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What’s the Difference Between Critical Thinking and Scientific Thinking?

critical thinking and scientific thinking

Thinking deeply about things is a defining feature of what it means to be human, but, surprising as it may seem, there isn’t just one way to ‘think’ about something; instead, humans have been developing organized and varied schools of thought for thousands of years.

Discussions about morality, religion, and the meaning of life often drive knowledge-seeking inquiry, leading people to wonder what the difference is between critical thinking and Scientific Thinking.

Critical thinkers prioritize objectivity to analyze a problem, deduce logical solutions, and examine what the ramifications of those solutions are.

While scientific thinking often relies heavily on critical thinking, scientific inquiry is more dedicated to acquiring knowledge rather than mere abstraction.

There are a lot of nuances between critical thinking and scientific thinking, and most of us probably utilize these skills in our everyday lives. The rest of this article will thoroughly define the two terms and relate how they are similar and different.

What Is Critical Thinking?

Critical thinking is a mindset ― a lens, if you will, through which one may view the world. Critical thinkers rely on a lot of introspection, constantly self-evaluating how they came to a conclusion, and what that conclusion naturally entails.

A critical thinker may discern what they already know about a subject, what that information suggests, why that information is relevant, and how that information could be linked to further lines of inquiry. Critical thinking is, therefore, simply the ability to think clearly and logically.

Systematic reasoning is prized over gut instinct, and determining relevance is crucial to parsing out useful data from extraneous information.

Naturally, the ability to think critically is highly prized in an academic setting, and most educators seek to enable their students to think critically.

What is the link between the styles and motivations of these two Romantic era poets? How can your current understanding of algebra be applied to geometry? How does our understanding of this historical figure influence our understanding of social life at the time?

So much information can be interlinked to develop our understanding of the world, and critical thinking is the basis for using objectivity to not only establish likely outcomes to a scenario, but also inquire on the repercussions of that outcome and reflect on the process by which one came to that conclusion.

What Is Scientific Thinking?

The objective of scientific thinking is the acquisition of knowledge. The more we know, the more we can hope to know.

Scientific thinking begins by imagining what the outcome of a problem may be, observing the situation, and then making notes and changing the initial hypothesis.

The commonly used scientific method is as follows:

  • Define the purpose of the experiment
  • Formulate a hypothesis
  • Study the phenomenon and collect data
  • Draw results

As you might imagine, this process can be repeated ad infinitum. So, you draw a conclusion that’s scientifically verifiable? Great! Now you can take that conclusion and use it as a basis for a new experiment. Of course, the scientific method has limits.

It’s hard to apply the scientific method when it comes to morality or religious beliefs. A revelation of a prophet cannot be empirically verified.

We can’t go inside said prophet’s mind and see exactly what neurons were firing to recreate the conditions under which the vision was made, and even if we could, the nature of such a revelation is spiritual and immaterial.

It’s impossible to influence the supernatural in the material world, and as such, creating a test that relies on changing something to see the outcome is impossible. Where scientific thinking does excel is in the fields of math and, well, science.

Physics is known as the perfect science because the forces that comprise our world are well understood and don’t tend to exhibit anomalies, making the empirically verified scientific method perfect for improving our understanding of the natural world.

How Are Critical Thinking and Scientific Thinking Similar and Different?

Both critical and scientific thinking rely on the use of empirical, objective evidence. Thinking scientifically or critically relies on using the data available and following it to its likely conclusion.

Scientific thinking can be seen as a stricter, more regulated version of critical thinking. It takes the tenets of critically thinking and narrows the focus.

Both fields of study eschew personal bias and gut instinct as both unreliable and unhelpful.

The main difference between the two, however, is the goal of each discipline.

While both prioritize learning and using data to make hypotheses, critical thinking is prone to much more abstraction and self-reflection.

With little variation in the scientific method, there’s not really any need to reflect on how those conclusions were drawn or if those conclusions are a result of any kind of bias. It’s just not useful information.

For a critical thinker, however, self-reflection is key to identifying inconsistencies and refining one’s argument.

Both scientific thinking and critical thinking tend to draw links between concepts, evaluating how they are related and what knowledge may be gleaned from that connection.

While critical thinking can be applied to most concepts, even those of morality and anthropology, scientific thinking is often problem oriented. If a problem exists, scientific inquiry attempts to gain the necessary information to solve it, overcoming obstacles along the way.

Both critical thinkers and scientific thinkers may very well end up at the same conclusion― they will just draw those conclusions differently. Critical thinkers are concerned with logic, order, and rational thinking.

Establishing already-understood information, applying that information to a query, and then establishing a defensible argument on the accuracy and relevance of the conclusion is the trademark of a critical thinker. Scientific thinkers, on the other hand, work towards solving knowledge almost exclusively through the acquisition of knowledge through the scientific method.

Scientific thinkers develop a hypothesis, test it, and then rinse and repeat until the phenomenon is understood. As such, scientific thinkers are obsessed with why questions. Why does this phenomenon happen?

Why does matter behave like this? In the end, both schools are thought have a lot of interesting ideas guiding them, and most of us probably use them throughout our daily lives.

https://www.vwaust.com/resource/what-is-scientific-thinking/

https://www.skillsyouneed.com/learn/critical-thinking.html#:~:text=Critical%20thinking%20is%20thinking%20about%20things%20in%20certain,to%20the%20best%20possible%20conclusion.%20Critical%20Thinking%20is%3A

https://psycnet.apa.org/record/2010-22950-019

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Critical Thinking

Critical thinking is a widely accepted educational goal. Its definition is contested, but the competing definitions can be understood as differing conceptions of the same basic concept: careful thinking directed to a goal. Conceptions differ with respect to the scope of such thinking, the type of goal, the criteria and norms for thinking carefully, and the thinking components on which they focus. Its adoption as an educational goal has been recommended on the basis of respect for students’ autonomy and preparing students for success in life and for democratic citizenship. “Critical thinkers” have the dispositions and abilities that lead them to think critically when appropriate. The abilities can be identified directly; the dispositions indirectly, by considering what factors contribute to or impede exercise of the abilities. Standardized tests have been developed to assess the degree to which a person possesses such dispositions and abilities. Educational intervention has been shown experimentally to improve them, particularly when it includes dialogue, anchored instruction, and mentoring. Controversies have arisen over the generalizability of critical thinking across domains, over alleged bias in critical thinking theories and instruction, and over the relationship of critical thinking to other types of thinking.

2.1 Dewey’s Three Main Examples

2.2 dewey’s other examples, 2.3 further examples, 2.4 non-examples, 3. the definition of critical thinking, 4. its value, 5. the process of thinking critically, 6. components of the process, 7. contributory dispositions and abilities, 8.1 initiating dispositions, 8.2 internal dispositions, 9. critical thinking abilities, 10. required knowledge, 11. educational methods, 12.1 the generalizability of critical thinking, 12.2 bias in critical thinking theory and pedagogy, 12.3 relationship of critical thinking to other types of thinking, other internet resources, related entries.

Use of the term ‘critical thinking’ to describe an educational goal goes back to the American philosopher John Dewey (1910), who more commonly called it ‘reflective thinking’. He defined it as

active, persistent and careful consideration of any belief or supposed form of knowledge in the light of the grounds that support it, and the further conclusions to which it tends. (Dewey 1910: 6; 1933: 9)

and identified a habit of such consideration with a scientific attitude of mind. His lengthy quotations of Francis Bacon, John Locke, and John Stuart Mill indicate that he was not the first person to propose development of a scientific attitude of mind as an educational goal.

In the 1930s, many of the schools that participated in the Eight-Year Study of the Progressive Education Association (Aikin 1942) adopted critical thinking as an educational goal, for whose achievement the study’s Evaluation Staff developed tests (Smith, Tyler, & Evaluation Staff 1942). Glaser (1941) showed experimentally that it was possible to improve the critical thinking of high school students. Bloom’s influential taxonomy of cognitive educational objectives (Bloom et al. 1956) incorporated critical thinking abilities. Ennis (1962) proposed 12 aspects of critical thinking as a basis for research on the teaching and evaluation of critical thinking ability.

Since 1980, an annual international conference in California on critical thinking and educational reform has attracted tens of thousands of educators from all levels of education and from many parts of the world. Also since 1980, the state university system in California has required all undergraduate students to take a critical thinking course. Since 1983, the Association for Informal Logic and Critical Thinking has sponsored sessions in conjunction with the divisional meetings of the American Philosophical Association (APA). In 1987, the APA’s Committee on Pre-College Philosophy commissioned a consensus statement on critical thinking for purposes of educational assessment and instruction (Facione 1990a). Researchers have developed standardized tests of critical thinking abilities and dispositions; for details, see the Supplement on Assessment . Educational jurisdictions around the world now include critical thinking in guidelines for curriculum and assessment.

For details on this history, see the Supplement on History .

2. Examples and Non-Examples

Before considering the definition of critical thinking, it will be helpful to have in mind some examples of critical thinking, as well as some examples of kinds of thinking that would apparently not count as critical thinking.

Dewey (1910: 68–71; 1933: 91–94) takes as paradigms of reflective thinking three class papers of students in which they describe their thinking. The examples range from the everyday to the scientific.

Transit : “The other day, when I was down town on 16th Street, a clock caught my eye. I saw that the hands pointed to 12:20. This suggested that I had an engagement at 124th Street, at one o’clock. I reasoned that as it had taken me an hour to come down on a surface car, I should probably be twenty minutes late if I returned the same way. I might save twenty minutes by a subway express. But was there a station near? If not, I might lose more than twenty minutes in looking for one. Then I thought of the elevated, and I saw there was such a line within two blocks. But where was the station? If it were several blocks above or below the street I was on, I should lose time instead of gaining it. My mind went back to the subway express as quicker than the elevated; furthermore, I remembered that it went nearer than the elevated to the part of 124th Street I wished to reach, so that time would be saved at the end of the journey. I concluded in favor of the subway, and reached my destination by one o’clock.” (Dewey 1910: 68–69; 1933: 91–92)

Ferryboat : “Projecting nearly horizontally from the upper deck of the ferryboat on which I daily cross the river is a long white pole, having a gilded ball at its tip. It suggested a flagpole when I first saw it; its color, shape, and gilded ball agreed with this idea, and these reasons seemed to justify me in this belief. But soon difficulties presented themselves. The pole was nearly horizontal, an unusual position for a flagpole; in the next place, there was no pulley, ring, or cord by which to attach a flag; finally, there were elsewhere on the boat two vertical staffs from which flags were occasionally flown. It seemed probable that the pole was not there for flag-flying.

“I then tried to imagine all possible purposes of the pole, and to consider for which of these it was best suited: (a) Possibly it was an ornament. But as all the ferryboats and even the tugboats carried poles, this hypothesis was rejected. (b) Possibly it was the terminal of a wireless telegraph. But the same considerations made this improbable. Besides, the more natural place for such a terminal would be the highest part of the boat, on top of the pilot house. (c) Its purpose might be to point out the direction in which the boat is moving.

“In support of this conclusion, I discovered that the pole was lower than the pilot house, so that the steersman could easily see it. Moreover, the tip was enough higher than the base, so that, from the pilot’s position, it must appear to project far out in front of the boat. Moreover, the pilot being near the front of the boat, he would need some such guide as to its direction. Tugboats would also need poles for such a purpose. This hypothesis was so much more probable than the others that I accepted it. I formed the conclusion that the pole was set up for the purpose of showing the pilot the direction in which the boat pointed, to enable him to steer correctly.” (Dewey 1910: 69–70; 1933: 92–93)

Bubbles : “In washing tumblers in hot soapsuds and placing them mouth downward on a plate, bubbles appeared on the outside of the mouth of the tumblers and then went inside. Why? The presence of bubbles suggests air, which I note must come from inside the tumbler. I see that the soapy water on the plate prevents escape of the air save as it may be caught in bubbles. But why should air leave the tumbler? There was no substance entering to force it out. It must have expanded. It expands by increase of heat, or by decrease of pressure, or both. Could the air have become heated after the tumbler was taken from the hot suds? Clearly not the air that was already entangled in the water. If heated air was the cause, cold air must have entered in transferring the tumblers from the suds to the plate. I test to see if this supposition is true by taking several more tumblers out. Some I shake so as to make sure of entrapping cold air in them. Some I take out holding mouth downward in order to prevent cold air from entering. Bubbles appear on the outside of every one of the former and on none of the latter. I must be right in my inference. Air from the outside must have been expanded by the heat of the tumbler, which explains the appearance of the bubbles on the outside. But why do they then go inside? Cold contracts. The tumbler cooled and also the air inside it. Tension was removed, and hence bubbles appeared inside. To be sure of this, I test by placing a cup of ice on the tumbler while the bubbles are still forming outside. They soon reverse” (Dewey 1910: 70–71; 1933: 93–94).

Dewey (1910, 1933) sprinkles his book with other examples of critical thinking. We will refer to the following.

Weather : A man on a walk notices that it has suddenly become cool, thinks that it is probably going to rain, looks up and sees a dark cloud obscuring the sun, and quickens his steps (1910: 6–10; 1933: 9–13).

Disorder : A man finds his rooms on his return to them in disorder with his belongings thrown about, thinks at first of burglary as an explanation, then thinks of mischievous children as being an alternative explanation, then looks to see whether valuables are missing, and discovers that they are (1910: 82–83; 1933: 166–168).

Typhoid : A physician diagnosing a patient whose conspicuous symptoms suggest typhoid avoids drawing a conclusion until more data are gathered by questioning the patient and by making tests (1910: 85–86; 1933: 170).

Blur : A moving blur catches our eye in the distance, we ask ourselves whether it is a cloud of whirling dust or a tree moving its branches or a man signaling to us, we think of other traits that should be found on each of those possibilities, and we look and see if those traits are found (1910: 102, 108; 1933: 121, 133).

Suction pump : In thinking about the suction pump, the scientist first notes that it will draw water only to a maximum height of 33 feet at sea level and to a lesser maximum height at higher elevations, selects for attention the differing atmospheric pressure at these elevations, sets up experiments in which the air is removed from a vessel containing water (when suction no longer works) and in which the weight of air at various levels is calculated, compares the results of reasoning about the height to which a given weight of air will allow a suction pump to raise water with the observed maximum height at different elevations, and finally assimilates the suction pump to such apparently different phenomena as the siphon and the rising of a balloon (1910: 150–153; 1933: 195–198).

Diamond : A passenger in a car driving in a diamond lane reserved for vehicles with at least one passenger notices that the diamond marks on the pavement are far apart in some places and close together in others. Why? The driver suggests that the reason may be that the diamond marks are not needed where there is a solid double line separating the diamond lane from the adjoining lane, but are needed when there is a dotted single line permitting crossing into the diamond lane. Further observation confirms that the diamonds are close together when a dotted line separates the diamond lane from its neighbour, but otherwise far apart.

Rash : A woman suddenly develops a very itchy red rash on her throat and upper chest. She recently noticed a mark on the back of her right hand, but was not sure whether the mark was a rash or a scrape. She lies down in bed and thinks about what might be causing the rash and what to do about it. About two weeks before, she began taking blood pressure medication that contained a sulfa drug, and the pharmacist had warned her, in view of a previous allergic reaction to a medication containing a sulfa drug, to be on the alert for an allergic reaction; however, she had been taking the medication for two weeks with no such effect. The day before, she began using a new cream on her neck and upper chest; against the new cream as the cause was mark on the back of her hand, which had not been exposed to the cream. She began taking probiotics about a month before. She also recently started new eye drops, but she supposed that manufacturers of eye drops would be careful not to include allergy-causing components in the medication. The rash might be a heat rash, since she recently was sweating profusely from her upper body. Since she is about to go away on a short vacation, where she would not have access to her usual physician, she decides to keep taking the probiotics and using the new eye drops but to discontinue the blood pressure medication and to switch back to the old cream for her neck and upper chest. She forms a plan to consult her regular physician on her return about the blood pressure medication.

Candidate : Although Dewey included no examples of thinking directed at appraising the arguments of others, such thinking has come to be considered a kind of critical thinking. We find an example of such thinking in the performance task on the Collegiate Learning Assessment (CLA+), which its sponsoring organization describes as

a performance-based assessment that provides a measure of an institution’s contribution to the development of critical-thinking and written communication skills of its students. (Council for Aid to Education 2017)

A sample task posted on its website requires the test-taker to write a report for public distribution evaluating a fictional candidate’s policy proposals and their supporting arguments, using supplied background documents, with a recommendation on whether to endorse the candidate.

Immediate acceptance of an idea that suggests itself as a solution to a problem (e.g., a possible explanation of an event or phenomenon, an action that seems likely to produce a desired result) is “uncritical thinking, the minimum of reflection” (Dewey 1910: 13). On-going suspension of judgment in the light of doubt about a possible solution is not critical thinking (Dewey 1910: 108). Critique driven by a dogmatically held political or religious ideology is not critical thinking; thus Paulo Freire (1968 [1970]) is using the term (e.g., at 1970: 71, 81, 100, 146) in a more politically freighted sense that includes not only reflection but also revolutionary action against oppression. Derivation of a conclusion from given data using an algorithm is not critical thinking.

What is critical thinking? There are many definitions. Ennis (2016) lists 14 philosophically oriented scholarly definitions and three dictionary definitions. Following Rawls (1971), who distinguished his conception of justice from a utilitarian conception but regarded them as rival conceptions of the same concept, Ennis maintains that the 17 definitions are different conceptions of the same concept. Rawls articulated the shared concept of justice as

a characteristic set of principles for assigning basic rights and duties and for determining… the proper distribution of the benefits and burdens of social cooperation. (Rawls 1971: 5)

Bailin et al. (1999b) claim that, if one considers what sorts of thinking an educator would take not to be critical thinking and what sorts to be critical thinking, one can conclude that educators typically understand critical thinking to have at least three features.

  • It is done for the purpose of making up one’s mind about what to believe or do.
  • The person engaging in the thinking is trying to fulfill standards of adequacy and accuracy appropriate to the thinking.
  • The thinking fulfills the relevant standards to some threshold level.

One could sum up the core concept that involves these three features by saying that critical thinking is careful goal-directed thinking. This core concept seems to apply to all the examples of critical thinking described in the previous section. As for the non-examples, their exclusion depends on construing careful thinking as excluding jumping immediately to conclusions, suspending judgment no matter how strong the evidence, reasoning from an unquestioned ideological or religious perspective, and routinely using an algorithm to answer a question.

If the core of critical thinking is careful goal-directed thinking, conceptions of it can vary according to its presumed scope, its presumed goal, one’s criteria and threshold for being careful, and the thinking component on which one focuses. As to its scope, some conceptions (e.g., Dewey 1910, 1933) restrict it to constructive thinking on the basis of one’s own observations and experiments, others (e.g., Ennis 1962; Fisher & Scriven 1997; Johnson 1992) to appraisal of the products of such thinking. Ennis (1991) and Bailin et al. (1999b) take it to cover both construction and appraisal. As to its goal, some conceptions restrict it to forming a judgment (Dewey 1910, 1933; Lipman 1987; Facione 1990a). Others allow for actions as well as beliefs as the end point of a process of critical thinking (Ennis 1991; Bailin et al. 1999b). As to the criteria and threshold for being careful, definitions vary in the term used to indicate that critical thinking satisfies certain norms: “intellectually disciplined” (Scriven & Paul 1987), “reasonable” (Ennis 1991), “skillful” (Lipman 1987), “skilled” (Fisher & Scriven 1997), “careful” (Bailin & Battersby 2009). Some definitions specify these norms, referring variously to “consideration of any belief or supposed form of knowledge in the light of the grounds that support it and the further conclusions to which it tends” (Dewey 1910, 1933); “the methods of logical inquiry and reasoning” (Glaser 1941); “conceptualizing, applying, analyzing, synthesizing, and/or evaluating information gathered from, or generated by, observation, experience, reflection, reasoning, or communication” (Scriven & Paul 1987); the requirement that “it is sensitive to context, relies on criteria, and is self-correcting” (Lipman 1987); “evidential, conceptual, methodological, criteriological, or contextual considerations” (Facione 1990a); and “plus-minus considerations of the product in terms of appropriate standards (or criteria)” (Johnson 1992). Stanovich and Stanovich (2010) propose to ground the concept of critical thinking in the concept of rationality, which they understand as combining epistemic rationality (fitting one’s beliefs to the world) and instrumental rationality (optimizing goal fulfillment); a critical thinker, in their view, is someone with “a propensity to override suboptimal responses from the autonomous mind” (2010: 227). These variant specifications of norms for critical thinking are not necessarily incompatible with one another, and in any case presuppose the core notion of thinking carefully. As to the thinking component singled out, some definitions focus on suspension of judgment during the thinking (Dewey 1910; McPeck 1981), others on inquiry while judgment is suspended (Bailin & Battersby 2009, 2021), others on the resulting judgment (Facione 1990a), and still others on responsiveness to reasons (Siegel 1988). Kuhn (2019) takes critical thinking to be more a dialogic practice of advancing and responding to arguments than an individual ability.

In educational contexts, a definition of critical thinking is a “programmatic definition” (Scheffler 1960: 19). It expresses a practical program for achieving an educational goal. For this purpose, a one-sentence formulaic definition is much less useful than articulation of a critical thinking process, with criteria and standards for the kinds of thinking that the process may involve. The real educational goal is recognition, adoption and implementation by students of those criteria and standards. That adoption and implementation in turn consists in acquiring the knowledge, abilities and dispositions of a critical thinker.

