted from prequestions asked at the beginning as much as those sprinkled throughout.

From Lab to Classroom

So, what should teachers DO with this information.

I think the conclusions are mostly straightforward.

A: The evidence pool supporting prequestions is growing. We should use them strategically.

B: This study highlights their benefts to reduce mind-wandering, especially for online classes or videos.

C: We don’t need to worry about the timing. If we want to ask all prequestions up front or jumble them throughout the class, either strategy (according to this study) gets the job done.

D: If you’re interested in specific suggestions on using and understanding prequestions, check out this blog post.

A Final Note

Research is, of course, a highly technical business. For that reason, most psychology studies make for turgid reading.

While this one certainly has its share of jargon heavy, data-laden sentences, its explanatory sections are unusually easy to read.

If you’d like to get a sense of how researchers think, check it out!

Pan, S. C., Sana, F., Schmitt, A. G., & Bjork, E. L. (2020). Pretesting reduces mind wandering and enhances learning during online lectures. Journal of Applied Research in Memory and Cognition, 9(4), 542-554.

Older posts

Newer posts

Enter the Steelman

Playground Physics

Persistent Doubts

First:

Second:

TL;DR

Reasonable Start

Getting Specific

I Am Not Alone

Lest I Be Misunderstood

Setting Goals, and Failing…

Troubling Examples

Problems Beget Solutions?

Time to Review

An Envelope, and LOTS of Questions…

Let the Sleuthing Commence…

The Perfect Combination

A Final Note

Useful…

…and (substantially) True

Exceptions Matter

Beyond Twitter Rage

Teaching Science is a Challenge

Time for a Change

Model Found in Cybernetics

Have Students Notice Differences by Predicting Outcomes

Practical Examples Inspire Teachers

Is It for Everybody?

Can I Be in This Class?

AHA + Working Memory

Answers, Plus

Thinking Aloud

There’s a First Time for Everything

TL;DR

A Case in Point

Curiouser and Curiouser

In Brief:

A Useful Start

The First Pendulum

The Second Pendulum

Towards a Balanced Framework

First: we can avoid the extremes.

Second: within this nuanced perspective, we should acknowledge the importance of responding to trauma appropriately.

Attempting a Summary

Strike That, Reverse That, Thank You

Questions Answered

From Lab to Classroom

A Final Note

Newsletter & Event Emails