Baron, J. (1993). Why teach thinking? - An essay. (Target
article with commentary.) Applied Psychology: An
International Review, 42, 191-237.*
Why teach thinking? - An essay
Recent efforts to teach thinking could be unproductive
without a theory of what needs to be taught and why. Analysis of
where thinking goes wrong suggests that emphasis is needed on
`actively open-minded thinking,' including the effort to search
for reasons why an initial conclusion might be wrong, and on
reflection about rules of inference, such as heuristics used for
making decisions and judgments. Such instruction has two
functions. First, it helps students think on their own. Second,
it helps them to understand the nature of expert knowledge, and,
more generally, the nature of academic disciplines. The second
function, largely neglected in discussions of thinking
instruction, can serve as the basis for thinking instruction in
the disciplines. Students should learn how knowledge is obtained
through actively open-minded thinking. Such learning will also
teach students to recognize false claims to systematic knowledge.
The last decade has seen a rebirth of the idea that schools
should teach students how to think. Several newsletters and
journals have begun, with such titles as Thinking and problem
solving (Erlbaum) and Thinking (Institute for Philosophy
and Children, Montclair State University). In addition, main
line educational publications such as Educational Leadership
have had a large number of articles about thinking. A major
review of recent programs was published (Nickerson, Perkins, and
Smith, 1986) and updated (Nickerson, 1989). Regular and special
conferences are devoted to the subjects, and at least one of
these, the International Conference on Thinking, has led to
several published volumes (e.g., Perkins, Lochhead, & Bishop,
In the U.S. and Canada, the idea of critical thinking,
creative thinking, or reflective thinking has been incorporated
into many statements of goals by state and provincial education
authorities, and some effort has been made to implement these
ideas on a large scale (Brown, 1991). In Venezuela, L. A.
Machado (see Machado, 1980) convinced the Campins administration
to institute several experiments on the teaching of thinking
between 1980 and 1984, some of which led to positive results
(Herrnstein, Nickerson, de Sánchez, & Swets, 1986).
But this is, of course, a rebirth, not a new idea. Ancient
Greek students learned mathematics and philosophy with the idea
that it would benefit them in whatever they did, even if they did
not become mathematicians or philosophers. Scholastic education
emphasized logic, and in 1662, the Port Royal Logic was
published (Arnauld, 1964), a general guide to good thinking that
was reprinted several times in several languages. In the 19th
century, the idea of formal discipline dominated educational
theory. The work of Thorndike and Woodworth (1901) was done in
opposition to this tradition, but, a little later, John Dewey
(e.g., 1933) resurrected the idea as a justification for
thoughtful education in general rather than as a justification
for teaching Latin and geometry to everyone. After World War II,
the idea of `critical thinking' was emphasized as a way of
teaching resistance to propaganda (Presseissen, 1986). Writers
such as DeBono (1976) have been busy teaching thinking to anyone
who would learn it, often corporations and institutions other
Of course, I have been speaking of current educational
publications and conferences, not practice. Even after Dewey was
no longer in fashion, many teachers and schools continued to
implement his ideas, and many teachers have discovered successful
ways to teaching thinking without the benefit of anyone else's
My concern here is with the psychological rationale for such
education. But pure psychology will not suffice here. We must
also consider the broader educational and social context. I
shall provide what I think is a somewhat novel rationale for
thoughtful education. This rationale leads to many of the same
recommendations as the most widely accepted rationales, but an
understanding of the purposes of thoughtful education can lead to
If my differences with current opinion were to be summarized
briefly, they would amount to an amendment to the current
rationale in terms of the relation between thinking and
democratic citizenship. Dewey felt that thoughtful education was
necessary in a democracy because citizens needed to be able to
think about the things that affected them and others. I agree
with this. But things have changed a bit since Dewey wrote, and
we can expect further changes in the same direction. The work of
the world has become more complex, more difficult for the average
person to understand. Citizens in a democracy must rely more and
more on experts. This leads us to think about the nature of
expertise itself, and the relationship between expertise and
critical thinking. I shall suggest that good thinking forms the
foundation of legitimate expertise, so that an understanding of
thinking is necessary for citizens who must increasingly be
guided by experts.
I shall begin with a summary of what I take to be the
current rationale for the teaching of thinking. I shall then
discuss the need for an understanding of expertise, provide some
examples of the kind of instruction that is implied by my view,
and close with a summary of the implications of my view for the
nature of education in the disciplines.
The Current Rationale
In previous writing, I have attempted to summarize the
rationale for the teaching of thinking (Baron, 1985a, 1988,
1990a,b), and I shall summarize that rationale here. It is in
the spirit of Dewey, and it is intended to be compatible with
other recent writing on the subject (Nickerson, 1989; Paul, 1984;
Perkins, in press; Schrag, 1988), although my terms are somewhat
different. Again, my current purpose is not to overturn this
rationale but to go beyond it, so I still accept what I am about
What is Thinking?
Thinking is a mental activity that is used to resolve doubt
about what to do, what to believe, or what to desire or seek.
Thinking about what to do is decision making. Thinking about
what to believe is part of learning. (Some learning, perhaps
most, does not involve thinking but is, rather, an automatic
consequence of certain experiences.) Other kinds of thinking
about what to believe are scientific thinking, hypothesis
testing, and making inferences about correlations or
contingencies. Thinking about what to desire is not studied
much, but it is analogous to thinking about beliefs: if decisions
are based on beliefs and desires, then we can think separately
about each of the elements.