Conceptions of critical thinking generally do not include moral integrity as part of the concept. Dewey, for example, took critical thinking to be the ultimate intellectual goal of education, but distinguished it from the development of social cooperation among school children, which he took to be the central moral goal. Ennis (1996, 2011) added to his previous list of critical thinking dispositions a group of dispositions to care about the dignity and worth of every person, which he described as a “correlative” (1996) disposition without which critical thinking would be less valuable and perhaps harmful. An educational program that aimed at developing critical thinking but not the correlative disposition to care about the dignity and worth of every person, he asserted, “would be deficient and perhaps dangerous” (Ennis 1996: 172).

Dewey thought that education for reflective thinking would be of value to both the individual and society; recognition in educational practice of the kinship to the scientific attitude of children’s native curiosity, fertile imagination and love of experimental inquiry “would make for individual happiness and the reduction of social waste” (Dewey 1910: iii). Schools participating in the Eight-Year Study took development of the habit of reflective thinking and skill in solving problems as a means to leading young people to understand, appreciate and live the democratic way of life characteristic of the United States (Aikin 1942: 17–18, 81). Harvey Siegel (1988: 55–61) has offered four considerations in support of adopting critical thinking as an educational ideal. (1) Respect for persons requires that schools and teachers honour students’ demands for reasons and explanations, deal with students honestly, and recognize the need to confront students’ independent judgment; these requirements concern the manner in which teachers treat students. (2) Education has the task of preparing children to be successful adults, a task that requires development of their self-sufficiency. (3) Education should initiate children into the rational traditions in such fields as history, science and mathematics. (4) Education should prepare children to become democratic citizens, which requires reasoned procedures and critical talents and attitudes. To supplement these considerations, Siegel (1988: 62–90) responds to two objections: the ideology objection that adoption of any educational ideal requires a prior ideological commitment and the indoctrination objection that cultivation of critical thinking cannot escape being a form of indoctrination.

Despite the diversity of our 11 examples, one can recognize a common pattern. Dewey analyzed it as consisting of five phases:

  • suggestions , in which the mind leaps forward to a possible solution;
  • an intellectualization of the difficulty or perplexity into a problem to be solved, a question for which the answer must be sought;
  • the use of one suggestion after another as a leading idea, or hypothesis , to initiate and guide observation and other operations in collection of factual material;
  • the mental elaboration of the idea or supposition as an idea or supposition ( reasoning , in the sense on which reasoning is a part, not the whole, of inference); and
  • testing the hypothesis by overt or imaginative action. (Dewey 1933: 106–107; italics in original)

The process of reflective thinking consisting of these phases would be preceded by a perplexed, troubled or confused situation and followed by a cleared-up, unified, resolved situation (Dewey 1933: 106). The term ‘phases’ replaced the term ‘steps’ (Dewey 1910: 72), thus removing the earlier suggestion of an invariant sequence. Variants of the above analysis appeared in (Dewey 1916: 177) and (Dewey 1938: 101–119).

The variant formulations indicate the difficulty of giving a single logical analysis of such a varied process. The process of critical thinking may have a spiral pattern, with the problem being redefined in the light of obstacles to solving it as originally formulated. For example, the person in Transit might have concluded that getting to the appointment at the scheduled time was impossible and have reformulated the problem as that of rescheduling the appointment for a mutually convenient time. Further, defining a problem does not always follow after or lead immediately to an idea of a suggested solution. Nor should it do so, as Dewey himself recognized in describing the physician in Typhoid as avoiding any strong preference for this or that conclusion before getting further information (Dewey 1910: 85; 1933: 170). People with a hypothesis in mind, even one to which they have a very weak commitment, have a so-called “confirmation bias” (Nickerson 1998): they are likely to pay attention to evidence that confirms the hypothesis and to ignore evidence that counts against it or for some competing hypothesis. Detectives, intelligence agencies, and investigators of airplane accidents are well advised to gather relevant evidence systematically and to postpone even tentative adoption of an explanatory hypothesis until the collected evidence rules out with the appropriate degree of certainty all but one explanation. Dewey’s analysis of the critical thinking process can be faulted as well for requiring acceptance or rejection of a possible solution to a defined problem, with no allowance for deciding in the light of the available evidence to suspend judgment. Further, given the great variety of kinds of problems for which reflection is appropriate, there is likely to be variation in its component events. Perhaps the best way to conceptualize the critical thinking process is as a checklist whose component events can occur in a variety of orders, selectively, and more than once. These component events might include (1) noticing a difficulty, (2) defining the problem, (3) dividing the problem into manageable sub-problems, (4) formulating a variety of possible solutions to the problem or sub-problem, (5) determining what evidence is relevant to deciding among possible solutions to the problem or sub-problem, (6) devising a plan of systematic observation or experiment that will uncover the relevant evidence, (7) carrying out the plan of systematic observation or experimentation, (8) noting the results of the systematic observation or experiment, (9) gathering relevant testimony and information from others, (10) judging the credibility of testimony and information gathered from others, (11) drawing conclusions from gathered evidence and accepted testimony, and (12) accepting a solution that the evidence adequately supports (cf. Hitchcock 2017: 485).

Checklist conceptions of the process of critical thinking are open to the objection that they are too mechanical and procedural to fit the multi-dimensional and emotionally charged issues for which critical thinking is urgently needed (Paul 1984). For such issues, a more dialectical process is advocated, in which competing relevant world views are identified, their implications explored, and some sort of creative synthesis attempted.

If one considers the critical thinking process illustrated by the 11 examples, one can identify distinct kinds of mental acts and mental states that form part of it. To distinguish, label and briefly characterize these components is a useful preliminary to identifying abilities, skills, dispositions, attitudes, habits and the like that contribute causally to thinking critically. Identifying such abilities and habits is in turn a useful preliminary to setting educational goals. Setting the goals is in its turn a useful preliminary to designing strategies for helping learners to achieve the goals and to designing ways of measuring the extent to which learners have done so. Such measures provide both feedback to learners on their achievement and a basis for experimental research on the effectiveness of various strategies for educating people to think critically. Let us begin, then, by distinguishing the kinds of mental acts and mental events that can occur in a critical thinking process.

  • Observing : One notices something in one’s immediate environment (sudden cooling of temperature in Weather , bubbles forming outside a glass and then going inside in Bubbles , a moving blur in the distance in Blur , a rash in Rash ). Or one notes the results of an experiment or systematic observation (valuables missing in Disorder , no suction without air pressure in Suction pump )
  • Feeling : One feels puzzled or uncertain about something (how to get to an appointment on time in Transit , why the diamonds vary in spacing in Diamond ). One wants to resolve this perplexity. One feels satisfaction once one has worked out an answer (to take the subway express in Transit , diamonds closer when needed as a warning in Diamond ).
  • Wondering : One formulates a question to be addressed (why bubbles form outside a tumbler taken from hot water in Bubbles , how suction pumps work in Suction pump , what caused the rash in Rash ).
  • Imagining : One thinks of possible answers (bus or subway or elevated in Transit , flagpole or ornament or wireless communication aid or direction indicator in Ferryboat , allergic reaction or heat rash in Rash ).
  • Inferring : One works out what would be the case if a possible answer were assumed (valuables missing if there has been a burglary in Disorder , earlier start to the rash if it is an allergic reaction to a sulfa drug in Rash ). Or one draws a conclusion once sufficient relevant evidence is gathered (take the subway in Transit , burglary in Disorder , discontinue blood pressure medication and new cream in Rash ).
  • Knowledge : One uses stored knowledge of the subject-matter to generate possible answers or to infer what would be expected on the assumption of a particular answer (knowledge of a city’s public transit system in Transit , of the requirements for a flagpole in Ferryboat , of Boyle’s law in Bubbles , of allergic reactions in Rash ).
  • Experimenting : One designs and carries out an experiment or a systematic observation to find out whether the results deduced from a possible answer will occur (looking at the location of the flagpole in relation to the pilot’s position in Ferryboat , putting an ice cube on top of a tumbler taken from hot water in Bubbles , measuring the height to which a suction pump will draw water at different elevations in Suction pump , noticing the spacing of diamonds when movement to or from a diamond lane is allowed in Diamond ).
  • Consulting : One finds a source of information, gets the information from the source, and makes a judgment on whether to accept it. None of our 11 examples include searching for sources of information. In this respect they are unrepresentative, since most people nowadays have almost instant access to information relevant to answering any question, including many of those illustrated by the examples. However, Candidate includes the activities of extracting information from sources and evaluating its credibility.
  • Identifying and analyzing arguments : One notices an argument and works out its structure and content as a preliminary to evaluating its strength. This activity is central to Candidate . It is an important part of a critical thinking process in which one surveys arguments for various positions on an issue.
  • Judging : One makes a judgment on the basis of accumulated evidence and reasoning, such as the judgment in Ferryboat that the purpose of the pole is to provide direction to the pilot.
  • Deciding : One makes a decision on what to do or on what policy to adopt, as in the decision in Transit to take the subway.

By definition, a person who does something voluntarily is both willing and able to do that thing at that time. Both the willingness and the ability contribute causally to the person’s action, in the sense that the voluntary action would not occur if either (or both) of these were lacking. For example, suppose that one is standing with one’s arms at one’s sides and one voluntarily lifts one’s right arm to an extended horizontal position. One would not do so if one were unable to lift one’s arm, if for example one’s right side was paralyzed as the result of a stroke. Nor would one do so if one were unwilling to lift one’s arm, if for example one were participating in a street demonstration at which a white supremacist was urging the crowd to lift their right arm in a Nazi salute and one were unwilling to express support in this way for the racist Nazi ideology. The same analysis applies to a voluntary mental process of thinking critically. It requires both willingness and ability to think critically, including willingness and ability to perform each of the mental acts that compose the process and to coordinate those acts in a sequence that is directed at resolving the initiating perplexity.

Consider willingness first. We can identify causal contributors to willingness to think critically by considering factors that would cause a person who was able to think critically about an issue nevertheless not to do so (Hamby 2014). For each factor, the opposite condition thus contributes causally to willingness to think critically on a particular occasion. For example, people who habitually jump to conclusions without considering alternatives will not think critically about issues that arise, even if they have the required abilities. The contrary condition of willingness to suspend judgment is thus a causal contributor to thinking critically.

Now consider ability. In contrast to the ability to move one’s arm, which can be completely absent because a stroke has left the arm paralyzed, the ability to think critically is a developed ability, whose absence is not a complete absence of ability to think but absence of ability to think well. We can identify the ability to think well directly, in terms of the norms and standards for good thinking. In general, to be able do well the thinking activities that can be components of a critical thinking process, one needs to know the concepts and principles that characterize their good performance, to recognize in particular cases that the concepts and principles apply, and to apply them. The knowledge, recognition and application may be procedural rather than declarative. It may be domain-specific rather than widely applicable, and in either case may need subject-matter knowledge, sometimes of a deep kind.

Reflections of the sort illustrated by the previous two paragraphs have led scholars to identify the knowledge, abilities and dispositions of a “critical thinker”, i.e., someone who thinks critically whenever it is appropriate to do so. We turn now to these three types of causal contributors to thinking critically. We start with dispositions, since arguably these are the most powerful contributors to being a critical thinker, can be fostered at an early stage of a child’s development, and are susceptible to general improvement (Glaser 1941: 175)

8. Critical Thinking Dispositions

Educational researchers use the term ‘dispositions’ broadly for the habits of mind and attitudes that contribute causally to being a critical thinker. Some writers (e.g., Paul & Elder 2006; Hamby 2014; Bailin & Battersby 2016a) propose to use the term ‘virtues’ for this dimension of a critical thinker. The virtues in question, although they are virtues of character, concern the person’s ways of thinking rather than the person’s ways of behaving towards others. They are not moral virtues but intellectual virtues, of the sort articulated by Zagzebski (1996) and discussed by Turri, Alfano, and Greco (2017).

On a realistic conception, thinking dispositions or intellectual virtues are real properties of thinkers. They are general tendencies, propensities, or inclinations to think in particular ways in particular circumstances, and can be genuinely explanatory (Siegel 1999). Sceptics argue that there is no evidence for a specific mental basis for the habits of mind that contribute to thinking critically, and that it is pedagogically misleading to posit such a basis (Bailin et al. 1999a). Whatever their status, critical thinking dispositions need motivation for their initial formation in a child—motivation that may be external or internal. As children develop, the force of habit will gradually become important in sustaining the disposition (Nieto & Valenzuela 2012). Mere force of habit, however, is unlikely to sustain critical thinking dispositions. Critical thinkers must value and enjoy using their knowledge and abilities to think things through for themselves. They must be committed to, and lovers of, inquiry.

A person may have a critical thinking disposition with respect to only some kinds of issues. For example, one could be open-minded about scientific issues but not about religious issues. Similarly, one could be confident in one’s ability to reason about the theological implications of the existence of evil in the world but not in one’s ability to reason about the best design for a guided ballistic missile.

Facione (1990a: 25) divides “affective dispositions” of critical thinking into approaches to life and living in general and approaches to specific issues, questions or problems. Adapting this distinction, one can usefully divide critical thinking dispositions into initiating dispositions (those that contribute causally to starting to think critically about an issue) and internal dispositions (those that contribute causally to doing a good job of thinking critically once one has started). The two categories are not mutually exclusive. For example, open-mindedness, in the sense of willingness to consider alternative points of view to one’s own, is both an initiating and an internal disposition.

Using the strategy of considering factors that would block people with the ability to think critically from doing so, we can identify as initiating dispositions for thinking critically attentiveness, a habit of inquiry, self-confidence, courage, open-mindedness, willingness to suspend judgment, trust in reason, wanting evidence for one’s beliefs, and seeking the truth. We consider briefly what each of these dispositions amounts to, in each case citing sources that acknowledge them.

  • Attentiveness : One will not think critically if one fails to recognize an issue that needs to be thought through. For example, the pedestrian in Weather would not have looked up if he had not noticed that the air was suddenly cooler. To be a critical thinker, then, one needs to be habitually attentive to one’s surroundings, noticing not only what one senses but also sources of perplexity in messages received and in one’s own beliefs and attitudes (Facione 1990a: 25; Facione, Facione, & Giancarlo 2001).
  • Habit of inquiry : Inquiry is effortful, and one needs an internal push to engage in it. For example, the student in Bubbles could easily have stopped at idle wondering about the cause of the bubbles rather than reasoning to a hypothesis, then designing and executing an experiment to test it. Thus willingness to think critically needs mental energy and initiative. What can supply that energy? Love of inquiry, or perhaps just a habit of inquiry. Hamby (2015) has argued that willingness to inquire is the central critical thinking virtue, one that encompasses all the others. It is recognized as a critical thinking disposition by Dewey (1910: 29; 1933: 35), Glaser (1941: 5), Ennis (1987: 12; 1991: 8), Facione (1990a: 25), Bailin et al. (1999b: 294), Halpern (1998: 452), and Facione, Facione, & Giancarlo (2001).
  • Self-confidence : Lack of confidence in one’s abilities can block critical thinking. For example, if the woman in Rash lacked confidence in her ability to figure things out for herself, she might just have assumed that the rash on her chest was the allergic reaction to her medication against which the pharmacist had warned her. Thus willingness to think critically requires confidence in one’s ability to inquire (Facione 1990a: 25; Facione, Facione, & Giancarlo 2001).
  • Courage : Fear of thinking for oneself can stop one from doing it. Thus willingness to think critically requires intellectual courage (Paul & Elder 2006: 16).
  • Open-mindedness : A dogmatic attitude will impede thinking critically. For example, a person who adheres rigidly to a “pro-choice” position on the issue of the legal status of induced abortion is likely to be unwilling to consider seriously the issue of when in its development an unborn child acquires a moral right to life. Thus willingness to think critically requires open-mindedness, in the sense of a willingness to examine questions to which one already accepts an answer but which further evidence or reasoning might cause one to answer differently (Dewey 1933; Facione 1990a; Ennis 1991; Bailin et al. 1999b; Halpern 1998, Facione, Facione, & Giancarlo 2001). Paul (1981) emphasizes open-mindedness about alternative world-views, and recommends a dialectical approach to integrating such views as central to what he calls “strong sense” critical thinking. In three studies, Haran, Ritov, & Mellers (2013) found that actively open-minded thinking, including “the tendency to weigh new evidence against a favored belief, to spend sufficient time on a problem before giving up, and to consider carefully the opinions of others in forming one’s own”, led study participants to acquire information and thus to make accurate estimations.
  • Willingness to suspend judgment : Premature closure on an initial solution will block critical thinking. Thus willingness to think critically requires a willingness to suspend judgment while alternatives are explored (Facione 1990a; Ennis 1991; Halpern 1998).
  • Trust in reason : Since distrust in the processes of reasoned inquiry will dissuade one from engaging in it, trust in them is an initiating critical thinking disposition (Facione 1990a, 25; Bailin et al. 1999b: 294; Facione, Facione, & Giancarlo 2001; Paul & Elder 2006). In reaction to an allegedly exclusive emphasis on reason in critical thinking theory and pedagogy, Thayer-Bacon (2000) argues that intuition, imagination, and emotion have important roles to play in an adequate conception of critical thinking that she calls “constructive thinking”. From her point of view, critical thinking requires trust not only in reason but also in intuition, imagination, and emotion.
  • Seeking the truth : If one does not care about the truth but is content to stick with one’s initial bias on an issue, then one will not think critically about it. Seeking the truth is thus an initiating critical thinking disposition (Bailin et al. 1999b: 294; Facione, Facione, & Giancarlo 2001). A disposition to seek the truth is implicit in more specific critical thinking dispositions, such as trying to be well-informed, considering seriously points of view other than one’s own, looking for alternatives, suspending judgment when the evidence is insufficient, and adopting a position when the evidence supporting it is sufficient.

Some of the initiating dispositions, such as open-mindedness and willingness to suspend judgment, are also internal critical thinking dispositions, in the sense of mental habits or attitudes that contribute causally to doing a good job of critical thinking once one starts the process. But there are many other internal critical thinking dispositions. Some of them are parasitic on one’s conception of good thinking. For example, it is constitutive of good thinking about an issue to formulate the issue clearly and to maintain focus on it. For this purpose, one needs not only the corresponding ability but also the corresponding disposition. Ennis (1991: 8) describes it as the disposition “to determine and maintain focus on the conclusion or question”, Facione (1990a: 25) as “clarity in stating the question or concern”. Other internal dispositions are motivators to continue or adjust the critical thinking process, such as willingness to persist in a complex task and willingness to abandon nonproductive strategies in an attempt to self-correct (Halpern 1998: 452). For a list of identified internal critical thinking dispositions, see the Supplement on Internal Critical Thinking Dispositions .

Some theorists postulate skills, i.e., acquired abilities, as operative in critical thinking. It is not obvious, however, that a good mental act is the exercise of a generic acquired skill. Inferring an expected time of arrival, as in Transit , has some generic components but also uses non-generic subject-matter knowledge. Bailin et al. (1999a) argue against viewing critical thinking skills as generic and discrete, on the ground that skilled performance at a critical thinking task cannot be separated from knowledge of concepts and from domain-specific principles of good thinking. Talk of skills, they concede, is unproblematic if it means merely that a person with critical thinking skills is capable of intelligent performance.

Despite such scepticism, theorists of critical thinking have listed as general contributors to critical thinking what they variously call abilities (Glaser 1941; Ennis 1962, 1991), skills (Facione 1990a; Halpern 1998) or competencies (Fisher & Scriven 1997). Amalgamating these lists would produce a confusing and chaotic cornucopia of more than 50 possible educational objectives, with only partial overlap among them. It makes sense instead to try to understand the reasons for the multiplicity and diversity, and to make a selection according to one’s own reasons for singling out abilities to be developed in a critical thinking curriculum. Two reasons for diversity among lists of critical thinking abilities are the underlying conception of critical thinking and the envisaged educational level. Appraisal-only conceptions, for example, involve a different suite of abilities than constructive-only conceptions. Some lists, such as those in (Glaser 1941), are put forward as educational objectives for secondary school students, whereas others are proposed as objectives for college students (e.g., Facione 1990a).

The abilities described in the remaining paragraphs of this section emerge from reflection on the general abilities needed to do well the thinking activities identified in section 6 as components of the critical thinking process described in section 5 . The derivation of each collection of abilities is accompanied by citation of sources that list such abilities and of standardized tests that claim to test them.

Observational abilities : Careful and accurate observation sometimes requires specialist expertise and practice, as in the case of observing birds and observing accident scenes. However, there are general abilities of noticing what one’s senses are picking up from one’s environment and of being able to articulate clearly and accurately to oneself and others what one has observed. It helps in exercising them to be able to recognize and take into account factors that make one’s observation less trustworthy, such as prior framing of the situation, inadequate time, deficient senses, poor observation conditions, and the like. It helps as well to be skilled at taking steps to make one’s observation more trustworthy, such as moving closer to get a better look, measuring something three times and taking the average, and checking what one thinks one is observing with someone else who is in a good position to observe it. It also helps to be skilled at recognizing respects in which one’s report of one’s observation involves inference rather than direct observation, so that one can then consider whether the inference is justified. These abilities come into play as well when one thinks about whether and with what degree of confidence to accept an observation report, for example in the study of history or in a criminal investigation or in assessing news reports. Observational abilities show up in some lists of critical thinking abilities (Ennis 1962: 90; Facione 1990a: 16; Ennis 1991: 9). There are items testing a person’s ability to judge the credibility of observation reports in the Cornell Critical Thinking Tests, Levels X and Z (Ennis & Millman 1971; Ennis, Millman, & Tomko 1985, 2005). Norris and King (1983, 1985, 1990a, 1990b) is a test of ability to appraise observation reports.

