Why Don’t Students Like School?

Daniel T. Willingham

Created on Sunday, June 9, 2013.
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About how cognitive science can inform teaching methods in the classroom.


Contrary to popular belief, the brain is not designed for thinking. It’s designed to save you from having to think, because the brain is actually not very good at thinking. Thinking is slow and unreliable. Nevertheless, people enjoy mental work if it is successful. People like to solve problems, but not to work on unsolvable problems.


Your brain serves many purposes, and thinking is not the one it serves best. Your brain also supports the ability to see and to move, for example, and these functions operate much more efficiently and reliably than your ability to think. It’s no accident that most of your brains real estate is devoted to these activities. The extra brain power is needed because seeing is actually more difficult than playing chess or solving calculus problems.


This problem illustrates three properties of thinking. First, thinking is slow. Your visual system instantly takes in a complex scene. When you enter a friends backyard you don’t think to yourself, Hmmm, there’s some green stuff. Probably grass, but it could be some other ground cover — and what’s that rough brown object sticking up there? A fence, perhaps? You take in the whole scene — lawn, fence, flowerbeds, gazebo — at a glance. Your thinking system does not instantly calculate the answer to a problem the way your visual system immediately takes in a visual scene. Second, thinking is effortful; you don’t have to try to see, but thinking takes concentration. You can perform other tasks while you are seeing, but you can’t think about something else while you are working on a problem. Finally, thinking is uncertain. Your visual system seldom makes mistakes, and when it does you usually think you see something similar to what is actually out there — you’re close, if not exactly right. Your thinking system might not even get you close; your solution to a problem may be far from correct. In fact, your thinking system may not produce an answer at all, which is what happens to most people when they try to solve the candle problem.


Solving problems brings pleasure. When I say problem solving in this book, I mean any cognitive work that succeeds; it might be understanding a difficult passage of prose, planning a garden, or sizing up an investment opportunity. There is a sense of satisfaction, of fulfillment, in successful thinking. In the last ten years neuroscientists have discovered that there is overlap between the brain areas and chemicals that are important in learning and those that are important in the brains natural reward system. Many neuroscientists suspect that the two systems are related.


It’s notable too that the pleasure is in the solving of the problem. Working on a problem with no sense that you’re making progress is not pleasurable. In fact, it’s frustrating. Then too, there’s not great pleasure in simply knowing the answer. I told you the solution to the candle problem; did you get any fun out of it? Think how much more fun it would have been if you had solved it yourself — in fact, the problem would have seemed more clever, just as a joke that you get is funnier than a joke that has to be explained.


So there is no inconsistency in claiming that people avoid thought and in claiming that people are naturally curious — curiosity prompts people to explore new ideas and problems, but when we do, we quickly evaluate how much mental work it will take to solve the problem. If it’s too much or too little, we stop working on the problem if we can.


In sum, successful thinking relies on four factors: information from the environment, facts in long-term memory, procedures in long-term memory, and the amount of space in working memory. If any one of these factors is inadequate, thinking will likely fail.


Every fact or demonstration that would puzzle students before they have the right background knowledge has the potential to be an experience that will puzzle students momentarily, and then lead to the pleasure of problem solving. It is worth thinking about when to use a marvelous device like the egg-in-the-bottle trick.


Research from cognitive science has shown that the sorts of skills that teachers want for students — such as the ability to analyze and to think critically — require extensive factual knowledge. The cognitive principle that guides this chapter is: Factual knowledge must precede skill.


Data from the last thirty years lead to a conclusion that is not scientifically challengeable: thinking well requires knowing facts, and that’s true not simply because you need something to think about. The very processes that teachers care about most — critical thinking processes such as reasoning and problem solving — are intimately intertwined with factual knowledge that is stored in long-term memory (not just found in the environment).


The amount of space in working memory doesn’t depend on the number of [objects]; it depends on the number of meaningful objects. If you can remember seven individual letters, you can remember seven (or just about seven) meaningful acronyms or words. The letters F, B, and I together count as only one object because combined they are meaningful.