Thinking is only one type of action, and only one kind of
determinant of overt behavior, among many others. Thus, the
theory of thinking is only a part of the theory of action, and
action errors are only sometimes cause by thinking errors
(Reason, 1990; Zapf et al., 1992). Thinking is worthy of special
attention, however, because, I shall suggest, certain principles
of good thinking are common across domains. This property
creates opportunities for the teaching of thinking.
Decision making is the final common path of thinking, in
this pragmatic view, but thinking about what to desire is part of
thinking about what to do. Creative tasks such as art and
science involve decision making, but they also involve
considerable thinking about what to desire or seek. Scientists
who do basic research must think not only about how to test their
hypotheses but also about what questions are worth hypothesizing
about. Creative artists spend much of their time thinking about
what they want to achieve in a given work or series of works
Thinking consists of search and inference. We
search for possibilities, evidence, and goals.
Possibilities are potential answers to the doubt that inspired
the thinking: they are potential courses of action, potential
beliefs, or potential desires. Evidence is whatever bears on the
strength of the possibility. Goals are the standards by which we
evaluate possibilities in the light of the evidence. This
process of evaluation is inference. Figure 1 shows this
structure: the evidence affects the strengths of the
possibilities, in a way that is determined by the goals.
- Figure 1 deleted. -
For example, in the case of a decision, the possibilities
are alternative options or courses of action, the goals are the
desires or personal goals that are relevant. The evidence
consists of facts about possible consequences and their
probability of occurrence and about the extent to which each
possibility will achieve each relevant goal. In buying a car,
the possibilities are the cars one might buy, some goals are
price, reliability, appearance, and cost of operation, and the
evidence comes from magazines, test drives, dealers, and friends.
Good thinking is likely to achieve the goals of the thinker,
but more than this can be said. Certain ways of thinking are
generally better at achieving the thinker's goals than other
ways. In particular, several normative models of inference have
been developed (Baron, 1988). These are standards for evaluating
inferences. Logic is such a standard, but traditional logic is
limited to inferences about beliefs held with certainty from
other beliefs held with certainty. Probability theory allows us
to deal with the more typical case of uncertainty. The various
forms of utility theory allow us to evaluate decisions. As yet,
no generally accepted normative model can be applied to the
choice of goals or desires, but Baron (in press a) makes some
suggestions about this.
We can regard search itself as an action, and we can apply
the normative model of utility theory to search itself. The main
conclusion to be drawn from this application (Baron, 1985a) is
that search has costs as well as benefits. Too much search, past
the point at which expected costs exceed expected benefits,
impairs the achievement of goals. We can think too much.
Of course, the determinants of successful thinking lie in
the domain-specific details. And good thinking cannot help much
when specific knowledge is lacking, although it can help both in
the acquisition of that knowledge and in its effective
application once it is acquired (Baron, 1985a). Here, however, I
am concerned with general properties of thinking that cut across
subject matters, although I shall also discuss briefly how even
these general properties must be adapted to specific subjects.
When we compare thinking to normative models, we find
several systematic departures in both search and inference. The
most general and pervasive departure is a bias toward
possibilities that are initially strong. Following Perkins, I
call this `myside bias,' although I do not mean to imply that it
always favors the thinker's side in a dispute. In the extreme,
depressives are subject to this bias when they interpret evidence
as favoring the hypothesis that bad outcomes are their fault.
Myside bias is not always present, but it accounts for many
common failures of thinking, so it is worth trying to prevent. I
shall give several examples.
One example is the selective exposure effect (Frey, 1986).
People tend to select information that would support their
present views if that evidence were selected randomly. For
example, political liberals tend to read articles written by
liberals, and vice versa. Then, having selected the evidence by
its content, they ignore this fact when they revise their
beliefs, so they change their beliefs as if the evidence were
selected randomly. They do not discount the evidence for the
fact that it was selected in a biased way. (If they did, they
would probably not spend so much time searching for it, for its
expected benefits would be small.) The same sort of selective
search is involved when the evidence comes from our own memory.
We tend to think of evidence that supports possibilities that are
already strong or beliefs that we desire to be true. Again,
wishful thinking (Kunda, 1990) requires forgetting the basis of
our selection of evidence when we respond to the evidence.
Several experiments indicate that this sort of bias
underlies both motivated and unmotivated errors. In one commonly
used experimental paradigm, subjects are instructed to make some
judgment under various conditions: without special instructions,
with instructions to think of reasons why their initial judgment
might be correct, with instructions to think of why it might be
incorrect, or with instructions to think or reasons on both
sides. (Not all experiments use all four conditions.) The
typical finding is that the latter two conditions improve
performance over the first two. Koriat, Fischhoff, &
Lichtenstein (1980) found that thinking of reasons on the `other
side' reduced inappropriate extreme confidence in the answers to
objective questions. Hoch (1985) found that it reduced
optimistic biases in the estimation of likely job offers by
business students. Arkes et al. (1988, also Slovic & Fischhoff,
1977, in a similar paradigm) found that it reduced the hindsight
effect, the tendency to say that one would have predicted an
outcome, once one has learned the outcome. And Anderson (1982)
found that it reduced the tendency to ignore total discrediting
of the evidence on which one has based an earlier judgment.