Emotional abilities : The emotions that drive a critical thinking process are perplexity or puzzlement, a wish to resolve it, and satisfaction at achieving the desired resolution. Children experience these emotions at an early age, without being trained to do so. Education that takes critical thinking as a goal needs only to channel these emotions and to make sure not to stifle them. Collaborative critical thinking benefits from ability to recognize one’s own and others’ emotional commitments and reactions.

Questioning abilities : A critical thinking process needs transformation of an inchoate sense of perplexity into a clear question. Formulating a question well requires not building in questionable assumptions, not prejudging the issue, and using language that in context is unambiguous and precise enough (Ennis 1962: 97; 1991: 9).

Imaginative abilities : Thinking directed at finding the correct causal explanation of a general phenomenon or particular event requires an ability to imagine possible explanations. Thinking about what policy or plan of action to adopt requires generation of options and consideration of possible consequences of each option. Domain knowledge is required for such creative activity, but a general ability to imagine alternatives is helpful and can be nurtured so as to become easier, quicker, more extensive, and deeper (Dewey 1910: 34–39; 1933: 40–47). Facione (1990a) and Halpern (1998) include the ability to imagine alternatives as a critical thinking ability.

Inferential abilities : The ability to draw conclusions from given information, and to recognize with what degree of certainty one’s own or others’ conclusions follow, is universally recognized as a general critical thinking ability. All 11 examples in section 2 of this article include inferences, some from hypotheses or options (as in Transit , Ferryboat and Disorder ), others from something observed (as in Weather and Rash ). None of these inferences is formally valid. Rather, they are licensed by general, sometimes qualified substantive rules of inference (Toulmin 1958) that rest on domain knowledge—that a bus trip takes about the same time in each direction, that the terminal of a wireless telegraph would be located on the highest possible place, that sudden cooling is often followed by rain, that an allergic reaction to a sulfa drug generally shows up soon after one starts taking it. It is a matter of controversy to what extent the specialized ability to deduce conclusions from premisses using formal rules of inference is needed for critical thinking. Dewey (1933) locates logical forms in setting out the products of reflection rather than in the process of reflection. Ennis (1981a), on the other hand, maintains that a liberally-educated person should have the following abilities: to translate natural-language statements into statements using the standard logical operators, to use appropriately the language of necessary and sufficient conditions, to deal with argument forms and arguments containing symbols, to determine whether in virtue of an argument’s form its conclusion follows necessarily from its premisses, to reason with logically complex propositions, and to apply the rules and procedures of deductive logic. Inferential abilities are recognized as critical thinking abilities by Glaser (1941: 6), Facione (1990a: 9), Ennis (1991: 9), Fisher & Scriven (1997: 99, 111), and Halpern (1998: 452). Items testing inferential abilities constitute two of the five subtests of the Watson Glaser Critical Thinking Appraisal (Watson & Glaser 1980a, 1980b, 1994), two of the four sections in the Cornell Critical Thinking Test Level X (Ennis & Millman 1971; Ennis, Millman, & Tomko 1985, 2005), three of the seven sections in the Cornell Critical Thinking Test Level Z (Ennis & Millman 1971; Ennis, Millman, & Tomko 1985, 2005), 11 of the 34 items on Forms A and B of the California Critical Thinking Skills Test (Facione 1990b, 1992), and a high but variable proportion of the 25 selected-response questions in the Collegiate Learning Assessment (Council for Aid to Education 2017).

Experimenting abilities : Knowing how to design and execute an experiment is important not just in scientific research but also in everyday life, as in Rash . Dewey devoted a whole chapter of his How We Think (1910: 145–156; 1933: 190–202) to the superiority of experimentation over observation in advancing knowledge. Experimenting abilities come into play at one remove in appraising reports of scientific studies. Skill in designing and executing experiments includes the acknowledged abilities to appraise evidence (Glaser 1941: 6), to carry out experiments and to apply appropriate statistical inference techniques (Facione 1990a: 9), to judge inductions to an explanatory hypothesis (Ennis 1991: 9), and to recognize the need for an adequately large sample size (Halpern 1998). The Cornell Critical Thinking Test Level Z (Ennis & Millman 1971; Ennis, Millman, & Tomko 1985, 2005) includes four items (out of 52) on experimental design. The Collegiate Learning Assessment (Council for Aid to Education 2017) makes room for appraisal of study design in both its performance task and its selected-response questions.

Consulting abilities : Skill at consulting sources of information comes into play when one seeks information to help resolve a problem, as in Candidate . Ability to find and appraise information includes ability to gather and marshal pertinent information (Glaser 1941: 6), to judge whether a statement made by an alleged authority is acceptable (Ennis 1962: 84), to plan a search for desired information (Facione 1990a: 9), and to judge the credibility of a source (Ennis 1991: 9). Ability to judge the credibility of statements is tested by 24 items (out of 76) in the Cornell Critical Thinking Test Level X (Ennis & Millman 1971; Ennis, Millman, & Tomko 1985, 2005) and by four items (out of 52) in the Cornell Critical Thinking Test Level Z (Ennis & Millman 1971; Ennis, Millman, & Tomko 1985, 2005). The College Learning Assessment’s performance task requires evaluation of whether information in documents is credible or unreliable (Council for Aid to Education 2017).

Argument analysis abilities : The ability to identify and analyze arguments contributes to the process of surveying arguments on an issue in order to form one’s own reasoned judgment, as in Candidate . The ability to detect and analyze arguments is recognized as a critical thinking skill by Facione (1990a: 7–8), Ennis (1991: 9) and Halpern (1998). Five items (out of 34) on the California Critical Thinking Skills Test (Facione 1990b, 1992) test skill at argument analysis. The College Learning Assessment (Council for Aid to Education 2017) incorporates argument analysis in its selected-response tests of critical reading and evaluation and of critiquing an argument.

Judging skills and deciding skills : Skill at judging and deciding is skill at recognizing what judgment or decision the available evidence and argument supports, and with what degree of confidence. It is thus a component of the inferential skills already discussed.

Lists and tests of critical thinking abilities often include two more abilities: identifying assumptions and constructing and evaluating definitions.

In addition to dispositions and abilities, critical thinking needs knowledge: of critical thinking concepts, of critical thinking principles, and of the subject-matter of the thinking.

We can derive a short list of concepts whose understanding contributes to critical thinking from the critical thinking abilities described in the preceding section. Observational abilities require an understanding of the difference between observation and inference. Questioning abilities require an understanding of the concepts of ambiguity and vagueness. Inferential abilities require an understanding of the difference between conclusive and defeasible inference (traditionally, between deduction and induction), as well as of the difference between necessary and sufficient conditions. Experimenting abilities require an understanding of the concepts of hypothesis, null hypothesis, assumption and prediction, as well as of the concept of statistical significance and of its difference from importance. They also require an understanding of the difference between an experiment and an observational study, and in particular of the difference between a randomized controlled trial, a prospective correlational study and a retrospective (case-control) study. Argument analysis abilities require an understanding of the concepts of argument, premiss, assumption, conclusion and counter-consideration. Additional critical thinking concepts are proposed by Bailin et al. (1999b: 293), Fisher & Scriven (1997: 105–106), Black (2012), and Blair (2021).

According to Glaser (1941: 25), ability to think critically requires knowledge of the methods of logical inquiry and reasoning. If we review the list of abilities in the preceding section, however, we can see that some of them can be acquired and exercised merely through practice, possibly guided in an educational setting, followed by feedback. Searching intelligently for a causal explanation of some phenomenon or event requires that one consider a full range of possible causal contributors, but it seems more important that one implements this principle in one’s practice than that one is able to articulate it. What is important is “operational knowledge” of the standards and principles of good thinking (Bailin et al. 1999b: 291–293). But the development of such critical thinking abilities as designing an experiment or constructing an operational definition can benefit from learning their underlying theory. Further, explicit knowledge of quirks of human thinking seems useful as a cautionary guide. Human memory is not just fallible about details, as people learn from their own experiences of misremembering, but is so malleable that a detailed, clear and vivid recollection of an event can be a total fabrication (Loftus 2017). People seek or interpret evidence in ways that are partial to their existing beliefs and expectations, often unconscious of their “confirmation bias” (Nickerson 1998). Not only are people subject to this and other cognitive biases (Kahneman 2011), of which they are typically unaware, but it may be counter-productive for one to make oneself aware of them and try consciously to counteract them or to counteract social biases such as racial or sexual stereotypes (Kenyon & Beaulac 2014). It is helpful to be aware of these facts and of the superior effectiveness of blocking the operation of biases—for example, by making an immediate record of one’s observations, refraining from forming a preliminary explanatory hypothesis, blind refereeing, double-blind randomized trials, and blind grading of students’ work. It is also helpful to be aware of the prevalence of “noise” (unwanted unsystematic variability of judgments), of how to detect noise (through a noise audit), and of how to reduce noise: make accuracy the goal, think statistically, break a process of arriving at a judgment into independent tasks, resist premature intuitions, in a group get independent judgments first, favour comparative judgments and scales (Kahneman, Sibony, & Sunstein 2021). It is helpful as well to be aware of the concept of “bounded rationality” in decision-making and of the related distinction between “satisficing” and optimizing (Simon 1956; Gigerenzer 2001).

Critical thinking about an issue requires substantive knowledge of the domain to which the issue belongs. Critical thinking abilities are not a magic elixir that can be applied to any issue whatever by somebody who has no knowledge of the facts relevant to exploring that issue. For example, the student in Bubbles needed to know that gases do not penetrate solid objects like a glass, that air expands when heated, that the volume of an enclosed gas varies directly with its temperature and inversely with its pressure, and that hot objects will spontaneously cool down to the ambient temperature of their surroundings unless kept hot by insulation or a source of heat. Critical thinkers thus need a rich fund of subject-matter knowledge relevant to the variety of situations they encounter. This fact is recognized in the inclusion among critical thinking dispositions of a concern to become and remain generally well informed.

Experimental educational interventions, with control groups, have shown that education can improve critical thinking skills and dispositions, as measured by standardized tests. For information about these tests, see the Supplement on Assessment .

What educational methods are most effective at developing the dispositions, abilities and knowledge of a critical thinker? In a comprehensive meta-analysis of experimental and quasi-experimental studies of strategies for teaching students to think critically, Abrami et al. (2015) found that dialogue, anchored instruction, and mentoring each increased the effectiveness of the educational intervention, and that they were most effective when combined. They also found that in these studies a combination of separate instruction in critical thinking with subject-matter instruction in which students are encouraged to think critically was more effective than either by itself. However, the difference was not statistically significant; that is, it might have arisen by chance.

Most of these studies lack the longitudinal follow-up required to determine whether the observed differential improvements in critical thinking abilities or dispositions continue over time, for example until high school or college graduation. For details on studies of methods of developing critical thinking skills and dispositions, see the Supplement on Educational Methods .

12. Controversies

Scholars have denied the generalizability of critical thinking abilities across subject domains, have alleged bias in critical thinking theory and pedagogy, and have investigated the relationship of critical thinking to other kinds of thinking.

McPeck (1981) attacked the thinking skills movement of the 1970s, including the critical thinking movement. He argued that there are no general thinking skills, since thinking is always thinking about some subject-matter. It is futile, he claimed, for schools and colleges to teach thinking as if it were a separate subject. Rather, teachers should lead their pupils to become autonomous thinkers by teaching school subjects in a way that brings out their cognitive structure and that encourages and rewards discussion and argument. As some of his critics (e.g., Paul 1985; Siegel 1985) pointed out, McPeck’s central argument needs elaboration, since it has obvious counter-examples in writing and speaking, for which (up to a certain level of complexity) there are teachable general abilities even though they are always about some subject-matter. To make his argument convincing, McPeck needs to explain how thinking differs from writing and speaking in a way that does not permit useful abstraction of its components from the subject-matters with which it deals. He has not done so. Nevertheless, his position that the dispositions and abilities of a critical thinker are best developed in the context of subject-matter instruction is shared by many theorists of critical thinking, including Dewey (1910, 1933), Glaser (1941), Passmore (1980), Weinstein (1990), Bailin et al. (1999b), and Willingham (2019).

McPeck’s challenge prompted reflection on the extent to which critical thinking is subject-specific. McPeck argued for a strong subject-specificity thesis, according to which it is a conceptual truth that all critical thinking abilities are specific to a subject. (He did not however extend his subject-specificity thesis to critical thinking dispositions. In particular, he took the disposition to suspend judgment in situations of cognitive dissonance to be a general disposition.) Conceptual subject-specificity is subject to obvious counter-examples, such as the general ability to recognize confusion of necessary and sufficient conditions. A more modest thesis, also endorsed by McPeck, is epistemological subject-specificity, according to which the norms of good thinking vary from one field to another. Epistemological subject-specificity clearly holds to a certain extent; for example, the principles in accordance with which one solves a differential equation are quite different from the principles in accordance with which one determines whether a painting is a genuine Picasso. But the thesis suffers, as Ennis (1989) points out, from vagueness of the concept of a field or subject and from the obvious existence of inter-field principles, however broadly the concept of a field is construed. For example, the principles of hypothetico-deductive reasoning hold for all the varied fields in which such reasoning occurs. A third kind of subject-specificity is empirical subject-specificity, according to which as a matter of empirically observable fact a person with the abilities and dispositions of a critical thinker in one area of investigation will not necessarily have them in another area of investigation.

The thesis of empirical subject-specificity raises the general problem of transfer. If critical thinking abilities and dispositions have to be developed independently in each school subject, how are they of any use in dealing with the problems of everyday life and the political and social issues of contemporary society, most of which do not fit into the framework of a traditional school subject? Proponents of empirical subject-specificity tend to argue that transfer is more likely to occur if there is critical thinking instruction in a variety of domains, with explicit attention to dispositions and abilities that cut across domains. But evidence for this claim is scanty. There is a need for well-designed empirical studies that investigate the conditions that make transfer more likely.

It is common ground in debates about the generality or subject-specificity of critical thinking dispositions and abilities that critical thinking about any topic requires background knowledge about the topic. For example, the most sophisticated understanding of the principles of hypothetico-deductive reasoning is of no help unless accompanied by some knowledge of what might be plausible explanations of some phenomenon under investigation.

Critics have objected to bias in the theory, pedagogy and practice of critical thinking. Commentators (e.g., Alston 1995; Ennis 1998) have noted that anyone who takes a position has a bias in the neutral sense of being inclined in one direction rather than others. The critics, however, are objecting to bias in the pejorative sense of an unjustified favoring of certain ways of knowing over others, frequently alleging that the unjustly favoured ways are those of a dominant sex or culture (Bailin 1995). These ways favour:

  • reinforcement of egocentric and sociocentric biases over dialectical engagement with opposing world-views (Paul 1981, 1984; Warren 1998)
  • distancing from the object of inquiry over closeness to it (Martin 1992; Thayer-Bacon 1992)
  • indifference to the situation of others over care for them (Martin 1992)
  • orientation to thought over orientation to action (Martin 1992)
  • being reasonable over caring to understand people’s ideas (Thayer-Bacon 1993)
  • being neutral and objective over being embodied and situated (Thayer-Bacon 1995a)
  • doubting over believing (Thayer-Bacon 1995b)
  • reason over emotion, imagination and intuition (Thayer-Bacon 2000)
  • solitary thinking over collaborative thinking (Thayer-Bacon 2000)
  • written and spoken assignments over other forms of expression (Alston 2001)
  • attention to written and spoken communications over attention to human problems (Alston 2001)
  • winning debates in the public sphere over making and understanding meaning (Alston 2001)

A common thread in this smorgasbord of accusations is dissatisfaction with focusing on the logical analysis and evaluation of reasoning and arguments. While these authors acknowledge that such analysis and evaluation is part of critical thinking and should be part of its conceptualization and pedagogy, they insist that it is only a part. Paul (1981), for example, bemoans the tendency of atomistic teaching of methods of analyzing and evaluating arguments to turn students into more able sophists, adept at finding fault with positions and arguments with which they disagree but even more entrenched in the egocentric and sociocentric biases with which they began. Martin (1992) and Thayer-Bacon (1992) cite with approval the self-reported intimacy with their subject-matter of leading researchers in biology and medicine, an intimacy that conflicts with the distancing allegedly recommended in standard conceptions and pedagogy of critical thinking. Thayer-Bacon (2000) contrasts the embodied and socially embedded learning of her elementary school students in a Montessori school, who used their imagination, intuition and emotions as well as their reason, with conceptions of critical thinking as

thinking that is used to critique arguments, offer justifications, and make judgments about what are the good reasons, or the right answers. (Thayer-Bacon 2000: 127–128)

Alston (2001) reports that her students in a women’s studies class were able to see the flaws in the Cinderella myth that pervades much romantic fiction but in their own romantic relationships still acted as if all failures were the woman’s fault and still accepted the notions of love at first sight and living happily ever after. Students, she writes, should

be able to connect their intellectual critique to a more affective, somatic, and ethical account of making risky choices that have sexist, racist, classist, familial, sexual, or other consequences for themselves and those both near and far… critical thinking that reads arguments, texts, or practices merely on the surface without connections to feeling/desiring/doing or action lacks an ethical depth that should infuse the difference between mere cognitive activity and something we want to call critical thinking. (Alston 2001: 34)

Some critics portray such biases as unfair to women. Thayer-Bacon (1992), for example, has charged modern critical thinking theory with being sexist, on the ground that it separates the self from the object and causes one to lose touch with one’s inner voice, and thus stigmatizes women, who (she asserts) link self to object and listen to their inner voice. Her charge does not imply that women as a group are on average less able than men to analyze and evaluate arguments. Facione (1990c) found no difference by sex in performance on his California Critical Thinking Skills Test. Kuhn (1991: 280–281) found no difference by sex in either the disposition or the competence to engage in argumentative thinking.

The critics propose a variety of remedies for the biases that they allege. In general, they do not propose to eliminate or downplay critical thinking as an educational goal. Rather, they propose to conceptualize critical thinking differently and to change its pedagogy accordingly. Their pedagogical proposals arise logically from their objections. They can be summarized as follows:

  • Focus on argument networks with dialectical exchanges reflecting contesting points of view rather than on atomic arguments, so as to develop “strong sense” critical thinking that transcends egocentric and sociocentric biases (Paul 1981, 1984).
  • Foster closeness to the subject-matter and feeling connected to others in order to inform a humane democracy (Martin 1992).
  • Develop “constructive thinking” as a social activity in a community of physically embodied and socially embedded inquirers with personal voices who value not only reason but also imagination, intuition and emotion (Thayer-Bacon 2000).
  • In developing critical thinking in school subjects, treat as important neither skills nor dispositions but opening worlds of meaning (Alston 2001).
  • Attend to the development of critical thinking dispositions as well as skills, and adopt the “critical pedagogy” practised and advocated by Freire (1968 [1970]) and hooks (1994) (Dalgleish, Girard, & Davies 2017).

A common thread in these proposals is treatment of critical thinking as a social, interactive, personally engaged activity like that of a quilting bee or a barn-raising (Thayer-Bacon 2000) rather than as an individual, solitary, distanced activity symbolized by Rodin’s The Thinker . One can get a vivid description of education with the former type of goal from the writings of bell hooks (1994, 2010). Critical thinking for her is open-minded dialectical exchange across opposing standpoints and from multiple perspectives, a conception similar to Paul’s “strong sense” critical thinking (Paul 1981). She abandons the structure of domination in the traditional classroom. In an introductory course on black women writers, for example, she assigns students to write an autobiographical paragraph about an early racial memory, then to read it aloud as the others listen, thus affirming the uniqueness and value of each voice and creating a communal awareness of the diversity of the group’s experiences (hooks 1994: 84). Her “engaged pedagogy” is thus similar to the “freedom under guidance” implemented in John Dewey’s Laboratory School of Chicago in the late 1890s and early 1900s. It incorporates the dialogue, anchored instruction, and mentoring that Abrami (2015) found to be most effective in improving critical thinking skills and dispositions.

What is the relationship of critical thinking to problem solving, decision-making, higher-order thinking, creative thinking, and other recognized types of thinking? One’s answer to this question obviously depends on how one defines the terms used in the question. If critical thinking is conceived broadly to cover any careful thinking about any topic for any purpose, then problem solving and decision making will be kinds of critical thinking, if they are done carefully. Historically, ‘critical thinking’ and ‘problem solving’ were two names for the same thing. If critical thinking is conceived more narrowly as consisting solely of appraisal of intellectual products, then it will be disjoint with problem solving and decision making, which are constructive.

Bloom’s taxonomy of educational objectives used the phrase “intellectual abilities and skills” for what had been labeled “critical thinking” by some, “reflective thinking” by Dewey and others, and “problem solving” by still others (Bloom et al. 1956: 38). Thus, the so-called “higher-order thinking skills” at the taxonomy’s top levels of analysis, synthesis and evaluation are just critical thinking skills, although they do not come with general criteria for their assessment (Ennis 1981b). The revised version of Bloom’s taxonomy (Anderson et al. 2001) likewise treats critical thinking as cutting across those types of cognitive process that involve more than remembering (Anderson et al. 2001: 269–270). For details, see the Supplement on History .

