Think for a moment what it means to be an expert at something. What is it that could be described as “expertness?” Expertness requires sophisticated uses of memory. Experts have to mobilize information, the right information, for the situation at hand.
Analogies Help Us Predict
Analogies are tools that help us become experts at learning how to predict. Let’s explore a concrete example to see how.
In the late eighteenth century, Robert Burns was collecting songs and writing poems in Scotland. Many of those still are well known today. One poem, also written as song, is “A Red, Red Rose.” The opening lines are immediately recognizable for a famous simile they contain:
O My luve’s like a red, red rose,
That’s newly sprung in June.
In this analogy, readers quickly realize that Burns’ love does not have petals or a long stem. In fact, that thought probably does not even occur as we read the poem. Immediately, readers begin mapping the similarities between roses and the ones they love (or the condition of being in love). The reader may be thinking:
- Love is beautiful, just as a rose is
- Love is sweet, like the fragrance of a rose
- Love is delicate and elegant just as the rose is
- Love is new, like a newly blossomed rose and its ‘newness’ is what makes it delicate like a rose, fragile as of yet
For some readers, these would be relatively shallow comparisons or similarities. Readers can also compare what they know about roses as they renew themselves every spring, thus love also renews itself and always remains fresh.
In constructing an analogy, the brain first constructs an initial search for similarities between the source and the target analog (the components of the analogy). One way to think about this is to think of the source analog as an already existing schema or as background knowledge. The target analog is the concept or schema that begs to be understood. Once an initial partial map of similarities is made, then the thinker may look for more detailed similarities or extensions of the initial partial mapping. An interesting series of experiments reported by Holyoak and Thagard (1995) described how a chimpanzee named Sarah learned to make analogies. Sarah was taught to use a series of plastic tokens which were used to represent ideas (or propositions). She learned to use tokens for the concepts of “same” and “different” as well. Many animals can pick out or match items that are the same in physical nature, but Sarah learned to do something far more interesting. She could identify what the relations were between objects that were not the same in physical appearance. For example, Sarah could correctly match objects that were smaller than or larger than a target even if physical shape was different. In one experiment, she correctly matched a source analog of a glass half-filled with water to a target analog when given the option of a half of an apple or three-fourths of an apple by correctly choosing the half apple. There is no reason to match a fraction of an apple with a glass of water based solely on physical properties. Sarah had to determine the relation of the glass of water and the apple based on the relation of concepts that are implicit.
Sarah’s accomplishments are remarkable for a couple of reasons. One is that Sarah was able to learn something based on the symbolic manipulation of ideas. Another is that such learning was the direct result of her ability to use symbols, an ability she was taught and which other chimps lack, to create abstractions that identify implicit relations between objects. Such implicit relations are the type of thinking that allows humans to compare a rose with one’s love. While Sarah used tokens to communicate in a language-like manner, people are much more adept at manipulating symbols. Of course, the symbol system readers most often use is language.
Analogies Clarify Understanding
Analogies help us refine and connect knowledge that we might not have been able to connect otherwise. Gick and Holyoak (1983) have studied analogies extensively and explain their functions: “The analogist notes correspondences between the known problem and a new unsolved one, and on that basis derives an analogous potential solution. More generally, the function of an analogy is to derive a new solution, hypothesis, or prediction…” (p. 5).
In creating analogies, the thinker must notice that there is a similarity that might prove useful. Then, through a process of abstraction, the thinker completely maps the similarities, which cognitive scientists call identities. The differences are also noted. The possibility of mapping every aspect of a source analog onto the target analog is very small, so identification of differences which are not helpful may also be important. If readers continue thinking about Burns’ rose, they might also think that their rose, like their love, is beautiful, elegant, and always fresh. Some readers might then think that roses also have thorns and our love also has… Hmmm, better not finish that analogy unless we want to spend the next week or so sleeping on the couch. Or in the doghouse.
Analogies Help Us Predict with Greater Detail
As noted, analogies can help us understand predictions. An incomplete, partial map of an analogy requires predictive ability to continue abstracting the rest of the map. However, a mapped analogy – one that provides a link between the source and the target ideas – may be useful in constructing additional analogies with greater detail. We may want to explore, for example, the analogy that human cognition is like the operation of a computer. Initially, we find that there are several identities or similarities that are useful and map them this way (see Table 1.1).