These results suggest that one source of many of these
errors is a bias toward initial or desired conclusions. Telling
people to think of evidence for these conclusions has no effect,
because that is what people do anyway. Telling them to think of
counterevidence helps, whether or not we tell them to think of
positive evidence too.
Perkins (in press) has provided additional evidence for the
existence of such myside bias. When subjects were asked to list
arguments relevant to some question of policy, such as whether
their state should enact a law requiring return of glass bottles
for recycling, they listed far more arguments on their own side
than on the other side of the issue. When they were pushed to
list extra arguments, they listed far more such arguments on
the other side. So their initial bias was the result of
lack of effort, not lack of knowledge. A course that taught
people to be fair to both sides and thorough in their thinking
substantially reduced this bias, although other kinds of courses
(e.g., a course in debating) did not.
Myside bias is related to other psychological concepts.
Janis describes similar errors in group decisions about policies
in both government (1982) and business (1989), and Jervis (1976)
provides other examples. A measure of `integrative complexity'
(Suedfeld & Tetlock, 1977; Tetlock, 1983, 1984, 1985) is also
related. This measure can be applied to all sorts of texts, such
as political speeches. It measures `differentiation' and
`integration,' and it is conceived as a series of stages, with
the middle stages characterized by more differentiation compared
to the lower stages and the higher stages characterized by more
integration compared to the middle stages. The absence of
differentiation is essentially the same as myside bias. It is
the failure to acknowledge the other side. Of interest is the
fact that most of the results obtained with this measure (e.g.,
Tetlock, 1983, 1984, 1985) are found largely in the
differentiation measure; the integration measure plays little
role, because the higher stages are rarely found.
Myside bias seems to occur in inference as well as search.
For example, Lord, Ross, and Lepper (1979) found that subjects
given arguments on both sides of a controversial question
(whether the death penalty should be used) responded more to
evidence on their own side. At the end of being presented with
mixed evidence, then, subjects who initially disagreed became
polarized, so that they disagreed even more. An even more
dramatic result has been found by Batson (1975): a subgroup of
high-school girls who believed in the divinity of Christ became
more convinced of this belief when they were given evidence
(in the form of an article about new scrolls found near the Dead
Sea) that the virgin birth and the resurrection were hoaxes.
Myside bias is not the only general failing that leads to
errors. Baron (1990b) has reviewed the evidence for another
source, the overgeneralization (i.e., misuse) of rules of
inference. Inferences are typically made by the use of heuristic
rules. (Most warrants, in the sense of Toulmin , are
heuristics in that they require qualifiers.) For example, we
decide on punishment on the basis of the magnitude of the harm
done, we stick to the status-quo (`a bird in the hand is worth
two in the bush'), and we judge harmful acts to be worse than
equally harmful omissions. These heuristic rules are discovered
by individuals and passed on from one person to another because
they work in certain situations, perhaps in most situations.
Their working can be understood when their results are best at
achieving the thinker's goals. For example, it usually best to
punish harmful acts more than we punish harmful omissions,
because harmful acts are more usually intentional and their
outcomes are easier to foresee, so they are more easily deterred.
But people use these heuristics without fully understanding their
relationship to their purposes. As a result, people often apply
them in cases in which they do not achieve their usual purposes
as well as some alternative might do so. Thus, people sometimes
judge acts to be worse than omission even when intention and
foreseeability are held constant (Spranca, Minsk, & Baron, 1991).
Subjects were easily persuaded that this was an error when
they were asked to reflect on the purpose of the information and
when they realized that they would make the same choice no matter
how the test turned out. In other examples that I have
attributed to overgeneralization of heuristic rules, such as the
bias toward omissions or the tendency to seek retribution even in
the absence of deterrence, subjects are not so easily persuaded
that the rule in question is being misused (Baron, 1992, in press
b; Baron & Ritov, 1992). Some heuristics have acquired a certain
commitment, through processes not yet fully understood. (In this
regard, they may differ from merely mindless habits in the sense
of Langer, 1989). Still, at some point in the course of learning
these heuristics, people might benefit from asking themselves
about purposes, that is (in terms of the theory of thinking
outlined above), they might benefit from searching for goals.
The term `overgeneralization' is somewhat misleading.
Overgeneralization of one rule goes hand in hand with
undergeneralization of whatever rule should be used in its
place. However, the term is still useful because it brings to
mind other examples of inappropriate transfer of a rule, e.g.,
Luchins (1942). Overgeneralization errors were taken by
Wertheimer (1959) to be a sign of misunderstanding, of `blind'
learning or transfer. We may account for such misunderstanding
in terms of ignorance of (or failure to recall) the arguments
about why a rule of inference serves its purposes (Perkins,
Wertheimer showed that such overgeneralization can apply to
rules of inference learned in school as well as `naive' rules.
