You are here

5+ Lessons from the Cognitive Sciences that every Professor should know

1.  Knowledge is organized and constructed
2.  An important goal of learning is to promote conceptual change
3.  The depth of processing predicts retention and transfer
4.  Metacognition improves learning outcomes
5.  Learners’ language and cultural background shape their thinking and learning
6.  There are individual differences in cognition, but not the ones you think about
7.  Conception and perception are tightly interwoven


 

1. Knowledge is organized and constructed

- Learning builds on prior knowledge or pre-existing beliefs.
- Learning involves actively integrating information acquired from different sources.

2. An important goal of learning is to promote conceptual change

- When new information doesn’t “fit” with existing structures, this creates “mental discomfort” that learners are driven to resolve.
- This attempt to resolve conflicting beliefs can result in erroneous beliefs (“misconceptions”) that are resistant to change.

3. The depth of processing predicts retention and transfer

- Levels of Processing principle
- The deeper and less contextualized the representation, the higher the likelihood of transfer to new cases.

4. Metacognition improves learning outcomes

- Involves strategies such as comprehension monitoring and asking questions.
- Beware of the “illusion of explanatory depth”

5. Learners’ language and cultural background shape their thinking and learning

- The different ways in which language carves up the world's existing beliefs/knowledge (i.e., content), but also the way they incorporate new information (i.e., process).
- There are cognitive advantages to being bilingual
- East-West differences in figure-ground, dealing with conflict/argumentation/debate (Nisbett, Norenzayan)

6. There are individual differences in cognition, but not the ones you think about (e.g., "learning styles")

- Working memory capacity and spatial thinking skills can predict learning outcomes.

7. Conception and perception are tightly interwoven

- Even the most symbolic and abstract (i.e., “higher-order”) cognitive processes are often grounded in “lower-order” perceptual experience
- Educators can use these experiences in service of abstraction.
“As cognitive scientists have emphasized in recent years, cognition is embodied; you think with your body, not only with your brain.”
― Daniel Kahneman, Thinking, Fast and Slow

In addition to the above, educational researchers often use Bloom’s Taxonomy to model thinking skills in students. 

From Ormrod, Human Learning, 6th Edition:

Bloom’s Taxonomy of Educational Objectives (B. S. Bloom, Englehart, Furst, Hill, & Krathwohl, 1956), describes six general levels of knowing and using information, shown in  Figure  5.5 . Bloom and his colleagues originally presented the six levels as a hierarchy, with each one depending on those preceding it in the list. Although the hierarchical nature of Bloom’s taxonomy is in doubt (L. W. Anderson et al., 2001; Krathwohl, 1994; Marzano & Kendall, 2007), it nevertheless provides a useful reminder that instructional objectives should often encompass higher-level thinking skills as well as the knowledge of simple, discrete facts.

 

http://www.nwlink.com/~donclark/hrd/bloom.html
https://zoe-s-wiki.wikispaces.com/Bloom%27s+taxonomy
http://www.edpsycinteractive.org/topics/cognition/bloom.html


 

1.  Knowledge is organized and constructed

 

- Learning builds on prior knowledge or pre-existing beliefs.
- Learning involves actively integrating information acquired from different sources.

From Ormrod:

People are actively involved in the learning process. Rather than being passive victims of environmental conditions, people are active participants in their learning and in fact ultimately control their own learning. Individual learners themselves determine how they mentally process their experiences, and these cognitive processes in turn determine what, if anything, is learned. To the extent that individual learners think differently about a situation, they’ll learn different things from it.

People organize what they learn. An individual’s knowledge, beliefs, attitudes, and emotions aren’t isolated from one another but instead are all either directly or indirectly interconnected. The learning process itself contributes to this organization: people usually learn most effectively when they relate new information and experiences to things they already know. When people are allowed to recall items of a serial learning task in whatever order they prefer—a task known as free recall —they typically don’t recall the items in the original presentation order. Instead, their recall order often reflects an organizational scheme of some kind (Bousfield, 1953; Buschke, 1977; Jenkins & Russell, 1952).

People even try to organize seemingly unorganized material (Tulving, 1962).

Potential applications:

Instruction is more effective when it helps students organize new material.
A variety of strategies can help students organize what they’re learning. One widely recommended strategy is an advance organizer, a general introduction to new material that’s typically designed to accomplish either or both of two purposes (Ausubel et al., 1978). An expository organizer provides a rough overview or outline of the material, describing the general topics that will be presented and their relationship to one another; thus, it provides the beginnings of an internal organizational scheme. A comparative organizer shows how the new material relates to students’ previous experiences, to information they’ve previously learned in school, or possibly to their purposes for studying the material—in other words, it activates students’ prior knowledge. Research consistently demonstrates the effectiveness of advance organizers in facilitating learning, especially when material isn’t clearly organized and students have trouble organizing and making sense of it on their own. A variety of formats—overviews, outlines, analogies, examples, and thought-provoking questions—all appear to be effective, especially when they’re fairly concrete (L. Alexander, Frankiewicz, & Williams, 1979; Corkill, 1992; Glynn & Di Vesta, 1977; Mayer, 1979, 1984; Mayer & Bromage, 1980; Naumann, Richter, Flender, Christmann, & Groeben, 2007; Zook, 1991). In some situations, an advance organizer might even take a graphic rather than strictly verbal form.

