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.
As long as I’ve been in this field – heck, as long as I’ve been a teacher – the concept of executive function has floated around as a core way to discuss students’ academic development.
Although the concept has a technical definition – in fact, more than one — it tends to be presented as a list of cognitive moves: “prioritizing, switching, planning, evaluating, focusing, deliberately ignoring…”
I myself have tended to think of executive functions this way: all the cognitive skills that don’t include academic content, but matter in every discipline. So, if I’m trying to execute a lab in science class, I need to …
… focus on this thing, not that thing,
… decide where to begin,
… decide when to switch to the next step,
…realize that I’ve made a mistake,
…evaluate options to fix my mistake,
And so forth.
Crucially, that list applies to almost any academic task: writing an essay, or evaluating the reliability of a historical source, or composing a sentence in Spanish using a new verb tense…
So: these executive functions help students in school – no matter the class that they are in.
To say all this in another ways: EFs resist easy definition but are mightily important in schools and classrooms. (Truthfully they’re important in life, but that broader range lies outside of this blog’s focus.)
Today’s News
I recently saw an enthusiastic response to a newly-published study that explores, reconceptualizes — and debunks? — EFs. Because EFs “are mightily important,” such reconceptualization & debunkage merits our thoughtful attention.
Here’s the story.
A research team led by Andreas Demetriou wanted to see if they could translate that long list (“prioritizing, switching, evaluating,” etc.) into a core set of mental processes.
So: a carbon atom might look different from an iron atom, but both are different ways of putting protons, neutrons, and electrons together. Likewise, “prioritizing” and “switching” might seem like two different processes, but they could instead be different arrangements of the same mental elements.
Demetriou’s team focuses on two core mental processes – their “protons and electrons.” Roughly, those mental processes are:
Forming and holding a mental model of the goal, and
Mapping that mental model onto the situation or problem.
For complicated reasons, Team D combines these two processes with a label: the AACog mechanism. They then run a lengthy series of studies using a GREAT variety of different tests (Stroop this, Raven’s that) across a wide range of ages.
When they run all the calculations, sure enough: the AACog mechanism underlies all those other EFs we’ve been taught about over the years.
As they write: “AACog is the common core running through all executive functions.” (That’s an extraordinary claim, no?)
And, development of the AACog mechanisms explains all sorts of increasing mental capacities: symbolic exploration, drawing inferences, using deductive reasoning, and so forth. (The concentric circles representing this argument challenge all of my AACog mechanisms!)
In other words, this model explains an ENORMOUS amount of human cognitive processing by focusing on two elements.
What It All Means
I wrote above that this study received an “enthusiastic response” when it came out.
In my twitter feed at least, it was packaged with basically this message:
“All those people who were nattering on about EF were having you on. Look: we can boil it down to basically one thing. No need to make it so complicated!”
I can understand why Twitter responded this way: the title of the Demetriou et al. study is: “Executive function: Debunking an overprized construct.” No wonder readers think that the idea of EFs has been debunked!
At the same time, I’m not so sure. I have three reasons to hesitate:
First:
Quoth Dan Willingham: “One study is just one study, folks.” Until MANY more people test out this idea is MANY more ways, we shouldn’t suddenly stop thinking one thing (“EFs exist!”) and start thinking another (“EFs are the AACog mechanism in disguise!”).
We need more research — LOTS — before we get all debunky.
Second:
Let’s assume for a moment that the AACog mechanism hypothesis is true. What effect will that have on discussions in schools?
Honestly, I doubt very much.
The “AACog mechanism” is itself so abstract — as are the “modeling” and “mapping” functions that go into it — that I doubt they’ll usefully replace “exective functions” in daily conversations.
Imagine that a learning specialist says to me: “This student has a diagnosed problem with her AACog mechanism.”
I’ll probably respond: “I don’t understand. What does that mean?”
The learning specialist will almost certainly respond: “Well, she has difficulty with prioritizing, task switching, initiating, and so forth.”
We’re back to EF language in seconds.
Third:
I’m not sure I buy the argument that the “AACog mechanism” DEBUNKS “executive function.”
Imagine this logical flow:
Carbon and iron are made up of the same sub-elements: protons, neutrons, and electrons.
Therefore, carbon and iron don’t really exist.
Voila: we’ve debunked the idea of carbon and iron.
Well, that logic just doesn’t hold up. Carbon and iron DO exist, even as meaningfully different arrangements of sub-particles.
So too:
EFs all boil down to the AACog mechanism, which is itself just “mental modelling” and “mapping of models onto reality.”