(For a beautiful example of overgeneralization of the law of
large numbers by sophisticated students of statistics, see
Ganzach & Krantz [1991, pp. 189-190].) For example, he found
that students who had learned the formula for the area of a
parallelogram (base times height) would apply the same formula to
other figures that could not be made into rectangles in the same
way that a parallelogram can be (by cutting off a triangle on one
side and moving it to the other). The area rule worked for the
parallelogram because it could be shown in this way to serve the
purpose or goal of finding area. The derivation of the rule
involves the subgoals of making the parallelogram into a
rectangle and conserving area. When the rule was applied
elsewhere, it did not serve the purpose of finding area, and the
subgoal of making the figure into a rectangle could not be
achieved. Analogously, when punishment is given for causing harm
even in the absence of the deterrence, the purpose that could
justify the punishment is absent. Thus, failing to ask whether a
rule serves its purpose can lead to mistaken application of the
In sum, two general errors in thinking are myside bias and
what we might call purposelessness, in particular, the use of
inferential heuristics without understanding their purposes in
terms of goal achievement. The first error may be countered by
instruction in what I have called actively open-minded
thinking (which seems to be identical to what Nickerson, 1989,
calls `actively fair-minded' thinking). The second error might
be corrected by reflection on thinking itself, by the study of
heuristics and their purposes in an actively open-minded way
(Baron, 1990a,b). If schools can improve thinking in these ways,
then people will think better and achieve their individual and
collective goals better in their work, in their personal lives,
and in their public lives as citizens.
What I have offered here is a partial justification of the
teaching of thinking in terms of psychological studies that
compare thinking to normative standards. In essence, we cannot
teach thinking well without knowing what is wrong with it, what
needs to be corrected through education. We teach Latin or
calculus because students do not already know how to speak Latin
or find integrals. But, by any reasonable description of
thinking, students already know how to think, and the problem is
that they do not do it as effectively as they might.
Beliefs about Thinking
How should thinking be taught if these are the problems?
Clearly, this is a topic for research, for we are capable of
measuring the types of errors made and evaluating their response
to instruction (as done by, for example, Nisbett, Fong, Lehman, &
Cheng, 1987; Larrick, Morgan, & Nisbett, 1990; as well as by many
others in work reviewed by Nickerson et al., 1985, and by
Let me make one more suggestion here. I have argued (Baron,
1989, 1991) that one of the determinants of how people think is
how they believe that they ought to think. For example, I have
found that people who are prone to myside bias in thinking about
abortion, for example, tend to evaluate one-sided thinking as
better than two-sided thinking, even when the one-sided
thinking favors a position opposite from their own (Baron, 1989).
Similarly, Schommer (1990) found that those who believed that
reflection was unnecessary were relatively poor at comprehension
of difficult passages. These sorts of result suggests that what
is most needed is to present people with convincing arguments
about why certain kinds of thinking are more likely to avoid
errors and to achieve goals more than other kinds of thinking.
These arguments should result in an understanding of the theory
of thinking that underlies the instruction (such as the theory of
actively open-minded thinking that I have outlined here).
Students can even be evaluated for such an understanding,
regardless of whether they accept the theory.
It might be said that people have the goal of thinking in a
certain way. But I would suggest that this is in fact a subgoal,
if it is a goal at all. People desire to think in one way or
another because they think it is good for some other purpose.
They should be encouraged to reflect upon their goals and the
real goals behind them. If people still have the goal of using
certain heuristics or not considering the other side or
supporting their initial beliefs even after such reflection, then
educators have done what they can.
Although the view I have presented may represent a consensus
among some writers, the battle to improve thinking through
education has not been won. In practice, many teachers who try
to improve thinking have little understanding of the theory I
have just sketched or of any competing theory. Instead, what has
developed, in the U.S. at least, is a kind of subculture of
thinking, maintained through in-service workshops and conference
presentations by self-appointed experts on thinking who have, by
and large, little contact with the scholarly literature on
thinking in either psychology, philosophy, or educational theory
itself (e.g., Dewey, 1933; Schrag, 1988). There is much of value
in what these people have discovered on their own and gleaned
from third-hand accounts of scholarship. But I fear that much of
it is off the point and will not succeed.
Examples of what this culture has to offer are found in
Brown (1991). These new advocates of thinking tend to hold the
following views: Students will learn to think if they are
challenged to think. Opportunities for thinking are everywhere.
Discussions can promote thinking. In class discussions, students
should respond to each other rather than just to the teacher.
School should build on what children bring to it, on their
interests, as exemplified in the whole-language approaches to
literacy instruction. All subjects must be taught thoughtfully;
thinking cannot be taught as a separate subject. Evaluation too
must emphasize thinking, whether this is done through projects or
through essay examinations that require it. Above all, learning
must be `active.'
What is missing from this new conventional wisdom is a
common understanding of what the problem is, why it needs to be
corrected, and how these various prescriptions will do the job.
The impetus for teaching thinking in the U.S. comes from many
desires: beating the Japanese in commerce; having demagogue-proof
citizens; having more creativity in the popular arts; etc. But
advocates of thinking instruction have no standard account of how
these prescriptions will meet even these concrete goals.
Take the value of discussion, for example. Students should,
of course, know how to conduct themselves in a group discussion
in which different points of view are expressed. Perhaps some
students will learn that others have different points of
view, or that it is possible to disagree politely. But is this
the big problem with the students that schools are now turning
out? When they participate in political discussions or solve
problems together in the workplace, are they inhibited or
Perhaps, but the evidence for this is lacking, and in any
case these reasons are not the driving force behind the support
of discussion in the classroom. What is? In what ways are
students deficient when they leave school, such that more
discussion will remove the deficiency? How is discussion
supposed to accomplish this end? In saying this, I do not assume
that the deficiency can be measured with a multiple-choice test,
but I do think that educational methods must be justified by
arguments about their ultimate consequences for what students do
and how they think in the future, even if we cannot easily
measure these consequences.
