|
| |
Book Smart or Street Smart?
From: Cambridge University Press
| By:
Robert J. Sternberg |
EDITOR'S INTRODUCTION |
The academic genius who cannot fathom practical detail has provided the basis for many comic plots. But does what we learn in the classroom readily transfer to everyday life? In this extract from his co-authored book Practical Intelligence in Everyday Life, Robert Sternberg draws on real-world examples to demonstrate that practical and academic intelligence are indeed distinct, both psychologically and statistically. |
 ractical, or everyday, intelligence is different from the kind of intelligence associated with academic success. There are any number of ways in which we see this difference in our everyday lives. We see people who succeed in school and fail in work or who fail in school but succeed in work. We meet people with high scores on intelligence tests who seem inept in their social interactions. And we meet people with low test scores who can get along effectively with practically anyone. Laypersons have long recognized a distinction between academic intelligence (book smarts) and practical intelligence (street smarts or common sense). This distinction is confirmed by research on the implicit theories of intelligence held by both laypersons and researchers (see R.J. Sternberg, Human Abilities, 1985; R. J. Sternberg et al., "People's Conceptions of Intelligence", Journal of Personality and Social Psychology 41, 1981). |
Academic versus practical intelligence
 | |
| On the surface, the job of trash collector appears to be more physically than intellectually demanding. | |
There may be any number of reasons for the apparent difference between academic and practical intelligence. A major source of this difference is the sheer disparity in the kinds of problems one faces in academic versus practical situations. The problems faced in everyday life often have little relation to the knowledge or skills acquired through formal education or the abilities used in classroom activities. Consider the following example of an observation made of a garbage collector in Tallahassee, Florida. |
Tallahassee, priding itself on the service it provides to its citizens, requires garbage collectors to retrieve trash containers from the backyards of residents. Each resident fills a large trash container in the backyard rather than placing standard-sized garbage cans on the curbside to be picked up. Trash collectors must locate and retrieve each full container from the backyard, heave it into the truck, and then drag the empty container back to each yard. Many of the garbage collectors are young high school dropouts, who because of their lack of education might be expected to score poorly on intelligence tests. On the surface, the job appears to be more physically than intellectually demanding. Each stop requires two trips to the backyard, one to retrieve the full can, and another to return it when it is empty. |
One summer it was noticed that the collection routine had changed after a new, older employee joined the crew. This change involved relaxing the constraint that each household retain the same container. Because the trash bins were issued by the city and not purchased with personal funds, they were identical. The new routine consisted of wheeling the previous house's empty container to the current house's backyard, leaving it to replace the full can, which was in turn wheeled to the truck to be emptied. Once emptied, this can was wheeled to the backyard of the next house to replace its full can, and so on. The new routine required only one trip to each house, whereas the previous one required two trips. The new employee's insights cut the work nearly in half. This solution had eluded other garbage collectors and the managers who trained them. |
Everyone encounters problems to which solutions are neither readily available nor readily derivable from acquired knowledge. This type of problem solving, frequently experienced in daily life, is referred to as practical problem solving. Such problems can be experienced in the workplace or in school, the household, stores, movie theaters, or anywhere. There is no consensus on how to define practical problems encountered in life, but building on a distinction made by U. Neisser (in L. Resnick (ed.), Human Intelligence, 1976, pp. 179-89), R. J. Sternberg (Beyond IQ, 1985; Successful Intelligence, 1997) and R. K. Wagner and R. J. Sternberg (Practical Intelligence, 1986, pp. 51-83) have classified problems as academic or practical in nature. Academic problems tend to be formulated by others; well-defined; complete in the information they provide; characterized by having only one correct answer; characterized by having only one method of obtaining the correct answer; outside of ordinary experience; and of little or no intrinsic interest. |
Practical problems, in contrast to academic problems, tend to be unformulated or in need of reformulation; of personal interest; lacking in information necessary for solution; related to everyday experience; poorly defined; characterized by multiple appropriate solutions, each with liabilities as well as assets; and characterized by multiple methods for picking a problem solution. Given the differences in the nature of academic and practical problems, it is no surprise that people who are adept at solving one kind of problem may well not be adept at solving problems of the other kind. |
The intellectual skills that individuals exhibit in finding solutions to practical problems may be referred to as practical intellectual skills (see, inter alia, P. B. Baltes and O. G. Brim (eds), Life-span Development and Behavior, 1984, vol. 6, pp. 33-76). When combined, these skills are often referred to as practical intelligence, which is defined as intelligence that serves to find a more optimal fit between the individual and the demands of the individual's environment, whether by adapting to the environment, changing (or shaping) the environment, or selecting a different environment (see Sternberg, Beyond IQ and Successful Intelligence). The concept of practical intelligence takes into account the distinction presented above between academic and practical tasks. The abilities emphasized in formal schooling have limited value if they cannot be used to address practical, everyday problems. |