As to creative thinking, it overlaps with critical thinking (Bailin 1987, 1988). Thinking about the explanation of some phenomenon or event, as in Ferryboat , requires creative imagination in constructing plausible explanatory hypotheses. Likewise, thinking about a policy question, as in Candidate , requires creativity in coming up with options. Conversely, creativity in any field needs to be balanced by critical appraisal of the draft painting or novel or mathematical theory.

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Thinking critically on critical thinking: why scientists’ skills need to spread

critical thinking and scientific thinking

Lecturer in Psychology, University of Tasmania

Disclosure statement

Rachel Grieve does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

University of Tasmania provides funding as a member of The Conversation AU.

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critical thinking and scientific thinking

MATHS AND SCIENCE EDUCATION: We’ve asked our authors about the state of maths and science education in Australia and its future direction. Today, Rachel Grieve discusses why we need to spread science-specific skills into the wider curriculum.

When we think of science and maths, stereotypical visions of lab coats, test-tubes, and formulae often spring to mind.

But more important than these stereotypes are the methods that underpin the work scientists do – namely generating and systematically testing hypotheses. A key part of this is critical thinking.

It’s a skill that often feels in short supply these days, but you don’t necessarily need to study science or maths in order gain it. It’s time to take critical thinking out of the realm of maths and science and broaden it into students’ general education.

What is critical thinking?

Critical thinking is a reflective and analytical style of thinking, with its basis in logic, rationality, and synthesis. It means delving deeper and asking questions like: why is that so? Where is the evidence? How good is that evidence? Is this a good argument? Is it biased? Is it verifiable? What are the alternative explanations?

Critical thinking moves us beyond mere description and into the realms of scientific inference and reasoning. This is what enables discoveries to be made and innovations to be fostered.

For many scientists, critical thinking becomes (seemingly) intuitive, but like any skill set, critical thinking needs to be taught and cultivated. Unfortunately, educators are unable to deposit this information directly into their students’ heads. While the theory of critical thinking can be taught, critical thinking itself needs to be experienced first-hand.

So what does this mean for educators trying to incorporate critical thinking within their curricula? We can teach students the theoretical elements of critical thinking. Take for example working through [statistical problems](http://wdeneys.org/data/COGNIT_1695.pdf](http://wdeneys.org/data/COGNIT_1695.pdf) like this one:

In a 1,000-person study, four people said their favourite series was Star Trek and 996 said Days of Our Lives. Jeremy is a randomly chosen participant in this study, is 26, and is doing graduate studies in physics. He stays at home most of the time and likes to play videogames. What is most likely? a. Jeremy’s favourite series is Star Trek b. Jeremy’s favourite series is Days of Our Lives

Some critical thought applied to this problem allows us to know that Jeremy is most likely to prefer Days of Our Lives.

Can you teach it?

It’s well established that statistical training is associated with improved decision-making. But the idea of “teaching” critical thinking is itself an oxymoron: critical thinking can really only be learned through practice. Thus, it is not surprising that student engagement with the critical thinking process itself is what pays the dividends for students.

As such, educators try to connect students with the subject matter outside the lecture theatre or classroom. For example, problem based learning is now widely used in the health sciences, whereby students must figure out the key issues related to a case and direct their own learning to solve that problem. Problem based learning has clear parallels with real life practice for health professionals.

Critical thinking goes beyond what might be on the final exam and life-long learning becomes the key. This is a good thing, as practice helps to improve our ability to think critically over time .

Just for scientists?

For those engaging with science, learning the skills needed to be a critical consumer of information is invaluable. But should these skills remain in the domain of scientists? Clearly not: for those engaging with life, being a critical consumer of information is also invaluable, allowing informed judgement.

Being able to actively consider and evaluate information, identify biases, examine the logic of arguments, and tolerate ambiguity until the evidence is in would allow many people from all backgrounds to make better decisions. While these decisions can be trivial (does that miracle anti-wrinkle cream really do what it claims?), in many cases, reasoning and decision-making can have a substantial impact, with some decisions have life-altering effects. A timely case-in-point is immunisation.

Pushing critical thinking from the realms of science and maths into the broader curriculum may lead to far-reaching outcomes. With increasing access to information on the internet, giving individuals the skills to critically think about that information may have widespread benefit, both personally and socially.

The value of science education might not always be in the facts, but in the thinking.

This is the sixth part of our series Maths and Science Education .

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The Oxford Handbook of Thinking and Reasoning

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35 Scientific Thinking and Reasoning

Kevin N. Dunbar, Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD

David Klahr, Department of Psychology, Carnegie Mellon University, Pittsburgh, PA

  • Published: 21 November 2012
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Scientific thinking refers to both thinking about the content of science and the set of reasoning processes that permeate the field of science: induction, deduction, experimental design, causal reasoning, concept formation, hypothesis testing, and so on. Here we cover both the history of research on scientific thinking and the different approaches that have been used, highlighting common themes that have emerged over the past 50 years of research. Future research will focus on the collaborative aspects of scientific thinking, on effective methods for teaching science, and on the neural underpinnings of the scientific mind.

There is no unitary activity called “scientific discovery”; there are activities of designing experiments, gathering data, inventing and developing observational instruments, formulating and modifying theories, deducing consequences from theories, making predictions from theories, testing theories, inducing regularities and invariants from data, discovering theoretical constructs, and others. — Simon, Langley, & Bradshaw, 1981 , p. 2

What Is Scientific Thinking and Reasoning?

There are two kinds of thinking we call “scientific.” The first, and most obvious, is thinking about the content of science. People are engaged in scientific thinking when they are reasoning about such entities and processes as force, mass, energy, equilibrium, magnetism, atoms, photosynthesis, radiation, geology, or astrophysics (and, of course, cognitive psychology!). The second kind of scientific thinking includes the set of reasoning processes that permeate the field of science: induction, deduction, experimental design, causal reasoning, concept formation, hypothesis testing, and so on. However, these reasoning processes are not unique to scientific thinking: They are the very same processes involved in everyday thinking. As Einstein put it:

The scientific way of forming concepts differs from that which we use in our daily life, not basically, but merely in the more precise definition of concepts and conclusions; more painstaking and systematic choice of experimental material, and greater logical economy. (The Common Language of Science, 1941, reprinted in Einstein, 1950 , p. 98)

Nearly 40 years after Einstein's remarkably insightful statement, Francis Crick offered a similar perspective: that great discoveries in science result not from extraordinary mental processes, but rather from rather common ones. The greatness of the discovery lies in the thing discovered.

I think what needs to be emphasized about the discovery of the double helix is that the path to it was, scientifically speaking, fairly commonplace. What was important was not the way it was discovered , but the object discovered—the structure of DNA itself. (Crick, 1988 , p. 67; emphasis added)

Under this view, scientific thinking involves the same general-purpose cognitive processes—such as induction, deduction, analogy, problem solving, and causal reasoning—that humans apply in nonscientific domains. These processes are covered in several different chapters of this handbook: Rips, Smith, & Medin, Chapter 11 on induction; Evans, Chapter 8 on deduction; Holyoak, Chapter 13 on analogy; Bassok & Novick, Chapter 21 on problem solving; and Cheng & Buehner, Chapter 12 on causality. One might question the claim that the highly specialized procedures associated with doing science in the “real world” can be understood by investigating the thinking processes used in laboratory studies of the sort described in this volume. However, when the focus is on major scientific breakthroughs, rather than on the more routine, incremental progress in a field, the psychology of problem solving provides a rich source of ideas about how such discoveries might occur. As Simon and his colleagues put it:

It is understandable, if ironic, that ‘normal’ science fits … the description of expert problem solving, while ‘revolutionary’ science fits the description of problem solving by novices. It is understandable because scientific activity, particularly at the revolutionary end of the continuum, is concerned with the discovery of new truths, not with the application of truths that are already well-known … it is basically a journey into unmapped terrain. Consequently, it is mainly characterized, as is novice problem solving, by trial-and-error search. The search may be highly selective—but it reaches its goal only after many halts, turnings, and back-trackings. (Simon, Langley, & Bradshaw, 1981 , p. 5)

The research literature on scientific thinking can be roughly categorized according to the two types of scientific thinking listed in the opening paragraph of this chapter: (1) One category focuses on thinking that directly involves scientific content . Such research ranges from studies of young children reasoning about the sun-moon-earth system (Vosniadou & Brewer, 1992 ) to college students reasoning about chemical equilibrium (Davenport, Yaron, Klahr, & Koedinger, 2008 ), to research that investigates collaborative problem solving by world-class researchers in real-world molecular biology labs (Dunbar, 1995 ). (2) The other category focuses on “general” cognitive processes, but it tends to do so by analyzing people's problem-solving behavior when they are presented with relatively complex situations that involve the integration and coordination of several different types of processes, and that are designed to capture some essential features of “real-world” science in the psychology laboratory (Bruner, Goodnow, & Austin, 1956 ; Klahr & Dunbar, 1988 ; Mynatt, Doherty, & Tweney, 1977 ).

There are a number of overlapping research traditions that have been used to investigate scientific thinking. We will cover both the history of research on scientific thinking and the different approaches that have been used, highlighting common themes that have emerged over the past 50 years of research.

A Brief History of Research on Scientific Thinking

Science is often considered one of the hallmarks of the human species, along with art and literature. Illuminating the thought processes used in science thus reveal key aspects of the human mind. The thought processes underlying scientific thinking have fascinated both scientists and nonscientists because the products of science have transformed our world and because the process of discovery is shrouded in mystery. Scientists talk of the chance discovery, the flash of insight, the years of perspiration, and the voyage of discovery. These images of science have helped make the mental processes underlying the discovery process intriguing to cognitive scientists as they attempt to uncover what really goes on inside the scientific mind and how scientists really think. Furthermore, the possibilities that scientists can be taught to think better by avoiding mistakes that have been clearly identified in research on scientific thinking, and that their scientific process could be partially automated, makes scientific thinking a topic of enduring interest.

The cognitive processes underlying scientific discovery and day-to-day scientific thinking have been a topic of intense scrutiny and speculation for almost 400 years (e.g., Bacon, 1620 ; Galilei 1638 ; Klahr 2000 ; Tweney, Doherty, & Mynatt, 1981 ). Understanding the nature of scientific thinking has been a central issue not only for our understanding of science but also for our understating of what it is to be human. Bacon's Novumm Organum in 1620 sketched out some of the key features of the ways that experiments are designed and data interpreted. Over the ensuing 400 years philosophers and scientists vigorously debated about the appropriate methods that scientists should use (see Giere, 1993 ). These debates over the appropriate methods for science typically resulted in the espousal of a particular type of reasoning method, such as induction or deduction. It was not until the Gestalt psychologists began working on the nature of human problem solving, during the 1940s, that experimental psychologists began to investigate the cognitive processes underlying scientific thinking and reasoning.

The Gestalt psychologist Max Wertheimer pioneered the investigation of scientific thinking (of the first type described earlier: thinking about scientific content ) in his landmark book Productive Thinking (Wertheimer, 1945 ). Wertheimer spent a considerable amount of time corresponding with Albert Einstein, attempting to discover how Einstein generated the concept of relativity. Wertheimer argued that Einstein had to overcome the structure of Newtonian physics at each step in his theorizing, and the ways that Einstein actually achieved this restructuring were articulated in terms of Gestalt theories. (For a recent and different account of how Einstein made his discovery, see Galison, 2003 .) We will see later how this process of overcoming alternative theories is an obstacle that both scientists and nonscientists need to deal with when evaluating and theorizing about the world.

One of the first investigations of scientific thinking of the second type (i.e., collections of general-purpose processes operating on complex, abstract, components of scientific thought) was carried out by Jerome Bruner and his colleagues at Harvard (Bruner et al., 1956 ). They argued that a key activity engaged in by scientists is to determine whether a particular instance is a member of a category. For example, a scientist might want to discover which substances undergo fission when bombarded by neutrons and which substances do not. Here, scientists have to discover the attributes that make a substance undergo fission. Bruner et al. saw scientific thinking as the testing of hypotheses and the collecting of data with the end goal of determining whether something is a member of a category. They invented a paradigm where people were required to formulate hypotheses and collect data that test their hypotheses. In one type of experiment, the participants were shown a card such as one with two borders and three green triangles. The participants were asked to determine the concept that this card represented by choosing other cards and getting feedback from the experimenter as to whether the chosen card was an example of the concept. In this case the participant may have thought that the concept was green and chosen a card with two green squares and one border. If the underlying concept was green, then the experimenter would say that the card was an example of the concept. In terms of scientific thinking, choosing a new card is akin to conducting an experiment, and the feedback from the experimenter is similar to knowing whether a hypothesis is confirmed or disconfirmed. Using this approach, Bruner et al. identified a number of strategies that people use to formulate and test hypotheses. They found that a key factor determining which hypothesis-testing strategy that people use is the amount of memory capacity that the strategy takes up (see also Morrison & Knowlton, Chapter 6 ; Medin et al., Chapter 11 ). Another key factor that they discovered was that it was much more difficult for people to discover negative concepts (e.g., not blue) than positive concepts (e.g., blue). Although Bruner et al.'s research is most commonly viewed as work on concepts, they saw their work as uncovering a key component of scientific thinking.

A second early line of research on scientific thinking was developed by Peter Wason and his colleagues (Wason, 1968 ). Like Bruner et al., Wason saw a key component of scientific thinking as being the testing of hypotheses. Whereas Bruner et al. focused on the different types of strategies that people use to formulate hypotheses, Wason focused on whether people adopt a strategy of trying to confirm or disconfirm their hypotheses. Using Popper's ( 1959 ) theory that scientists should try and falsify rather than confirm their hypotheses, Wason devised a deceptively simple task in which participants were given three numbers, such as 2-4-6, and were asked to discover the rule underlying the three numbers. Participants were asked to generate other triads of numbers and the experimenter would tell the participant whether the triad was consistent or inconsistent with the rule. They were told that when they were sure they knew what the rule was they should state it. Most participants began the experiment by thinking that the rule was even numbers increasing by 2. They then attempted to confirm their hypothesis by generating a triad like 8-10-12, then 14-16-18. These triads are consistent with the rule and the participants were told yes, that the triads were indeed consistent with the rule. However, when they proposed the rule—even numbers increasing by 2—they were told that the rule was incorrect. The correct rule was numbers of increasing magnitude! From this research, Wason concluded that people try to confirm their hypotheses, whereas normatively speaking, they should try to disconfirm their hypotheses. One implication of this research is that confirmation bias is not just restricted to scientists but is a general human tendency.

It was not until the 1970s that a general account of scientific reasoning was proposed. Herbert Simon, often in collaboration with Allan Newell, proposed that scientific thinking is a form of problem solving. He proposed that problem solving is a search in a problem space. Newell and Simon's theory of problem solving is discussed in many places in this handbook, usually in the context of specific problems (see especially Bassok & Novick, Chapter 21 ). Herbert Simon, however, devoted considerable time to understanding many different scientific discoveries and scientific reasoning processes. The common thread in his research was that scientific thinking and discovery is not a mysterious magical process but a process of problem solving in which clear heuristics are used. Simon's goal was to articulate the heuristics that scientists use in their research at a fine-grained level. By constructing computer programs that simulated the process of several major scientific discoveries, Simon and colleagues were able to articulate the specific computations that scientists could have used in making those discoveries (Langley, Simon, Bradshaw, & Zytkow, 1987 ; see section on “Computational Approaches to Scientific Thinking”). Particularly influential was Simon and Lea's ( 1974 ) work demonstrating that concept formation and induction consist of a search in two problem spaces: a space of instances and a space of rules. This idea has influenced problem-solving accounts of scientific thinking that will be discussed in the next section.

Overall, the work of Bruner, Wason, and Simon laid the foundations for contemporary research on scientific thinking. Early research on scientific thinking is summarized in Tweney, Doherty and Mynatt's 1981 book On Scientific Thinking , where they sketched out many of the themes that have dominated research on scientific thinking over the past few decades. Other more recent books such as Cognitive Models of Science (Giere, 1993 ), Exploring Science (Klahr, 2000 ), Cognitive Basis of Science (Carruthers, Stich, & Siegal, 2002 ), and New Directions in Scientific and Technical Thinking (Gorman, Kincannon, Gooding, & Tweney, 2004 ) provide detailed analyses of different aspects of scientific discovery. Another important collection is Vosnadiau's handbook on conceptual change research (Vosniadou, 2008 ). In this chapter, we discuss the main approaches that have been used to investigate scientific thinking.

How does one go about investigating the many different aspects of scientific thinking? One common approach to the study of the scientific mind has been to investigate several key aspects of scientific thinking using abstract tasks designed to mimic some essential characteristics of “real-world” science. There have been numerous methodologies that have been used to analyze the genesis of scientific concepts, theories, hypotheses, and experiments. Researchers have used experiments, verbal protocols, computer programs, and analyzed particular scientific discoveries. A more recent development has been to increase the ecological validity of such research by investigating scientists as they reason “live” (in vivo studies of scientific thinking) in their own laboratories (Dunbar, 1995 , 2002 ). From a “Thinking and Reasoning” standpoint the major aspects of scientific thinking that have been most actively investigated are problem solving, analogical reasoning, hypothesis testing, conceptual change, collaborative reasoning, inductive reasoning, and deductive reasoning.

Scientific Thinking as Problem Solving

One of the primary goals of accounts of scientific thinking has been to provide an overarching framework to understand the scientific mind. One framework that has had a great influence in cognitive science is that scientific thinking and scientific discovery can be conceived as a form of problem solving. As noted in the opening section of this chapter, Simon ( 1977 ; Simon, Langley, & Bradshaw, 1981 ) argued that both scientific thinking in general and problem solving in particular could be thought of as a search in a problem space. A problem space consists of all the possible states of a problem and all the operations that a problem solver can use to get from one state to the next. According to this view, by characterizing the types of representations and procedures that people use to get from one state to another it is possible to understand scientific thinking. Thus, scientific thinking can be characterized as a search in various problem spaces (Simon, 1977 ). Simon investigated a number of scientific discoveries by bringing participants into the laboratory, providing the participants with the data that a scientist had access to, and getting the participants to reason about the data and rediscover a scientific concept. He then analyzed the verbal protocols that participants generated and mapped out the types of problem spaces that the participants search in (e.g., Qin & Simon, 1990 ). Kulkarni and Simon ( 1988 ) used a more historical approach to uncover the problem-solving heuristics that Krebs used in his discovery of the urea cycle. Kulkarni and Simon analyzed Krebs's diaries and proposed a set of problem-solving heuristics that he used in his research. They then built a computer program incorporating the heuristics and biological knowledge that Krebs had before he made his discoveries. Of particular importance are the search heuristics that the program uses, which include experimental proposal heuristics and data interpretation heuristics. A key heuristic was an unusualness heuristic that focused on unusual findings, which guided search through a space of theories and a space of experiments.

Klahr and Dunbar ( 1988 ) extended the search in a problem space approach and proposed that scientific thinking can be thought of as a search through two related spaces: an hypothesis space and an experiment space. Each problem space that a scientist uses will have its own types of representations and operators used to change the representations. Search in the hypothesis space constrains search in the experiment space. Klahr and Dunbar found that some participants move from the hypothesis space to the experiment space, whereas others move from the experiment space to the hypothesis space. These different types of searches lead to the proposal of different types of hypotheses and experiments. More recent work has extended the dual-space approach to include alternative problem-solving spaces, including those for data, instrumentation, and domain-specific knowledge (Klahr & Simon, 1999 ; Schunn & Klahr, 1995 , 1996 ).

Scientific Thinking as Hypothesis Testing

Many researchers have regarded testing specific hypotheses predicted by theories as one of the key attributes of scientific thinking. Hypothesis testing is the process of evaluating a proposition by collecting evidence regarding its truth. Experimental cognitive research on scientific thinking that specifically examines this issue has tended to fall into two broad classes of investigations. The first class is concerned with the types of reasoning that lead scientists astray, thus blocking scientific ingenuity. A large amount of research has been conducted on the potentially faulty reasoning strategies that both participants in experiments and scientists use, such as considering only one favored hypothesis at a time and how this prevents the scientists from making discoveries. The second class is concerned with uncovering the mental processes underlying the generation of new scientific hypotheses and concepts. This research has tended to focus on the use of analogy and imagery in science, as well as the use of specific types of problem-solving heuristics.

Turning first to investigations of what diminishes scientific creativity, philosophers, historians, and experimental psychologists have devoted a considerable amount of research to “confirmation bias.” This occurs when scientists only consider one hypothesis (typically the favored hypothesis) and ignore other alternative hypotheses or potentially relevant hypotheses. This important phenomenon can distort the design of experiments, formulation of theories, and interpretation of data. Beginning with the work of Wason ( 1968 ) and as discussed earlier, researchers have repeatedly shown that when participants are asked to design an experiment to test a hypothesis they will predominantly design experiments that they think will yield results consistent with the hypothesis. Using the 2-4-6 task mentioned earlier, Klayman and Ha ( 1987 ) showed that in situations where one's hypothesis is likely to be confirmed, seeking confirmation is a normatively incorrect strategy, whereas when the probability of confirming one's hypothesis is low, then attempting to confirm one's hypothesis can be an appropriate strategy. Historical analyses by Tweney ( 1989 ), concerning the way that Faraday made his discoveries, and experiments investigating people testing hypotheses, have revealed that people use a confirm early, disconfirm late strategy: When people initially generate or are given hypotheses, they try and gather evidence that is consistent with the hypothesis. Once enough evidence has been gathered, then people attempt to find the boundaries of their hypothesis and often try to disconfirm their hypotheses.