Therefore, EFs don’t really exist.
Misson Debunk Accomplished!
I just don’t track that logic.
We understand human cognitive complexity better, but the complexity hasn’t gone away. (We understand carbon and iron better now that we know about protons and neutrons, but the periodic table is still complicated.)
This model helps us think differently about mental functions across academic disciplines. Those new thought patterns might indeed be helpful — especially to people who create conceptual diagrams of cognition.
But I don’t think it will radically change the way teachers think and talk about our students.
TL;DR
A group of thoughtful scholars have put together a new model of cognition explaining executive functions (and a whole lot more).
What does this mean for us?
In ten or fifteen years, EF experts might be talking to us differently about understanding and promoting these cognitive moves.
In the meantime, don’t let oversimplications on the interwebs distract you. Yes: “executive function” is a mushy and complicated category — and yes, people do go too far with this label. But something like EFs exist, and we do need to understand their complexity.
Demetriou, A., Kazali, E., Spanoudis, G., Makris, N., & Kazi, S. (2024). Executive function: Debunking an overprized construct. Developmental Review, 74, 101168.
At first glance, honesty might seem like a straightforward, even mundane topic. When I picked up Honesty: the Philosophy and Psychology of a Neglected Virtue, I wasn’t expecting much—the title suggested a dry, philosophical dive into a concept we all assume we understand. Isn’t honesty just common sense? But from the opening chapter, the author, Christian Miller, intrigued me and continually pulled me deeper. The complications and questions offered important challenge at the individual and societal level. The author masterfully introduces honesty not merely as telling the truth but as a profound and complex character trait—an enduring virtue that influences how we think, feel, and act. What starts as a critique of common assumptions grows into a refined and compelling argument, presenting honesty as an “honest disposition”—a trait defined by consistency across different contexts. Eventually settling in as honesty perhaps as a mixed trait. This nuanced perspective elevates the discussion, moving beyond surface-level ideas to explore the motivations, reasoning, and inner consistency required to truly embody honesty as a virtue.
The first half of the book dives into the philosophical and psychological underpinnings of honesty. One section raises compelling questions about whether acting against what one perceives as morally right could also be considered dishonest. Another challenges traditional models of practical wisdom, questioning its necessity as a distinct trait for other virtues. These discussions are enriched with insights into the motivations behind honest actions—such as friendship, caring, justice, and duty—demonstrating how honesty transcends mere self-interest.
Through this exploration of honesty, the book offers a detailed examination of vices of dishonesty. It highlights how dishonesty manifests in everyday life through behaviors like lying, cheating, stealing, promise-breaking, and self-deception. Each vice has a corresponding virtue, such as truthfulness and respectfulness, which collectively frame honesty as a higher-level virtue. A unifying theme emerges: honesty involves resisting the intentional distortion of facts as we perceive them. This definition evolves throughout the book, as the author refines their argument by presenting premises and challenging them with thought-provoking examples.
The second half of the book takes a more empirical turn, exploring psychological studies on lying, cheating, and related behaviors. While the author notes a surprising lack of research on some facets of honesty, such as promise-breaking and stealing, studies on lying and cheating offer valuable insights. These range from participants reflecting on their everyday dishonest behaviors to controlled experiments involving vignettes or games where cheating and misleading are possible. While these studies don’t provide a complete picture, they shed light on how honesty and dishonesty play out in different situations and how individual dispositions influence these behaviors.
One of the book’s most striking conclusions is that most people do not fully embody either virtue or vice but instead exhibit “mixed character” traits, existing somewhere between honesty and dishonesty. These mixed traits reflect a blend of beliefs and desires that lead to inconsistent yet predictable behavior across situations. For example, a person might believe cheating is wrong but still feel tempted to cheat to avoid failure. Such traits are neither wholly virtuous nor wholly vicious but lie on a spectrum, varying in degree and evolving over time. This perspective moves beyond traditional virtue/vice labels, offering a more realistic understanding of human character.
The book also addresses how external situations can either enhance or suppress the application of honesty as a character trait. It acknowledges the significant gap between our current character and the ideal, emphasizing the importance of aligning thoughts, feelings, motivations, and actions to avoid distorting reality. The author suggests practical ways to cultivate honesty, such as reducing the temptation to cheat, minimizing our inclination to present a dishonest image to others, and fostering self-reflection and honest self-assessment.
Additionally, the book grapples with the complexities of moral decision-making, recognizing that virtues do not always align seamlessly. Honesty can sometimes conflict with other moral priorities, and this tension—along with the acknowledgment of human imperfections—makes the book relatable and profoundly thought-provoking.