Arguably, discussion in a class of several students (as
opposed to a tutorial, or an academic colloquium) is filled with
talk by people who don't have much to say that others can learn
from. Many good students find discussions boring and
time-wasting for this reason. Many of the discussions that Brown
(1991) records have these qualities. They seem pointless. It
can be argued that the students have not yet learned how to carry
on a good discussion, but good discussions are rare, even in
graduate school. Can we rely on them as a major tool of reform?
Similarly, student projects surely create interest in
schoolwork, but they can degenerate into (relatively)
time-wasting activities such as coloring in the cover in detail
or looking through magazines for pictures to cut out. Projects
are often used because they seem more `real.' The trouble is
that most of them are not real. They have as little resemblance
to the worlds of work, personal relationships, or politics as do
most of the other activities that children do in school. And
even such resemblance would not insure real relevance, which
often comes only at the expense of abstraction. Nor do projects
necessarily involve much thought. The support for projects may
come from a desire to give every child a chance to do something
successfully. But, if such success is important, it can be
arranged in more intellectual areas.
Much the same could be said for the whole-language approach.
When this approach is used as an excuse to neglect phonics
instruction, it could even work to the long-run detriment of
children's reading (see Adams, 1990). It may feel good to the
teachers, who see children having fun and doing things that are
officially declared `authentic' because they are close to home.
But it may also miss opportunities to expand children's
worlds, opportunities that will come most easily after children
have mastered the skill of decoding print. And does the
whole-language approach involve more thinking than figuring out
how to pronounce and understand a new word in context?
Most seriously, why should we assume that simply doing more
thinking in school helps children think better out of school? Is
it the case that children simply do not think, so that they do
not learn how to think unless they think in school? Does
thinking improve with practice? The theory I have sketched
suggests that students get lots of practice thinking all the time
and that, therefore, additional practice alone will not help
unless it is coupled with explicit discussion of the kind of
thinking that should be done, and why. Without such redirection,
students will simply practice their mistakes.
Another type of abuse is, fortunately, becoming less common.
This is the idea that thinking is exactly a skill that can
improve with practice at its subskills. Students who are the
victims of this approach fill out worksheets in which they
supposedly practice the basic elements of thinking such as
finding similarities and differences or deciding whether an
object belongs in a category. The source of the analysis of
thinking that generates these exercises is difficult to find. It
comes most directly from educational writers such as Bloom
(1956), but the ultimate source is probably Aristotle as worked
over by the scholastic philosophers. Those who apply this
approach seem unaware that psychology and philosophy have had
some other ideas in the last 500 years. And even if the analysis
were correct, the evidence is firmly against the idea that
anything general can be learned from practice at component
subskills (Baron, 1985b).
The Growth of Knowledge
In the remainder of this essay I shall try to provide a
different kind of rationale for the teaching of thinking than I
(or to my knowledge others) have provided so far. I hope that
this will provide the kind of clarity of connection between means
and ends that is lacking in the conventional wisdom and to some
extent in my own earlier proposals.
In the Middle Ages, one individual might have been able to
learn everything of the world of scholarship that was worth
learning. This is not to say that anyone knew what that was.
Then, as now, the scholarly world was filled with false starts,
fads, and nonsense that is difficult to recognize without benefit
of hindsight. But if someone could sift what was valuable from
the rest, a student might have learned it all. Indeed, the ideal
of the `Renaissance man' was someone who did just that.
Today, the world of scholarship has grown like the human
population. No individual can hope to master more than a small
fraction of the useful scholarship that has accumulated over the
years and across fields. In the future, this trend will
Most of education is about scholarship, that is, about
the work of writers who have tried to add to knowledge
self-consciously, however digested and packaged their work might
be, and however long ago they wrote. The mathematics curriculum,
for example, is an accumulation of at least three thousand years
of mathematical insights (Hogben, 1951). History and social
studies represent the work of historians and social scientists.
Even the language curriculum, beyond the first few years of
teaching basic skills of native and foreign languages, is based
heavily on scholars who wrote about literature itself.
This much is recognized by the widespread consensus that
students must learn how to find things out, how to use a library,
how to use computer databases, and so on. The assumption here is
that students will be able to get the information that they need
to make decisions on their own. One problem, though, is that
using the library takes time. In some cases, a graduate degree
in economics or chemistry is required to understand some public
issue. A person who `knows how to use the library' could in
principle acquire the knowledge-equivalent of such a degree, but
that is beside the point. Ultimately, most people have to rely
on experts. As knowledge increases, people will have to rely on
experts more and more.
The conventional approach to thinking instruction tries to
deal with this problem by teaching people to evaluate judgments
critically. Often this amounts to a kind of cynicism that looks
only at whether the expert in question stands to gain from
whatever she is saying. This is relevant, of course, but so is
much else. What students - and the adults they become - often
miss is a positive understanding of the basis of expert opinion.
As a result, even experts of different kinds have trouble
understanding each other (Roberts, 1992).
