If ideas like superior intelligence, heightened perceptual skills, and photographic memory come to mind, you may be thinking of superheroes, or perhaps savants.

If you’re on the more reserved end of the expert-worshipping continuum, you may instead be thinking that people with expertise really just know more and perform better than those around them.

There is a sense in which expertise is relative. That’s the key idea that makes the movie Idiocracy so entertaining. In it, an ordinary Joe who is completely average on every measurable dimension is transported to a future where humanity has devolved to the lowest common denominator. Suddenly Joe is the resident expert on everything.

In fact, one measure researchers often use of when they try to decide whether someone is an expert is whether or not other people in their area think they are one. Other common indicators they sometimes use are: ‘has a lot of knowledge and experience,’ ‘has an advanced degree or certification,’ ‘is always (or almost always) right,’ and ‘can solve very difficult problems.’

The question ‘what is expertise?’ is a complicated one. But what most of us really just want to know is: How do you get expertise, and how do you know when you have it?

How Do You Know if You have Expertise?

I’ll start with the last question because what you think it means to have expertise can have implications for what you think are good ways to get it. Just like your ideas about intelligence influence how you approach increasing it—and whether you try in the first place.

It turns out even the experts on expertise don’t agree on what it is.

Ericsson and Hatano are two expertise experts who think about expertise differently. As a result, they also give different advice about how you would go about building expertise.

Ericsson is a well-known cognitive psychologist who has studied learning and expertise for decades. His definition of expertise focuses on consistency. In a discussion of the Superior Performance of Experts in Current Directions in Psychological Science, Ericsson and his colleagues define expertise as the thinking and qualities that lead to consistently superior performance.

Hatano and his colleague Inagaki, in their studies of expertise, noticed that there seemed to be two kinds of expertise. In groups of recognized experts; some appeared to be even more expert than others. They discovered that what set these ‘experts among the experts’ apart were their ability to not just solve problems, but solve them in new ways by inventing new procedures and strategies. Hatano and Inagaki called what these experts had adaptive expertise. They contrasted that with the routine expertise they believed the other experts had. The routine experts could consistently solve the problems, but relied on routine procedures they had used many times before.

According to Hatano you know you have adaptive expertise when you can perform with understanding. You know you have understanding if you can do the following:

• you can explain why certain strategies or procedures work and others don’t
• you can distinguish between appropriate and inappropriate ways to modify strategies
• you can change your strategies in response to changes in the environment

How do You Get Expertise?

The two different ways of thinking about expertise lead to different ideas about how to go about building it.

Practice, Seek Feedback, Analyze

Thinking about expertise as consistently superior performance has led Ericsson to recommend a training regimen that includes lots and lots of repetitions of tasks and activities. According to Ericsson, you can build expertise through:

• Practice: Your goal when practicing should be to concentrate deeply and perform just a little bit better than last time
• Feedback: When you complete a task or problem, seek feedback about the accuracy.
• Analysis: When you’re not practicing, study past moves or solutions—your own or those of accomplished experts

Following this recipe you should gradually (over the course of 10,000 hours) get to the point where you can perform the skill at a superior level consistently.

Practice, Explain, Modify

Hatano and Inagaki recommend a similar approach (lots of practice) but with important modifications.

• Practice: Your objective when practicing skills should be to discover what happens when you apply a variety of strategies.
• Explain: Each time you apply a strategy and observe an outcome you should try to explain why it worked, or didn’t.
• Modify: When practicing the skill again, either change some aspect of the task or problem or your strategy for approaching it.

The key to Hatano’s approach to building expertise is that you continue to seek out new problems that challenge your current state of skills and knowledge.

Pick Your Poison, I Mean, Practice

Ericsson’s ideas about how to build expertise seem appropriate for building what Hatano calls routine expertise. Especially if you’re trying to build expertise in physical skills, like tennis or ballet, or areas with manageable problem spaces, like chess.

In many domains the problem spaces are much more open and dynamically changing. New diseases, new economic crises, new weather patterns are constantly emerging. To tackle these kinds of problems you need adaptive expertise.