In today’s world, where the rapid spread of misinformation tests our commitment to honesty, this book’s insights feel especially timely. It challenges readers to think deeply about how they consume and deliver information, urging us to reflect on the broader implications of honesty in our lives.
Honesty offers a rich, multi-dimensional exploration of this often-overlooked virtue. By blending philosophy, psychology, and empirical research, it provides a compelling framework for understanding and cultivating honesty. Whether you’re interested in moral philosophy, psychology, or personal growth, this book is a thought-provoking and rewarding read that will leave you reflecting long after the final page.
I hope that one of my strengths as a blogger is: I know what I don’t know — and I don’t write about those topics.
While I DO know a lot about cognitive science — working memory, self-determination theory, retrieval practice — I DON’T know a lot about technology. And: I’m only a few miles into my own A.I. journey; no doubt there will be thousands of miles to go. (My first foray along the ChatGPT path, back in February of this year, did not go well…)
Recently I came across research that looks at A.I.’s potential benefits for studying. Because I know studying research quite well, I feel confident enough to describe this particular experiment and consider its implications for our work.
But before I describe that study…
Guiding Principles
Although I’m not a student of A.I., I AM a student of thinking. Few cognitive principles have proven more enduring than Dan Willingham’s immortal sentence: “memory is the residue of thought.”
In other words, if teachers want students to remember something, we must ensure that they think about it.
More specifically:
they should think about it successfully (so we don’t want to overload working memory)
they should think about it many times (so spacing and interleaving will be important cognitive principles
they should think hard about it (so desirable difficulty is a thing)
And so forth.
This core principle — “memory is the residue of thought” — prompts an obvious concern about A.I. in education.
In theory, A.I. simplifies complex tasks. In other words, it reduces the amount of time I think about that complexity.
If artificial intelligence reduces the amount of time I that I’m required to think about doing the thing, it necessarily reduces the amount of learning I’ll do about the thing.
If “memory is the residue of thought,” then less thinking means less memory, and less learning…
Who Did What?
Although discussions of generative A.I. often sound impenetrable to me, this study followed a clear and sensible design.
Researchers from the University of Pennslyvania worked with almost 1000 students at a high school in Turkey. (In this kind of research, 1000 is an unusually high number.)
These students spent time REVIEWING math concepts they had already learned. This review happened in three phases:
Phase 1: the teacher re-explained math concepts.
Phase 2: the students practiced independently.
Phase 3: the students took a test on those math concepts. (No book; no notes; nada.)
For all students, phases 1 and 3 were identical. Phase 2, however, gave researchers a chance to explore their question.
Some students (let’s call them Group A) practiced in the usual way: the textbook, their notes, paper and pencil.
Group B, on the other hand, practiced with ChatGPT at hand. They could ask it questions to assist with their review.
Group C practiced with a specially designed ChatGPT tutor. This tutor was programed not to give answers to students’ questions, but to provide hints. (There were other differences between the ChatGPT and the ChatGPT tutor, but this difference strikes me as most pertinent.)
So: did ChatGPT help?
Did the students in Groups B and C have greater success on the practice problems, compared to Group A?
Did they do better on the test?
Intriguing Results
The students who used A.I. did better on the practice problems.
Those who used ChatGPT scored 48% higher than their peers in Group A.
Those who used the ChatGPT tutor scored (are you sitting down?) 127% higher than their peers in Group A.
Numbers like these really get our attention!
And yet…we’re more interested in knowing how they did on the test; that is, how well did they do when they couldn’t look at their books, or ask Chatty questions.
In brief: had they LEARNED the math concepts?
The students who used regular ChatGPT scored 17% lower than their notes-n-textbook peers.
Those who used the ChatGPT tutor scored the same as those peers.
In brief:
A.I. helped students succeed during practice.
But, because it reduced the amount of time they had to THINK about the problems, it didn’t help them learn.
Case closed.
Case Closed?
In education, we all too easily rush to extremes. In this case, we might easily summarize this study in two sentences:
“A.I. certainly didn’t help students learn; in some cases it harmed their learning. Banish A.I.!”
While I understand that summary, I don’t think it captures the full message that this study gives us.
Yes: if we let students ask ChatGPT questions, they think less and therefore learn less. (Why do they think less? Probably they simply ask for the answer to the question.)