In an interesting variant on the confirmation bias paradigm, Gorman ( 1989 ) showed that when participants are told that there is the possibility of error in the data that they receive, participants assume that any data that are inconsistent with their favored hypothesis are due to error. Thus, the possibility of error “insulates” hypotheses against disconfirmation. This intriguing hypothesis has not been confirmed by other researchers (Penner & Klahr, 1996 ), but it is an intriguing hypothesis that warrants further investigation.

Confirmation bias is very difficult to overcome. Even when participants are asked to consider alternate hypotheses, they will often fail to conduct experiments that could potentially disconfirm their hypothesis. Tweney and his colleagues provide an excellent overview of this phenomenon in their classic monograph On Scientific Thinking (1981). The precise reasons for this type of block are still widely debated. Researchers such as Michael Doherty have argued that working memory limitations make it difficult for people to consider more than one hypothesis. Consistent with this view, Dunbar and Sussman ( 1995 ) have shown that when participants are asked to hold irrelevant items in working memory while testing hypotheses, the participants will be unable to switch hypotheses in the face of inconsistent evidence. While working memory limitations are involved in the phenomenon of confirmation bias, even groups of scientists can also display confirmation bias. For example, the controversy over cold fusion is an example of confirmation bias. Here, large groups of scientists had other hypotheses available to explain their data yet maintained their hypotheses in the face of other more standard alternative hypotheses. Mitroff ( 1974 ) provides some interesting examples of NASA scientists demonstrating confirmation bias, which highlight the roles of commitment and motivation in this process. See also MacPherson and Stanovich ( 2007 ) for specific strategies that can be used to overcome confirmation bias.

Causal Thinking in Science

Much of scientific thinking and scientific theory building pertains to the development of causal models between variables of interest. For example, do vaccines cause illnesses? Do carbon dioxide emissions cause global warming? Does water on a planet indicate that there is life on the planet? Scientists and nonscientists alike are constantly bombarded with statements regarding the causal relationship between such variables. How does one evaluate the status of such claims? What kinds of data are informative? How do scientists and nonscientists deal with data that are inconsistent with their theory?

A central issue in the causal reasoning literature, one that is directly relevant to scientific thinking, is the extent to which scientists and nonscientists alike are governed by the search for causal mechanisms (i.e., how a variable works) versus the search for statistical data (i.e., how often variables co-occur). This dichotomy can be boiled down to the search for qualitative versus quantitative information about the paradigm the scientist is investigating. Researchers from a number of cognitive psychology laboratories have found that people prefer to gather more information about an underlying mechanism than covariation between a cause and an effect (e.g., Ahn, Kalish, Medin, & Gelman, 1995 ). That is, the predominant strategy that students in simulations of scientific thinking use is to gather as much information as possible about how the objects under investigation work, rather than collecting large amounts of quantitative data to determine whether the observations hold across multiple samples. These findings suggest that a central component of scientific thinking may be to formulate explicit mechanistic causal models of scientific events.

One type of situation in which causal reasoning has been observed extensively is when scientists obtain unexpected findings. Both historical and naturalistic research has revealed that reasoning causally about unexpected findings plays a central role in science. Indeed, scientists themselves frequently state that a finding was due to chance or was unexpected. Given that claims of unexpected findings are such a frequent component of scientists' autobiographies and interviews in the media, Dunbar ( 1995 , 1997 , 1999 ; Dunbar & Fugelsang, 2005 ; Fugelsang, Stein, Green, & Dunbar, 2004 ) decided to investigate the ways that scientists deal with unexpected findings. In 1991–1992 Dunbar spent 1 year in three molecular biology laboratories and one immunology laboratory at a prestigious U.S. university. He used the weekly laboratory meeting as a source of data on scientific discovery and scientific reasoning. (He termed this type of study “in vivo” cognition.) When he looked at the types of findings that the scientists made, he found that over 50% of the findings were unexpected and that these scientists had evolved a number of effective strategies for dealing with such findings. One clear strategy was to reason causally about the findings: Scientists attempted to build causal models of their unexpected findings. This causal model building results in the extensive use of collaborative reasoning, analogical reasoning, and problem-solving heuristics (Dunbar, 1997 , 2001 ).

Many of the key unexpected findings that scientists reasoned about in the in vivo studies of scientific thinking were inconsistent with the scientists' preexisting causal models. A laboratory equivalent of the biology labs involved creating a situation in which students obtained unexpected findings that were inconsistent with their preexisting theories. Dunbar and Fugelsang ( 2005 ) examined this issue by creating a scientific causal thinking simulation where experimental outcomes were either expected or unexpected. Dunbar ( 1995 ) has called the study of people reasoning in a cognitive laboratory “in vitro” cognition. These investigators found that students spent considerably more time reasoning about unexpected findings than expected findings. In addition, when assessing the overall degree to which their hypothesis was supported or refuted, participants spent the majority of their time considering unexpected findings. An analysis of participants' verbal protocols indicates that much of this extra time was spent formulating causal models for the unexpected findings. Similarly, scientists spend more time considering unexpected than expected findings, and this time is devoted to building causal models (Dunbar & Fugelsang, 2004 ).

Scientists know that unexpected findings occur often, and they have developed many strategies to take advantage of their unexpected findings. One of the most important places that they anticipate the unexpected is in designing experiments (Baker & Dunbar, 2000 ). They build different causal models of their experiments incorporating many conditions and controls. These multiple conditions and controls allow unknown mechanisms to manifest themselves. Thus, rather than being the victims of the unexpected, they create opportunities for unexpected events to occur, and once these events do occur, they have causal models that allow them to determine exactly where in the causal chain their unexpected finding arose. The results of these in vivo and in vitro studies all point to a more complex and nuanced account of how scientists and nonscientists alike test and evaluate hypotheses about theories.

The Roles of Inductive, Abductive, and Deductive Thinking in Science

One of the most basic characteristics of science is that scientists assume that the universe that we live in follows predictable rules. Scientists reason using a variety of different strategies to make new scientific discoveries. Three frequently used types of reasoning strategies that scientists use are inductive, abductive, and deductive reasoning. In the case of inductive reasoning, a scientist may observe a series of events and try to discover a rule that governs the event. Once a rule is discovered, scientists can extrapolate from the rule to formulate theories of observed and yet-to-be-observed phenomena. One example is the discovery using inductive reasoning that a certain type of bacterium is a cause of many ulcers (Thagard, 1999 ). In a fascinating series of articles, Thagard documented the reasoning processes that Marshall and Warren went through in proposing this novel hypothesis. One key reasoning process was the use of induction by generalization. Marshall and Warren noted that almost all patients with gastric entritis had a spiral bacterium in their stomachs, and he formed the generalization that this bacterium is the cause of stomach ulcers. There are numerous other examples of induction by generalization in science, such as Tycho De Brea's induction about the motion of planets from his observations, Dalton's use of induction in chemistry, and the discovery of prions as the source of mad cow disease. Many theories of induction have used scientific discovery and reasoning as examples of this important reasoning process.

Another common type of inductive reasoning is to map a feature of one member of a category to another member of a category. This is called categorical induction. This type of induction is a way of projecting a known property of one item onto another item that is from the same category. Thus, knowing that the Rous Sarcoma virus is a retrovirus that uses RNA rather than DNA, a biologist might assume that another virus that is thought to be a retrovirus also uses RNA rather than DNA. While research on this type of induction typically has not been discussed in accounts of scientific thinking, this type of induction is common in science. For an influential contribution to this literature, see Smith, Shafir, and Osherson ( 1993 ), and for reviews of this literature see Heit ( 2000 ) and Medin et al. (Chapter 11 ).

While less commonly mentioned than inductive reasoning, abductive reasoning is an important form of reasoning that scientists use when they are seeking to propose explanations for events such as unexpected findings (see Lombrozo, Chapter 14 ; Magnani, et al., 2010 ). In Figure 35.1 , taken from King ( 2011 ), the differences between inductive, abductive, and deductive thinking are highlighted. In the case of abduction, the reasoner attempts to generate explanations of the form “if situation X had occurred, could it have produced the current evidence I am attempting to interpret?” (For an interesting of analysis of abductive reasoning see the brief paper by Klahr & Masnick, 2001 ). Of course, as in classical induction, such reasoning may produce a plausible account that is still not the correct one. However, abduction does involve the generation of new knowledge, and is thus also related to research on creativity.

The different processes underlying inductive, abductive, and deductive reasoning in science. (Figure reproduced from King 2011 ).)

Turning now to deductive thinking, many thinking processes that scientists adhere to follow traditional rules of deductive logic. These processes correspond to those conditions in which a hypothesis may lead to, or is deducible to, a conclusion. Though they are not always phrased in syllogistic form, deductive arguments can be phrased as “syllogisms,” or as brief, mathematical statements in which the premises lead to the conclusion. Deductive reasoning is an extremely important aspect of scientific thinking because it underlies a large component of how scientists conduct their research. By looking at many scientific discoveries, we can often see that deductive reasoning is at work. Deductive reasoning statements all contain information or rules that state an assumption about how the world works, as well as a conclusion that would necessarily follow from the rule. Numerous discoveries in physics such as the discovery of dark matter by Vera Rubin are based on deductions. In the dark matter case, Rubin measured galactic rotation curves and based on the differences between the predicted and observed angular motions of galaxies she deduced that the structure of the universe was uneven. This led her to propose that dark matter existed. In contemporary physics the CERN Large Hadron Collider is being used to search for the Higgs Boson. The Higgs Boson is a deductive prediction from contemporary physics. If the Higgs Boson is not found, it may lead to a radical revision of the nature of physics and a new understanding of mass (Hecht, 2011 ).

The Roles of Analogy in Scientific Thinking

One of the most widely mentioned reasoning processes used in science is analogy. Scientists use analogies to form a bridge between what they already know and what they are trying to explain, understand, or discover. In fact, many scientists have claimed that the making of certain analogies was instrumental in their making a scientific discovery, and almost all scientific autobiographies and biographies feature one particular analogy that is discussed in depth. Coupled with the fact that there has been an enormous research program on analogical thinking and reasoning (see Holyoak, Chapter 13 ), we now have a number of models and theories of analogical reasoning that suggest how analogy can play a role in scientific discovery (see Gentner, Holyoak, & Kokinov, 2001 ). By analyzing several major discoveries in the history of science, Thagard and Croft ( 1999 ), Nersessian ( 1999 , 2008 ), and Gentner and Jeziorski ( 1993 ) have all shown that analogical reasoning is a key aspect of scientific discovery.

Traditional accounts of analogy distinguish between two components of analogical reasoning: the target and the source (Holyoak, Chapter 13 ; Gentner 2010 ). The target is the concept or problem that a scientist is attempting to explain or solve. The source is another piece of knowledge that the scientist uses to understand the target or to explain the target to others. What the scientist does when he or she makes an analogy is to map features of the source onto features of the target. By mapping the features of the source onto the target, new features of the target may be discovered, or the features of the target may be rearranged so that a new concept is invented and a scientific discovery is made. For example, a common analogy that is used with computers is to describe a harmful piece of software as a computer virus. Once a piece of software is called a virus, people can map features of biological viruses, such as that it is small, spreads easily, self-replicates using a host, and causes damage. People not only map individual features of the source onto the target but also the systems of relations. For example, if a computer virus is similar to a biological virus, then an immune system can be created on computers that can protect computers from future variants of a virus. One of the reasons that scientific analogy is so powerful is that it can generate new knowledge, such as the creation of a computational immune system having many of the features of a real biological immune system. This analogy also leads to predictions that there will be newer computer viruses that are the computational equivalent of retroviruses, lacking DNA, or standard instructions, that will elude the computational immune system.

The process of making an analogy involves a number of key steps: retrieval of a source from memory, aligning the features of the source with those of the target, mapping features of the source onto those of the target, and possibly making new inferences about the target. Scientific discoveries are made when the source highlights a hitherto unknown feature of the target or restructures the target into a new set of relations. Interestingly, research on analogy has shown that participants do not easily use remote analogies (see Gentner et al., 1997 ; Holyoak & Thagard 1995 ). Participants in experiments tend to focus on the sharing of a superficial feature between the source and the target, rather than the relations among features. In his in vivo studies of science, Dunbar ( 1995 , 2001 , 2002 ) investigated the ways that scientists use analogies while they are conducting their research and found that scientists use both relational and superficial features when they make analogies. Whether they use superficial or relational features depends on their goals. If their goal is to fix a problem in an experiment, their analogies are based upon superficial features. However, if their goal is to formulate hypotheses, they focus on analogies based upon sets of relations. One important difference between scientists and participants in experiments is that the scientists have deep relational knowledge of the processes that they are investigating and can hence use this relational knowledge to make analogies (see Holyoak, Chapter 13 for a thorough review of analogical reasoning).

Are scientific analogies always useful? Sometimes analogies can lead scientists and students astray. For example, Evelyn Fox-Keller ( 1985 ) shows how an analogy between the pulsing of a lighthouse and the activity of the slime mold dictyostelium led researchers astray for a number of years. Likewise, the analogy between the solar system (the source) and the structure of the atom (the target) has been shown to be potentially misleading to students taking more advanced courses in physics or chemistry. The solar system analogy has a number of misalignments to the structure of the atom, such as electrons being repelled from each other rather than attracted; moreover, electrons do not have individual orbits like planets but have orbit clouds of electron density. Furthermore, students have serious misconceptions about the nature of the solar system, which can compound their misunderstanding of the nature of the atom (Fischler & Lichtfeld, 1992 ). While analogy is a powerful tool in science, like all forms of induction, incorrect conclusions can be reached.

Conceptual Change in Science

Scientific knowledge continually accumulates as scientists gather evidence about the natural world. Over extended time, this knowledge accumulation leads to major revisions, extensions, and new organizational forms for expressing what is known about nature. Indeed, these changes are so substantial that philosophers of science speak of “revolutions” in a variety of scientific domains (Kuhn, 1962 ). The psychological literature that explores the idea of revolutionary conceptual change can be roughly divided into (a) investigations of how scientists actually make discoveries and integrate those discoveries into existing scientific contexts, and (b) investigations of nonscientists ranging from infants, to children, to students in science classes. In this section we summarize the adult studies of conceptual change, and in the next section we look at its developmental aspects.

Scientific concepts, like all concepts, can be characterized as containing a variety of “knowledge elements”: representations of words, thoughts, actions, objects, and processes. At certain points in the history of science, the accumulated evidence has demanded major shifts in the way these collections of knowledge elements are organized. This “radical conceptual change” process (see Keil, 1999 ; Nersessian 1998 , 2002 ; Thagard, 1992 ; Vosniadou 1998, for reviews) requires the formation of a new conceptual system that organizes knowledge in new ways, adds new knowledge, and results in a very different conceptual structure. For more recent research on conceptual change, The International Handbook of Research on Conceptual Change (Vosniadou, 2008 ) provides a detailed compendium of theories and controversies within the field.

While conceptual change in science is usually characterized by large-scale changes in concepts that occur over extensive periods of time, it has been possible to observe conceptual change using in vivo methodologies. Dunbar ( 1995 ) reported a major conceptual shift that occurred in immunologists, where they obtained a series of unexpected findings that forced the scientists to propose a new concept in immunology that in turn forced the change in other concepts. The drive behind this conceptual change was the discovery of a series of different unexpected findings or anomalies that required the scientists to both revise and reorganize their conceptual knowledge. Interestingly, this conceptual change was achieved by a group of scientists reasoning collaboratively, rather than by a scientist working alone. Different scientists tend to work on different aspects of concepts, and also different concepts, that when put together lead to a rapid change in entire conceptual structures.

Overall, accounts of conceptual change in individuals indicate that it is indeed similar to that of conceptual change in entire scientific fields. Individuals need to be confronted with anomalies that their preexisting theories cannot explain before entire conceptual structures are overthrown. However, replacement conceptual structures have to be generated before the old conceptual structure can be discarded. Sometimes, people do not overthrow their original conceptual theories and through their lives maintain their original views of many fundamental scientific concepts. Whether people actively possess naive theories, or whether they appear to have a naive theory because of the demand characteristics of the testing context, is a lively source of debate within the science education community (see Gupta, Hammer, & Redish, 2010 ).

Scientific Thinking in Children

Well before their first birthday, children appear to know several fundamental facts about the physical world. For example, studies with infants show that they behave as if they understand that solid objects endure over time (e.g., they don't just disappear and reappear, they cannot move through each other, and they move as a result of collisions with other solid objects or the force of gravity (Baillargeon, 2004 ; Carey 1985 ; Cohen & Cashon, 2006 ; Duschl, Schweingruber, & Shouse, 2007 ; Gelman & Baillargeon, 1983 ; Gelman & Kalish, 2006 ; Mandler, 2004 ; Metz 1995 ; Munakata, Casey, & Diamond, 2004 ). And even 6-month-olds are able to predict the future location of a moving object that they are attempting to grasp (Von Hofsten, 1980 ; Von Hofsten, Feng, & Spelke, 2000 ). In addition, they appear to be able to make nontrivial inferences about causes and their effects (Gopnik et al., 2004 ).

The similarities between children's thinking and scientists' thinking have an inherent allure and an internal contradiction. The allure resides in the enthusiastic wonder and openness with which both children and scientists approach the world around them. The paradox comes from the fact that different investigators of children's thinking have reached diametrically opposing conclusions about just how “scientific” children's thinking really is. Some claim support for the “child as a scientist” position (Brewer & Samarapungavan, 1991 ; Gelman & Wellman, 1991 ; Gopnik, Meltzoff, & Kuhl, 1999 ; Karmiloff-Smith 1988 ; Sodian, Zaitchik, & Carey, 1991 ; Samarapungavan 1992 ), while others offer serious challenges to the view (Fay & Klahr, 1996 ; Kern, Mirels, & Hinshaw, 1983 ; Kuhn, Amsel, & O'Laughlin, 1988 ; Schauble & Glaser, 1990 ; Siegler & Liebert, 1975 .) Such fundamentally incommensurate conclusions suggest that this very field—children's scientific thinking—is ripe for a conceptual revolution!

A recent comprehensive review (Duschl, Schweingruber, & Shouse, 2007 ) of what children bring to their science classes offers the following concise summary of the extensive developmental and educational research literature on children's scientific thinking:

Children entering school already have substantial knowledge of the natural world, much of which is implicit.

What children are capable of at a particular age is the result of a complex interplay among maturation, experience, and instruction. What is developmentally appropriate is not a simple function of age or grade, but rather is largely contingent on children's prior opportunities to learn.

Students' knowledge and experience play a critical role in their science learning, influencing four aspects of science understanding, including (a) knowing, using, and interpreting scientific explanations of the natural world; (b) generating and evaluating scientific evidence and explanations, (c) understanding how scientific knowledge is developed in the scientific community, and (d) participating in scientific practices and discourse.

Students learn science by actively engaging in the practices of science.

In the previous section of this article we discussed conceptual change with respect to scientific fields and undergraduate science students. However, the idea that children undergo radical conceptual change in which old “theories” need to be overthrown and reorganized has been a central topic in understanding changes in scientific thinking in both children and across the life span. This radical conceptual change is thought to be necessary for acquiring many new concepts in physics and is regarded as the major source of difficulty for students. The factors that are at the root of this conceptual shift view have been difficult to determine, although there have been a number of studies in cognitive development (Carey, 1985 ; Chi 1992 ; Chi & Roscoe, 2002 ), in the history of science (Thagard, 1992 ), and in physics education (Clement, 1982 ; Mestre 1991 ) that give detailed accounts of the changes in knowledge representation that occur while people switch from one way of representing scientific knowledge to another.

One area where students show great difficulty in understanding scientific concepts is physics. Analyses of students' changing conceptions, using interviews, verbal protocols, and behavioral outcome measures, indicate that large-scale changes in students' concepts occur in physics education (see McDermott & Redish, 1999 , for a review of this literature). Following Kuhn ( 1962 ), many researchers, but not all, have noted that students' changing conceptions resemble the sequences of conceptual changes in physics that have occurred in the history of science. These notions of radical paradigm shifts and ensuing incompatibility with past knowledge-states have called attention to interesting parallels between the development of particular scientific concepts in children and in the history of physics. Investigations of nonphysicists' understanding of motion indicate that students have extensive misunderstandings of motion. Some researchers have interpreted these findings as an indication that many people hold erroneous beliefs about motion similar to a medieval “impetus” theory (McCloskey, Caramazza, & Green, 1980 ). Furthermore, students appear to maintain “impetus” notions even after one or two courses in physics. In fact, some authors have noted that students who have taken one or two courses in physics can perform worse on physics problems than naive students (Mestre, 1991 ). Thus, it is only after extensive learning that we see a conceptual shift from impetus theories of motion to Newtonian scientific theories.