There is a final point to be made about knowledge and thinking skills. Much of what experts tell us they do in the course of thinking about their field requires background knowledge, even if it’s not described that way. Lets take science as an example. We could tell students a lot about how scientists think, and they could memorize those bits of advice. For example, we could tell students that when interpreting the results of an experiment, scientists are especially interested in anomalous (that is, unexpected) outcomes. Unexpected outcomes indicate that their knowledge is incomplete and that this experiment contains hidden seeds of new knowledge. But for results to be unexpected, you must have an expectation! An expectation about the outcome would be based on your knowledge of the field. Most or all of what we tell students about scientific thinking strategies is impossible to use without appropriate background knowledge.


The paragraph on the right is easier to understand (and therefore will be better remembered) because you can tie it to things you already know. Your experience tells you that a good cake tastes buttery, not oily, so the interest value of the fact that some are made with oil is apparent. Similarly, when the final sentence refers to what characteristics are desired for a cake, you can imagine what those characteristics might be — fluffiness, moistness, and so on. Note that these effects aren’t about comprehension; you can comprehend the paragraph on the left pretty well despite a lack of background knowledge. But some richness, some feeling of depth to the comprehension is missing. That’s because when you have background knowledge your mind connects the material you’re reading with what you already know about the topic, even if you’re not aware that it’s happening.


This final effect of background knowledge — that having factual knowledge in long-term memory makes it easier to acquire still more factual knowledge — is worth contemplating for a moment. It means that the amount of information you retain depends on what you already have. So, if you have more than I do, you retain more than I do, which means you gain more than me. To


If you want to be exposed to new vocabulary and new ideas, the places to go are books, magazines, and newspapers. Television, video games, and the sorts of Internet content that students lean toward (for example, social networking sites, music sites, and the like) are for the most part unhelpful. Researchers have painstakingly analyzed the contents of the many ways that students can spend their leisure time. Books, newspapers, and magazines are singularly helpful in introducing new ideas and new vocabulary to students.


Scientists are good at thinking like scientists, but doing so depends not just on knowing and practicing the thinking strategies, but also on having background knowledge that allows them to use the thinking strategies. This may be why a well-known geologist, H. H. Read, said, The best geologist is the one who has seen the most rocks.


I offer a Spanish proverb that emphasizes the importance of experience and, by inference, knowledge: Mas sabe El Diablo por viejo que por Diablo. Roughly translated: The Devil is not wise because he’s the Devil. The Devil is wise because he’s old.


Cognitive science leads to the rather obvious conclusion that students must learn the concepts that come up again and again — the unifying ideas of each discipline. Some educational thinkers have suggested that a limited number of ideas should be taught in great depth, beginning in the early grades and carrying through the curriculum for years as different topics are taken up and viewed through the lens of one or more of these ideas. From the cognitive perspective, that makes sense.


How can the memory system know what you’ll need to remember later? Your memory system lays its bets this way: if you think about something carefully, you’ll probably have to think about it again, so it should be stored. Thus your memory is not a product of what you want to remember or what you try to remember; it’s a product of what you think about.


To teach well, you should pay careful attention to what an assignment will actually make students think about (not what you hope they will think about), because that is what they will remember.


Later, experimenters administered a memory test for the words, with some hints. For piano, the hint was either something heavy or something that makes music.The results showed that the subjects memories were really good if the hint matched the way they had thought about piano, but poor if it didn’t. That is, if the subjects read the moving men version of the sentence, hearing the cue something that makes music didn’t help them remember piano. So it’s not even enough to say, You should think about meaning. You have to think about the right aspect of meaning.


The human mind seems exquisitely tuned to understand and remember stories — so much so that psychologists sometimes refer to stories as psychologically privileged, meaning that they are treated differently in memory than other types of material. I’m going to suggest that organizing a lesson plan like a story is an effective way to help students comprehend and remember. It also happens to be the organizing principle used by the four teachers I described. The way in which each of them related emotionally to their students was very different, but the way they got their students to think about the meaning of material was identical.