But: if we design a tutor that offers hints not answers, we reduce that problem … and eliminate the difference in learning. (Yes: the reseachers have data showing that the students spent more time asking the tutor questions; presumably they had to think harder while doing so.)
As a non-expert in this field, I suspect that — sooner or later — wise people somewhere will be able to design A.I. tutors that are better at asking thought-provoking hints. That is: perhaps an A.I. tutor might cause students to think even MORE than other students praticing the old-fashioned way.
That two sentence summary above might hold true today. But we’ve learned this year that A.I. evolves VERY rapidly. Who knows what next month will bring.
TL;DR
Although THIS study suggests that A.I. doesn’t help (and might harm) learning, it also suggests that more beneficial A.I. tutors might exist in the future.
If — and this is the ESSENTIAL “if” — if A.I. can prompt students to THINK MORE than they currently do while practicing, then well-established cog-sci principles suggest that our students will learn more.
* A note about the publication status of this study. It has not yet been peer reviewed and published, although it is “under review” at a well-known journal. So, it’s technically a “working paper.” If you want to get your research geek on, you can check out the link above.
Bastani, H., Bastani, O., Sungu, A., Ge, H., Kabakcı, O., & Mariman, R. (2024). Generative ai can harm learning. Available at SSRN, 4895486.
I recently attended a (non-Learning-and-the-Brain) conference, and saw a thoughtful presentation that included a discussion of teachers’ professional standing.
In this blog post, I want to …
summarize this speaker’s thoughtful argument,
explain my own reasons for doubting it, and
consider some weaknesses in my own argument.
Teachers as Pilots
The speaker started by summarizing a common claim about teachers’ professional independence:
“Because teachers are highly-trained professionals, we should have the same freedom for independent action and creativity that other professionals enjoy. Rather than scripting and constraining teachers, schools should allow them the leeway to think, act, and teach with meaningful independence.”
I should be clear, by the way, that this speaker’s summary is NOT a straw man. I know that people make (roughly) this argument because a) I’ve heard other people make it, and b) I’m going to make a similar argument in just a few paragraphs.
To interrogate this pro-independence argument, the speaker asks us to think about other highly esteemed professionals: doctors, airline pilots, and engineers.
In every case, these professionals work in highly constrained conditions. In fact, we would be rightly shocked were they to want to escape those constraints. Imagine:
If a pilot were to say: “today I think it would be fun to go through this pre-flight check list in reverse order. And, heck, I think I’ll skip step #27; I’ve never understood what it was for!”
If an ER doctor were to say: “I understand that you’re experiencing chest pains, and the protocols suggest several tests. But honestly: you look healthy to me. And, I’m feeling lucky today. So let’s assume it’s a touch of nerves and get you some nice chamomile tea.”
We would be horrified!
Pilots and doctors work within well-established constraints, and have a strict ethical obligation to follow them.
For this reason, society rightly condemns instances where these professionals go outside those constraints.
Another example: when engineers get sloppy and bridges fall down mid-construction, we feel both horror and surprise to learn that they didn’t follow professional codes that govern their work.
These examples — the speaker said — show that teachers’ demands for professional freedom are misplaced.
Tight constraints do not violate our professional standing; they embody our professional standing.
Pushing Back: Reason #1
Although I understand these arguments as far as they go, I disagree with the speaker’s conclusion. Let me see if I can persuade you.
I think that doctors, pilots, and engineers are not good analogues for teachers, because the systems on which doctors, pilots, and engineers operate are unified, coherent, and designed to function as a whole.
Here’s what I mean:
An airplane’s ailerons and flaps have been scrupulously designed to act upon the wings in a specific way. So too the engines and the rudders. And, frankly, almost everything else about the airplane.
Because airplane parts have been structured to function together, we can have specific, precise, and absolute rules the operation of planes. When the flaps do this, the airflow over the wing changes in entirely predictable ways. The plane will, in these circumstances, always turn, or ascend, or land, or whatever.
Yes, special circumstances exist: turbulence, wind shear, or thunderstorms. But even these special circumstances call for predictable and consistent responses: responses that can be trained, and should be executed precisely.
A bridge has been designed to balance the forces that act upon it and the materials used to build it. Steel does not wake up one day and decide to have the strength of aluminum. Gravity does not vary unpredictably from day to day or mood to mood.
Because bridges have been structured to function in a particular way, engineers can have specific, precise, and absolute rules about their construction. Engineers don’t insist on moment-by-moment freedom because the bridges they build have entirely predictable constraints.
If, however, you have worked in a classroom, you know that such absolute predictability – based on the unity of the system being operated – has almost nothing to do with a teacher’s daily function.