The internal organization of a body of information facilitates its retrieval.
When material is presented in an organized fashion—for instance, when hierarchical structures, cause-and-effect relationships, and so on are clearly specified—students are more likely to store it in a similar organizational network. And when information in long-term memory is organized, it can more easily be retrieved.

Showing how material should be organized and interrelated may be especially important for students with little relevant background knowledge or a history of learning difficulties (deLeeuw & Chi, 2003; Krajcik, 1991; Mayer, 2010a; Niederhauser, 2008).

Yet another effective strategy is a concept map or knowledge map —a diagram of a unit’s concepts or main ideas (often identified by circles) and the interrelationships among them (often designated by lines and by words or phrases that link two concepts or ideas together). But students, too, can be shown how to construct concept maps that help them organize what they’re learning. Both teacher- and student-constructed organizational maps have often been shown to facilitate learning, provided that they’re not  so detailed that they overwhelm students’ working memory capacities (Hofman & van Oostendorp, 1999; Novak, 1998; O’Donnell, Dansereau, & Hall, 2002; Stull & Mayer, 2007).

Instruction is more effective when it activates and builds on students’ prior knowledge.
Even when students have existing knowledge to which they can relate new material, they aren’t always aware of connections they might make (Paris & Lindauer, 1976; Spires & Donley, 1998; Spires, Donley, & Penrose, 1990; Stodolsky, Salk, & Glaessner, 1991). Thus, effective instruction includes prior knowledge activation: It begins with what students already know and continues to remind students of additional things they know that relate to the topic at hand. For example, teachers and students might discuss a topic in class before students begin a reading assignment about it (Hansen & Pearson, 1983; P. T. Wilson & Anderson, 1986). And when content studied at a previous time is important for understanding something new, teachers might provide a quick refresher of that content. When students have virtually no prior knowledge about a topic, teachers might provide actual experiences on which subsequent instruction can build. Another effective strategy is to provide analogies that relate classroom subject matter to familiar concepts and situations (Bulgren, Deshler, Schumaker, & Lenz, 2000; Donnelly & McDaniel, 1993; Pinker, 2007; Zook, 1991). Analogies help students learn information more meaningfully and retrieve it more easily, especially when the topic is a new one for students or when the material is fairly abstract. At the same time, teachers must be careful to point out ways in which the two things being compared are different . Otherwise, students may take an analogy too far and draw incorrect conclusions (Duit, 1990; Sfard, 1997; Zook & Di Vesta, 1991).

--

In most situations, integrated knowledge is more useful than fragmented knowledge. When we integrate the things we know, we’re more likely to draw inferences that go beyond the specific things we’ve learned. Furthermore, organized information is easier to remember—in other words, to retrieve—than unorganized information. Many contemporary learning theorists stress the importance of teaching an integrated body of knowledge—knowledge that includes general principles, cause-and-effect relationships, and so on—rather than simply teaching isolated facts. In the case of mathematics, for example, teachers should help students make associations between general concepts and principles of mathematics, on the one hand, and specific procedures for solving mathematical problems, on the other (Carr, 2010; Hiebert et al., 1997; Rittle-Johnson, Siegler, & Alibali, 2001). When students learn specific mathematical procedures (e.g., how to do long division or how to add two fractions by finding a common denominator) in association with the overall logic of math, they’re more likely to apply problem-solving procedures appropriately and to recognize occasions when they’ve obtained illogical and thus probably incorrect problem solutions.

Learning information primarily through repetition is sometimes called rote learning. In rote learning, there’s little or no attempt to make the information meaningful or to understand it in terms of things one already knows. If such information is stored in long-term memory at all, it’s stored in relative isolation from other information. Information stored in this unconnected fashion is hard to retrieve.  Back to Top


 

2.   An important goal of learning is to promote conceptual change
 

- When new information doesn’t “fit” with existing structures, this creates “mental discomfort” that learners are driven to resolve.
- This attempt to resolve conflicting beliefs can result in erroneous beliefs (“misconceptions”) that are resistant to change.

From Ormrod:

Learners’ existing beliefs affect their interpretations of new information.
Thanks to the processes of meaningful learning and elaboration—processes that usually facilitate learning—learners are more likely to interpret new information in ways that are consistent with what they already “know” about the world, to the point where they continue to believe some or all of what they’ve always believed (Brewer, 2008; Kendeou & van den Broek, 2005; Porat, 2004).

Learners’ existing beliefs are often consistent with their everyday experiences.
Truly accurate explanations of physical phenomena (e.g., commonly accepted principles or theories in physics) can be fairly abstract and difficult to relate to everyday reality (P. A. Alexander, 1997; D. B. Clark, 2006; Wiser & Smith, 2008). For example, although physicists agree that all matter has weight, a tiny piece of Styrofoam doesn’t feel as if it weighs anything (C. L. Smith, 2007). And although the law of inertia tells us that force is needed to start an object in motion but not to keep it in motion, we know from experience that if we want to move a heavy object across the floor, we must keep on pushing it until we get it where we want it (Driver, Asoko, Leach, Mortimer, & Scott, 1994).

On misconceptions:

When people engage in elaboration, they use what they already know about a topic to expand on and presumably make better sense of new information. But what happens when people use inaccurate “knowledge”—misconceptions—to elaborate? If people think that new information is clearly wrong within the context of what they currently believe about the world, they may ignore the information altogether. Alternatively, they may distort the information to be consistent with their “knowledge” and as a result learn something quite different from what they actually saw, heard, or read (P. K. Murphy & Mason, 2006; Porat, 2004; Sneider & Pulos, 1983; Vosniadou, 2008). In some instances, then, having misinformation is more detrimental than having no information about a topic.