Even though Ericsson and Hatano agree that lots and lots of practice is needed they have different ideas about how you should practice. Both approaches seem like they would help you get better at whatever it is you’re practicing. But, before you start solving endless numbers of calculus problems or examining unending arrays of chest x-rays, you may want to think about what kind of expertise you’re hoping to develop. That way you can adopt a practice strategy that will help you get there.

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Ericsson, K., & Ward, P. (2007). Capturing the Naturally Occurring Superior Performance of Experts in the Laboratory: Toward a Science of Expert and Exceptional Performance Current Directions in Psychological Science, 16 (6), 346-350 DOI: 10.1111/j.1467-8721.2007.00533.x
Hatano, G., & Inagaki, K. (1984). Two courses of expertise Research and Clinical Center for Child Development Annual Report, 6, 27-36

Discovery learning finds its roots in constructivism. It extends from ideas that learners actively seek to make sense of their environments. They learn best by exploring and actively building knowledge. Good principles for sure. Is discovery learning a successful application of them?

Richard Mayer of the University of California, Santa Barbara, reviewed three bodies of research to find out how well pure discovery learning really works. He published his paper on pure discovery learning in American Psychologist.

In his review, Mayer distinguished between pure discovery learning and guided discovery learning. In the pure form, learners are given tasks and problem to tangle with, but essentially no guidance from their teacher. They might work in groups and discuss with other students. Otherwise, they figure it out on their own.

With the guided version of discovery learning, teachers are much more involved. They might coach the student, give hints and feedback, and perhaps even model solution strategies. Teachers systematically guide students towards the learning objectives.

Mayer compared pure and guided discovery learning for the three tasks. The first had students discover problem-solving rules. The second was about learning the principle of conservation. And the third was about a discovery learning approaches to computer programming.

Mayer found that in each of these cases that had been carefully tested, students learned more from guided discovery learning than from pure discovery learning. The main reason for the failure for the pure form of discovery learning is that students tended to miss the relevant information when given the complete freedom to explore. They also developed more misconceptions when learning on their own, at least when learning about how computer programs work.

The researchers from all these studies discovered something important. It’s actually something that adventurers have known through the ages. When you venture into the dark unknown, it’s quite helpful to take a guide.

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Image Credit: Black Rock Solar

Mayer, R. (2004). Should There Be a Three-Strikes Rule Against Pure Discovery Learning? American Psychologist, 59 (1), 14-19 DOI: 10.1037/0003-066X.59.1.14

Fortunately, cognitive and educational psychologists have been conducting painstaking scientific research on study skills and strategies for ages. There is currently a wealth of good science about what study skills work and what methods don’t.

John Dunlosky of Kent State University and his collaborators reviewed a large collection of research related to ten different study skills. Their paper on improving students’ learning was published in Psychological Science in the Public Interest. Dunlosky found that about half of the study skills did not work very well. Current research suggests that the other half are effective. We have covered several of these 5 best study skills in previous posts on Head Smart, though a brief overview still seemed useful.

Here are the 5 study skills that Dunlosky and team found to be the most effective:

• Elaborative interrogation: Answering why a fact is true
• Self-explanation: Explaining what a section of text or an example problem means to you
• Practice testing: Testing yourself on the material you are trying to learn
• Distributed practice: Spreading your studying out over several sessions
• Interleaved practice: Mixing different kinds of problems together when studying

Best Study Skills #1: Elaborative interrogation (Asking “Why”)

A great way to learn is to ask yourself questions. Little kids know this intuitively, as they run around asking “Why, why, why?” A great deal of research has proven that the kids are on to something. Getting students to answer questions, such as “Why is this fact true?” aids learning.

The main reason asking “why” questions seems to work is that it encourages you to integrate the new fact with things you already know. Doing so improves your memory for the new fact by giving you more “hooks” to find it. Recent research also suggests that some ways of questioning yourself work better than others.

Best Study Skills #2: Self-Explanation

The idea behind self-explanation as a reading strategy is to pause from reading your textbook periodically and explain to yourself what it means to you. You can do this after a section of text, or when studying an example problem. When trying to self-explain, you may find that you need to look back over parts of the text to fully understand what’s being said.