Gravity doesn’t work differently before and after lunch, but students do.
An airplane’s rudder doesn’t have a different response to the pilot’s input, but this student might have a very different response than that student to a teacher’s input.
An EKG (I assume) shows a particular kind of result for a healthy heart and a distinct one for an unhealthy heart. A student’s test results might mean all sorts of things depending on all sorts of variables.
By the way: all of these examples so far focus on one student at a time. They don’t begin to explore the infinite, often-unpredictable interactions among students…
…or the differences among the topics that students learn…
…or the cultures within which the students learn.
We shouldn’t treat a classroom system as a straightforwardly stimulus-response system (like an airplane, like a bridge) because classrooms include an unpredictable vortex of possibilities between stimulus and response.
The best way – in many cases, the ONLY way – to manage that vortex: give teachers professional leeway to act, decide, change, and improvise.
The Continuum of Professionalism
Let’s pause for a moment to consider other kinds of professionls — say, ARCHITECTS, or THERAPISTS.
We allow — in fact we expect — subsantial freedom and creativity and variety as they do their work.
Of course, these professionals work within some constraints, and follow a well-defined code of ethics.
But those modest contraints allow for great freedom because…
… this client wants her house to look like a Tudor castle, while that client wants his to look like Falling Water, or
… this client struggles with PTSD and doesn’t want to take meds, while that client is managing bipolar disorder.
In other words: some professions do permit — in fact, require — strict limitations. But not all professions do. And, in my view, ours doesn’t. We’re more like architects than engineers.
Pushing Back: Reason #2
I promised two reasons that I resist the call for doctor-like-narrow-constraints. Here’s the second.
The analogies provided in this case all focus on people dying. The plane crashed. The heart-attack patient perished in agony. The bridge crushed workers and passers by.
In these professions, constraints literally save lives.
Now, I (like all teachers) think that education is important, and can transform lives and societies for the better. Bad educational practices do have damaging results for individuals and communities.
But: no one ever died from a bad English class. (I know; I’ve taught bad English classes. My students didn’t learn much, but they survived.)
If, in fact, teachers should work within tight constraints — checklists, scripts, step-by-step codes — the argument in favor of that position should be persuasive without the threat of death to energize it.
I’m Right, but I Might be Wrong
I promised up top that I’d include the weaknesses in my argument. I see at least four.
One:
Obviously, novice teachers require lots of support, and should work within tighter constraints than experienced teachers.
And, some teachers aren’t very good at their jobs. We might reasonably decline to trust their professional judgments.
Also: some people LIKE scripted curricula. I don’t think we should take them away from people who want them.
Two:
Teachers shouldn’t be scripted or managed detail by detail, but we should operate within well-established cognitive science principles. For instance:
Retrieval practice is, in almost all cases, better than simple review
Working memory overload is, in ALL cases, a detriment to learning
Myths like “learning styles” or “right/left brain learning” should not be a guide for instruction.
In other words: my call for independence isn’t absolute. We should know our subject and our craft — and then work flexibly and appropriately with those knowledge bases.
Three:
I suspect that a few specific disciplines might allow for precise scripting.
The teaching of reading or early math, for instance, might really benefit from doing EXACTLY step A and then EXACTLY step B, and so forth.
However:
Even in this case, I suspect we would need LOTS of different scripts:
… “typical” readers,
… students with dyslexia, or diagnosably low working memory,
… students with unusually low background knowledge,
… students whose home language isn’t the instructional language,
and so forth.
In other words: even a scriptable subject matter requires teacherly expertise and inventiveness in moving from script to script.
Four:
My own biases no doubt shape my argument.
I myself am a VERY independent person, and I have a penchant for holding teachers in great esteem.
For these reasons, I probably react more strongly than others do to the suggestion that teachers should be tightly constrained to meet our professional obligations.
In other words: I do have a logical argument in support of my position.
a) Flying an airplane is an inappropriate analogue for teaching a class;
b) Tight constraints are almost certainly impossible in the work we do.
But: that logical argument almost certainly gets much of its passion from a realm beyond logic.
In Sum
Ideas about pedagogy often rest on assumptions about teacher independence. For that reason, I’m glad that the speaker raised this point specifically, and made a strong argument to support one point of view.
I hope I’ve persuaded you that teachers — like architects — need informed independence (within some constraints) to do our work.
Even if not, I’m hopeful that thinking through these questions has proven helpful to you.