How one's conceptual representation shifts from “naive” to Newtonian is a matter of contention, as some have argued that the shift involves a radical conceptual change, whereas others have argued that the conceptual change is not really complete. For example, Kozhevnikov and Hegarty ( 2001 ) argue that much of the naive impetus notions of motion are maintained at the expense of Newtonian principles even with extensive training in physics. However, they argue that such impetus principles are maintained at an implicit level. Thus, although students can give the correct Newtonian answer to problems, their reaction times to respond indicate that they are also using impetus theories when they respond. An alternative view of conceptual change focuses on whether there are real conceptual changes at all. Gupta, Hammer and Redish ( 2010 ) and Disessa ( 2004 ) have conducted detailed investigations of changes in physics students' accounts of phenomena covered in elementary physics courses. They have found that rather than students possessing a naive theory that is replaced by the standard theory, many introductory physics students have no stable physical theory but rather construct their explanations from elementary pieces of knowledge of the physical world.

Computational Approaches to Scientific Thinking

Computational approaches have provided a more complete account of the scientific mind. Computational models provide specific detailed accounts of the cognitive processes underlying scientific thinking. Early computational work consisted of taking a scientific discovery and building computational models of the reasoning processes involved in the discovery. Langley, Simon, Bradshaw, and Zytkow ( 1987 ) built a series of programs that simulated discoveries such as those of Copernicus, Bacon, and Stahl. These programs had various inductive reasoning algorithms built into them, and when given the data that the scientists used, they were able to propose the same rules. Computational models make it possible to propose detailed models of the cognitive subcomponents of scientific thinking that specify exactly how scientific theories are generated, tested, and amended (see Darden, 1997 , and Shrager & Langley, 1990 , for accounts of this branch of research). More recently, the incorporation of scientific knowledge into computer programs has resulted in a shift in emphasis from using programs to simulate discoveries to building programs that are used to help scientists make discoveries. A number of these computer programs have made novel discoveries. For example, Valdes-Perez ( 1994 ) has built systems for discoveries in chemistry, and Fajtlowicz has done this in mathematics (Erdos, Fajtlowicz, & Staton, 1991 ).

These advances in the fields of computer discovery have led to new fields, conferences, journals, and even departments that specialize in the development of programs devised to search large databases in the hope of making new scientific discoveries (Langley, 2000 , 2002 ). This process is commonly known as “data mining.” This approach has only proved viable relatively recently, due to advances in computer technology. Biswal et al. ( 2010 ), Mitchell ( 2009 ), and Yang ( 2009 ) provide recent reviews of data mining in different scientific fields. Data mining is at the core of drug discovery, our understanding of the human genome, and our understanding of the universe for a number of reasons. First, vast databases concerning drug actions, biological processes, the genome, the proteome, and the universe itself now exist. Second, the development of high throughput data-mining algorithms makes it possible to search for new drug targets, novel biological mechanisms, and new astronomical phenomena in relatively short periods of time. Research programs that took decades, such as the development of penicillin, can now be done in days (Yang, 2009 ).

Another recent shift in the use of computers in scientific discovery has been to have both computers and people make discoveries together, rather than expecting that computers make an entire scientific discovery. Now instead of using computers to mimic the entire scientific discovery process as used by humans, computers can use powerful algorithms that search for patterns on large databases and provide the patterns to humans who can then use the output of these computers to make discoveries, ranging from the human genome to the structure of the universe. However, there are some robots such as ADAM, developed by King ( 2011 ), that can actually perform the entire scientific process, from the generation of hypotheses, to the conduct of experiments and the interpretation of results, with little human intervention. The ongoing development of scientific robots by some scientists (King et al., 2009 ) thus continues the tradition started by Herbert Simon in the 1960s. However, many of the controversies as to whether the robot is a “real scientist” or not continue to the present (Evans & Rzhetsky, 2010 , Gianfelici, 2010 ; Haufe, Elliott, Burian, & O' Malley, 2010 ; O'Malley 2011 ).

Scientific Thinking and Science Education

Accounts of the nature of science and research on scientific thinking have had profound effects on science education along many levels, particularly in recent years. Science education from the 1900s until the 1970s was primarily concerned with teaching students both the content of science (such as Newton's laws of motion) or the methods that scientists need to use in their research (such as using experimental and control groups). Beginning in the 1980s, a number of reports (e.g., American Association for the Advancement of Science, 1993; National Commission on Excellence in Education, 1983; Rutherford & Ahlgren, 1991 ) stressed the need for teaching scientific thinking skills rather than just methods and content. The addition of scientific thinking skills to the science curriculum from kindergarten through adulthood was a major shift in focus. Many of the particular scientific thinking skills that have been emphasized are skills covered in previous sections of this chapter, such as teaching deductive and inductive thinking strategies. However, rather than focusing on one particular skill, such as induction, researchers in education have focused on how the different components of scientific thinking are put together in science. Furthermore, science educators have focused upon situations where science is conducted collaboratively, rather than being the product of one person thinking alone. These changes in science education parallel changes in methodologies used to investigate science, such as analyzing the ways that scientists think and reason in their laboratories.

By looking at science as a complex multilayered and group activity, many researchers in science education have adopted a constructivist approach. This approach sees learning as an active rather than a passive process, and it suggests that students learn through constructing their scientific knowledge. We will first describe a few examples of the constructivist approach to science education. Following that, we will address several lines of work that challenge some of the assumptions of the constructivist approach to science education.

Often the goal of constructivist science education is to produce conceptual change through guided instruction where the teacher or professor acts as a guide to discovery, rather than the keeper of all the facts. One recent and influential approach to science education is the inquiry-based learning approach. Inquiry-based learning focuses on posing a problem or a puzzling event to students and asking them to propose a hypothesis that could explain the event. Next, the student is asked to collect data that test the hypothesis, make conclusions, and then reflect upon both the original problem and the thought processes that they used to solve the problem. Often students use computers that aid in their construction of new knowledge. The computers allow students to learn many of the different components of scientific thinking. For example, Reiser and his colleagues have developed a learning environment for biology, where students are encouraged to develop hypotheses in groups, codify the hypotheses, and search databases to test these hypotheses (Reiser et al., 2001 ).

One of the myths of science is the lone scientist suddenly shouting “Eureka, I have made a discovery!” Instead, in vivo studies of scientists (e.g., Dunbar, 1995 , 2002 ), historical analyses of scientific discoveries (Nersessian, 1999 ), and studies of children learning science at museums have all pointed to collaborative scientific discovery mechanisms as being one of the driving forces of science (Atkins et al., 2009 ; Azmitia & Crowley, 2001 ). What happens during collaborative scientific thinking is that there is usually a triggering event, such as an unexpected result or situation that a student does not understand. This results in other members of the group adding new information to the person's representation of knowledge, often adding new inductions and deductions that both challenge and transform the reasoner's old representations of knowledge (Chi & Roscoe, 2002 ; Dunbar 1998 ). Social mechanisms play a key component in fostering changes in concepts that have been ignored in traditional cognitive research but are crucial for both science and science education. In science education there has been a shift to collaborative learning, particularly at the elementary level; however, in university education, the emphasis is still on the individual scientist. As many domains of science now involve collaborations across scientific disciplines, we expect the explicit teaching of heuristics for collaborative science to increase.

What is the best way to teach and learn science? Surprisingly, the answer to this question has been difficult to uncover. For example, toward the end of the last century, influenced by several thinkers who advocated a constructivist approach to learning, ranging from Piaget (Beilin, 1994 ) to Papert ( 1980 ), many schools answered this question by adopting a philosophy dubbed “discovery learning.” Although a clear operational definition of this approach has yet to be articulated, the general idea is that children are expected to learn science by reconstructing the processes of scientific discovery—in a range of areas from computer programming to chemistry to mathematics. The premise is that letting students discover principles on their own, set their own goals, and collaboratively explore the natural world produces deeper knowledge that transfers widely.

The research literature on science education is far from consistent in its use of terminology. However, our reading suggests that “discovery learning” differs from “inquiry-based learning” in that few, if any, guidelines are given to students in discovery learning contexts, whereas in inquiry learning, students are given hypotheses and specific goals to achieve (see the second paragraph of this section for a definition of inquiry-based learning). Even though thousands of schools have adopted discovery learning as an alternative to more didactic approaches to teaching and learning, the evidence showing that it is more effective than traditional, direct, teacher-controlled instructional approaches is mixed, at best (Lorch et al., 2010 ; Minner, Levy, & Century, 2010 ). In several cases where the distinctions between direct instruction and more open-ended constructivist instruction have been clearly articulated, implemented, and assessed, direct instruction has proven to be superior to the alternatives (Chen & Klahr, 1999 ; Toth, Klahr, & Chen, 2000 ). For example, in a study of third- and fourth-grade children learning about experimental design, Klahr and Nigam ( 2004 ) found that many more children learned from direct instruction than from discovery learning. Furthermore, they found that among the few children who did manage to learn from a discovery method, there was no better performance on a far transfer test of scientific reasoning than that observed for the many children who learned from direct instruction.

The idea of children learning most of their science through a process of self-directed discovery has some romantic appeal, and it may accurately describe the personal experience of a handful of world-class scientists. However, the claim has generated some contentious disagreements (Kirschner, Sweller, & Clark, 2006 ; Klahr, 2010 ; Taber 2009 ; Tobias & Duffy, 2009 ), and the jury remains out on the extent to which most children can learn science that way.

Conclusions and Future Directions

The field of scientific thinking is now a thriving area of research with strong underpinnings in cognitive psychology and cognitive science. In recent years, a new professional society has been formed that aims to facilitate this integrative and interdisciplinary approach to the psychology of science, with its own journal and regular professional meetings. 1 Clearly the relations between these different aspects of scientific thinking need to be combined in order to produce a truly comprehensive picture of the scientific mind.

While much is known about certain aspects of scientific thinking, much more remains to be discovered. In particular, there has been little contact between cognitive, neuroscience, social, personality, and motivational accounts of scientific thinking. Research in thinking and reasoning has been expanded to use the methods and theories of cognitive neuroscience (see Morrison & Knowlton, Chapter 6 ). A similar approach can be taken in exploring scientific thinking (see Dunbar et al., 2007 ). There are two main reasons for taking a neuroscience approach to scientific thinking. First, functional neuroimaging allows the researcher to look at the entire human brain, making it possible to see the many different sites that are involved in scientific thinking and gain a more complete understanding of the entire range of mechanisms involved in this type of thought. Second, these brain-imaging approaches allow researchers to address fundamental questions in research on scientific thinking, such as the extent to which ordinary thinking in nonscientific contexts and scientific thinking recruit similar versus disparate neural structures of the brain.

Dunbar ( 2009 ) has used some novel methods to explore Simon's assertion, cited at the beginning of this chapter, that scientific thinking uses the same cognitive mechanisms that all human beings possess (rather than being an entirely different type of thinking) but combines them in ways that are specific to a particular aspect of science or a specific discipline of science. For example, Fugelsang and Dunbar ( 2009 ) compared causal reasoning when two colliding circular objects were labeled balls or labeled subatomic particles. They obtained different brain activation patterns depending on whether the stimuli were labeled balls or subatomic particles. In another series of experiments, Dunbar and colleagues used functional magnetic resonance imaging (fMRI) to study patterns of activation in the brains of students who have and who have not undergone conceptual change in physics. For example, Fugelsang and Dunbar ( 2005 ) and Dunbar et al. ( 2007 ) have found differences in the activation of specific brain sites (such as the anterior cingulate) for students when they encounter evidence that is inconsistent with their current conceptual understandings. These initial cognitive neuroscience investigations have the potential to reveal the ways that knowledge is organized in the scientific brain and provide detailed accounts of the nature of the representation of scientific knowledge. Petitto and Dunbar ( 2004 ) proposed the term “educational neuroscience” for the integration of research on education, including science education, with research on neuroscience. However, see Fitzpatrick (in press) for a very different perspective on whether neuroscience approaches are relevant to education. Clearly, research on the scientific brain is just beginning. We as scientists are beginning to get a reasonable grasp of the inner workings of the subcomponents of the scientific mind (i.e., problem solving, analogy, induction). However, great advances remain to be made concerning how these processes interact so that scientific discoveries can be made. Future research will focus on both the collaborative aspects of scientific thinking and the neural underpinnings of the scientific mind.

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Understanding the Complex Relationship between Critical Thinking and Science Reasoning among Undergraduate Thesis Writers

  • Jason E. Dowd
  • Robert J. Thompson
  • Leslie A. Schiff
  • Julie A. Reynolds

*Address correspondence to: Jason E. Dowd ( E-mail Address: [email protected] ).

Department of Biology, Duke University, Durham, NC 27708

Search for more papers by this author

Department of Psychology and Neuroscience, Duke University, Durham, NC 27708

Department of Microbiology and Immunology, University of Minnesota, Minneapolis, MN 55455

Developing critical-thinking and scientific reasoning skills are core learning objectives of science education, but little empirical evidence exists regarding the interrelationships between these constructs. Writing effectively fosters students’ development of these constructs, and it offers a unique window into studying how they relate. In this study of undergraduate thesis writing in biology at two universities, we examine how scientific reasoning exhibited in writing (assessed using the Biology Thesis Assessment Protocol) relates to general and specific critical-thinking skills (assessed using the California Critical Thinking Skills Test), and we consider implications for instruction. We find that scientific reasoning in writing is strongly related to inference , while other aspects of science reasoning that emerge in writing (epistemological considerations, writing conventions, etc.) are not significantly related to critical-thinking skills. Science reasoning in writing is not merely a proxy for critical thinking. In linking features of students’ writing to their critical-thinking skills, this study 1) provides a bridge to prior work suggesting that engagement in science writing enhances critical thinking and 2) serves as a foundational step for subsequently determining whether instruction focused explicitly on developing critical-thinking skills (particularly inference ) can actually improve students’ scientific reasoning in their writing.

INTRODUCTION

Critical-thinking and scientific reasoning skills are core learning objectives of science education for all students, regardless of whether or not they intend to pursue a career in science or engineering. Consistent with the view of learning as construction of understanding and meaning ( National Research Council, 2000 ), the pedagogical practice of writing has been found to be effective not only in fostering the development of students’ conceptual and procedural knowledge ( Gerdeman et al. , 2007 ) and communication skills ( Clase et al. , 2010 ), but also scientific reasoning ( Reynolds et al. , 2012 ) and critical-thinking skills ( Quitadamo and Kurtz, 2007 ).

Critical thinking and scientific reasoning are similar but different constructs that include various types of higher-order cognitive processes, metacognitive strategies, and dispositions involved in making meaning of information. Critical thinking is generally understood as the broader construct ( Holyoak and Morrison, 2005 ), comprising an array of cognitive processes and dispostions that are drawn upon differentially in everyday life and across domains of inquiry such as the natural sciences, social sciences, and humanities. Scientific reasoning, then, may be interpreted as the subset of critical-thinking skills (cognitive and metacognitive processes and dispositions) that 1) are involved in making meaning of information in scientific domains and 2) support the epistemological commitment to scientific methodology and paradigm(s).

Although there has been an enduring focus in higher education on promoting critical thinking and reasoning as general or “transferable” skills, research evidence provides increasing support for the view that reasoning and critical thinking are also situational or domain specific ( Beyer et al. , 2013 ). Some researchers, such as Lawson (2010) , present frameworks in which science reasoning is characterized explicitly in terms of critical-thinking skills. There are, however, limited coherent frameworks and empirical evidence regarding either the general or domain-specific interrelationships of scientific reasoning, as it is most broadly defined, and critical-thinking skills.

The Vision and Change in Undergraduate Biology Education Initiative provides a framework for thinking about these constructs and their interrelationship in the context of the core competencies and disciplinary practice they describe ( American Association for the Advancement of Science, 2011 ). These learning objectives aim for undergraduates to “understand the process of science, the interdisciplinary nature of the new biology and how science is closely integrated within society; be competent in communication and collaboration; have quantitative competency and a basic ability to interpret data; and have some experience with modeling, simulation and computational and systems level approaches as well as with using large databases” ( Woodin et al. , 2010 , pp. 71–72). This framework makes clear that science reasoning and critical-thinking skills play key roles in major learning outcomes; for example, “understanding the process of science” requires students to engage in (and be metacognitive about) scientific reasoning, and having the “ability to interpret data” requires critical-thinking skills. To help students better achieve these core competencies, we must better understand the interrelationships of their composite parts. Thus, the next step is to determine which specific critical-thinking skills are drawn upon when students engage in science reasoning in general and with regard to the particular scientific domain being studied. Such a determination could be applied to improve science education for both majors and nonmajors through pedagogical approaches that foster critical-thinking skills that are most relevant to science reasoning.

Writing affords one of the most effective means for making thinking visible ( Reynolds et al. , 2012 ) and learning how to “think like” and “write like” disciplinary experts ( Meizlish et al. , 2013 ). As a result, student writing affords the opportunities to both foster and examine the interrelationship of scientific reasoning and critical-thinking skills within and across disciplinary contexts. The purpose of this study was to better understand the relationship between students’ critical-thinking skills and scientific reasoning skills as reflected in the genre of undergraduate thesis writing in biology departments at two research universities, the University of Minnesota and Duke University.

In the following subsections, we discuss in greater detail the constructs of scientific reasoning and critical thinking, as well as the assessment of scientific reasoning in students’ thesis writing. In subsequent sections, we discuss our study design, findings, and the implications for enhancing educational practices.

Critical Thinking

The advances in cognitive science in the 21st century have increased our understanding of the mental processes involved in thinking and reasoning, as well as memory, learning, and problem solving. Critical thinking is understood to include both a cognitive dimension and a disposition dimension (e.g., reflective thinking) and is defined as “purposeful, self-regulatory judgment which results in interpretation, analysis, evaluation, and inference, as well as explanation of the evidential, conceptual, methodological, criteriological, or contextual considera­tions upon which that judgment is based” ( Facione, 1990, p. 3 ). Although various other definitions of critical thinking have been proposed, researchers have generally coalesced on this consensus: expert view ( Blattner and Frazier, 2002 ; Condon and Kelly-Riley, 2004 ; Bissell and Lemons, 2006 ; Quitadamo and Kurtz, 2007 ) and the corresponding measures of critical-­thinking skills ( August, 2016 ; Stephenson and Sadler-McKnight, 2016 ).

Both the cognitive skills and dispositional components of critical thinking have been recognized as important to science education ( Quitadamo and Kurtz, 2007 ). Empirical research demonstrates that specific pedagogical practices in science courses are effective in fostering students’ critical-thinking skills. Quitadamo and Kurtz (2007) found that students who engaged in a laboratory writing component in the context of a general education biology course significantly improved their overall critical-thinking skills (and their analytical and inference skills, in particular), whereas students engaged in a traditional quiz-based laboratory did not improve their critical-thinking skills. In related work, Quitadamo et al. (2008) found that a community-based inquiry experience, involving inquiry, writing, research, and analysis, was associated with improved critical thinking in a biology course for nonmajors, compared with traditionally taught sections. In both studies, students who exhibited stronger presemester critical-thinking skills exhibited stronger gains, suggesting that “students who have not been explicitly taught how to think critically may not reach the same potential as peers who have been taught these skills” ( Quitadamo and Kurtz, 2007 , p. 151).

Recently, Stephenson and Sadler-McKnight (2016) found that first-year general chemistry students who engaged in a science writing heuristic laboratory, which is an inquiry-based, writing-to-learn approach to instruction ( Hand and Keys, 1999 ), had significantly greater gains in total critical-thinking scores than students who received traditional laboratory instruction. Each of the four components—inquiry, writing, collaboration, and reflection—have been linked to critical thinking ( Stephenson and Sadler-McKnight, 2016 ). Like the other studies, this work highlights the value of targeting critical-thinking skills and the effectiveness of an inquiry-based, writing-to-learn approach to enhance critical thinking. Across studies, authors advocate adopting critical thinking as the course framework ( Pukkila, 2004 ) and developing explicit examples of how critical thinking relates to the scientific method ( Miri et al. , 2007 ).

In these examples, the important connection between writing and critical thinking is highlighted by the fact that each intervention involves the incorporation of writing into science, technology, engineering, and mathematics education (either alone or in combination with other pedagogical practices). However, critical-thinking skills are not always the primary learning outcome; in some contexts, scientific reasoning is the primary outcome that is assessed.

Scientific Reasoning

Scientific reasoning is a complex process that is broadly defined as “the skills involved in inquiry, experimentation, evidence evaluation, and inference that are done in the service of conceptual change or scientific understanding” ( Zimmerman, 2007 , p. 172). Scientific reasoning is understood to include both conceptual knowledge and the cognitive processes involved with generation of hypotheses (i.e., inductive processes involved in the generation of hypotheses and the deductive processes used in the testing of hypotheses), experimentation strategies, and evidence evaluation strategies. These dimensions are interrelated, in that “experimentation and inference strategies are selected based on prior conceptual knowledge of the domain” ( Zimmerman, 2000 , p. 139). Furthermore, conceptual and procedural knowledge and cognitive process dimensions can be general and domain specific (or discipline specific).