Micki Chi offers a nice account of why self-explanation works. Her ideas were published as a chapter in the book, Advances in Instructional Psychology. The idea is that self-explaining encourages you to make inferences based on what you are reading. You don’t just summarize the text, but say a little more than what was in it. As you try to explain, you also identify problems and so revise your explanation. These serve to enrich and repair your understanding.

Best Study Skills #3: Practice testing

The main idea behind practice testing is that testing yourself improves learning. Tests are not just for evaluation anymore.

Testing improves learning by exercising memory retrieval. When you answer a test question, you have to actively search your long-term memory. Doing so creates more and better pathways to the answer. This makes the answer easier to find the next time around. Scientists sometimes call it, “retrieval practice.”

Practice testing is easy to do. You can make flash cards or answer questions from your textbook. You can often find free practice tests online. Make sure you can get the correct answers. Practice testing works best when you can find out whether got the answers right or wrong.

Best Study Skills #4: Distributed Practice

You have a test coming up, oh say, tomorrow. You haven’t studied at all. Should you cram for it? Sure. And, good luck.

For the next time, you’d really do better to space your studying out over the time you have. Do a little at a time over several study sessions. That’s the idea behind distributed practice.

One reason distributed practice aids learning is that you have to re-start your memory for the topic during each study session. Once your memory for the topic is warmed up and moving, doing more is fairly easy. Like a car coasting downhill, it’s too easy. Stopping and starting is harder on your memory. That’s good (unlike the car), because it strengthens your memory.

Distributed practice seems to work regardless of how you go about studying. Yet, you can do best by combining it with practice testing. Don’t be mad at your instructor for giving you lots of quizzes. They give you a double dose of good learning. Try (and try again) to get in the habit of doing it yourself!

Best Study Skills #5: Interleaved Practice

When studying math, you need to learn a few different kinds of formulas. For example, you learn one equation to compute the area of a circle. You learn another to figure out the perimeter. The idea behind interleaved practice is that you are better off mixing some area problems with some perimeter problems when you study.

The reason this works is that you need to learn a bit more than how to apply each formula. You also need to learn when to use one formula and when to use another. When you see a new problem, you first have to figure out what kind of problem it is. By interleaving the problems during your study sessions, you give yourself practice at telling the problems apart.

Conclusion

There is one caveat about this list. The team who reviewed the research recommends a couple of these 5 study skills more strongly than the others. The main reason is that the team would like to see even more research to answer a few additional questions. It’s not that the researchers know for sure that some of these study skills work X% better than others. I mention this because some summaries of the paper may give that suggestion.

Based on the research to date, these five study skills all appear to work quite well. The study skills are far more complementary than competing, in any case. They can readily work together.

Each of these study skills is also fairly easy to use. Training and practice can make some of them more effective, but there is no need to wait. The best way to begin is to start trying them out.

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Dunlosky, J., Rawson, K., Marsh, E., Nathan, M., & Willingham, D. (2013). Improving Students’ Learning With Effective Learning Techniques: Promising Directions From Cognitive and Educational Psychology Psychological Science in the Public Interest, 14 (1), 4-58 DOI: 10.1177/1529100612453266

Everyday arguments are often messy. The parties involved in the argument may well commit an informal reasoning fallacy at one point or another. They try to prove their ideas with bad or fallacious arguments. Fallacious arguments can seem convincing, even though they shouldn’t, as the conclusions do not follow from the premises.

It can be easy to fall prey to fallacious arguments, especially when the issue is complex, or the argument is offered by a charming person. Being able to readily recognize argument fallacies helps avoid being led astray. What cognitive abilities help us spot fallacious arguments? One possibility is that knowing deductive logic would help. Another is that detecting fallacious arguments is more related to comprehension skills.

Yair Neuman of Ben-Gurion University studied high-school students’ ability to identify fallacious arguments. The point was to determine whether students’ ability to identify fallacious arguments was related to deductive logic ability and/or a specific aspect of reading comprehension. Neuman’s results were published in the journal, Learning and Instruction.