If were trying to communicate with others, using a story structure brings several important advantages. First, stories are easy to comprehend, because the audience knows the structure, which helps to interpret the action. For example, the audience knows that events don’t happen randomly in stories. There must be a causal connection, so if the cause is not immediately apparent, the audience will think carefully about the previous action to try to connect it to present events. […] Second, stories are interesting. Reading researchers have conducted experiments in which people read lots of different types of material and rate each for how interesting it is. Stories are consistently rated as more interesting than other formats (for example, expository prose), even if the same information is presented. Stories may be interesting because they demand the kind of inferences I discussed in Chapter One. Recall that problems (such as crossword puzzles) are interesting if they are neither too difficult nor too easy. Stories demand these medium-difficulty inferences. […] Third, stories are easy to remember. There are at least two contributing factors here. Because comprehending stories requires lots of medium-difficulty inferences, you must think about the story’s meaning throughout. As described earlier in the chapter, thinking about meaning is excellent for memory because it is usually meaning that you want to remember. Your memory for stories is also aided by their causal structure. If you remember one part of the plot, it’s a good guess that the next thing that happened was caused by what you remember.


My intention here is not to suggest that you simply tell stories, although there’s nothing wrong with doing so. Rather, I’m suggesting something one step removed from that. Structure your lessons the way stories are structured, using the four Cs: causality, conflict, complications, and character. This doesn’t mean you must do most of the talking. Small group work or projects or any other method may be used. The story structure applies to the way you organize the material that you encourage your students to think about, not to the methods you use to teach the material.


When it comes to teaching, I think of it this way:The material I want students to learn is actually the answer to a question. On its own, the answer is almost never interesting. But if you know the question, the answer may be quite interesting. That’s why making the question clear is so important. But I sometimes feel that we, as teachers, are so focused on getting to the answer, we spend insufficient time making sure that students understand the question and appreciate its significance.


Learning is influenced by many factors, but one factor trumps the others: students remember what they think about. That principle highlights the importance of getting students to think about the right thing at the right time.


We understand new things in the context of things we already know, and most of what we know is concrete. Thus it is difficult to comprehend abstract ideas, and difficult to apply them in new situations. The surest way to help students understand an abstraction is to expose them to many different versions of the abstraction — that is, to have them solve area calculation problems about tabletops, soccer fields, envelopes, doors, and so on.


Now you see why I claim that understanding is remembering in disguise. No one can pour new ideas into a students head directly. Every new idea must build on ideas that the student already knows. To get a student to understand, a teacher (or a parent or book or television program) must ensure that the right ideas from the students long-term memory are pulled up and put into working memory. In addition, the right features of these memories must be attended to, that is, compared or combined or somehow manipulated.


When people read the tumor-and-rays problem, their cognitive system narrows the interpretation of it ( just as it does for the hurricane sentences) according to what sort of background knowledge the reader has, and that’s likely to be some knowledge of tumors, rays, doctors, and so forth. When the person later reads the other version of the problem, the background knowledge that seems relevant concerns dictators, armies, and fortresses. That’s why transfer is so poor. The first problem is taken to be one about tumors, and the second problem is interpreted as being about armies.


Suppose I said, What do a butterfly, a dragonfly, a chopstick, a pillbox, and a scarecrow have in common?* These are simply too many items to compare simultaneously. As you’re thinking about how to relate a pillbox to a chopstick, you’ve already forgotten what the other items are. This lack of space in working memory is a fundamental bottleneck of human cognition. You could dream up lots of ways that your cognitive system could be improved — more accurate memory, more focused attention, sharper vision, and so on — but if a genie comes out of a lamp and offers you one way to improve your mind, ask for more working memory capacity.

* These items may have other features in common, but I selected them because they are all compound words.


Thus, the first way to cheat the limited size of your working memory is through factual knowledge. There is a second way: you can make the processes that manipulate information in working memory more efficient. In fact, you can make them so efficient that they are virtually cost free. Think about learning to tie your shoes. Initially it requires your full attention and thus absorbs all of working memory, but with practice you can tie your shoes automatically. What used to take all of the room in working memory now takes almost no room. As an adult you can tie your shoes while holding a conversation or even while working math problems in your head (in the unlikely event that the need arises).