Some erroneous beliefs are integrated into a cohesive whole, with many interrelationships existing among various ideas.
In such instances, changing misconceptions involves changing an entire organized body of knowledge—an entire theory or worldview—rather than a single belief (Derry, 1996; Koltko-Rivera, 2004; P. K. Murphy & Mason, 2006; Vosniadou et al., 2008). For example, the belief that the sun revolves around the earth may be part of a more general earth-centered view of things, perhaps one that includes the moon, stars, and various other heavenly bodies revolving around the earth as well. In reality, of course, the moon revolves around the earth, the earth revolves around the sun, and the other stars aren’t significantly involved with the earth one way or the other. Yet the earth-centered view is a much easier one to understand and accept (on the surface, at least), and everything fits so nicely together.

People’s erroneous notions about the world probably have a variety of sources. Sometimes misconceptions result from how things appear to be; for example, from our perspective living here on the earth’s surface, the sun looks as if it moves around the earth, rather than vice versa. Sometimes misconceptions are fostered by common expressions in language; for instance, we often talk about the sun “rising” and “setting.” Sometimes people infer incorrect cause-and-effect relationships between two events simply because the events often occur at the same time—a problem of mistaking correlation for causation. Perhaps fairy tales and popular television cartoons play a role in promoting misconceptions; as an example, after cartoon “bad guys” run off the edge of a cliff, they usually remain suspended in air until they realize there’s nothing solid holding them up. And unfortunately it’s sometimes the case that students acquire erroneous ideas from textbooks or teachers. For example, geometry textbooks often portray rectangles as long and skinny and parallelograms as slanted, even though some rectangles are squares and some parallelograms are rectangles (A. C. Butler, Zaromb, Lyle, & Roediger, 2009; Byrnes, 1996; diSessa, 1996; Glynn, Yeany, & Britton, 1991; Marcus 2008; Masters et al., 2010).

Suggestions for getting students on the road to conceptual change:

Before beginning instruction, teachers should determine what beliefs and misconceptions students currently have about a topic.
Teachers can more easily address students’ counterproductive beliefs when they know what those beliefs are (P. K. Murphy & Alexander, 2008). Thus, a lesson might begin with informal questioning or a formal pretest to probe students’ current views of the subject matter at hand.

In some instances, effective teacher probing involves delving into students’ unspoken, implicit beliefs about a topic. For example, middle school and high school students may think of numbers as being discrete entities used for counting (1, 2, 3, etc.) rather than a continuum of all possible quantities of something; this overly simplistic understanding of numbers can wreak havoc on students’ ability to understand and use fractions, decimals, and negative numbers (Vosniadou et al., 2008). And college students may think about such concepts as heat, light , and electricity as being physical substances rather than as emerging, dynamic processes (Slotta & Chi, 2006). In such cases, instruction may require a concerted, ongoing effort to help students reconceptualize old, seemingly familiar concepts to enable more flexible and productive thinking and learning—a paradigm shift, if you will (Chi, 2008; Clement, 2008).

Students should learn correct information in a meaningful rather than rote fashion. Students will notice inconsistencies between new information and prior beliefs only when they try to make connections between the new and the old. To use levels-of-processing terminology for a moment, students are most likely to modify their misconceptions in light of new data if they process those data in depth —in other words, if they become actively engaged in learning and truly try to understand the information being presented. Thus instruction is most likely to encourage meaningful learning, and therefore to promote conceptual change, when it intensively focuses on a few key ideas rather than superficially covering many topics (D. B. Clark, 2006; diSessa, 1996, 2008; C. Howe, Tolmie, Greer, & Mackenzie, 1995; M. C. Linn, 2008; Pintrich, Marx, & Boyle, 1993; Slusher & Anderson, 1996). Carefully chosen analogies are often useful in bringing about conceptual change, provided that students don’t draw inappropriate parallels (D. B. Clark, 2006; Clement, 2008). For example, if students in a science class resist the idea that a table and a book on its surface each exerts a force on the other—the table pushing up, the book pushing down—a teacher might first show a book resting on a spring (where the upward and downward forces are both obvious), then on a foam pad, then on a thin and bendable piece of wood, and finally on a hard table (Clement, 2008).

Students can sometimes build effectively on kernels of truth in their existing understandings.
Often students’ current understandings have a partly-right-and-partly-wrong quality (diSessa, 1996, 2006, 2008). For example, in the earlier question-and-answer session about rain, the child correctly understands that (1) clouds have water, (2) evaporation is somehow involved in the water cycle, and (3) rain is the result of water being too heavy to remain suspended in air. Such knowledge provides a good starting point for further instruction. For instance, it would be important to explain where in the water cycle evaporation occurs (i.e., in cloud formation) and how a cloud actually  is water rather than being a shakerlike water container.

Students are more likely to revise their current way of thinking when they believe revision is in order.
Many theorists suggest that conceptual change is most likely to occur when learners encounter evidence that blatantly contradicts what they currently believe. Such contradictory evidence can create a sense of mental discomfort—something that some theorists call disequilibrium and others call cognitive dissonance.