Neuman examined three common types of fallacious arguments:

• The false cause fallacy.
• The appeal to the people fallacy.
• The appeal to ignorance fallacy.

An argument using the false cause fallacy implies that something that happens before an event occurs is the cause of the event. For example:

I watered my lawn and then it rained. It must have rained because I watered my lawn.

An argument that appeals to the people is one that reaches the conclusion by saying it must be so because popular opinion favors it. For example:

Most people believe that extraterrestrials exist, so you should too.

An argument that includes an appeal to ignorance suggests that not knowing for sure that something is false implies that it is true. For example:

We know that Big Foot exists, because no one has been able to prove that it doesn’t.

Neuman’s idea is that the ability to detect fallacious arguments, such as these, is related to skill in drawing inferences from text. In order to test his idea, Neuman measured student’s performance on detection of argument fallacies, deductive logic, and the inference process in reading comprehension.

He found that comprehension was significantly related to spotting fallacies. Performance on the pure deductive logic task was not.

His results suggest that if you are actively generating inferences when you read (or listen), then you may be more likely to detect a gap or disconnect between what comes next and what you were expecting. Actively trying to put together the story on your own helps to see that the other person has made a bad move in their argument.

Logic puzzles and games can be quite a lot of fun. Yet little of what appears in our natural arguments is likely to resemble pure deduction. This may be why skill in deductive logic appears to be less important to detecting fallacious arguments than the ability to make your own connections between everything that’s been said.

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Image Credit: Br3nda

Neuman, Y. (2003). Go ahead, prove that God does not exist! On high school students’ ability to deal with fallacious arguments Learning and Instruction, 13 (4), 367-380 DOI: 10.1016/S0959-4752(02)00011-7

Cognition is about how the mind does amazing things like:

• Recognize that a flying object is a goose
• Understand a paragraph or poem
• Remember a new friend’s name
• Play chess
• Come up with a bright idea

In short, cognition refers to the ways the mind goes about perceiving, remembering and thinking.

The science of cognition is a branch of psychology. As far as sciences go, it hasn’t been around all that long. Cognition really took hold as a serious science in the 1950’s. It sprung up and started advancing rapidly, right along with some of its partner sciences, such as computer science, neuroscience, and linguistics.

Cognitive psychologists are fond of talking about a cognitive revolution that occurred around that time. One of the leaders of that revolution, George Miller, wrote a nice essay reflecting back on his experience in those early years. His take on the cognitive revolution, published in Trends in Cognitive Sciences, serves as my primary reference. My favorite book on the topic is Howard Gardner’s, The Mind’s New Science.

The revolution started because psychologists weren’t allowed to use the word “mind.” At the time, experimental psychologists wanted psychology to be more objective. They did this by focusing on what they could all see. Behavior can be witnessed first-hand. Stuff happening in the mind could not. It was out of bounds.

The rigid focus on behavior led to some interesting gymnastics of jargon. You couldn’t say language in professional circles, for example. Instead, the scientific term was verbal behavior. You couldn’t talk about memory, because that was inside the mind, so you had to restrict yourself to discussing learning.  Intelligence was interesting, because it sure sounds like something on the inside. The way around this was to define intelligence as what an IQ test measures. You could see that.

Psychologists actually learned a lot by stripping down their theories, and focusing on what they could objectively measure. One thing they learned was that they really did need more than behavior to explain human psychology.  Miller put it like this: “As Chomsky remarked, defining psychology as the science of behavior was like defining physics as the science of meter reading.”

It started to become clear that to really get anywhere, psychological scientists would have to think about how the mind works. They would need to use some theory about the mind to explain the behavioral data. But, they didn’t want to go back to old words that had been used before Behaviorism came along. They needed a new word. The word that won out was cognition.

Cognitive psychologists came into being, who, according to Miller, were “unafraid of words like mind and expectation and perception and memory.” A flurry of fascinating cognitive research began to emerge by the mid 50’s. Two quick examples were:

• George Miller published what may be the most famous result from cognitive psychology, The magical number seven, plus or minus two. The “magic number” has often been taken as the span of short-term memory.
• A Study of Thinking, by Jerry Bruner, Jackie Goodenough and George Austin took seriously the notion of cognitive strategies, which brings intention into the mix.