With regard to conceptual knowledge, attention has been focused on the acquisition of core methodological concepts fundamental to scientists’ causal reasoning and metacognitive distancing (or decontextualized thinking), which is the ability to reason independently of prior knowledge or beliefs ( Greenhoot et al. , 2004 ). The latter involves what Kuhn and Dean (2004) refer to as the coordination of theory and evidence, which requires that one question existing theories (i.e., prior knowledge and beliefs), seek contradictory evidence, eliminate alternative explanations, and revise one’s prior beliefs in the face of contradictory evidence. Kuhn and colleagues (2008) further elaborate that scientific thinking requires “a mature understanding of the epistemological foundations of science, recognizing scientific knowledge as constructed by humans rather than simply discovered in the world,” and “the ability to engage in skilled argumentation in the scientific domain, with an appreciation of argumentation as entailing the coordination of theory and evidence” ( Kuhn et al. , 2008 , p. 435). “This approach to scientific reasoning not only highlights the skills of generating and evaluating evidence-based inferences, but also encompasses epistemological appreciation of the functions of evidence and theory” ( Ding et al. , 2016 , p. 616). Evaluating evidence-based inferences involves epistemic cognition, which Moshman (2015) defines as the subset of metacognition that is concerned with justification, truth, and associated forms of reasoning. Epistemic cognition is both general and domain specific (or discipline specific; Moshman, 2015 ).

There is empirical support for the contributions of both prior knowledge and an understanding of the epistemological foundations of science to scientific reasoning. In a study of undergraduate science students, advanced scientific reasoning was most often accompanied by accurate prior knowledge as well as sophisticated epistemological commitments; additionally, for students who had comparable levels of prior knowledge, skillful reasoning was associated with a strong epistemological commitment to the consistency of theory with evidence ( Zeineddin and Abd-El-Khalick, 2010 ). These findings highlight the importance of the need for instructional activities that intentionally help learners develop sophisticated epistemological commitments focused on the nature of knowledge and the role of evidence in supporting knowledge claims ( Zeineddin and Abd-El-Khalick, 2010 ).

Scientific Reasoning in Students’ Thesis Writing

Pedagogical approaches that incorporate writing have also focused on enhancing scientific reasoning. Many rubrics have been developed to assess aspects of scientific reasoning in written artifacts. For example, Timmerman and colleagues (2011) , in the course of describing their own rubric for assessing scientific reasoning, highlight several examples of scientific reasoning assessment criteria ( Haaga, 1993 ; Tariq et al. , 1998 ; Topping et al. , 2000 ; Kelly and Takao, 2002 ; Halonen et al. , 2003 ; Willison and O’Regan, 2007 ).

At both the University of Minnesota and Duke University, we have focused on the genre of the undergraduate honors thesis as the rhetorical context in which to study and improve students’ scientific reasoning and writing. We view the process of writing an undergraduate honors thesis as a form of professional development in the sciences (i.e., a way of engaging students in the practices of a community of discourse). We have found that structured courses designed to scaffold the thesis-­writing process and promote metacognition can improve writing and reasoning skills in biology, chemistry, and economics ( Reynolds and Thompson, 2011 ; Dowd et al. , 2015a , b ). In the context of this prior work, we have defined scientific reasoning in writing as the emergent, underlying construct measured across distinct aspects of students’ written discussion of independent research in their undergraduate theses.

The Biology Thesis Assessment Protocol (BioTAP) was developed at Duke University as a tool for systematically guiding students and faculty through a “draft–feedback–revision” writing process, modeled after professional scientific peer-review processes ( Reynolds et al. , 2009 ). BioTAP includes activities and worksheets that allow students to engage in critical peer review and provides detailed descriptions, presented as rubrics, of the questions (i.e., dimensions, shown in Table 1 ) upon which such review should focus. Nine rubric dimensions focus on communication to the broader scientific community, and four rubric dimensions focus on the accuracy and appropriateness of the research. These rubric dimensions provide criteria by which the thesis is assessed, and therefore allow BioTAP to be used as an assessment tool as well as a teaching resource ( Reynolds et al. , 2009 ). Full details are available at www.science-writing.org/biotap.html .

In previous work, we have used BioTAP to quantitatively assess students’ undergraduate honors theses and explore the relationship between thesis-writing courses (or specific interventions within the courses) and the strength of students’ science reasoning in writing across different science disciplines: biology ( Reynolds and Thompson, 2011 ); chemistry ( Dowd et al. , 2015b ); and economics ( Dowd et al. , 2015a ). We have focused exclusively on the nine dimensions related to reasoning and writing (questions 1–9), as the other four dimensions (questions 10–13) require topic-specific expertise and are intended to be used by the student’s thesis supervisor.

Beyond considering individual dimensions, we have investigated whether meaningful constructs underlie students’ thesis scores. We conducted exploratory factor analysis of students’ theses in biology, economics, and chemistry and found one dominant underlying factor in each discipline; we termed the factor “scientific reasoning in writing” ( Dowd et al. , 2015a , b , 2016 ). That is, each of the nine dimensions could be understood as reflecting, in different ways and to different degrees, the construct of scientific reasoning in writing. The findings indicated evidence of both general and discipline-specific components to scientific reasoning in writing that relate to epistemic beliefs and paradigms, in keeping with broader ideas about science reasoning discussed earlier. Specifically, scientific reasoning in writing is more strongly associated with formulating a compelling argument for the significance of the research in the context of current literature in biology, making meaning regarding the implications of the findings in chemistry, and providing an organizational framework for interpreting the thesis in economics. We suggested that instruction, whether occurring in writing studios or in writing courses to facilitate thesis preparation, should attend to both components.

Research Question and Study Design

The genre of thesis writing combines the pedagogies of writing and inquiry found to foster scientific reasoning ( Reynolds et al. , 2012 ) and critical thinking ( Quitadamo and Kurtz, 2007 ; Quitadamo et al. , 2008 ; Stephenson and Sadler-­McKnight, 2016 ). However, there is no empirical evidence regarding the general or domain-specific interrelationships of scientific reasoning and critical-thinking skills, particularly in the rhetorical context of the undergraduate thesis. The BioTAP studies discussed earlier indicate that the rubric-based assessment produces evidence of scientific reasoning in the undergraduate thesis, but it was not designed to foster or measure critical thinking. The current study was undertaken to address the research question: How are students’ critical-thinking skills related to scientific reasoning as reflected in the genre of undergraduate thesis writing in biology? Determining these interrelationships could guide efforts to enhance students’ scientific reasoning and writing skills through focusing instruction on specific critical-thinking skills as well as disciplinary conventions.

To address this research question, we focused on undergraduate thesis writers in biology courses at two institutions, Duke University and the University of Minnesota, and examined the extent to which students’ scientific reasoning in writing, assessed in the undergraduate thesis using BioTAP, corresponds to students’ critical-thinking skills, assessed using the California Critical Thinking Skills Test (CCTST; August, 2016 ).

Study Sample

The study sample was composed of students enrolled in courses designed to scaffold the thesis-writing process in the Department of Biology at Duke University and the College of Biological Sciences at the University of Minnesota. Both courses complement students’ individual work with research advisors. The course is required for thesis writers at the University of Minnesota and optional for writers at Duke University. Not all students are required to complete a thesis, though it is required for students to graduate with honors; at the University of Minnesota, such students are enrolled in an honors program within the college. In total, 28 students were enrolled in the course at Duke University and 44 students were enrolled in the course at the University of Minnesota. Of those students, two students did not consent to participate in the study; additionally, five students did not validly complete the CCTST (i.e., attempted fewer than 60% of items or completed the test in less than 15 minutes). Thus, our overall rate of valid participation is 90%, with 27 students from Duke University and 38 students from the University of Minnesota. We found no statistically significant differences in thesis assessment between students with valid CCTST scores and invalid CCTST scores. Therefore, we focus on the 65 students who consented to participate and for whom we have complete and valid data in most of this study. Additionally, in asking students for their consent to participate, we allowed them to choose whether to provide or decline access to academic and demographic background data. Of the 65 students who consented to participate, 52 students granted access to such data. Therefore, for additional analyses involving academic and background data, we focus on the 52 students who consented. We note that the 13 students who participated but declined to share additional data performed slightly lower on the CCTST than the 52 others (perhaps suggesting that they differ by other measures, but we cannot determine this with certainty). Among the 52 students, 60% identified as female and 10% identified as being from underrepresented ethnicities.

In both courses, students completed the CCTST online, either in class or on their own, late in the Spring 2016 semester. This is the same assessment that was used in prior studies of critical thinking ( Quitadamo and Kurtz, 2007 ; Quitadamo et al. , 2008 ; Stephenson and Sadler-McKnight, 2016 ). It is “an objective measure of the core reasoning skills needed for reflective decision making concerning what to believe or what to do” ( Insight Assessment, 2016a ). In the test, students are asked to read and consider information as they answer multiple-choice questions. The questions are intended to be appropriate for all users, so there is no expectation of prior disciplinary knowledge in biology (or any other subject). Although actual test items are protected, sample items are available on the Insight Assessment website ( Insight Assessment, 2016b ). We have included one sample item in the Supplemental Material.

The CCTST is based on a consensus definition of critical thinking, measures cognitive and metacognitive skills associated with critical thinking, and has been evaluated for validity and reliability at the college level ( August, 2016 ; Stephenson and Sadler-McKnight, 2016 ). In addition to providing overall critical-thinking score, the CCTST assesses seven dimensions of critical thinking: analysis, interpretation, inference, evaluation, explanation, induction, and deduction. Scores on each dimension are calculated based on students’ performance on items related to that dimension. Analysis focuses on identifying assumptions, reasons, and claims and examining how they interact to form arguments. Interpretation, related to analysis, focuses on determining the precise meaning and significance of information. Inference focuses on drawing conclusions from reasons and evidence. Evaluation focuses on assessing the credibility of sources of information and claims they make. Explanation, related to evaluation, focuses on describing the evidence, assumptions, or rationale for beliefs and conclusions. Induction focuses on drawing inferences about what is probably true based on evidence. Deduction focuses on drawing conclusions about what must be true when the context completely determines the outcome. These are not independent dimensions; the fact that they are related supports their collective interpretation as critical thinking. Together, the CCTST dimensions provide a basis for evaluating students’ overall strength in using reasoning to form reflective judgments about what to believe or what to do ( August, 2016 ). Each of the seven dimensions and the overall CCTST score are measured on a scale of 0–100, where higher scores indicate superior performance. Scores correspond to superior (86–100), strong (79–85), moderate (70–78), weak (63–69), or not manifested (62 and below) skills.

Scientific Reasoning in Writing

At the end of the semester, students’ final, submitted undergraduate theses were assessed using BioTAP, which consists of nine rubric dimensions that focus on communication to the broader scientific community and four additional dimensions that focus on the exhibition of topic-specific expertise ( Reynolds et al. , 2009 ). These dimensions, framed as questions, are displayed in Table 1 .

Student theses were assessed on questions 1–9 of BioTAP using the same procedures described in previous studies ( Reynolds and Thompson, 2011 ; Dowd et al. , 2015a , b ). In this study, six raters were trained in the valid, reliable use of BioTAP rubrics. Each dimension was rated on a five-point scale: 1 indicates the dimension is missing, incomplete, or below acceptable standards; 3 indicates that the dimension is adequate but not exhibiting mastery; and 5 indicates that the dimension is excellent and exhibits mastery (intermediate ratings of 2 and 4 are appropriate when different parts of the thesis make a single category challenging). After training, two raters independently assessed each thesis and then discussed their independent ratings with one another to form a consensus rating. The consensus score is not an average score, but rather an agreed-upon, discussion-based score. On a five-point scale, raters independently assessed dimensions to be within 1 point of each other 82.4% of the time before discussion and formed consensus ratings 100% of the time after discussion.

In this study, we consider both categorical (mastery/nonmastery, where a score of 5 corresponds to mastery) and numerical treatments of individual BioTAP scores to better relate the manifestation of critical thinking in BioTAP assessment to all of the prior studies. For comprehensive/cumulative measures of BioTAP, we focus on the partial sum of questions 1–5, as these questions relate to higher-order scientific reasoning (whereas questions 6–9 relate to mid- and lower-order writing mechanics [ Reynolds et al. , 2009 ]), and the factor scores (i.e., numerical representations of the extent to which each student exhibits the underlying factor), which are calculated from the factor loadings published by Dowd et al. (2016) . We do not focus on questions 6–9 individually in statistical analyses, because we do not expect critical-thinking skills to relate to mid- and lower-order writing skills.

The final, submitted thesis reflects the student’s writing, the student’s scientific reasoning, the quality of feedback provided to the student by peers and mentors, and the student’s ability to incorporate that feedback into his or her work. Therefore, our assessment is not the same as an assessment of unpolished, unrevised samples of students’ written work. While one might imagine that such an unpolished sample may be more strongly correlated with critical-thinking skills measured by the CCTST, we argue that the complete, submitted thesis, assessed using BioTAP, is ultimately a more appropriate reflection of how students exhibit science reasoning in the scientific community.

Statistical Analyses

We took several steps to analyze the collected data. First, to provide context for subsequent interpretations, we generated descriptive statistics for the CCTST scores of the participants based on the norms for undergraduate CCTST test takers. To determine the strength of relationships among CCTST dimensions (including overall score) and the BioTAP dimensions, partial-sum score (questions 1–5), and factor score, we calculated Pearson’s correlations for each pair of measures. To examine whether falling on one side of the nonmastery/mastery threshold (as opposed to a linear scale of performance) was related to critical thinking, we grouped BioTAP dimensions into categories (mastery/nonmastery) and conducted Student’s t tests to compare the means scores of the two groups on each of the seven dimensions and overall score of the CCTST. Finally, for the strongest relationship that emerged, we included additional academic and background variables as covariates in multiple linear-regression analysis to explore questions about how much observed relationships between critical-thinking skills and science reasoning in writing might be explained by variation in these other factors.

Although BioTAP scores represent discreet, ordinal bins, the five-point scale is intended to capture an underlying continuous construct (from inadequate to exhibiting mastery). It has been argued that five categories is an appropriate cutoff for treating ordinal variables as pseudo-continuous ( Rhemtulla et al. , 2012 )—and therefore using continuous-variable statistical methods (e.g., Pearson’s correlations)—as long as the underlying assumption that ordinal scores are linearly distributed is valid. Although we have no way to statistically test this assumption, we interpret adequate scores to be approximately halfway between inadequate and mastery scores, resulting in a linear scale. In part because this assumption is subject to disagreement, we also consider and interpret a categorical (mastery/nonmastery) treatment of BioTAP variables.

We corrected for multiple comparisons using the Holm-Bonferroni method ( Holm, 1979 ). At the most general level, where we consider the single, comprehensive measures for BioTAP (partial-sum and factor score) and the CCTST (overall score), there is no need to correct for multiple comparisons, because the multiple, individual dimensions are collapsed into single dimensions. When we considered individual CCTST dimensions in relation to comprehensive measures for BioTAP, we accounted for seven comparisons; similarly, when we considered individual dimensions of BioTAP in relation to overall CCTST score, we accounted for five comparisons. When all seven CCTST and five BioTAP dimensions were examined individually and without prior knowledge, we accounted for 35 comparisons; such a rigorous threshold is likely to reject weak and moderate relationships, but it is appropriate if there are no specific pre-existing hypotheses. All p values are presented in tables for complete transparency, and we carefully consider the implications of our interpretation of these data in the Discussion section.

CCTST scores for students in this sample ranged from the 39th to 99th percentile of the general population of undergraduate CCTST test takers (mean percentile = 84.3, median = 85th percentile; Table 2 ); these percentiles reflect overall scores that range from moderate to superior. Scores on individual dimensions and overall scores were sufficiently normal and far enough from the ceiling of the scale to justify subsequent statistical analyses.

a Scores correspond to superior (86–100), strong (79–85), moderate (70–78), weak (63–69), or not manifested (62 and lower) skills.

The Pearson’s correlations between students’ cumulative scores on BioTAP (the factor score based on loadings published by Dowd et al. , 2016 , and the partial sum of scores on questions 1–5) and students’ overall scores on the CCTST are presented in Table 3 . We found that the partial-sum measure of BioTAP was significantly related to the overall measure of critical thinking ( r = 0.27, p = 0.03), while the BioTAP factor score was marginally related to overall CCTST ( r = 0.24, p = 0.05). When we looked at relationships between comprehensive BioTAP measures and scores for individual dimensions of the CCTST ( Table 3 ), we found significant positive correlations between the both BioTAP partial-sum and factor scores and CCTST inference ( r = 0.45, p < 0.001, and r = 0.41, p < 0.001, respectively). Although some other relationships have p values below 0.05 (e.g., the correlations between BioTAP partial-sum scores and CCTST induction and interpretation scores), they are not significant when we correct for multiple comparisons.

a In each cell, the top number is the correlation, and the bottom, italicized number is the associated p value. Correlations that are statistically significant after correcting for multiple comparisons are shown in bold.

b This is the partial sum of BioTAP scores on questions 1–5.

c This is the factor score calculated from factor loadings published by Dowd et al. (2016) .

When we expanded comparisons to include all 35 potential correlations among individual BioTAP and CCTST dimensions—and, accordingly, corrected for 35 comparisons—we did not find any additional statistically significant relationships. The Pearson’s correlations between students’ scores on each dimension of BioTAP and students’ scores on each dimension of the CCTST range from −0.11 to 0.35 ( Table 3 ); although the relationship between discussion of implications (BioTAP question 5) and inference appears to be relatively large ( r = 0.35), it is not significant ( p = 0.005; the Holm-Bonferroni cutoff is 0.00143). We found no statistically significant relationships between BioTAP questions 6–9 and CCTST dimensions (unpublished data), regardless of whether we correct for multiple comparisons.

The results of Student’s t tests comparing scores on each dimension of the CCTST of students who exhibit mastery with those of students who do not exhibit mastery on each dimension of BioTAP are presented in Table 4 . Focusing first on the overall CCTST scores, we found that the difference between those who exhibit mastery and those who do not in discussing implications of results (BioTAP question 5) is statistically significant ( t = 2.73, p = 0.008, d = 0.71). When we expanded t tests to include all 35 comparisons—and, like above, corrected for 35 comparisons—we found a significant difference in inference scores between students who exhibit mastery on question 5 and students who do not ( t = 3.41, p = 0.0012, d = 0.88), as well as a marginally significant difference in these students’ induction scores ( t = 3.26, p = 0.0018, d = 0.84; the Holm-Bonferroni cutoff is p = 0.00147). Cohen’s d effect sizes, which reveal the strength of the differences for statistically significant relationships, range from 0.71 to 0.88.

a In each cell, the top number is the t statistic for each comparison, and the middle, italicized number is the associated p value. The bottom number is the effect size. Correlations that are statistically significant after correcting for multiple comparisons are shown in bold.

Finally, we more closely examined the strongest relationship that we observed, which was between the CCTST dimension of inference and the BioTAP partial-sum composite score (shown in Table 3 ), using multiple regression analysis ( Table 5 ). Focusing on the 52 students for whom we have background information, we looked at the simple relationship between BioTAP and inference (model 1), a robust background model including multiple covariates that one might expect to explain some part of the variation in BioTAP (model 2), and a combined model including all variables (model 3). As model 3 shows, the covariates explain very little variation in BioTAP scores, and the relationship between inference and BioTAP persists even in the presence of all of the covariates.

** p < 0.01.

*** p < 0.001.

The aim of this study was to examine the extent to which the various components of scientific reasoning—manifested in writing in the genre of undergraduate thesis and assessed using BioTAP—draw on general and specific critical-thinking skills (assessed using CCTST) and to consider the implications for educational practices. Although science reasoning involves critical-thinking skills, it also relates to conceptual knowledge and the epistemological foundations of science disciplines ( Kuhn et al. , 2008 ). Moreover, science reasoning in writing , captured in students’ undergraduate theses, reflects habits, conventions, and the incorporation of feedback that may alter evidence of individuals’ critical-thinking skills. Our findings, however, provide empirical evidence that cumulative measures of science reasoning in writing are nonetheless related to students’ overall critical-thinking skills ( Table 3 ). The particularly significant roles of inference skills ( Table 3 ) and the discussion of implications of results (BioTAP question 5; Table 4 ) provide a basis for more specific ideas about how these constructs relate to one another and what educational interventions may have the most success in fostering these skills.

Our results build on previous findings. The genre of thesis writing combines pedagogies of writing and inquiry found to foster scientific reasoning ( Reynolds et al. , 2012 ) and critical thinking ( Quitadamo and Kurtz, 2007 ; Quitadamo et al. , 2008 ; Stephenson and Sadler-McKnight, 2016 ). Quitadamo and Kurtz (2007) reported that students who engaged in a laboratory writing component in a general education biology course significantly improved their inference and analysis skills, and Quitadamo and colleagues (2008) found that participation in a community-based inquiry biology course (that included a writing component) was associated with significant gains in students’ inference and evaluation skills. The shared focus on inference is noteworthy, because these prior studies actually differ from the current study; the former considered critical-­thinking skills as the primary learning outcome of writing-­focused interventions, whereas the latter focused on emergent links between two learning outcomes (science reasoning in writing and critical thinking). In other words, inference skills are impacted by writing as well as manifested in writing.

Inference focuses on drawing conclusions from argument and evidence. According to the consensus definition of critical thinking, the specific skill of inference includes several processes: querying evidence, conjecturing alternatives, and drawing conclusions. All of these activities are central to the independent research at the core of writing an undergraduate thesis. Indeed, a critical part of what we call “science reasoning in writing” might be characterized as a measure of students’ ability to infer and make meaning of information and findings. Because the cumulative BioTAP measures distill underlying similarities and, to an extent, suppress unique aspects of individual dimensions, we argue that it is appropriate to relate inference to scientific reasoning in writing . Even when we control for other potentially relevant background characteristics, the relationship is strong ( Table 5 ).