Students must explicitly compare their existing beliefs with alternative explanations.
Students are more likely to replace a misconception with a more accurate understanding—rather than to accept the accurate understanding while also retaining the misconception—if they’re thinking about both ideas at the same time. In other words, the erroneous and accurate beliefs should simultaneously be in working memory. Unfortunately, many textbook authors seem to be oblivious to this point: When they present new ideas in science or history, they neglect to point out that these ideas may be inconsistent with what students currently believe. The result is that  students often  don’t shed their misconceptions in the face of contradictory information (deLeeuw & Chi, 2003; Kowalski, Taylor, & Guggia, 2004; Mason, Gava, & Boldrin, 2008; McKeown & Beck, 1990; Otero, 1998; Southerland & Sinatra, 2003). One strategy for encouraging students to compare various beliefs and explanations is to engage them in discussions about the pros and cons of each one (P. K. Murphy & Mason, 2006; Siegler & Lin, 2010; C. L. Smith, 2007; Vosniadou et al., 2008). Textbooks can present the pros and cons of various perspectives as well. One effective approach is refutational text , in which possible objections to a particular explanation are presented and then discredited. In this way, students are persuaded to buy into the preferred explanation and also “inoculated” against accepting counterarguments they might encounter at a future time (Hynd, 2003; Kowalski et al., 2004; Mason et al., 2008; C. Shanahan, 2004).

Throughout a lesson, students’ understanding should be monitored for particularly tenacious misconceptions . Because of human beings’ natural tendency to reinterpret new information in light of what they already “know,” some misconceptions may persist in spite of a teacher’s best efforts. These misconceptions are sometimes blatantly incorrect; at other times they may be sort-of-but-not-quite correct.

Suggestions for how teachers might create mental disequilibrium and then encourage students to address it:

 •  Ask questions that challenge students’ current beliefs.
•  Present phenomena that students can’t adequately explain within their existing perspectives.
•  Ask students to make predictions about what will happen in various circumstances—predictions that, given their present beliefs, are likely to be wrong.
•  Encourage students to conduct experiments to test various hypotheses.
•  Ask students to provide possible explanations for puzzling phenomena.
•  Engage students in discussions of the pros and cons of various explanations.
•  Show how one explanation of an event or phenomenon is more plausible (i.e., makesmore sense) than others. (Andre & Windschitl, 2003; Chinn & Malhotra, 2002; Echevarria, 2003; Guzzetti, Snyder, Glass, & Gamas, 1993; Hatano & Inagaki, 2003; C. Howe et al., 1995; P. K. Murphy & Mason, 2006; Pine  & Messer, 2000; G. J. Posner, Strike, Hewson, & Gertzog, 1982; K. Roth, 2002; C. L. Smith, 2007; Vosniadou, 2008)

Such strategies encompass a wide variety of instructional methods, including demonstrations, hands-on experiments, teacher explanations, and student discussions. There’s certainly no single “best” instructional method for promoting conceptual change.  Back to Top


3.  The depth of processing predicts retention and transfer

From Ormrod:

The ways in which people store new information affect both the nature of the knowledge they possess and the ease with which they can retrieve that knowledge later on.

Levels of Processing Principle
How long and how well information is remembered depends on how thoroughly the central processor deals with it. Information that isn’t processed at all leaves only a very brief impression, much as it does in the sensory register of the dual-store model. Information that is processed superficially, with attention only to surface characteristics (e.g., appearance, brightness), lasts a bit longer—perhaps as long as information lasts in the dual-store model’s working memory.

We’re far more likely to remember information for the long haul if it undergoes “deep” processing—that is, if we interpret it and relate it to our previous knowledge (e.g., S. Kapur et al., 1994).

A study by Hyde and Jenkins (1969) provides an example of successful incidental learning as a result of deep processing. [The study showed that…] students who rated words for pleasantness remembered more words than students who counted letters. More interesting, however, is the fact that incidental-learning students who rated the words for pleasantness generally remembered as many words as any of the intentional-learning groups (in fact, they did better than the intentional-counting groups). Here was a case where learning was facilitated simply by virtue of the fact that students had to focus on the underlying meaning of the material to be learned. Depth of processing—not intention to learn—was the critical factor affecting learning.

Quite possibly the key to learning and more successful recall is to process new information not only semantically but also elaboratively —that is, a learner embellishes on new material in such a way that the material is encoded more precisely, meaningfully, and completely (Craik & Tulving, 1975).

When people receive new information, they often impose their own interpretations on it—making assumptions, drawing inferences, and so on—and learn those interpretations right along with the information they’ve actually been given. In general, elaboration involves using prior knowledge to embellish on new information and storing the embellished version. Thus, elaboration is a process of learning more than the material presented; I like to think of it as learning between the lines.

Potential applications:

Instruction is more effective when it encourages students to elaborate on what they’re learning.
Many classroom activities can potentially promote student elaboration of classroom subject matter. For example, asking students to talk about a topic, perhaps within the context of a class discussion or cooperative learning activity, almost forces them to do something (mentally) with the material. Asking questions that require students to draw inferences from what they’re learning—and also having students themselves formulate and then answer such questions—can be helpful as well (Croninger & Valli, 2009; A. King, 1994, 1999; McCrudden & Schraw, 2007). And when students tutor their classmates on subject matter they presumably know quite well, they learn it at an even higher level of understanding (Inglis & Biemiller, 1997; O’Donnell, 2006; Roscoe & Chi, 2007; Semb, Ellis, & Araujo, 1993). Teachers must remember, however, that elaboration can sometimes lead students to acquire erroneous ideas, especially if the students already have misconceptions about the topic at hand. Teachers must continually monitor students’ understanding of classroom material—perhaps by asking questions, assigning regular homework, or giving occasional quizzes—and then take steps to correct any misinterpretations that students’ responses reveal. Some of their misconceptions may be stubborn ones that aren’t easily corrected.