Theory May be Fun, but has Cognition Done Anything Useful?

In the 1970’s, cognitive psychology was ready to start tackling practical problems. For example, it started to take on problems in education. This was a way for the science of cognition to show what it could do. It was also a way for cognitive psychologists to test and improve their theories with more realistic problems. Richard Mayer’s essay on cognition and instruction, published in Educational Psychologist, provides the basis for my remarks here.

As Mayer points out, problems in education are often addressed by, “well-intended fads, expert opinions, and doctrine-based agendas.” This hinders the progress of educational practice. What’s needed are instructional techniques based on research evidence and tested theory. Cognitive psychology helps with this. Mayer describes two general areas in which cognition has contributed to education:

• Cognition of Core Subjects
• Teaching of Cognitive Strategies

Cognition of Core Subjects

Cognitive psychologists have helped identify specific concepts and skills needed for subject areas. These include reading, math, science, and history. A quick example for math is figuring out the precise knowledge needed to learn how to add and subtract two numbers. One aspect that cognitive psychologists have examined is “number sense.” Number sense means that a person has the concept of a number line and skill to use it.  It can be tested by asking questions like “Is 6 or 2 closer to 5?”

Mayer describes evidence that having the mental number line down is related to learning arithmetic. Cognitive research with 6-year old children first showed that those who knew the number line could learn simple arithmetic fairly easily. Those without number sense had much more trouble. It’s been estimated that half the entering students don’t have it.

In a next step, cognitive researchers tried training kids on the mental number line. They used various games, such as comparing two dice to see which number is higher. They tested their ideas by comparing to a control group who did not receive the training. By end of the year, twice as many of trained students mastered basic arithmetic as did students in the control. The evidence showed the training to improve number sense was effective.

This is but one example. Cognitive psychologists have conducted many studies that contribute to learning specific subjects in school.

Cognitive Strategies

Cognitive strategies are ways the learner intentionally influences learning and cognition. Examples include strategies to improve reading comprehension and scientific thinking.

Lots of research has yielded evidence attesting to the effectiveness of a number of cognitive strategies. Mayer points out that work on cognitive strategies represents a paramount contribution of cognition applied to education. With a solid research base built over the past several decades, we can now improve how students learn and think by helping them cultivate and use cognitive strategies.

Understanding the cognition underlying specific subjects and cognitive strategies are just two ways cognition has aided education so far.

Other areas of improvement include new ways of assessing learning outcomes, new ways of thinking about intellectual ability (well beyond what IQ tests measure), and new ways of using computers for instruction.

Cognition has also made contributions in numerous other fields, such as medical practice, jury decision-making in law, design of airplane cockpits and the dashboard in your car, making computers that anyone can use, marketing, and voter behavior in politics. At Global Cognition, we’ve recently been working on cognitive strategies for getting along in new cultures. And the list goes on. There’s a good bet that’s why the word is becoming increasingly popular and widely used. And perhaps even why you’re reading this article.

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Image Credit: fdecomite

Mayer, R. (2001). What Good is Educational Psychology? The Case of Cognition and Instruction Educational Psychologist, 36 (2), 83-88 DOI: 10.1207/S15326985EP3602_3
Miller, G. (2003). The cognitive revolution: a historical perspective Trends in Cognitive Sciences, 7 (3), 141-144 DOI: 10.1016/S1364-6613(03)00029-9

Overconfidence is a real problem. When you are too sure that you’ve got it right, then you don’t try to improve your understanding. You tend to not check your facts, or try to learn more.

People will often say that you need to have confidence in yourself. Confidence can help motivate us to action. Overconfidence can make us wish we’d prepared a little better first.

Winston Sieck, Ed Merkle, and Trish Van Zandt of the Ohio State University studied overconfidence among college students using a test of financial knowledge. The researchers created a cognitive model that explains why overconfidence occurs. They also tested ways of overcoming overconfidence based on the model. Their paper, “option fixation: a cognitive contributor to overconfidence” was published in the journal Organizational Behavior and Human Decision Processes.

