Education As the Cultivation of Intelligence

Michael E. Martinez

• Publisher: Routledge
• Year: 2014
• ISBN:
  9781135668792
• Edition: 1
• Topic: Education
• Subtopic: Education General

Martinez defines intelligence from a cognitive perspective as a repertoire of those skills, strategies, and knowledge structures that are most instrumental in human effectiveness. He posits that in today's complex, fast-paced, technologically dense, and information-rich society, intelligence is the supreme human resource. The current social context not only demands intelligence, but rewards it economically, psychically, and in other ways. His central argument in this book is this: The intellectual abilities that are crucial to modern life, including economic viability and effectiveness in daily living, correspond to the cognitive functions that are reasonably called intelligence; these intellectual abilities are learnable; we now know enough about the structure and mechanisms of intelligent thought and behavior to teach them directly. Martinez explicates his argument and provides research-based evidence to support his claim.

Part I

Introduction

1

The Age of Intelligence

In the fast-paced, technology-dense world indwelled by much of the earth’s population, the supreme human resource is intelligence. That is because intelligence consists of the knowledge, skills, and strategies necessary to be effective in a world that is complex and information-rich, a world in which daily life consists largely in a concatenation of problems to be solved. Intelligence shapes the economic prospects of individuals, organizations, and nations. Intelligence also connects to the causes and potential solutions of deep-rooted social problems. My thesis is this: The intellectual abilities that are crucial to modern life, including economic viability and effectiveness in daily living, correspond to the cognitive functions that are reasonably called intelligence. Moreover, those intellectual abilities are learnable.

This differs radically from the conventional view that intelligence is invariant and determined by heredity. In this book, I present evidence that intelligence is not an innate, immutable, and strictly inherited capacity, but instead is a repertoire of learnable cognitive functions. And although genetics does have a role in shaping intelligence, the acquisition of competencies composing intelligence depends profoundly on experience. Tremendous economic and social implications follow. Most fundamentally, it becomes possible to reconceptualize the goals of education to embrace the cultivation of intelligence, and to redefine education as consisting in all those experiences, through the life span, that lead to its enhancement.

INTELLIGENCE AND THE ECONOMY

The rising importance of intelligence to work is demonstrated in the way that economists have redefined the job sectors composing the labor market. Long-recognized distinctions in the labor market—especially between “white-collar” managers and “blue-collar” laborers—are giving way to divisions that recognize the importance of intellect to contemporary work. One alternative typology of jobs was set forth by Reich (1992), who divided the labor market into three sectors: symbolic-analytic workers, in-person workers, and routine production workers (cf. Stewart, 1997). In modern economies, the most important and rewarding of these is the symbolic-analytic sector, which comprises workers who specialize in “problem solving, problem-identifying, and strategic-brokering” (Reich, 1992, p. 177). The symbolic analyst is a professional problem solver whose work consists of tasks that are novel and complex, that resist routinization, and that entail the gathering and transformation of information in symbolic form. These task characteristics—problem solving, complexity, and symbolic information—have strong associations with the qualities of thinking collectively called intelligence.

The division of the labor market proposed by Reich explicitly recognizes the importance of intellect to work. Likewise, a U.S. government report on workplace skills needed for the 21st century called for a “new set of competencies” that are predominantly cognitive. These “new” competencies include a foundation of literacy and personal skills, but also such thinking skills as problem solving, reasoning, and decision making—the “true raw materials” from which workplace competency is now built (Secretary’s Commission on Achieving Necessary Skills, 1991, pp. vi, 17). More technical analyses also support the view that contemporary jobs can be differentiated according to their cognitive demand. In a factor analysis of jobs in the petrochemical industry, Arvey (1986) found that a “judgment and reasoning” factor accounted for almost half of the common variance in job similarity ratings.¹ This factor, according to Arvey (1986) “corresponds quite well to a general [intelligence] g factor” (p. 418). In another study, Hunter and Hunter (1984) found that the key organizing dimension of diverse jobs was their complexity. These analyses are consistent with the intuitive categories proposed by Reich, and with the hypothesis that both people and jobs can be compared along a similar intelligence axis.