The Basis of Expertise
The modern cognitive psychology of expertise does not help
much here. The literature on expertise is full of comparisons of
experts and novices, and much has been learned. Experts have
richer representations of problem domains (Voss, Tyler, & Yengo,
1983), they carry out certain operations more automatically and
more quickly (Bryan & Harter, 1899). They are able to classify
textbook problems according to the type of solution required
(Chi, Feltovich, & Glaser, 1981). When solving problems, they
work forward instead of backward (Sweller, Mawer, & Ward, 1983).
Another type of research attempts to distinguish successful
and less successful experts in terms of cognitive processes
(e.g., Ceci & Liker, 1986; Charness, 1981) or personality traits
(e.g., Klemp & McClelland, 1986). In some cases, the differences
found are most easily ascribed either to specific knowledge or to
biologically determined capacities (such as mental speed - see
Baron, 1985a, ch. 5). In other cases, the results provide
evidence for the role of actively open-minded thinking. Klemp
and McClelland, for example, derived empirically a taxonomy for
distinguishing successful and less successful managers. A number
of the traits they found seem to represent thorough and
open-minded search for possibilities, evidence and goals, for
example: `makes strategies, plans steps to reach a goal'; `seeks
information from multiple sources to clarify a situation'; `sees
implications, consequences, alternatives, or if-then
relationships'; and `identifies the most important issues in a
complex situation' (Klemp & McClelland, 1986, Table 3, p. 41).
Although these results are useful, they do not tell us what
makes knowledge qualify as expert knowledge, or, in other words,
what gives experts their legitimate authority. They do not
distinguish true expertise and false expertise. Undoubtedly a
study of expert and novice astrologers would yield the same sorts
of results as studies of expert and novice physicists.
To understand the justification of expertise in the relevant
way, we must turn to philosophy. Karl Popper (e.g. 1962) has had
the most to say about the difference between true and false
theoretical understanding, although he is concerned mostly with
science. He holds that true scientific theories are falsifiable.
The most useful theories make the boldest predictions, those that
are most likely to be falsified but which are then not falsified.
More generally, science works because it is, as an institution,
self-critical. Scientific theories that experts learn acquire
their legitimacy from the fact that they have withstood attempts
to prove them wrong.
Popper himself has been criticized in many ways. Lakatos
(1978), for example, argued that scientific theories are
essentially never falsifiable in the way that Popper suggests,
since they can always be modified to deal with discrepant results
by changing some nonessential assumption and thereby protecting
their core. This argument in itself, however, does not dispute
the central insight that science is successful because it is
self-critical. (Lakatos himself does not emphasize this aspect,
though.) Other views of the nature of scientific inquiry can
explain how science advances even in the absence of decisive
falsification, e.g., by acquiring probabilistic knowledge
(Horwich, 1982; Baron, 1985a, ch. 4).
Another problem with Popper is that his arguments are
limited to science. He does not explain how his own work is an
advance over previous accounts or how someone else might improve
on his account. Putnam (1981) argues that other disciplines work
in much the same way as science, although the particular form of
self-criticism might be different. Thus, philosophy can and does
Notice that Putnam's claim is essentially that disciplines
advance through what I have called actively open-minded thinking.
Just as in thinking done by an individual, the way to avoid error
within a discipline is to consider criticisms of current views
and to allow those views to change in response to criticism.
Reflection on methods of inferences is helpful as well, just as
it is for individuals in considering their own heuristic methods.
So an understanding of good thinking, imparted through the
schools, has a second function. In addition to teaching students
how to think themselves, about their own concerns, it teaches
them to understand the nature of the expert knowledge on which
they must increasingly rely.
Some disciplines, such as astrology, do not advance through
good thinking and reflection on their methods. Such fields may
change over time, but the changes do not result from
self-criticism and reflection. The problem with such disciplines
is not in the abstractness of their theories or in the complexity
of their applications but, rather, in the role of self-criticism
in their development (Horton, 1967; Popper, 1962).
Self-criticism, of course, requires rules of inference by which
criticisms can be weighed.
Although all legitimate disciplinary knowledge must be
subject to a process of actively open-minded criticism, the
understanding of methods of thinking and inference must be
somewhat specific to the disciplines. Students need to learn
what counts as evidence for a mathematician, a historian, an
environmental scientist, a medical researcher, etc., and they
must learn some of the methods of inference specific to each of
these fields. For example, in applied fields such as medical
research and economics, and in retrospective fields such as
history and archaeology, the level of certainty required for
conclusions to be taken as warranted is lower than in
experimental physics or cognitive psychology. In computer
science and education, arguments often concern practicability
rather than truth. In psychology and philosophy, but not in most
branches of chemistry, much of the argumentation concerns the
proper statement of questions, e.g., `How can we state Skinner's
law of effect so that it is testable and not tautological?' In
linguistics and philosophy, but not in physics, agreement with
intuition is sometimes an important argument. Some fields rely
heavily on statistical inference, and others do not.
In each field, the structure of thinking involves goals
(questions), possibilities (hypotheses, conjectures), evidence
(arguments), and inference from the evidence about the
possibilities in the light of the goals (Baron, 1988). But the
fields differ in their goals, the types of possibilities that are
considered, the kinds of evidence that are brought to bear, and
the forms of inference that are used. Thus, what counts as a
good argument may vary from field to field, in part because of
different goals. So the standards of good thinking are the same
in that a search for alternative possibilities and
counterevidence is always required, but the standards by which
inferences are made from what is found - and hence what is
searched for - may vary considerably. We may think of actively
open-minded thinking as a general schema that requires filling in
for a given case.