The researchers chose finance, because knowledge of financial management is important in everyday life. Failing to understand finance can lead to poor financial decisions and loss of real money. If people are overconfident about their knowledge of finance, they won’t try to understand it better.

The test was based on one used by the Jump$tart Coalition for Personal Financial Literacy. It covered general financial topics, such as bank accounts, insurance, and interest. An example question was:

If you have caused an accident, which type of automobile insurance would cover damage to your own car? (a) collision, or (b) liability

For each question, students first chose between the two options. Then, they reported their confidence from 50% to 100% that their answer was correct.

For this kind of test, an overconfidence effect is found when average confidence is higher than the proportion of correct answers. For example, suppose a person is 85% sure of their answers on average. Yet, they only get 65% of the questions correct. That is a sizeable overconfidence effect.

Lots of experiments have found overconfidence using tests about lots of different things. It is most often found for challenging tests. In this case, the research team did find an overconfidence effect for the financial knowledge test. So, why does overconfidence occur?

Sieck and colleagues described a cognitive process model of how people answered questions on the test, and the confidence they felt in their answers. They called the model, the Assess-Search-Construct (ASC) model. Then, the research team used the model to identify points in the cognitive process that produce overconfidence.

According to ASC, there are three basic steps people go through to make a choice on this kind of test and appraise their confidence in it.

1. Take the option that seems more familiar as a first guess.
2. Search memory for facts that relate the question and the first guess.
3. Explain why that first guess is true

The model is called Assess-Search-Construct (ASC), because people first Assess the familiarity of the options, then Search memory in order to Construct an explanation about why the high-familiarity option is true.

ASC does not say overconfidence happens in every instance. People can feel like they are guessing in some cases, and be completely certain in others.

On the low-confidence end, no facts are retrieved from memory. The memory search fails. Without facts, the person cannot explain why his or her choice is true. The end choice feels like a guess. If possible, the individual might do more research. They might ask someone else or look for more information on the web.

At the other extreme, facts about the topic flood in from memory. An explanation forms in the person’s mind that really seems right. They feel extremely confident that their initial impression is spot on.

There are a couple of points in this cognitive process that lead to overconfidence:

• People tend to focus on that first guess. They mostly ignore the other alternative. This is called “option fixation.”
• People are too easily satisfied with their explanations. When we keep them to ourselves, our explanations tend to be far more shallow than we think they are.

Sieck and colleagues tested these ideas about why overconfidence occurs. They designed several methods to reduce overconfidence. The main idea was to get people to think about each option separately and independently. In the final method, they also had students write out their explanations.  Here’s how it worked, using the example above:

1. Read the question and choose between the two options: (a) collision, or (b) liability
2. Look only at option (a): collision
3. Assume you have found that option (a) is true. Explain why this option is true.
4. Look only at option (b): liability
5. Assume you have found that option (b) is true. Explain why this option is true.

The method was found to work in several experiments. This and similar methods greatly reduced overconfidence. You can adapt it to other kinds of situations, as well. Why does it work?

As you can see, this method keeps you from fixating on that initial guess. It’s important to just focus attention on that one option. The trick of assuming you already learned it’s true also helps break the fixation. Writing out the explanation keeps you from fooling yourself about how deep your understanding really is. Talking with a friend about it would do the same.

Sometimes we feel so sure that we cannot be wrong or that our plans can’t possibly fail. That is in itself a good sign of a momentary lapse into overconfidence. Try the technique above as a way to explore other possibilities, and keep yourself grounded.

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Image Credit: Graeme Newcomb

Sieck, W., Merkle, E., & Van Zandt, T. (2007). Option fixation: A cognitive contributor to overconfidence Organizational Behavior and Human Decision Processes, 103 (1), 68-83 DOI: 10.1016/j.obhdp.2006.11.001

In fact, questioning yourself can help you overcome two common obstacles to learning. One is the Lazy Learner syndrome and the other the Total Recall delusion. (These are entirely fictitious labels designed to pique your curiosity and increase the likelihood that you remember this information about questioning later.)