In this tweet threat, the invaluable Peps Mccrea gives an excellent example of a situation where teachers’ communal adherence to the same norms benefits students and schools.
Back in 2010, Professor Dan Willingham launched a movement with his now-classic book Why Don’t Students Like School?
In that book — one of the first to make cognitive science clear and practical for classroom teachers — Willingham wrote this immortal sentence: “Memory is the residue of thought.”
In other words: if we want students to learn something — and we do! — we need to make them think hard about it.
When I first read that sentence, it sounded almost too obvious to write down … until I recalled all the things that we (and I) do that DON’T require students to think about the topic we want them to learn.
Once we take this insight onboard, all sorts of other ideas start making more sense.
For instance: why is retrieval practice better at creating long-term memories than simple review?
Well, because students have to think harder when they retrieve than when they review. And because they think harder, they remember more. (“Memory is the residue of thought.”)
Simply put: we teachers strive to foster as much focused, hard thought as possible.
Voila — teaching made easy!
Not So Fast…
This core advice offers such practical insights: what could possibly be the problem?
Well, imagine this hypothetical:
If our students believe that fruits and vegetables are bad for their health, they face two compelling reasons not to eat ’em:
vegetables taste bad (I’m sorry, I don’t get kale), and
vegetable are bad for health.
That second belief might be wrong. But if the students believe it, it will influence their thinking and behavior.
Let’s translate this hypothetical to classrooms.
If our students believe that hard thinking is proof that they didn’t learn, they face two compelling reasons not to think hard:
thinking hard is unpleasant, and
thinking hard is proof that students don’t get the concept.
Now, you and I know that this second belief is false. Hard thinking is NOT proof that students don’t get the concept; it is — quite the contrary — an essential step on the way to getting the concept.
Hard thinking is — as the technology people say — not a bug; it’s a feature.
For these reasons, we really want to know: do our students hold this false belief? Do they think that hard thinking is both unpleasant and a sign of cognitive ineffectiveness?
In the highly technical language of research, they conclude:
“The amount of mental effort experienced during a learning task is usually negatively correlated with learners’ perception of learning.”
In other words: just as we feared, students (on average) perceive mental effort to be unpleasant AND a sign that they’re not learning.
In other words:
“If a learner judges their perceived mental effort to be high, they will judge their perceived learning…as low.”
In 1984, this situation would be described as “double-plus-un-good.”
As is usually the case, the methodology for this meta-analysis involves technical yada-yada that’s more complicated than is worth exploring. But those headlines should be clear enough to get our teacherly attention.
Translating Research for the Classroom
What should school people do with this information?
I’ve got three suggestions:
First:
“Just tell them.” *
That is:
Specifically say to students that, although it seems PLAUSIBLE that hard thought is a sign of academic failure,
Hard mental work actually helps them learn.
This truth feels obvious to adults, but is counter-intuitive to younger learners.
Of course, we will need to make this point over and over. I regularly say to my students: “That feeling in your head right now…that feeling ‘this is really complicated!’ … that feeling is you learning the concept.”
Second:
Explain to students — in an age appropriate way — that research supports this claim.
For instance:
You could ask your students: “which is more difficult: a) trying to remember information, or b) rereading it?”
You can then show them LOTS O’ RESEARCH showing that trying to remember — a.k.a. “retrieval practice” — is MUCH more beneficial for learning.
Third:
Depending on the student, an analogy might be helpful: specifically a sports or arts analogy.
In my experience, most students accept without question that sports and arts require repetitious hard work.
If you want to get better at your dance steps, repeat your dance steps
If you’re bad at hitting 3-point shots, practice 3 point shots
If you hope to feel more natural with the blocking at the top of act II, you know what to do.
In every case, the need for all this hard work isn’t a sign that you can’t dance/score/act; instead, it’s a perfectly normal part of getting better at dancing, scoring, and acting.
By the way, I should probably include a fourth strategy:
“You will know what works best for you and your students.”
As is so often the case when translating research for the classroom, the teacher’s perspective often provides the best ideas.
Once you and your classroom colleagues know that students mis-interpret hard thought to be a bad thing, you will start coming up with the solutions likeliest to update this misconception.
Research can help us spot problems; we teachers are often even better at developing effective solutions.
* Yes, I’m slyly quoting the title of my friend Zach Groshell’s book.
David, L., Biwer, F., Baars, M., Wijnia, L., Paas, F., & De Bruin, A. (2024). The relation between perceived mental effort, monitoring judgments, and learning outcomes: A meta-analysis. Educational Psychology Review, 36(3), 66.