The importance of intelligence to contemporary work is also demonstrated by the power of intelligence, measured as IQ, to predict job performance. It is important to note that the terms IQ and intelligence, although often used interchangeably, are in fact not identical in meaning. The difference is that IQ is a rough approximation, a convenient stand-in, for the diverse collection of powerful cognitive competencies to which we give the name intelligence. Whereas IQ is a test-based metric expressed as a single numeric value, intelligence is the underlying construct that is so complex it may never be understood completely. As a test-based metric, IQ is never a perfect predictor of job performance, and in some cases it is only a mediocre predictor (Wigdor & Green, 1991). However, it is hard to find a better one. Of all personal traits studied, IQ predicts workplace performance best (Schmidt & Hunter, 1998). The predictive power of IQ is strongest when correlations are “corrected” statistically (or disattenuated) for the imprecision of the job performance measure and for restriction of ability range.² Over a wide span of occupations, the mean predictive validity (disattentuated) of IQ on job performance is about 0.51 (Schmidt & Hunter, 1998).

The predictive validity of IQ increases as jobs become more complex (Hunter, 1986; Hunter & Hunter, 1984). In one study, the range of disattenuated validity coefficients extended from 0.23 for low-complexity jobs to 0.58 for high-complexity jobs (Gottfredson, 1997; Schmidt & Hunter, 1998). Hunter and Hunter (1984) obtained similar results: Correlations between cognitive tests and job performance ranged from 0.27 to 0.61, with higher validity coefficients associated with more intellectually demanding jobs.³ These studies suggest that it is possible to order jobs, job families, and job holders along a cognitive dimension defined by information-processing complexity, and that this dimension can account for variation among jobs in their demand qualities, as well as variation among people in job success (Arvey, 1986).

INTELLIGENCE, EDUCATION, AND ECONOMIC REWARD

IQ tests were not originally designed to predict workplace performance. The first intelligence tests, devised about a century ago by the Frenchman Alfred Binet, were constructed for the purpose of predicting success in school. Commissioned by the Paris public school system, Binet invented IQ tests to separate failing students into two groups: those who had the ability to succeed in regular classes and those who were mentally challenged and needed special instruction. IQ proved to be effective in making that distinction, and continues to be a successful, although not perfect, predictor of academic success, correlating about 0.50 with academic achievement (Brody, 1992; Hunt, 1995; Neisser et al., 1996).

Why should IQ have any predictive power at all in forecasting success—career or academic? The answer is that it is not IQ (the number) that is important, although IQ-like tests clearly do function as socially important screening devices, but instead that IQ quantifies a more functionally relevant set of cognitive competencies that make success possible. When intelligence is viewed as a set of competencies, the implication is that intelligence also can be learned. That is, intelligence is not just an input to education, but also an output, or product, of educational experience (Snow, 1982a). When Binet invented the first IQ test, he saw intelligence as an important input to education in that students who were more intelligent (i.e., had higher IQs) were more likely to succeed. Since Binet’s time it has become clear that intelligence is equally a product of education or, more exactly, of all experiences that have educational value. Actually, Binet himself understood this quite well. This view of intelligence—as a product of experience—sharply contrasts with preconceptions of intelligence as invariable and as determined by each person’s DNA code.

One potent form of experience in engendering the cognitive competencies associated with intelligence is formal education. Reich (1992) regarded a university-level education as a crucial phase in the development of the symbolic analyst. The investment reaps rewards: College and postbaccalaureate education greatly increase the likelihood of high pay. Moreover, the wage premium that accompanies higher levels of education is growing. In 1979, the average annual pay for a “prime age” (30 to 59) male high school graduate was $37,800, and for the holder of a bachelor’s degree, $53,600 (in constant 1996 dollars; Carnevale & Rose, 1998). Just 16 years later, in 1995, the annual earnings of the high school graduate dropped 18% to $31,000, whereas for the college graduate annual earnings rose 10% to $58,700.⁴ In other words, during this 16-year period, the wage premium for a college education over a high school education more than doubled, rising from 42% to 89%. Table 1.1 illustrates the association of income with formal education. Representation in the wealthiest quintile rises sharply with education level. The negative slope associated with the poorest quintile tells the reciprocal story. Opportunities for work success drop off steeply with lower levels of education, so much so that by the 1990s “low skill level” could be reasonably interpreted to mean no college experience (Carnevale & Rose, 1998; Hunt, 1995).