The Lazy Learner syndrome afflicts all of us at some point or another. It’s difficult to self-diagnose because our minds often trick us into believing that we aren’t being lazy even when we are. A lazy learner is someone who creates only the simplest possible mental representation of what they hear or read. This is a representation that allows them to verify whether or not they understand the information right then and there.

This simple representation can be deceptive. It provides a comforting feeling of understanding. But, it’s pretty useless for remembering the information later on.

Just because you understand something doesn’t mean you’ll remember it.

This self-deception may in fact be what makes us vulnerable to the Total Recall delusion. You know you’ve experienced it if this sounds familiar… It’s 2AM and you’re cramming for a test tomorrow. The Doritos are all gone and yours is the only light still on. You stare at a richly detailed diagram of the reproductive system, and think “There’s no way I won’t remember this tomorrow.”

It’s hard to imagine forgetting when the information is right in front of you.

To overcome these afflictions and learn effectively you need mental representations that help you both understand and remember.

Research has shown that questioning, or asking and answering your own questions when you read a textbook can help you create such representations. But, you may be asking, are there some questioning strategies that will help me more?

Do All Questions Help You Learn?

Julie Bugg and Mark McDaniel at Washington University in St. Louis set out to answer that question. They designed a study that would help determine if the Benefits of Question Self-Generation and Answering depend on the types of questions learners ask. They published their findings recently in the Journal of Educational Psychology.

In their study three groups of participants read several paragraphs of text. Two groups were instructed to generate and answer their own questions as they read these paragraphs. Only, each group was asked to generate different types of questions.

One group was asked to generate detail questions. The other was asked to generate conceptual questions.

• Detail questions can be answered by referring to a detail or fact that could be found within a single sentence in the text. An example of a detail question was, “How many square miles in size is Antarctica’s great ice cap?” The answer, “six million,” could be found in one sentence.
• Conceptual questions can only be answered by integrating information from at least two different sentences. For example, “Give two reasons why it is impossible to create a map of the crevasses in Antarctica.” To answer this question, participants had to combine two pieces of information.

Both groups were given examples of their question type and the opportunity to practice generating questions. The last group of participants was asked to simply read the paragraphs twice.

After studying the paragraphs all participants were asked to judge how well they would remember the information. Then they were all given the same test. This test contained both detail and conceptual questions.

No Stupid Questions, Only Better Questions

There may be no such thing as a stupid question, but some questions don’t help you get smarter. Bugg and McDaniel found that the conceptual questions helped the participants learn—the detail questions didn’t.

The participants who generated conceptual questions did much better than the other groups on the conceptual parts of the test. Their evaluations of how well they’d learned the information were also more accurate. Participants in the other two groups were much more likely to overestimate how much they would remember.

Interestingly there were no differences between the three groups in how well they did on the detail questions on the test.

Use Questioning To Take Control of Your Learning

Questioning works because it makes you an active learner instead of a passive recipient of information. When you interact with information by elaborating on it, thinking about its context, or relating some pieces of information to others you increase the likelihood that you will remember it.

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Image Credit: Don Moyer

Bugg, J., & McDaniel, M. (2012). Selective benefits of question self-generation and answering for remembering expository text. Journal of Educational Psychology, 104 (4), 922-931 DOI: 10.1037/a0028661

Learning styles has become a popular term to use when we talk about the idea that people have different ways of learning.

The concept of learning styles has a lot of personal and political appeal. It comes in handy when we want to explain why we didn’t learn much from a class. “The teacher just talked and talked the whole time. I’m more of a visual learner. I need more pictures.”

It can help us make the case that the instruction formats and learning environments we’re most comfortable with are more effective for us. “I’m an aural (auditory) learner, so I need to listen to music when I study.” Finally, it appeals to administrators and policy makers. This is because the learning styles idea supports the argument that it’s possible to develop instruction that is optimally effective for the majority of students. Teachers just need to try harder.

But, buyer (and learner) beware. A recent review of research in this area suggests that in spite of its personal and political appeal, not only is the concept of learning styles not helpful, it can lead you astray when you’re trying to gain skills and knowledge.