Research on practical problem solving ability
The research on practical ability is becoming more and more central to mainstream psychology (see C. A. Berg and P. Klaczynski, "Practical Intelligence and Problem Solving", in F. Blanchard-Fields and T. M. Hess (eds), Perspectives on Cognition in Adulthood and Aging, 1996, pp. 323-57). Initially, the examination of practical intelligence issued from a concern that the intelligence of adults functioning largely outside the academic environment from the moment they obtained their academic credentials and virtually for the rest of their lives was evaluated primarily by traditional tests of intelligence constructed to predict academic success. |
Various aspects of the concept of practical intelligence are expressed in a number of diverse constructs. Some researchers define everyday intelligence as a specific expression of conventional abilities that permit adaptive behavior within a distinct class of everyday situations (S. L. Willis and K. W. Schaie, in Wagner and Sternberg, (eds), Practical Intelligence, pp. 236-70), whereas others stress the unique nature of practical abilities (Neisser, op. cit.; R. K. Wagner, "Tacit knowledge in everyday intelligent behavior", Journal of Personality and Social Psychology 52, 1987). Most psychological studies of practical abilities focus on solving problems that are ill-structured in their goals and solutions and are frequently encountered in daily life at home, at work, and in dealing with people (S. W. Cornelius and A. Caspi, "Everyday problem solving in adulthood and old age", Psychology and Aging 2, 1987; N. W. Denney, "Everyday problem solving", in I. W. Poon et al(eds), Everyday Cognition in Adulthood and Late Life, 1989). |
A number of studies have addressed the relation between practical and academic intelligence. These studies have been carried out in a wide range of settings, using a variety of tasks, and with diverse populations. We review some examples of research on problem solving and reasoning. Taken together, these studies show that ability measured in one setting (e.g., school) does not necessarily transfer to another setting (e.g., real-world task). |
 | |
| Although the assemblers were the least educated workers in the plant, they routinely outperformed the more highly educated white collar workers. | |
Several studies compared performance on mathematical types of problems across different contexts. Scribner studied the strategies used by milk processing plant workers to fill orders (see S. Scribner, in B. Rogoff and J. Lave (eds), Everyday Cognition, 1984; and in Wagner and Sternberg, Practical Intelligence, pp. 13-30). Workers who assemble orders for cases of various quantities (e.g., gallons, quarts, or pints) and products (e.g., whole milk, 2% milk, or buttermilk) are called assemblers. Rather than employing typical mathematical algorithms learned in the classroom, Scribner found that experienced assemblers used complex strategies for combining partially filled cases in a manner that minimized the number of moves required to complete an order. Although the assemblers were the least educated workers in the plant, they were able to calculate in their heads quantities expressed in different base number systems, and they routinely outperformed the more highly educated white collar workers who substituted when assemblers were absent. Scribner found that the order-filling performance of the assemblers was unrelated to measures of school performance, including intelligence test scores, arithmetic test scores, and grades. |
Another series of studies of everyday mathematics involved shoppers in California grocery stores who sought to buy at the cheapest cost when the same products were available in different-sized containers (see J. Lave, M. Murtaugh, and O. de la Roche, in B. Rogoff and J. Lace (eds), Everyday Cognition, 1984, pp. 67-94; M. Murtaugh, "The practice of arithmetic by American grocery shoppers", Anthropology and Education Quarterly 16, 1985). (These studies were performed before cost per unit quantity information was routinely posted.) For example, oatmeal may come in two sizes, 10 ounces for $0.98 or 24 ounces for $2.29. One might adopt the strategy of always buying the largest size, assuming that the larger size is always the most economical. However, the researchers (and savvy shoppers) learned that the larger size did not represent the least cost per unit quantity for about one third of the items purchased. The findings of these studies were that effective shoppers used mental shortcuts to get an easily obtained answer, accurate enough to determine which size to buy. A common strategy, for example, was mentally to change the size and price of an item to make it more comparable with the other size available. For example, one might mentally double the smaller size, thereby comparing 20 ounces at $1.96 versus 24 ounces at $2.29. The difference of 4 ounces for about 35 cents, or about 9 cents per ounce, seems to favor the 24-ounce size, given that the smaller size of 10 ounces for $0.98 is about 10 cents per ounce. These mathematical shortcuts yield approximations that are as useful as the actual values of 9.80 and 9.33 cents per ounce for the smaller and larger sizes, respectively, and are much more easily computed in the absence of a calculator. When the shoppers were given a mental arithmetic test, no relation was found between test performance and accuracy in picking the best values. |
S. J. Ceci and J. Liker (in Wagner and Sternberg, Practical Intelligence, pp. 119-42; and "Stalking the IQ--expertise relationship", Journal of Experimental Psychology: General, 117, 1988) and Ceci and A. Ruiz (in R. Hoffman (ed.), The Psychology of Expertise, 1991) studied expert racetrack handicappers. Ceci and Liker (1986) found that expert handicappers used a highly complex algorithm for predicting post time odds that involved interactions among seven kinds of information. By applying the complex algorithm, handicappers adjusted times posted for each quarter mile on a previous outing by factors such as whether the horse was attempting to pass other horses and if so, the speed of the other horses passed and where the attempted passes took place. By adjusting posted times for these factors, a better measure of a horse's speed is obtained. It could be argued that the use of complex interactions to predict a horse's speed would require considerable cognitive ability (at least as it is traditionally measured). However, Ceci and Liker reported that the successful use of these interactions by handicappers was unrelated to their IQ. |