Information that must be retrieved within a particular context should ideally be stored within that context.
People are most likely to retrieve information relevant to a situation when they’ve stored that information in close association with other aspects of the situation. Accordingly, teachers should give students numerous opportunities to relate classroom material to the various situations that are later likely to require its retrieval. For example, a student is more likely to retrieve mathematical ideas relevant to accounting, surveying, or engineering if the math teacher incorporates problems involving accounting, surveying, and engineering into instruction. Similarly, a student studying for a psychology test that stresses application will be better prepared if he or she uses study time to consider numerous situations in which psychological principles can be applied.

Questions about previously learned material can promote both review and further elaboration.
In our discussion of instructional objectives, we made a distinction between lower-level skills and higher-level skills. This was essentially a distinction between simply knowing something versus  doing something (mentally) with that knowledge—for instance, applying, analyzing, synthesizing, or evaluating it. We can make a similar distinction between lower-level questions and higher-level questions: The former ask students to retrieve something pretty much as they’ve stored it in memory, whereas the latter ask students to elaborate on the retrieved information.

Teacher questioning is a widely used teaching technique, probably because it has several potential benefits. Questions help focus students’ attention on classroom activities and can alert teachers to students’ misconceptions about a topic. Questions also provide a feedback mechanism through which teachers and students alike can discover how much students have learned from a current lesson or can remember from previous lessons. When questions focus students’ attention on previously studied material, they encourage review of the material that should promote greater recall later on (S. K. Carpenter, Pashler, & Cepeda, 2009; McDaniel, Anderson, Derbish, & Morrisette, 2007; Wixson, 1984). And higher-level questions have the additional advantage of encouraging students to go beyond the information itself and construct more sophisticated understandings (Aulls, 1998; Brophy, Alleman, & Knighton, 2009; Mayer, 2010a; Minstrell & Stimpson, 1996).

Students should learn correct information in a meaningful rather than rote fashion. Students will notice inconsistencies between new information and prior beliefs only when they try to make connections between the new and the old. To use levels-of-processing terminology for a moment, students are most likely to modify their misconceptions in light of new data if they process those data in depth —in other words, if they become actively engaged in learning and truly try to understand the information being presented. Thus instruction is most likely to encourage meaningful learning, and therefore to promote conceptual change, when it intensively focuses on a few key ideas rather than superficially covering many topics (D. B. Clark, 2006; diSessa, 1996, 2008; C. Howe, Tolmie, Greer, & Mackenzie, 1995; M. C. Linn, 2008; Pintrich, Marx, & Boyle, 1993; Slusher & Anderson, 1996).

Meaningful learning promotes better transfer than rote learning.
Meaningful learning—connecting new information with things one already knows—leads to more effective long-term memory storage and retrieval than does rote learning. Now we see an additional advantage of meaningful learning: It increases the odds of positive transfer (Brooks & Dansereau, 1987; Mayer & Wittrock, 1996; Schwamborn, Mayer, Thillmann, Leopold, & Leutner, 2010). For example, in one experiment (Mayer & Greeno, 1972), college students received one of two methods of instruction about a particular formula useful in calculating probabilities. Group 1 received instruction that focused on the formula itself, whereas Group 2 received instruction that emphasized how the formula was consistent with students’ general knowledge. Group 1 students were better able to apply the formula to problems similar to those they’d studied during instruction, but Group 2 students were better able to use the formula in ways that instruction hadn’t specifically addressed—that is, they could transfer the formula to a wider variety of situations.

The more thoroughly something is learned, the more likely it is to be transferred to a new situation.
Research is clear on this point: The probability of transfer increases when students know something  well ( J. M. Alexander, Johnson, Scott, & Meyer, 2008; Cormier & Hagman, 1987a; Haskell, 2001; Voss, 1987). Thoroughly mastering knowledge and skills takes time, of course. In fact, some conditions that make initial learning slower and more difficult may actually be beneficial both for retention and for transfer over the long run. For example, increasing the variability of tasks that learners practice during instruction—having them perform several different tasks or several variations on the same task within a single instructional unit—lowers their performance initially but enhances their ability to transfer what they’ve learned to new situations (Z. Chen, 1999; Kornell & Bjork, 2008; R. A. Schmidt & Bjork, 1992; van Merriënboer & Kester, 2008). Clearly, then, there’s a trade-off between expediency and transfer. Teachers who teach a few things in depth are more likely to promote transfer than those who teach many things quickly—the less-is-more principle.