For the experienced reader, those processes happen in a flash and are a good example of the properties of automatic processes: (1) They happen very quickly. Experienced readers read common words in less than a quarter of a second. (2) They are prompted by a stimulus in the environment, and if that stimulus is present, the process may occur even if you wish it wouldn’t. (3) You are not aware of the components of the automatic process. That is, the component processes of reading (for example, identifying letters) are never conscious. The word pants ends up in consciousness, but the mental processes necessary to arrive at the conclusion that the word is pants do not. The process is very different for a beginning reader, who is aware of each constituent step (that’s a p, which makes a puh sound…).


I’ve said that working memory is the place in the mind where thinking happens — where we bring together ideas and transform them into something new. The difficulty is that there is only so much room in working memory, and if we try to put too much stuff in there, we get mixed up and lose the thread of the problem we were trying to solve, or the story we were trying to follow, or the factors we were trying to weigh in making a complex decision. People with larger working-memory capacities are better at these thinking tasks. Although we can’t make our working memory larger, we can, as I have said, make the contents of working memory smaller in two ways: by making facts take up less room through chunking, which requires knowledge in long-term memory and is discussed in Chapter Two; and by shrinking the processes we use to bring information into working memory or to manipulate it once it is there.


You may have mastered reading in the sense that you know which sounds go with which letters, and you can reliably string together sounds into words. So why keep practicing if you know the letters? You practice not just to get faster. What’s important is getting so good at recognizing letters that retrieving the sound becomes automatic. If it’s automatic, you have freed working-memory space that used to be devoted to retrieving the sounds from long-term memory — space that can now be devoted to thinking about meaning.


The great philosopher Alfred North Whitehead captured this phenomenon in this comment:It is a profoundly erroneous truism, repeated by all copybooks and by eminent people when they are making speeches, that we should cultivate the habit of thinking of what we are doing. The precise opposite is the case. Civilization advances by extending the number of important operations which we can perform without thinking about them.


Well, the truth is that I remember a little geometry. Certainly I know much less now than I did right after I finished the class — but I do know more than I did before I took it. Researchers have examined student memory more formally and have drawn the same conclusion: we forget much (but not all) of what we have learned, and the forgetting is rapid.


Cognition early in training is fundamentally different from cognition late in training. It’s not just that students know less than experts; it’s also that what they know is organized differently in their memory. Expert scientists did not think like experts-in-training when they started out. They thought like novices. In truth, no one thinks like a scientist or a historian without a great deal of training. This conclusion doesn’t mean that students should never try to write a poem or conduct a scientific experiment; but teachers and administrators should have a clear idea of what such assignments will do for students.


Compared to novices, experts are better able to single out important details, produce sensible solutions, and transfer their knowledge to similar domains. These abilities are seen not only in doctors but also in writers, mathematicians, chess players — and teachers.


In this experiment, people get a brief look at a chess board and then must replicate the configuration of pieces on a blank board. Experts and novices both do so in chunks — they put a few pieces on the board, then pause as they recall the next cluster from memory, then place the next few pieces, and so on. Novices tend to group pieces based on proximity — nearby pieces go in the same chunk, as shown on the right board whereas experts group pieces by function — pieces that are strategically related in the game go in the same chunk.


Over the last fifty years there have been a few instances in which a researcher has gained access to a good number (ten or more) of prominent scientists, who have agreed to be interviewed at length, take personality and intelligence tests, and so forth. The researcher has then looked for similarities in the backgrounds, interests, and abilities of these great men and women of science. The results of these studies are fairly consistent in one surprising finding. The great minds of science were not distinguished as being exceptionally brilliant, as measured by standard IQ tests; they were very smart, to be sure, but not the standouts that their stature in their fields might suggest. What was singular was their capacity for sustained work. Great scientists are almost always workaholics. Each of us knows his or her limit; at some point we need to stop working and watch a stupid television program, read People magazine, or something similar. Great scientists have incredible persistence, and their threshold for mental exhaustion is very high.


The goal is to provide students with some understanding of how others create knowledge rather than to ask students to engage in activities of knowledge creation.


