The Challenge of Defining Good Thinking

Any program that aspires to teach thinking needs to face the challenge of defining good thinking, not necessarily in any ultimate and comprehensive sense but at least in some practical, operational sense. With the foregoing examples of programs in mind, it will come as no surprise that many different approaches have been taken to answer this challenge.

To begin, it is useful to examine some general notions about the nature of good thinking. There are a number of very broad characterizations. Folk notions of intelligence, in contrast with technical notions, boil down to good thinking. A number of years ago, Sternberg et al. (1981) reported research synthesizing the characteristics people envision when they think of someone as intelligent. Intelligent individuals reason systematically, solve problems well, think in a logical way, deploy a good vocabulary, make use of a rich stock of information, remain focused on their goals, and display intelligence in practical as well as academic ways. Perkins (1 995) summed up a range of research on difficulties of thinking by noting the human tendency to think in ways that are hasty (impulsive, insufficient investment in deep processing and examining alternatives), narrow (failure to challenge assumptions, examine other points of view), fuzzy (careless, imprecise, full of conflations), and sprawling (general disorganization, failure to advance or conclude). Baron (1985) advanced a search-and-inference framework that emphasized effective search and inference around forming beliefs, making decisions, and choosing goals. Ennis (1986) offered a list of critical thinking abilities and dispositions, including traits such as seeking and offering reasons, seeking alternatives, and being open-minded. There are many others as well.

The overlap among such conceptions is apparent. They can be very useful for a broad overview and for the top level of program design, but they are not virtues of thinking that learners can straightforwardly learn or teachers teach. They do not constitute a good theory of action (e.g., Argyris, 1993; Argyris & Schon, 1996) that would guide and advise learners about how to improve their thinking, or guide and advise teachers and program designers about how to cultivate thinking. With this general challenge in mind, we turn to describing three approaches through which researchers and educators have constructed theories of action that characterize good thinking - by way of norms and heuristics, models of intelligence, and models of human development.

Norms and Heuristics

One common approach to defining good thinking is to characterize concepts, standards, and cognitive strategies that serve a particular kind of thinking well. These guide performance as norms and heuristics. When people know the norms and heuristics, they can strive to improve their practice accordingly. The result is a kind of "craft" conception: Good thinking is a matter of mastering knowledge, skills, and habits appropriate to the kind of thinking in question as guided by the norms and heuristics.

Norms provide criteria of adequacy for products of thinking such as arguments or grounded decisions. Examples of norms include suitable conditions for formal deduction or statistical adequacy, formal (e.g., affirming the consequent) or informal (e.g., ad hominem argument) fallacies to be avoided, or maximized payoffs in game theory (Ham-blin, 1970; Nisbett, 1993; Voss, Perkins, & Segal, 1 991). Heuristics guide the process of thinking, but without the guarantees of success that an algorithm provides. For instance, mathematical problem solvers often do well to examine specific cases before attempting a general proof or to solve a simpler related problem before tackling the principal problem (Polya, 1954, 1957).

The norms and heuristics approach figures widely in educational endeavors. Training in norms of argument goes back at least to the Greek rhetoriticians (Hamblin, 1970) and continues in numerous settings of formal education today with many available texts. Heuristic analyses have been devised and taught for many generic thinking practices - everyday decision making, problem solving, evaluating of claims, creative thinking, and so on.

Looking to programs mentioned earlier for examples, we note that the CoRT program teaches "operations" such as PMI (consider plus, minus, and interesting factors in a situation) and OPV (consider other points of view) (de Bono, 1973). The Odyssey program teaches strategies for decision-making, problem solving, and creative design, among others, foregrounding familiar strategies such as looking for options beyond the obvious, trial and error, and articulation of purposes (Adams, 1986). Polya (1954, 1957) offered a well-known analysis of strategies for mathematical problem solving, including examining special cases, addressing a simplified form of the problem first, and many others. This led to a number of efforts to teach mathematical problem solving, with unimpressive results, until Schoenfeld (1982; Schoenfeld & Herrmann, 1982) demonstrated a very effective intervention that included the instructor's working problems while commenting on strategies as they were deployed, plus emphasis on the students' self-management of the problem-solving process. Many simple reading strategies have been shown to improve student retention and understanding when systematically applied, including, for example, the previously mentioned "reciprocal teaching" framework in which young readers interact conversationally in small groups around a text to question, clarify, summarize, and predict (Brown & Palincsar, 1982).