Table I.1: Considering the Analogy Between Humans and Computers
|The random access memory (RAM) in a computer is like…
||Short-term and working memory
||Each stores information for temporary processing until it is pushed aside by other information or is no longer needed.
|The hard drive is like…
||Long term memory
||Each stores information indefinitely.
|The modern computer’s processor is very fast like…
||The human brain
||Each processes information through distributed and parallel processes
With this initial mapping, we can predict that humans might be overshadowed by computers at some point in the future. However, we also find that there are several differences. If these differences are not relevant to the problem we are trying to solve or the concept we are trying to understand, then the analogy still works. If not, then we will have to abandon the analogy. As we construct this analogy, we remember what Norman (1997) wrote about humans and computers. David Norman (1997) compared humans and computers by addressing the fear that computers will eventually surpass human abilities. Computers, he says, do not present a threat to humans because there are inherent differences in how human systems and computer systems operate. Computers can produce repeatable and accurate results, but humans follow “a complex-history-dependent mode of operation and yield approximate, variable results” (p. 29). Computers do not handle errors very well, but human systems are adaptable to a changing environment and conditions.
Norman’s comparison of computers and humans helps us understand a bit about why people are so good at making predictions and why we have come to rely on our predictive skills. Predictions are the means we use to reduce those elements of our world about which we are uncertain. The way we do that is to continually aggregate or collect information and compare that against other knowledge we have stored away. Without stored information and the ability to gather new information, predictions are little more than wild guesses. Such guesses are useless in making sense of an environment upon which we depend for survival and for meaning. Predictions allow people to examine their past and present situations to make meaningful new estimations about what the future might hold. Let’s continue and find some differences:
- Computers handle calculations based on precise algorithms that do not tolerate error.
- Humans seek patterns and process information in spite of errors and ambiguity.
Based on this analogy, we can then use the information comparing humans and computers to predict that computers will not surpass human abilities.
Analogies Provide a Bridge to New Learning
In their experiments with analogies and how people use them, Gick and Holyoak (1983) conducted an instructive experiment. They had participants in the experiment read a story that identified a specific solution to a problem. Next they had those participants read a story that presented a similar problem, but the solution was not provided. In the first story, a military situation is presented. A general must attack a fortress in the center of enemy territory. He can’t mount a frontal assault with a large force because the roads leading to the fortress are mined. A large force would not be able to move quickly through the minefield. A solution is to send small groups of soldiers to attack by coming at the fort through the minefields from several directions.
In the next story, the subject will encounter a different domain. Instead of a military problem, it might be a medical problem. In this scenario, a tumor must be removed; however, the dose of radiation needed to kill the tumor will also destroy the surrounding tissue. The subjects were asked then to solve the problem.
Because the source analog (the fortress) was in a different domain (the military) than the tumor problem (medical), Gick and Holyoak found something interesting that is useful for teachers. Their participants were successful 75 percent of the time in mapping the military source analog with the medical target analog to solve the problem. The means for removing the tumor is to target the radiation from several directions so that the surrounding tissue does not receive a lethal dose of radiation, but the tumor does. In each analog, the solution lies in coming at the object of the attack (either with soldiers or radiation) from varying directions. However, Gick and Holyoak were able to achieve the 75 percent rate of correct solutions only when the participants were given a hint. That is, they were told that the military problem could be used to solve the medical problem. Then it was up to the participants to correctly map the two different domains to solve the problem.
This experiment and the resulting conclusions suggest to us as teachers that we need to provide direction, or hints and clues, to help students scaffold their specific predictions about texts they read. We believe that such hints help students learn to construct reasonable and meaningful predictions in specific situations, and also that this type of help assists students in learning to predict and create other types of analogies as part of a process of making meaning of the challenging texts they read.
Comparison Activities as an Expert Skill
Teachers of English language arts often use Venn diagrams to help students compare information from the books they read. Indeed, Marzano, Pickering, and Pollock (2001) in their meta-analysis of effective classroom strategies found that comparison activities produce the highest effect size of all the strategies studied in increasing student achievement. These researchers identified four important points regarding instruction that makes use of classification based on similarities and comparisons. They are:
- Present students with explicit guidance in identifying similarities and differences.
- Ask students to independently identify similarities and differences.
- Represent similarities and differences in graphic or symbolic form.
- Identify similarities and differences in a variety of other ways. Such practice helps learners develop a repertoire of approaches and reinforces the use of the cognitive strategy (pp. 15-16).