The more similar two situations are, the more likely it is that something learned in one situation will be applied to the other situation. Transfer depends on retrieval of relevant information at the appropriate time, and thus the perceived similarity (rather than actual similarity) of the two sets of circumstances is important (Bassok & Holyoak, 1993; Di Vesta & Peverly, 1984; Haskell, 2001; Voss, 1987). Either way, similarity enhances the probability of transfer.  Back to Top


4.  Metacognition improves learning outcomes

From Ormrod:

Metacognition includes knowledge and skills such as the following:
•  Knowing what one’s own learning and memory capabilities are and what learning tasks one can realistically accomplish (e.g., recognizing that it isn’t possible to memorize 200 pages of text in a single evening)
•  Knowing which learning strategies are effective and which are not (e.g., realizing that meaningful learning is more effective than rote learning)
•  Planning a viable approach to a new learning task (e.g., finding a quiet place to study)
•  Tailoring learning strategies to the circumstances (e.g., taking detailed notes when lecture material might be hard to remember)
•  Monitoring one’s present knowledge state (e.g., determining whether information has or hasn’t been successfully learned)
•  Knowing effective strategies for retrieval of previously stored information (e.g., thinking about the context in which a certain piece of information was probably learned)

In essence, then, metacognition is thinking about thinking.

Good readers—those who understand and effectively remember what they read—do many of the following:
•  Clarify their purpose for reading something and tailor their reading strategies to fit their purpose
•  Determine what’s most important to learn and remember, and focus their attention and efforts accordingly
•  Draw on their prior knowledge to make sense of what they’re reading
•  Make use of illustrations, diagrams, and other embedded visual materials to help them in their sense-making efforts
•  Elaborate on what they read—for instance, by drawing inferences, identifying logical relationships, making predictions, and envisioning possible examples or applications
•  Ask themselves questions that they try to answer as they read
•  Periodically check themselves to make sure they understand and remember what they’ve read
•  Try to clarify seemingly ambiguous points
•  Persist in their efforts to understand when they initially have trouble understanding something
•  Read for possible conceptual change—in other words, read with the understanding that they may encounter ideas that are inconsistent with what they currently believe
•  Critically evaluate what they read
•  Summarize what they’ve read
(L. Baker, 1989; C. Chan, Burtis, & Bereiter, 1997; Cromley, Snyder, Luciw, & Tanaka, 2008; Cromley, Snyder-Hogan, & Luciw-Dubas, 2010a; Dole, Duffy, Roehler, & Pearson, 1991; E. Fox, 2009; Graesser & Bower, 1990; Hacker, 1998b; Palincsar & Brown, 1989)

As people who learn new things every day, we all have ideas about what “knowledge” and “learning” are—ideas that are collectively known as epistemic beliefs. In many cases, such beliefs are pulled together into cohesive, although not necessarily accurate, personal theories about human learning and cognition (B. Hofer & Pintrich, 1997; D. Kuhn, 2000; Lampert, Rittenhouse, & Crumbaugh, 1996; Muis, Bendixen, & Haerle, 2006). Included in these theories are beliefs about such things as:
•  The certainty of knowledge: Whether knowledge is a fixed, unchanging, absolute “truth”  or a tentative, dynamic entity that will continue to evolve over time
•  The simplicity and structure of knowledge: Whether knowledge is a collection of discrete, independent facts or a set of complex and interrelated ideas
•  The source of knowledge: Whether knowledge comes from outside of learners (i.e., from a teacher or other authority figure) or is derived and constructed by learners themselves
•  The criteria for determining truth: Whether an idea is accepted as true when it’s communicated by an expert  or  when it’s logically evaluated based on available evidence
•  The speed of learning: Whether knowledge is acquired quickly, if at all (in which case learners either know something or they don’t, in an all-or-none fashion) or is acquired gradually over a period of time (in which case learners can partially know something)
•  The nature of learning ability: Whether people’s ability to learn is fixed at birth (i.e., inherited)  or can improve over time with practice and use of better strategies (Bendixen & Rule, 2004; Elder, 2002; J. A. Greene, Torney-Purta, & Azevedo, 2010; Hammer, 1994; B. Hofer, 2004; B. Hofer & Pintrich, 1997; P. M. King & Kitchener, 2002; M. C. Linn, Songer, & Eylon, 1996; Schommer, 1994a; Schommer-Aikins, Duell, & Hutter, 2005; P. Wood & Kardash, 2002)

Keep in mind that epistemic beliefs aren’t as either–or as I’ve just portrayed them. Most or all of the dimensions I’ve listed probably reflect continuums rather than strict dichotomies (Baxter Magolda, 2002; J. A. Greene et al., 2010; P. M. King & Kitchener, 2002; D. Kuhn & Weinstock, 2002; Marton & Booth, 1997).

Learners’ epistemic beliefs may be specific to particular content domains. Following are examples:
•  Many students believe that knowledge is more certain in some disciplines than in others. For example, they may believe that knowledge in mathematics, the natural sciences, and history is pretty much a sure thing, whereas knowledge in some social sciences—psychology, for one—is more tentative (De Corte, Op’t Eynde, & Verschaffel, 2002; D. Estes, Chandler, Horvath, & Backus, 2003; J. A. Greene et al., 2010; Haenen, Schrijnemakers, & Stufkens, 2003; B. Hofer, 2000; Schommer, 1994b).
•  Many students think that learning math and physics means memorizing procedures and formulas and finding a single right answer and that, furthermore, there’s usually only one correct way to solve a problem and only one correct answer to it (De Corte et al., 2002; Hammer, 1994; Muis, 2004).
•  Many students think that when they work on math problems, they’ll either solve the problems within a few minutes or else not solve them at all. Many also think that when an answer to a math problem isn’t a whole number, it’s probably wrong (Muis, 2004; Schoenfeld, 1988).