Nisbett (1993) reported a series of studies conducted by himself and colleagues about the effectiveness of teaching norms and heuristics of statistical, if-then, cost-benefit, and other sorts of reasoning, mainly to college students. Nisbett concluded that instruction in rules of reasoning was considerably more effective than critics of general, context-free rules for reasoning had claimed. To be sure, student performance displayed a range of lapses and could have been better. Nonetheless, students often applied the patterns of reasoning that they were studying quite widely, well beyond the content foregrounded in the instruction. Relatively abstract and concise formulations of principle alone led to some practical use of rules for reasoning, and this improved when instruction included rich exploration of examples. Nisbett emphasized that we could certainly teach rules for reasoning much better than we do. Nonetheless, the basic enterprise appeared to be sound.

To summarize, the characteristic pedagogy of the approach through norms and heuristics follows from its emphasis on thinkers' theories of action. Programs of this sort typically introduce norms and heuristics directly, demonstrate their application, and engage learners in practice with a range of problems, often with an emphasis on metacognitive awareness, self-management, and reflection on the strategies, general character, and challenges of thinking.

Readily grasped concepts and standards, strategies with three or four steps, and the like characterize the majority of norms and heuristics approaches. One objection to such simplicity is that it can seem simpleminded. "Everyone knows" that people should consider both sides of the case in reasoning or look for options beyond the obvious. However, as emphasized in the introduction to this article, such lapses are commonplace. Everyone does not know, and those who do know often fail to do so. The point of norms and heuristics most often is not to reveal novel or startling secrets of a particular kind of thinking but to articulate some basics and help bridge from inert knowledge to active practice.

Models of Intelligence

The norms and heuristics approach to defining and cultivating good thinking may be the most common, but another avenue looks directly to models of intelligence (see Sternberg, Chap. 31). Not so often encountered in the teaching of thinking is good thinking defined through classic intelligence quotient (IQ) theory. On the one hand, many, although by no means all, scholars consider general intelligence in the sense of Spearman's g factor to be unmodifiable by direct instructional interventions (Brody, 1992; Jensen, 1980, 1998). On the other hand, a single factor does not afford much of a theory of action, because it does not break down the learning problem into components that can be addressed systematically.

Models of intelligence with components offer more toward a theory of action. J. P. Guilford's 1967 (Guilford & Hoepfner, 1971) Structure of Intellect (SOI) model, for example, proposes that intelligence involves no fewer than 150 different components generated by a three-dimensional analysis involving several cognitive operations (cognition, memory, evaluation, convergent production, divergent production) crossed with several kinds of content (behavioral, visual figural, and more) and cognitive products (units, classes, relations, and more). An intervention developed by Meeker (1 969) aims to enhance the functioning of a key subset of these components. Feuerstein (1980) argues that intelligence is modifiable through mediated learning (with a mediator scaffolding learners on the right kinds of tasks). His Instrumental Enrichment program offers a broad range of mediated activities organized around three broad categories of cognitive process - information input, elaboration, and output - to work against problems such as blurred and sweeping perception, impulsiveness, unsystematic hypothesis testing, and egocentric communication.

Sternberg (1985) developed the triarchic theory of intelligence over a number of years, featuring three dimensions of intelligence -analytic (as in typical IQ tests), practical (expert "streetwise" behavior in particular domains), and creative (invention, innovation). Sternberg, et al. (1996) report an intervention based on Sternberg's (1985) tri-archic theory of intelligence: High school students taking an intensive summer college course were grouped by their strengths according to Sternberg's three dimensions and taught the same content in ways building on their strengths. The study included other groups not matched with their strengths. Matched students exhibited superior performance.

The typical pedagogy of interventions based on models of intelligence emphasizes not teaching norms and heuristics but rather providing abundant experience with the thinking processes in question in motivated contexts with strong emphasis on attention and self-regulation. Often, although by no means always - the Sternberg intervention is an exception here, for example -the tasks have a rather abstract character on the theory that the learning activities are enhancing the functioning of fundamental cognitive operations and content is best selected for minimal dependence on background knowledge. That said, it is important to recognize that no matter what the underlying theory - norms and heuristics, intelligence-based, or developmental, as in the following section - interventions often pragmatically combine a variety of methods rather than proceeding in a purist manner.

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