As these researchers noted, the identification of similarities and differences is a highly robust activity and one that leads to increased student achievement. Making effective predictions involves the use of making comparisons of similar patterns of events, situations, personalities, and geographic locations against new information in order to make an inference about what may happen. To do so with precision and creativity reduces uncertainty on a journey across country, in a movie or novel, and in one’s journey through life.
Is Making Predictions Enough?
The simple answer is no. Even as students are taught all of the skills required to make predictions, simply making a prediction will likely not be sufficient for students to think deeply about text. Instead, as teachers, we need to extend the making of predictions to helping students learn from their predictions. For example, Ms. Martinez ensured that her students revisit their predictions and develop a strong sense of which predictions worked and why. She also asked them to figure out what they missed when their predictions were not confirmed. This critical analysis of predictions can improve student learning.
Similarly, illustrated through Ms. James’s classroom, asking students to make predictions when there isn’t really anything to predict or when the question in focus has been answered in an obvious way, will not help students learn. Re-focusing student predictions in Ms. James’s class might have changed the nature and depth of the classroom discussions about the Old Man and the Sea and created a more motivating reason for students to read the text and begin to understand its value for readers. Part II explains several cognitive strategies that students might use to make good predictions. Part III then puts these cognitive strategies in context of the classroom structures teachers might use to promote better thinking through predictions. And finally, Part IV provides an analysis of which students need which types of instruction to be successful.
Learning from Predictions: I Predict … Now What?
In Part II, several cognitive strategies students might use along with suggestions about how teachers might promote prediction among students are presented. Part III puts predicting to learn in the spotlight of several classroom structures or routines teachers might employ. We also highlight the idea that readers can learn from predictions. You might say, “Readers learn by making increasingly accurate predictions as they read, learn new information, put that in context of their existing knowledge, and move ahead to see what else there is to learn in the text. Students learn from prediction by making the predictions in the first place.” And you are right, but there’s more. There are at least two reasons:
- Readers make predictions because doing so brings relevant existing knowledge to bear as the reader makes meaning.
- Readers learn from prediction by applying what they know about the text as they read reducing uncertainty about the content, whether it is primarily aesthetic or efferent in nature (Rosenblatt, 1995), as they go.
Teachers are in the unique position of guiding students’ reading, through continuous feedback, direct instruction, modeling, and so on. Through these processes, teachers point out to students what good readers do and assist students to become expert readers. More specifically, through a scaffolding or helping interaction, teachers can assist students to learn what aspects of a text need their attention. Children are less likely to efficiently regulate attention (Berninger & Richards, 2002), and as a result, require more guidance. The significance for teachers is that it is simply not enough to ask students to make predictions. Instead, teachers must actively show students what needs their attention and how to access that through the cognitive processes explored in Part II and promote those their use through practice and classroom structures or routines like those in Part III. Teachers must also help students learn to attend to the predicting process itself. An example may help.
A popular (but fictional) television personality, Chef Lecteur, has just shown his adoring viewers how to make New England clam chowder. At the beginning, he predicted that a few pinches of salt and few grinds of the pepper mill will suffice. However, in the process of making the chowder, variables crept in: the potatoes weren’t as fresh because they’ve been in storage, the onions had a slightly stronger flavor than usual, and so on. A viewer might predict that Lecteur’s was following a recipe that will turn out a delicious soup if the correct ingredients in the correct amounts are assembled. Just as Lecteur is ready to serve the chowder, he dips a spoon into the pots, blows on the soup in the bowl of the spoon, and tries it. Wait! He reaches for pepper mill again. Didn’t he get it right the first time? He’s an expert, and his name is on products that fill his fans’ kitchens. As he tastes the soup, he points out how important it is to re-season the food prior to serving: the taste may change through the cooking process. The variables have changed the taste and could not be predicted precisely. What is more important, and what his fans admire, is that he tells them what he did and why he did it. In this way, reading is like cooking because the good cook reassesses the taste of the food right up until it’s time to serve the food; the reader re-evaluates predictions right up until the cover of the book is closed. Teachers live for the moments when the metaphorical light bulb comes on for students; Lecteur’s fans clap and cheer when he grabs the pepper mill. Like our make-believe chef’s fans, students will smile, and the lights will illuminate their faces when they understand why revisiting an earlier prediction creates a more meaningful reading experience. They get it.