Guidelines to promote the development of more sophisticated metacognitive knowledge and skills:

Students can use sophisticated learning strategies only when they have a knowledge base to which they can relate new material.
One of the most important factors influencing such processes as meaningful learning and elaboration is what a learner already knows. And as we discovered in earlier sections of the chapter, students’ prior knowledge affects their ability to separate important ideas from trivial facts and to effectively monitor their comprehension. Perhaps the limited capacity of working memory comes into play here: Students can do only so much (mentally) at a time, and they may not have the “room” to use sophisticated learning strategies if they must struggle to make even preliminary sense of what they’re studying (e.g., see Demetriou & Kazi, 2001; Lehmann & Hasselhorn, 2007; Waters & Kunnmann, 2010). Thus, teachers must be careful to present difficult material only after students have sufficiently mastered prerequisite knowledge and skills to genuinely understand it.

Students should learn a wide variety of strategies, as well as the situations in which each one is appropriate
(Jetton & Dole, 2004; Nist, Simpson, Olejnik, & Mealey, 1991; Pressley, Harris, & Marks, 1992; C. E. Weinstein, Goetz, & Alexander, 1988). Different strategies are useful in different situations; for instance, meaningful learning may be more effective for learning general principles within a discipline, whereas mnemonics may be more effective for learning hard-to-remember pairs and lists. Organizing ideas in a hierarchical fashion may be appropriate for one unit; organizing them in a two-dimensional matrix may be appropriate for another. Some students need assistance not only in organizing information but also in organizing what they need to do; for instance, many can benefit from explicit instruction in how to organize their notebooks and assignments, keep track of appointments and due dates, establish priorities for a study session, and the like (Belfiore & Hornyak, 1998; Meltzer, Pollica, & Barzillai, 2007).

Effective strategies should be practiced with a variety of tasks and on an ongoing basis (A. Collins, Brown, & Newman, 1989; Nokes & Dole, 2004; Pressley, El-Dinary, Marks, Brown, & Stein, 1992; Pressley et al., 1990). When students learn a strategy only for one particular task, they’re unlikely to use it in other contexts. But when they apply the same strategy to many different tasks over a long period, they’re apt to recognize the strategy’s value and apply it in new situations. Effective strategy instruction, then, cannot be a one-shot deal.

Strategy instruction should include covert as well as overt strategies (Kardash & Amlund, 1991). Certainly students stand to benefit from guidance about how to take notes, create outlines, and write summaries of what they’ve learned. But the sophisticated cognitive processes that underlie these behaviors—learning meaningfully, elaborating, monitoring comprehension, and so on—are ultimately the most important strategies for students to acquire.

Teachers can model effective strategies by thinking aloud about new material (Brophy, Alleman, & Knighton, 2009; McKeown & Beck, 2009; P. A. Ornstein et al., 2010). When teachers think aloud about material their classes are studying (e.g., “I remember that  Au is the symbol or gold by remembering, ‘ Ay, you stole my gold watch!’” or “Hmm . . . it seems to me that Napoleon’s military tactics were similar to those of the ancient Assyrians”), they give students specific, concrete examples of how to process information effectively.

Students can also benefit from reflecting on and describing their current study strategies. Even at the college level, low achievers don’t always have much metacognitive insight into how they approach classroom learning tasks. Regularly encouraging students to think about how they know something or how they went about learning it—as well as about how they might learn it more effectively—can sometimes help them bring implicit metacognitive strategies to the surface for careful scrutiny and reflection (Cornoldi, 2010; Großschedl & Harms, 2010; May & Etkina, 2002; S. Miller, Heafner, & Massey, 2009). Also beneficial is having students tell their peers about strategies that they’ve personally found to be useful (McGovern, Davis, & Ogbu, 2008; Meltzer et al., 2007).

Students should have epistemic beliefs that are consistent with effective strategies. As we’ve seen, students’ epistemic beliefs influence the learning strategies they use. Study-strategies training, in and of itself, might change those beliefs but won’t necessarily do so (Muis, 2007; Schraw & Moshman, 1995). Because students’ beliefs about the nature of knowledge and learning are often in the form of implicit rather than explicit knowledge, they may be especially resistant to change (Schraw & Moshman, 1995).  Back to Top


5.  Learners’ language and cultural background shapes their thinking and learning

- Learners’ language and cultural background shapes not only their pre-existing beliefs/knowledge (i.e., content), but also the way they incorporate new information (i.e., process) (e.g., Miura et al., 1994; Nisbett et al., 2001)
- There are cognitive consequences to being bilingual (e.g., Barac & Bialystok, 2002)
- Language supports learning and higher-order abstraction (e.g., Gentner, 2010).

Example from Ormrod – on critical thinking:

To some degree, learners’ tendency to think or not think critically depends on their personality characteristics: On average, critical thinkers are open-minded, enjoy intellectual challenges, and can emotionally handle the idea that they might occasionally be wrong about a topic (Halpern, 2008; Moon, 2008; West, Toplak, & Stanovich, 2008). Learners’ epistemic beliefs also come into play. Learners are more likely to look analytically and critically at new information if they believe that even experts’ understanding of a topic continues to evolve as new evidence accumulates. They’re less likely to engage in critical thinking if they believe that knowledge is an absolute, unchanging entity (Kardash & Scholes, 1996; P. M. King & Kitchener, 2002; D. Kuhn, 2001a; Muis & Franco, 2009; Schommer-Aikins, 2002).