In the future, prospects for economic reward are likely to depend even more on the type of work in which one engages. The reason for this, according to Reich (1992), is that the economy has evolved to the point where “the value of new designs and concepts continues to grow relative to the value placed on standard products,” and because of this “the demand for symbolic analysts will continue to surge” (p. 225). This trend places some workers at risk. Routine production workers will find themselves increasingly confined to low-wage jobs and vulnerable to replacement by machines. This is the case even now. Robots are displacing workers whose jobs can be reduced to a sequence of movements controlled by machine actuators. And, increasingly, robots can be programmed to produce short runs of customized products demanded by consumers. Thus, despite the economic transition from high-volume manufacturing to low-volume customization, robots threaten to displace workers whose primary skill is the routinized manipulation of materials (Hunt, 1995). Compounding this economic jeopardy, the globalization of commerce means that routine production workers must now compete with workers in developing countries—workers whose costs of living are much lower and who are quite happy to work for a fraction of the American minimum wage (Reich, 1992).

TABLE 1.1
Educational Attainment and Income Quintile Within the United States: 1995

	Income Quintiles
Education Level Attained	Lowest	Highest
9th to 12th grade (no diploma)	39.9	5.0
High school graduate	19.5	11.9
Some college, no degree	15.6	18.5
Associate degree	11.0	21.4
Bachelor’s degree	6.1	39.2
Master’s degree	4.2	51.9
Professional degree	3.6	66.3
Doctorate degree	2.2	61.2

Note. Adapted from Statistical Abstract of the United States (p. 470; Table 726), by U.S. Department of Commerce, 1997, Washington DC: US. Author. Public Domain.

Although global economic evolution portends a dire future for routine production workers, opportunities for symbolic-analytic workers will continue to expand. Where will this lead? According to Herrnstein and Murray (1994), the divergence of opportunity paths has already produced a two-tiered society, segmented into a “cognitive elite” and a cognitive underclass. The division arises from the compounding prosperity of an insular cognoscenti—Reich’s symbolic analysts—and the economic descent of the routine production laborer. Although there is disagreement among economists about the degree and seriousness of the cognitive-economic rift in American society, the fact of increasing social fractionation along economic lines is not disputed.

INTELLIGENCE AND SOCIAL PROBLEMS

In the foregoing paragraphs, I drew a conceptual triangle whose corners are intelligence, education, and economic reward. A fourth concept, already implied, is the vast arena of social problems. Social problems are of course multidimensional, but one especially salient dimension is the perennial inequality associated with race and ethnicity. Inequality is manifest in marked differences between racial/ethnic groups in academic opportunity and attainment, occupational achievement, health, and physical safety. These differences are yoked to income inequalities. Among the wealthiest fifth of Americans, by annual income, White families have two to three times the representation of African American and Hispanic families. At the other end of the economic spectrum the pattern is reversed: African American and Hispanic families have more than double the representation of White families among the poorest fifth (U.S. Department of Commerce, 1997). Although African American families, and to a lesser degree Hispanic families, have gained representation among the wealthy, the median income of White families has been rising even faster (U.S. Department of Commerce, 1997).

Race-based inequalities are manifest in measures of symbolic-analytic attainment (i.e., cognitive test scores), and in experiences that produce symbolic-analytic ability (i.e., formal education). And yet, within groups, the functional associations among cognitive ability, education, and economic reward are comparable: Those who have high...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Dedication
• Table of Contents
• Preface
• PART I: INTRODUCTION
• PART II: WHAT IS INTELLIGENCE?
• PART III: CAN INTELLIGENCE BE LEARNED?
• PART IV: LEARNABLE INTELLIGENCE AND SOCIETY
• References
• Author Index
• Subject Index