I’ve judged a lot of science fairs, and the projects are mostly — not to put too fine a point on it — terrible. The questions that students try to answer are usually lousy, because they aren’t really fundamental to the field; and students don’t appear to have learned much about the scientific method, because their experiments are poorly designed and they haven’t analyzed their data sensibly. But some of the students are really proud of what they have done, and their interest in science or engineering has gotten a big boost. So although the creative aspect of the project is usually a flop, science fairs seem to be good bets for motivation.


Now were getting to the heart of the visual-auditory-kinesthetic theory. It is true that some people have especially good visual or auditory memories. In that sense there are visual learners and auditory learners. But that’s not the key prediction of the theory. The key prediction is that students will learn better when instruction matches their cognitive style. That is, suppose Anne is an auditory learner and Victor is a visual learner. Suppose further that I give Anne and Victor two lists of new vocabulary words to learn. To learn the first list, they listen to a tape of the words and definitions several times; to learn the second list, they view a slide show of pictures depicting the words. The theory predicts that Anne should learn more words on the first list than on the second whereas Victor should learn more words on the second list than on the first. Dozens of studies have been conducted along these general lines, including studies using materials more like those used in classrooms, and overall the theory is not supported. Matching the preferred modality of a student doesn’t give that student any edge in learning. How can that be? Why doesn’t Anne learn better when the presentation is auditory, given that she’s an auditory learner? Because auditory information is not what’s being tested! Auditory information would be the particular sound of the voice on the tape. What’s being tested is the meaning of the words. Anne’s edge in auditory memory doesn’t help her in situations where meaning is important. Similarly, Victor might be better at recognizing the visual details of the pictures used to depict the words on the slides, but again, that ability is not being tested.


Children do differ in intelligence, but intelligence can be changed through sustained hard work. It is a good idea to model the belief in malleable intelligence for students. You can do so in how you administer praise and in how you talk to students about their successes and failures.


Praise Effort, Not Ability This principle should be obvious from the research I’ve described. You want to encourage your students to think of their intelligence as under their control, and especially that they can develop their intelligence through hard work. Therefore, you should praise processes rather than ability. In addition to praising effort (if appropriate), you might praise a student for persistence in the face of challenges, or for taking responsibility for her work. Avoid insincere praise, however. Dishonest praise is actually destructive. If you tell a student, Wow, you really worked hard on this project! when the student knows good and well that she didn’t, you lose credibility.


Michael Jordan put it this way: I’ve missed more than nine thousand shots in my career. I’ve lost almost three hundred games. Twenty-six times I’ve been trusted to take the game-winning shot and missed. I’ve failed over and over and over again in my life. And that is why I succeed.


In considering how to communicate that confidence to your students, we return to the subject of praise. Be wary of praising second-rate work in your slower students. Suppose you have a student who usually fails to complete his work. He manages to submit a project on time, although it’s not very good. It’s tempting to praise the student — after all, the fact that he submitted something is an improvement over his past performance. But consider the message that praising a mediocre project sends. You say good job, but that really means good job for someone like you. The student is probably not so naive as to think that his project is really all that great. By praising substandard work, you send the message that you have lower expectations for this student. Better to say, I appreciate that you finished the project on time, and I thought your opening paragraph was interesting, but I think you could have done a better job of organizing it. Lets talk about how.


Until now, I have been a bit casual in how I have talked about practice. I have made it sound synonymous with experience. It is not. Experience means you are simply engaged in the activity. Practice means you are trying to improve your performance. For example, I’m not an especially good driver, even though I’ve been driving for about thirty years. Like most people my age I’m experienced — that is, I’ve done a lot of driving — but I’m not well practiced, because for almost all of that thirty years I didn’t try to improve. I did work at my driving skills when I first got behind the wheel. After perhaps fifty hours of practice, I was driving with skill that seemed adequate to me, so I stopped trying to improve. That’s what most people do for driving, golf, typing, and indeed most of the skills they learn.


As the author must convince the reader not to drop the book, so too must the teacher persuade the student not to discontinue the journey. Teaching is an act of persuasion.

That's all there is, there isn't any more.
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