Although students cannot acquire all the knowledge of all
disciplines, they can be expected to understand the rules of
inference of the major disciplines. In this way they will know
where to look for what they need to know, and they will be able
to understand the strengths of expert knowledge as well as the
weaknesses of fallible human experts.
Some Instructional Illustrations
Let me illustrate the sort of education this argument implies
with a few examples. The first comes from a high-school lesson
in American history by Kevin O'Reilly (Swartz, 1987). The
students were asked to read a passage from their textbook
describing the 1775 battle of Concord and Lexington, the
beginning of the Revolutionary War against England. The passage
asserted that the battle began when `[t]he English fired a volley
of shots that killed eight patriots.' After calling attention to
some of the loaded language in the passage (patriots, etc.),
O'Reilly then gets the students to focus on the question of who,
in fact, fired the first shot by presenting them with a passage
from Churchill (1956-58), which said, `The colonial committees
were very anxious not to fire the first shot, and there were
strict orders not to provoke open conflict with the British
regulars. But in the confusion someone fired.'
The class was then asked how they might resolve the
discrepancy. The class quickly arrived at the idea of using
eyewitness testimony. O'Reilly had anticipated this by
photocopying all known eyewitness accounts for the class,
complete with background information about the origin of each
account. The accounts indeed conflict, and it turns out to be
impossible to know with certainty what happened, although
probabilities can be assigned. In this case, the political bias
of each witness is a relevant consideration, but so are other
factors such as the extent to which the total account agrees with
other accounts and the amount of time that elapsed until the
account was given. Although a superficial handling of this
lesson could teach that `everyone is biased and there is no real
truth,' a more adept handling could give students some insight
into the nature of historical inquiry itself. The students are
doing a bit of the work that historians do - without the dust
from poking around in old libraries, of course - and they will
thereby come to understand how historical knowledge grows out of
actively open-minded thinking about evidence. The teacher must
at some point make sure that students appreciate that this is
what they are doing, if necessary by saying so explicitly.
Many other teachers do this sort of thing. Constance Kamii
(as described in a conference presentation some years ago) has
taught elementary arithmetic by letting students invent their own
algorithms through class discussion. Sometimes students invent
unconventional but successful algorithms, such using negative
numbers in subtraction: 23 - 16 would thus yield -3 for the ones
column (6-3, with the sign reversed) and 1 for the tens column,
so the result would be 10 - 3 or 7. Although this method works
it was eventually abandoned in favor of the usual `regrouping'
method because the latter is more efficient, requiring fewer
symbols to be written down. The students thus learned that the
methods of mathematics are not arbitrary but are, rather, designs
to serve purposes (Perkins, 1986). Some of these purposes are
direct requirements of the task, such as making sure that numbers
are conserved or that results are unique, but others are matters
of computational efficiency. Although almost everyone is an
`expert' at this sort of arithmetic, students even at this level
can begin to understand where knowledge comes from.
At a higher level, students can learn where more advanced
mathematics comes from through the same kind of inquiry. Lempert
(1990) describes her work on teaching what amounts to actively
open-minded thinking about mathematics to a fifth-grade class.
The instruction involved the conscious creation of a
`participation structure,' in which students learned, mostly
implicitly, a set of social rules for stating conjectures,
alternatives, arguments, and revisions, e.g., `I want to question
In one series of classes, for example, Lempert asked the
students to tabulate the squares of the integers from 1 to 100
(with calculators). The students then spent 45 minutes trying to
find patterns in the table they had made. The students became
interested in the last digits. These digits alternated odd and
even. Squares of multiples of 10 always ended in 0, and squares
of numbers ending in 5 always ended in 5. In between the zeros
and fives, the squares always ended in 1, 4, 9, 6, or 6, 9, 4, 1.
Students then asked themselves whether these patterns would hold
for all integers, and they managed to find arguments for these
hypotheses. Later, the students were encouraged to examine
higher powers. Lempert suggested to students that they discuss
what the last digit of 74 and 75 would be, without
calculating. Although the students initially disagreed about
they answers to these questions, they were able to come to
agreement about them on the basis of general principles.
Although Lempert does not suggest it, open-ended questions
such as `tell me about the last digits of the squares' could be
used as examination questions, even if the students had not
previously encountered the particular problem. (A well taught
student at a high level should spontaneously think of
generalizing to higher powers.) These kinds of explorations for
the basis of mathematical knowledge. In mathematics, the
self-criticism comes when mathematicians ask `how do we know?'
Students can come to understand this through such activities as
this. They do not need to reinvent all of mathematics. They
need to reinvent just enough of it so that they can understand
the nature of mathematical invention and discovery.
In the sciences, students already spend time doing projects
and laboratory exercises. Students and teachers alike are
typically uncertain about the purpose of these exercises,
although students usually find them to be more fun than doing
worksheets or reading textbooks. Some of these exercises could
be used to repeat the course of scientific inquiry as a
For example, Newton's experiment showing that white light
can make a spectrum if passed through a prism is often cited as
showing that white light is a mixture. But Newton was not the
first to show this, and this was not his problem. Rather, he was
concerned with an alternative explanation of the basic result.
Instead of separating the white light into its components, the
prism could modify the light, thus creating the colors.