Another influential factor is one’s cultural upbringing. For example, if a culture places high value on respecting one’s elders or certain religious leaders, it’s likely to foster the epistemic belief that “truth” is a cut-and-dried entity that’s best gained from authority figures (Delgado-Gaitan, 1994; Losh, 2003; Qian & Pan, 2002; Tyler et al., 2008). In addition, a cultural emphasis on maintaining group harmony may discourage children from hashing out differences in perspectives, which critical thinking often entails (Kag˘itçibas¸i, 2007; D. Kuhn & Park, 2005; Moon, 2008). Perhaps as a result of such factors, critical thinking may be less common in some groups—for instance, in some very traditional Asian and Native American communities and in some fundamentalist religious groups—than in others (D. Kuhn, Daniels, & Krishnan, 2003; D. Kuhn & Park, 2005; Tyler et al., 2008; see Heyman, Fu, & Lee, 2007, for an exception).       Back to Top


6.  There are individual differences in cognition, but not the ones you think about
(e.g., "learning styles")

- Working memory capacity and spatial thinking skills can predict learning outcomes.

"[Working memory capacity] predicts performance on a wide range of higher-order cognitive tasks, including tasks involving reading and listening comprehension, language comprehension, ability to follow directions, vocabulary learning, note taking, writing, reasoning, bridge playing, and learning to write computer programs." (Engle, 2002, p. 19)

From Ormrod:

Even with attention-getting and appropriately paced instruction and activities, learners differ in their ability to control what they attend to and consciously think about. As noted earlier, working memory—especially its central-executive component—is a key player in guiding attention and thinking processes, and some learners have better central-executive skills than others. Fortunately, many of these skills can be taught.

The limited capacity of working memory isn’t necessarily a bad thing. The working memory bottleneck forces learners to condense, organize, and synthesize the information they receive (e.g., Cowan, 2010; R. M. Gagné & Driscoll, 1988).

"People who had high scores on spatial tests in high school were much more likely to major in STEM disciplines and go into STEM careers than those with lower scores, even after accounting for the fact that they tended to have higher verbal and mathematical scores as well. Similar results have been found in other longitudinal studies: one began in the 1970s and tracked the careers of a sample of gifted students first studied in middle school; another began in the 1980s with observing the block play of preschoolers and followed their mathematics learning through high school. In short, the relation between spatial thinking and STEM is a robust one, emerging for ordinary students and for gifted students, for men and for women, and for people who grew up during different historical periods. Spatial thinkers are likely to be more interested in science and math than less spatial thinkers, and are more likely to be good enough at STEM research to get advanced degrees." (Newcombe, 2010, p. 29-30)   Back to Top

See also Ganley et al. (2014) on spatial ability mediating gender differences in science performance.
 


7.  Conception and perception are tightly interwoven

- Even the most symbolic and abstract (i.e., “higher-order”) cognitive processes are often grounded in “lower-order” perceptual experience

“As cognitive scientists have emphasized in recent years, cognition is embodied; you think with your body, not only with your brain.”
― Daniel Kahneman, Thinking, Fast and Slow

"It is common to think of perception as delivering basic information in a relatively unchanging way. According to this view, high-level learning happens elsewhere—in committing facts to memory, acquiring procedures, or generating more complex or abstract products from raw perceptual inputs by means of reasoning processes. Contemporary experimental and neuroscientific research in perception, as well as new discoveries in [perceptual learning] require revision of these assumptions in at least three ways: 1) perceptual mechanisms provide complex and abstract descriptions of reality, overlapping and interacting deeply with what have traditionally been considered “higher” cognitive functions; 2) the representations generated by these perceptual mechanisms are not limited to low-level sensory features bound to separate sensory modalities; and 3) what perception delivers is not fixed, but progressively changing and adaptive." (Kellman & Massey, 2013, p. 119)

"Newborn infants imitate the facial expressions and bodily movements of adults, simulating the actions that they see physically (Meltzoff & Moore 1983). As infants grow older, they understand the perceived actions of others in terms of what they have come to understand about their own actions and intentions (Meltzoff 2007). Once infants experience the occluding effects of a blindfold, for example, they understand that an adult wearing a blindfold cannot see. Thus, mirroring plays a central role in development, as infants use simulations of their own experience to understand the goals and actions of others. Researchers increasingly demonstrate that development depends critically on bodily states (e.g., L. Smith 2005b) and situated action (e.g., L. Smith & Gasser 2005). For example, L. Smith et al. (1999) showed that the development of object permanence is not simply a cognitive achievement (as long believed) but also a grounded one. Specifically, motor perseveration plays a major role in tasks that measure object permanence. Longo & Bertenthal (2006) similarly showed that motor simulations contribute to perseveration. Other developmental tasks also exhibit strong dependence on action. For example, the motor actions performed while learning a category influence the visual features abstracted into its representation (L. Smith 2005a). Similarly, the actions performed on objects during play later cause children to place the objects in spatial clusters that reflect shared categories (Namy et al. 1997). In general, extensive amounts of learning occur between perception, action, and cognition as development progresses (e.g., Greco et al. 1990, Rochat & Striano 1999)." (Barsalou, 2008; p, 631)  Back to Top

See Son et al.