So, how did the idea of learning styles come about in the first place? It came about because we all want the answer to the million dollar question…

Why do some people learn better?

We all wonder, at some point in our lives, why some people seem to get more out of learning opportunities than others; be it when we’re in Middle School, High School, Hairdressing School, or the Army. Often we conclude that ‘they’re just smarter’—or at least smarter about the particular topic of instruction.

Some scientists have wondered, though, if the notions of intelligence, or aptitude, or talent really tell the whole story. Maybe there’s something else that can explain why some get more from some instruction than others. Maybe, they thought, it’s like we’re all kind of like radios—tuned to different channels. And, if instruction isn’t broadcasting on our frequency—then… Well then how could we possibly hear it? In other words, we each have a ‘learning style’ and instruction is most effective if it meshes with that style.

Notice that in this learning styles way of thinking, the burden of responsibility is placed on the instruction, not the learner. This makes the learning styles idea especially appealing if you’re the recipient of the instruction. And, probably less appealing if you’re a teacher with 32 radios in your classroom tuned to 15 different channels.

But I digress. What you really want know is…

Do learning styles exist and do they matter?

Harold Pashler from the University of California, San Diego and his colleagues Mark McDaniel, Doug Rohrer, and Robert Bjork scoured the research literature in search of evidence that learning styles exist and matter. That is, is it true that instruction that meshes with learners’ learning styles lead to better learning outcomes? They published what they discovered about Learning Styles in the journal Psychological Science in the Public Interest.

First, Pashler and colleagues came up with criteria that research studies had to meet in order to provide credible evidence. They had to:

• Divide students into groups based on their learning styles and randomly assign students from each group to receive one of multiple instructional methods.
• Make all students take the same final test to assess their learning outcomes.
• Show that the instructional method that was most effective for students with one learning style was not the most effective method for students with a different learning style.

The researchers found consistent evidence across the studies that both children and adults have preferences about how they would like to have information presented to them. This is hardly surprising. But, it suggests an important distinction to keep in mind—there’s a difference between what you want and what actually works better.

Sellers of tests to assess learning styles, or instruction tailored to your or your child’s learning style will be careful to describe learning styles as ‘preferences’ rather than dispositions. Instead of saying that people HAVE different learning styles—they will say that people PREFER different learning styles. Pashler did confirm this as true. Thus, they stay on the respectable side of the line between truth and fiction.

Pashler also found many studies confirming that people have different thinking styles and are more skilled at processing certain kinds of information than others. For example, you might be better at extracting information from pictures than your friend Joe; but Joe is better at extracting information from text than you are.

But, as for the central question: Do students learn better from instructional methods that mesh with their learning styles?

Pashler and his colleagues found no studies with results that support this claim that also met the credibility criteria above. In fact, many of the studies that used a scientifically sound method showed no effect of matching instructional methods to learning styles.

This means that there is no scientific evidence to suggest that people learn better with instruction that’s tailored to their preferences and thinking styles.

Earlier I suggested that not only is the concept of learning styles not helpful, it can lead you on the wrong path. But, if learning outcomes aren’t any better or any worse when instruction is matched with your learning style, then what’s the harm?

Why learning styles can be misleading 

In some cases, adopting certain beliefs about ourselves and our abilities, such as beliefs about intelligence, can help us improve our skills and knowledge. In others, our beliefs may instead lead us astray.

The belief that instruction should match our learning style is misleading because it can give us the illusion that we’ve made a clever move. If we’ve expended the energy taking a learning style assessment and found instruction that speaks to our presumed learning styles, we may believe that we’ve done the best we can do. And, this may prevent us from looking for and adopting strategies that could actually make a difference.

Pashler argues that we’d do better if we forget about learning styles and instead tune into the learning strategies that have been shown to work for people in general. So, stop looking for teachers or classes that are ‘tuned to your frequency.’ Instead, start beefing up your learning strategies.

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Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2009). Learning Styles: Concepts and Evidence Psychological Science in the Public Interest, 9 (3), 105-119 DOI: 10.1111/j.1539-6053.2009.01038.x