A subsequent study attempted to relate performance at the racetrack to making stock market predictions in which the same algorithm was involved. Ceci and Ruiz (1991) asked racetrack handicappers to solve a stock market prediction task that was structured similarly to the racetrack problem. After 611 trials on the stock market task, the handicappers performed no better than chance, and there was no difference in performance as a function of IQ. Ceci and A. Roazzi (in R. J. Sternberg and R. K. Wagner (eds), Mind in Context, 1994, pp. 74-101) attribute this lack of transfer to the low correlation between performance on problems and their isomorphs (problem isomorphs are two or more problems that involve the same cognitive processes but use different terminology or take place in different contexts). |
The same principle that applies to adults appears also to apply to children. T. N. Carraher, D. Carraher, and A. D. Schliemann ("Mathematics in the streets and in schools", British Journal of Developmental Psychology 3, 1985) studied Brazilian children who, for economic reasons, often worked as street vendors (T. Nuñes, "Street intelligence", in R. J. Sternberg (ed.), Encylopedia of Human Intelligence, 1994). Most of these children had very little formal schooling. Carraher et al. compared the performance of these children on mathematical problems that were embedded in a real-life situation (i.e., vending) with their performance on problems presented in an academic context (e.g., 2 + 4 = ?). The children correctly solved significantly more questions that related to vending than mathematical problems that were academic in nature. When the academic problems were presented as word problems (e.g., "If an orange costs 76 cruzeiros and a passion fruit costs 50, how much do the two cost together?"), the rate of correct responses was substantially better, but still not as high as when the problems were presented in the context of vending. |
This lack of transfer also appears to work in the reverse direction. For example, A. N. Perret-Clermont (Social Interaction and Cognitive Development in Children, 1980) found that many schoolchildren had no problem solving paper-and-pencil arithmetic questions but could not solve the same type of problem in a different context (e.g., counting bunches of flowers). That is, schoolchildren may fail to transfer academic knowledge to everyday problems. |
Roazzi ("Effects of context on cognitive development", in J. F. Cruz and R. A. Goncalves (eds), Psicologia e Eduçao, 1987) found similar results when comparing the performance of street vendor children and middle-class schoolchildren on a class inclusion task. To assess the performance of the street vendor children, the researcher posed as a customer and asked questions about the items to find out if the children understood the relationship among classes and subclasses of food (e.g., mint and strawberry chewing gum as part of the class chewing gum). At a later time, the same children were given a formal test that had the same logical structure but was irrelevant to their street vending jobs. The middle-class children were given the same two tests. Street vendor children performed significantly better on the class inclusion task in the natural than in the formal context, whereas middle-class children were more successful on the formal version of the task. |
Additional research has shown that the use of complex reasoning strategies does not necessarily correlate with IQ. D. Dörner and H. Kreuzig ("Problemlösefähigkeit und Intelligenz", Psychologische Rundschaus 34, 1983) and Dörner, Kreuzig, F. Reither, and T. Staudel (Lohhausen, 1983) studied individuals who were asked to play the role of city managers for the computer-simulated city of Lohhausen. A variety of problems were presented to these individuals, such as how best to raise revenue to build roads. The simulation involved more than 1,000 variables. Performance was quantified in terms of a hierarchy of strategies, ranging from the simplest (trial and error) to the most complex (hypothesis testing with multiple feedback loops). No relation was found between IQ and complexity of strategies used. A second problem was created to cross-validate these results. This problem, called the Sahara problem, required participants to determine the number of camels that could be kept alive by a small oasis. Once again, no relation was found between IQ and complexity of strategies employed. |
Conclusion
The above studies indicate that demonstrated abilities do not necessarily correspond between everyday tasks (e.g., price comparison shopping) and traditional academic tasks (e.g., mathematics achievement tests). In other words, some people are able to solve concrete, ill-defined problems better than well-defined, abstract problems that have little relevance to their personal lives, and vice versa. Few of these researchers would claim, however, that IQ is totally irrelevant to performance in these various contexts. There is evidence that conventional tests of intelligence predict both school performance and job performance (see, inter alia, G. V. Barrett and R. L. Depinet, "A reconsideration of testing for competence rather than intelligence", American Psychologist 46, 1991). What these studies do suggest is that there are other aspects of intelligence that may be independent of IQ and that are important to performance but have largely been neglected in the measurement of intelligence. We also observe this incongruity between conventional notions of ability and real-world abilities in research on age-related changes in intellectual ability. |
This is an extract from pages 32-38 of Practical Intelligence in Everyday Life, by Robert J. Sternberg, George B. Forsythe, Jennifer Hedlund, Joseph A. Horvath, Richard K. Wagner, Wendy M. Williams, Scott A. Snook and Elena L. Grigorenko, published by Cambridge University Press. Copyright Cambridge University Press, 2000. |
|
| |