To cast doubt on this account, Newton showed that the white light
could be reconstituted with another prism. (This still does not
tie down the theory, but it renders certain alternatives less
plausible.) The prism experiments could therefore be used to
show how experiments arise out of a process of criticism: finding
alternative explanations and then thinking of experiments to test
A combined exercise in high-school mathematics, European
history, and physics could lead students through the discovery of
Newton's law of gravity and its application to planetary motion.
Such a course could begin with data about the regression of
Mars's orbit. Ptolomy's theory of orbitals could be worked out
in detail for this one case. Then Copernicus's alternative could
be introduced. Students would come to understand that this was
mathematically equivalent to Ptolomy's theory and had only the
advantage of simplicity (Margolis, 1987). Hence, Copernicus was
unsure that he was correct, and those who resisted his theory did
not necessarily do so out of superstition and obedience alone.
Galileo's observation of the phases of Venus helped to make the
Copernican account more plausible but even this was not
convincing (as Tycho Brahe showed). It fell to Kepler and Newton
to provide both a firmer mathematical foundation for the
Copernican theory and a physical account in terms of gravitation.
By this account, of course, the earth did not really `rotate
around the sun,' but the earth and sun both rotated around a
common center of gravity. By the time that students understood
this, of course, they would be using calculus, as it was first
used, thereby understanding why its invention was needed. They
would also understand how scientific theories grow from criticism
of earlier theories, arguments based on plausibility, refutation
of those arguments, and so on.
The teaching of social studies is particularly problematical
because it derives ultimately from the social sciences, which are
relatively new and still controversial. The tendency to `water
down' the social studies curriculum is therefore great. In the
1960s, a group of scholars in the U.S., funded by the National
Science Foundation, developed `Man: A course of study' (MACOS),
which, among other things, drew heavily on the social sciences as
they were taught at the university level. It was too
controversial to be widely implemented. Among other problems, it
presented a view of culture as variable, with many possible
options. Conservatives in the U.S. did not want their own
culture presented as merely one option among many.
Understanding of the nature of knowledge in the social
sciences (except history, perhaps) is thus largely limited to
those who have been to college. (At least this is true in the
U.S.) This is particularly unfortunate because the social
sciences, especially economics, form much of the expertise
relevant to government. To most citizens, the economists
consulted routinely by government leaders might as well be
astrologers and fortunetellers who use the stars or tarot cards
rather than computers to predict the future. Students need to go
through miniature exercises in economics and the other social
sciences in order to understand the origin of this kind of
What the Educated Person should Know
The argument I have made provides another justification for
the understanding of actively open-minded thinking and reflection
on methods of inference. Indeed, it reinforces the argument made
on the basis of correlations between beliefs about thinking and
the conduct of thinking. Those results suggested that the
teaching of thinking involved not just teaching a particular set
of skills or habits or methods but, rather, or in addition,
imparting to students an understanding of the value of these
methods. My argument suggests that the same sort of
understanding is required for students to grasp the nature of
true expertise, for them to be able to distinguish true and false
experts, and for them to know the limits of true expertise.
One way to learn these things is to experience them
firsthand, in carefully prepared lessons. This is `active
learning,' but it is not just any sort of activity associated
with learning. It is active learning with a specific purpose.
It can replace much of the active learning now going on in the
form of laboratory exercises and projects without interfering
with the learning of substance.
In addition, students need to learn the geography of
expertise. Even those who make it through graduate school are
often ignorant of the fact that they are making statements about
issues on which someone else is an expert. Psychologists, for
example, frequently step into philosophy as if the discipline
didn't exist, and economists do the same with psychology. As it
is, the secondary curriculum in most countries is a watered-down
version of the university curriculum of decades (or centuries)
earlier, with no other particular justification. One way to
remedy this problem is for universities to work harder, with the
help of outside funds, to inform high-school teachers and
students of the full range of their activities.
Universities themselves could do more to teach students to
understand expertise in fields outside of their own. In the
U.S., college students are required to take courses in several
different kinds of disciplines. Ideally, such `distribution
requirements' should allow students to learn not only about the
methods of inference in each discipline they study but also about
how to learn the methods of yet other disciplines and how to ask
good questions of experts in each discipline. Learning about
these things will facilitate teamwork among members of different
disciplines. If I am correct, what is central in all of these
types of learning are the kinds of evidence used to establish
claims in a discipline and the kinds of inferences and criticisms
that are made. If students focus on these, they will quickly
learn what a discipline is about, even if they know little of its
substance (although they must know some of the substance if
only to understand the methods of inquiry). Many current courses
may actually do well at imparting this sort of knowledge.
I do not mean to imply that an understanding of the
different sources of expert knowledge is all that students need.
They do need to learn how to do some things on their own,
and they need to learn enough so that they do not need to take
the time to look up fundamental facts or acquire basic skills
after they leave school. But my point here has been to emphasize
the rest of what they need, which is an understanding of the
way in which knowledge is acquired in the disciplines that are
necessary for the operation of the modern world. Conceivably,
efforts to teach for such understanding will even increase the
acquisition for basic facts, because students will have a
framework for integrating them.
In sum, I have tried to provide a rationale for teaching the
standards of actively open-minded thinking in terms of learning
about the disciplines themselves. This rationale does not depend
on the assumption that such thinking is not done enough in daily
life. Rather, it depends on the idea that an understanding of
thinking is essential to an understanding of scholarship itself,
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