In taking the complementary view and focusing on BioTAP, when we compared students who exhibit mastery with those who do not, we found that the specific dimension of “discussing the implications of results” (question 5) differentiates students’ performance on several critical-thinking skills. To achieve mastery on this dimension, students must make connections between their results and other published studies and discuss the future directions of the research; in short, they must demonstrate an understanding of the bigger picture. The specific relationship between question 5 and inference is the strongest observed among all individual comparisons. Altogether, perhaps more than any other BioTAP dimension, this aspect of students’ writing provides a clear view of the role of students’ critical-thinking skills (particularly inference and, marginally, induction) in science reasoning.

While inference and discussion of implications emerge as particularly strongly related dimensions in this work, we note that the strongest contribution to “science reasoning in writing in biology,” as determined through exploratory factor analysis, is “argument for the significance of research” (BioTAP question 2, not question 5; Dowd et al. , 2016 ). Question 2 is not clearly related to critical-thinking skills. These findings are not contradictory, but rather suggest that the epistemological and disciplinary-specific aspects of science reasoning that emerge in writing through BioTAP are not completely aligned with aspects related to critical thinking. In other words, science reasoning in writing is not simply a proxy for those critical-thinking skills that play a role in science reasoning.

In a similar vein, the content-related, epistemological aspects of science reasoning, as well as the conventions associated with writing the undergraduate thesis (including feedback from peers and revision), may explain the lack of significant relationships between some science reasoning dimensions and some critical-thinking skills that might otherwise seem counterintuitive (e.g., BioTAP question 2, which relates to making an argument, and the critical-thinking skill of argument). It is possible that an individual’s critical-thinking skills may explain some variation in a particular BioTAP dimension, but other aspects of science reasoning and practice exert much stronger influence. Although these relationships do not emerge in our analyses, the lack of significant correlation does not mean that there is definitively no correlation. Correcting for multiple comparisons suppresses type 1 error at the expense of exacerbating type 2 error, which, combined with the limited sample size, constrains statistical power and makes weak relationships more difficult to detect. Ultimately, though, the relationships that do emerge highlight places where individuals’ distinct critical-thinking skills emerge most coherently in thesis assessment, which is why we are particularly interested in unpacking those relationships.

We recognize that, because only honors students submit theses at these institutions, this study sample is composed of a selective subset of the larger population of biology majors. Although this is an inherent limitation of focusing on thesis writing, links between our findings and results of other studies (with different populations) suggest that observed relationships may occur more broadly. The goal of improved science reasoning and critical thinking is shared among all biology majors, particularly those engaged in capstone research experiences. So while the implications of this work most directly apply to honors thesis writers, we provisionally suggest that all students could benefit from further study of them.

There are several important implications of this study for science education practices. Students’ inference skills relate to the understanding and effective application of scientific content. The fact that we find no statistically significant relationships between BioTAP questions 6–9 and CCTST dimensions suggests that such mid- to lower-order elements of BioTAP ( Reynolds et al. , 2009 ), which tend to be more structural in nature, do not focus on aspects of the finished thesis that draw strongly on critical thinking. In keeping with prior analyses ( Reynolds and Thompson, 2011 ; Dowd et al. , 2016 ), these findings further reinforce the notion that disciplinary instructors, who are most capable of teaching and assessing scientific reasoning and perhaps least interested in the more mechanical aspects of writing, may nonetheless be best suited to effectively model and assess students’ writing.

The goal of the thesis writing course at both Duke University and the University of Minnesota is not merely to improve thesis scores but to move students’ writing into the category of mastery across BioTAP dimensions. Recognizing that students with differing critical-thinking skills (particularly inference) are more or less likely to achieve mastery in the undergraduate thesis (particularly in discussing implications [question 5]) is important for developing and testing targeted pedagogical interventions to improve learning outcomes for all students.

The competencies characterized by the Vision and Change in Undergraduate Biology Education Initiative provide a general framework for recognizing that science reasoning and critical-thinking skills play key roles in major learning outcomes of science education. Our findings highlight places where science reasoning–related competencies (like “understanding the process of science”) connect to critical-thinking skills and places where critical thinking–related competencies might be manifested in scientific products (such as the ability to discuss implications in scientific writing). We encourage broader efforts to build empirical connections between competencies and pedagogical practices to further improve science education.

One specific implication of this work for science education is to focus on providing opportunities for students to develop their critical-thinking skills (particularly inference). Of course, as this correlational study is not designed to test causality, we do not claim that enhancing students’ inference skills will improve science reasoning in writing. However, as prior work shows that science writing activities influence students’ inference skills ( Quitadamo and Kurtz, 2007 ; Quitadamo et al. , 2008 ), there is reason to test such a hypothesis. Nevertheless, the focus must extend beyond inference as an isolated skill; rather, it is important to relate inference to the foundations of the scientific method ( Miri et al. , 2007 ) in terms of the epistemological appreciation of the functions and coordination of evidence ( Kuhn and Dean, 2004 ; Zeineddin and Abd-El-Khalick, 2010 ; Ding et al. , 2016 ) and disciplinary paradigms of truth and justification ( Moshman, 2015 ).

Although this study is limited to the domain of biology at two institutions with a relatively small number of students, the findings represent a foundational step in the direction of achieving success with more integrated learning outcomes. Hopefully, it will spur greater interest in empirically grounding discussions of the constructs of scientific reasoning and critical-thinking skills.

This study contributes to the efforts to improve science education, for both majors and nonmajors, through an empirically driven analysis of the relationships between scientific reasoning reflected in the genre of thesis writing and critical-thinking skills. This work is rooted in the usefulness of BioTAP as a method 1) to facilitate communication and learning and 2) to assess disciplinary-specific and general dimensions of science reasoning. The findings support the important role of the critical-thinking skill of inference in scientific reasoning in writing, while also highlighting ways in which other aspects of science reasoning (epistemological considerations, writing conventions, etc.) are not significantly related to critical thinking. Future research into the impact of interventions focused on specific critical-thinking skills (i.e., inference) for improved science reasoning in writing will build on this work and its implications for science education.

ACKNOWLEDGMENTS

We acknowledge the contributions of Kelaine Haas and Alexander Motten to the implementation and collection of data. We also thank Mine Çetinkaya-­Rundel for her insights regarding our statistical analyses. This research was funded by National Science Foundation award DUE-1525602.

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  • Jason E. Dowd ,
  • Robert J. Thompson ,
  • Leslie Schiff ,
  • Kelaine Haas ,
  • Christine Hohmann ,
  • Chris Roy ,
  • Warren Meck ,
  • John Bruno , and
  • Rebecca Price, Monitoring Editor
  • Kari L. Nelson ,
  • Claudia M. Rauter , and
  • Christine E. Cutucache
  • Elisabeth Schussler, Monitoring Editor

Submitted: 17 March 2017 Revised: 19 October 2017 Accepted: 20 October 2017

© 2018 J. E. Dowd et al. CBE—Life Sciences Education © 2018 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

ORIGINAL RESEARCH article

Conspiratorial beliefs and cognitive styles: an integrated look on analytic thinking, critical thinking, and scientific reasoning in relation to (dis)trust in conspiracy theories.

\nBiljana Gjoneska

  • Macedonian Academy of Sciences and Arts, Skopje, North Macedonia

The tendency to believe in conspiracy theories (implying secret and malevolent plots by scheming groups or individuals), incites growing decennial interest among psychological researchers (exploring the associated personality traits, worldviews and cognitive styles of people). The link between the conspiratorial beliefs and the cognitive styles remains of particular interest to scholars, requiring integrated theoretical considerations. This perspective article will focus on the relationship between the propensity to (dis)trust conspiracy theories and three cognitive styles: analytic thinking, critical thinking, and scientific reasoning. Analytic thinking (inclination toward slow and deliberate processing of information in a conscious effort to mitigate biases and reach objective understanding of facts), is a well-studied concept in the context of conspiratorial beliefs, while the negative mutual relationship seems well-evidenced. On the other hand, the evidence on the link with the critical thinking (readiness to consider, reason, appraise, review, and interpret facts to update existing beliefs) has only started to emerge in the last years. Finally, scientific reasoning (ability to apply principles of scientific inquiry to formulate, test, revise and update knowledge in accordance with new evidence), is the least studied of the three cognitive styles in relation to conspiracy theories. The present article will: (a) revise the (lack of) scientific consensus on the definitional and conceptual aspects (by providing theoretical framework); (b) summarize the state of the art on the subject (by providing overview of empirical evidence); (c) discuss directions for future research (especially in relation to the COVID-19 pandemic). An integrated perspective on the relationship between conspiratorial beliefs and cognitive styles of people, may serve to inspire future behavioral interventions.

1. Introduction

One of the powerful academic portrayals of a world filled with conspiracies, depicts an inhospitable environment, dominated by “ a gigantic and yet subtle machinery of influence set in motion to undermine and destroy a way of life ” ( Hofstadter, 1964 , p. 29). This portrait however, pertains less to the external world, and more to some internal worldviews. Hence, it is not a depiction, but a reflection of sorts, offering a glimpse into the mental states of people with pronounced tendency to endorse conspiratorial narratives as explanations for important events, and cultivate persistent beliefs that powerful others are secretly plotting to harm them ( Hofstadter, 1964 ; Moscovici, 1987 ; Goertzel, 1994 ; Swami et al., 2011 ; Bruder et al., 2013 ; Wood and Douglas, 2015 ; van der Linden et al., 2021 ).

Interestingly, conspiratorial narratives are often regarded as both a most probable scenario (by people who subscribe to conspiratorial beliefs) and a least probable account of events (by others). In a similar fashion, people who are inclined toward conspiratorial thinking might believe to be “critical freethinkers” themselves ( Lantian et al., 2021 ), while being regarded as gullible by others ( van Prooijen, 2019 ). Psychological researchers have intensified their effort to understand the complexities of these radically opposing perceptions, and in doing so have amassed an impressive body of knowledge on personality traits, cognitive styles and worldviews that are frequently associated with beliefs in conspiracy theories (for an overview see Douglas et al., 2017 ; Goreis and Voracek, 2019 ; Lantian et al., 2020 ). However, the topic remains complex, multilayered and intricate, with real-life implications for individuals, groups and whole societies. This has been especially evident in the time of the COVID-19 pandemic during which the so-called “contagious conspiracism” has been a prominent feature of the global cultural landscape ( Sturm and Albrecht, 2021 ) and has negatively affected the health of many citizens worldwide ( Freeman et al., 2020 ; Jolley and Paterson, 2020 ; Marinthe et al., 2020 ). To adequately tackle the problem, it is the belief of this author that a joint effort by experts in several psychological disciplines (including social, political, educational, personality, and cognitive psychology) is required. Thus far, cognitive and educational psychologists together with philosophers have mainly focused on postulating conceptual frameworks of cognition and rationality ( Stanovich and Stanovich, 2010 ; Díaz et al., 2021 ), while social and political psychologists have mainly directed their effort toward experimental investigations of the conspiratorial beliefs.

The present article outlines a unified perspective on susceptibility to (dis)trust conspiracy theories, in relation to three distinctive cognitive styles: analytic thinking, critical thinking and scientific reasoning. Specifically, the study addresses three crucial questions:

1. What is the appropriate scientific model to use when researching the three cognitive styles, considered in reference to the psychological research on conspiratorial beliefs? An integrated theoretical framework (with clear and delineated definitions), will be introduced in response to this question. This is as a novel perspective on the explored subject matter.

2. What are some of the most important contributions in psychological literature on the link between the conspiratorial beliefs and the three cognitive styles? A broad overview of existing evidence (highlighting most important findings), will be offered in response to this question.

3. What is the potential for applying findings from psychological research on conspiracy theories to benefit our daily lives? In the concluding section, existing methodology and potential implications will be discussed, with hopes they will serve to inspire future behavioral interventions and inform public policies.

Henceforth, the term “conspiratorial beliefs” ( Goertzel, 1994 ) will be used as an umbrella for other labels that are frequently utilized in psychological literature on conspiracy theories including: “conspiracist ideation” ( Swami et al., 2011 ), “conspiracy mentality” ( Bruder et al., 2013 ), “conspiratorial mindset” ( Moscovici, 1987 ; van der Linden et al., 2021 ), or “conspiratorial worldview” ( Wood and Douglas, 2015 ). Therefore, the term will imply a “monological belief system,” Goertzel (1994) marked by a general propensity to believe in conspiracy theories, rather than a content-specific belief ( Sternisko et al., 2020 ) in a particular conspiracy theory ( Sutton and Douglas, 2020 ).

The term “cognitive styles” is also used in a variety of related contexts within psychological research on conspiracy theories ( Georgiou et al., 2019 ; Ballová Mikušková and Čavojová, 2020 ; Lantian et al., 2020 ). The basic description however, is borrowed from a comprehensive review of psychological studies on cognitive styles ( Kozhevnikov, 2007 ) to outline “a psychological dimension representing consistencies in an individual's manner of cognitive functioning” (ibid, p. 464). As such, cognitive styles are relatively stable, partly fixed and innate. However, they are not entirely “inborn structures, dependent only on an individual's internal characteristics, but, rather, are interactive constructs that develop in response to social, educational, professional, and other environmental requirements” (ibid, p. 477). Hence, they are “complex, multifaceted psychological variables that affect the way a person prefers to process information” and refer “to the way people solve problems, make decisions and undertake tasks” ( Peterson et al., 2009 , p. 521). In the present article the label will be used in reference to analytic thinking, critical thinking and scientific reasoning .

Each of the three cognitive styles is guided by rationality and goals for reliable information processing, decision making, and problem solving , and they all rely on thinking dispositions, metacognitive strategies, and advanced cognitive skills ( Halpern, 1998 ; Dunbar and Fugelsang, 2005 ; Ku and Ho, 2010 ). The dispositional tendencies direct the execution of tasks, metacognitive strategies regulate execution of tasks, while advanced cognitive skills enable acquisition, retention and transfer of knowledge from executed tasks ( Ku and Ho, 2010 ). In this regard, metacognitive strategies and advanced cognitive skills are highly reminiscent of the term “mindware” that is used in reference to “rules, knowledge, procedures, and strategies” that can be retrieved from memory to assist in decision making and problem solving processes ( Stanovich and Stanovich, 2010 , p. 215).

2. Analytic Thinking, Critical Thinking, and Scientific Reasoning: An Integrated Theoretical Framework

First, let us focus on the three cognitive styles, by providing definitions of terms, clear descriptions of their meanings, and delineation of mutual relationships.

• Analytic thinking predominantly implies proneness to engage in a slow, controlled and deliberate processing of information. The thinking disposition is engaged to mitigate biases and establish reliable understanding of facts ( Sloman, 1996 ; Kahneman and Frederick, 2002 ; Kozhevnikov, 2007 ; Franssens and De Neys, 2009 ; Kahneman, 2011 ).

• Critical thinking implies readiness and willingness to (re)consider, reason, (re)appraise, review and interpret facts, in order to facilitate good judgment, and secure reliable updating of beliefs ( Lai, 2011 ). It contains the three components ( Halpern, 1998 ), but is probably most reliant on the disposition toward analytic thinking and the metacognitive strategies for repeated engagement in analytic thinking . This might be the reason why critical thinking is described as “a self-directed, self-disciplined, self-monitored, and self-corrective thinking” ( Elder and Paul, 2000 , p. 29).

• Scientific reasoning also comprises of the three components, but is especially dependent on the advanced cognitive skills . It includes induction, deduction, analogy, causal reasoning, and other competencies which are employed for the purposes of scientific inquiry and problem solving during the critical thinking processes. They help people formulate, test, and revise hypotheses to solve problems, as a way to integrate new evidence into their existing system of knowledge ( Dunbar and Fugelsang, 2005 ; Han, 2013 ; Díaz et al., 2021 ).

The proposed theoretical framework is informed by existing social psychological research, and conceptualized in consultation with related literature from cognitive psychology, educational psychology and philosophy (see corresponding references above). Most notably, the proposed tripartite model of cognitive styles (including analytic thinking, critical thinking and scientific reasoning), could be considered as complementary to the existing tripartite model of the mind (including the autonomous, algorithmic and reflective mind) by Stanovich and Stanovich (2010) . Specifically, the three cognitive styles rise above the basic cognitive abilities as “microstrategies for cognitive operations” ( Stanovich and Stanovich, 2010 , p. 215), since they are driven by goals, beliefs and general knowledge. Therefore, the three cognitive styles are also related to the concept of rationality, providing an upgraded and fine-grained perspective on the properties of the reflective mind.

In addition to advancing the existing model by Stanovich and Stanovich (2010) , the proposed framework is useful in shedding light on the hierarchical organization of cognitive styles and their hypothetical relationships ( Figure 1 ). Namely, the three cognitive styles can be represented within a nested structure, with analytic thinking considered as a lowest-order and broadest construct (comprising of most general set of dispositions, metacognitive strategies and advanced skills), while the scientific reasoning considered as a narrowest and highest-order construct (comprising a most specialized subset of the three).

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Figure 1 . Conceptual framework of cognitive styles: analytic thinking (the broadest and lowest in order), critical thinking, and scientific reasoning (the narrowest and highest in order) are conceptualized as related and nestled constructs.

Specifically, analytic thinking and critical thinking can be considered as neighboring but distinct cognitive styles ( Lantian et al., 2021 ), with the former usually referred as a broader set of the latter. In critical thinking, the general tendency for slow, deliberate, explicit ( Kahneman and Frederick, 2002 ; Kahneman, 2011 ), detail-oriented ( Kozhevnikov, 2007 ) and resource-demanding analysis ( Franssens and De Neys, 2009 ), is coupled with more elaborate dispositions and metacognitive strategies, sometimes referred as a “mindware” ( Stanovich and Stanovich, 2010 ). These include the critical thinking dispositions for persistent, honest, clear, caring and concerned pursuit of the truth ( Ennis, 1996 ), the instrumental rationality (as motivation to achieve one's goals) and the epistemic rationality (as motivation to endorse evidence-based beliefs, but refrain from beliefs that are unfounded) ( Kelly, 2003 ).

In this framework, and as seen on Figure 1 , critical thinking and scientific reasoning can also be considered as related yet separate constructs, with the latter understood as a subset of the former ( Dowd et al., 2018 ). Broadly speaking, the acts of thinking and reasoning differ in that thinking involves more general cognitive processes for systemic transformation of mental representation of knowledge, while reasoning includes specialized cognitive processes aimed at drawing conclusions from initial premises ( Holyoak and Morrison, 2005 ; Díaz et al., 2021 ). More specifically, the acts of critical thinking and scientific reasoning also differ from each other. The former is related to interpretation of facts, updating of beliefs, making sound judgments and delivering reliable decisions . On the other hand, scientific reasoning is related to evaluation of facts, updating of knowledge and problem solving strategies . Overall, critical thinking is grounded in principles of logical inquiry, while scientific reasoning in scientific principles and methods ( Zimmerman, 2007 ).

Scientific reasoning in particular, encompasses a specialized subset of advanced cognitive abilities, metacognitive strategies and thinking dispositions that permeate the field of science, and include (but may not be limited to) the following operations: exploration of a problem (i.e., identification of main variables and their mutual relationships via inductive and deductive reasoning), generation of hypotheses (i.e., concept formation, formulation of premises and expected outcomes), hypothesis testing (i.e., isolation, controlling and manipulation of variables via experimentation), and evaluation of consequences ( Dunbar and Fugelsang, 2005 ; Han, 2013 ). Scientific reasoning is important for individuals, because it improves their ability to formulate, test, revise and update knowledge. The societal benefits are evident across all levels of education, career opportunities and daily social interactions that require problem solving competencies ( Han, 2013 ). Nonetheless, its relationship with conspiratorial beliefs remains scarcely explored (as explained in the following section).

3. Cognitive Styles and Conspiratorial Beliefs: An Overview of Empirical Evidence

Next, let us consider the link between the cognitive styles described in section 2 and conspiratorial beliefs. All three styles are essential for reliable interpretation of events, and making sense of one's environment. Broadly speaking, analytic thinking helps us to discern a truth from a lie or a fact from a fiction (in everyday processing of information), critical thinking helps us to decide whether to believe or not an (un)reliable information (when making decisions and judgments), while scientific reasoning helps us to gain wholesome understanding of the observed subject matter (by solving problems and finding solutions). A failure in any of these domains might be associated with increased conspiracism, because it is a signal of “crippled epistemology” ( Vermeule and Sunstein, 2009 ). This has already been evidenced in literature on flawed heuristics, cognitive biases and logical fallacies. The prominent examples include the tendency to perceive illusory patterns ( Prooijen et al., 2018 ; van der Wal et al., 2018 ), the illusion of explanatory depth ( Vitriol and Marsh, 2018 ), and the proneness toward conjunction fallacy ( Brotherton and French, 2014 ), all of which have been associated with conspiratorial beliefs. On a more complex level, people with pronounced propensity toward conspiracy theories, also exhibited a tendency to endorse belief systems that are epistemically unsubstantiated. These include supernatural, superstitious, spiritualistic, paranormal, pseudo-scientific beliefs, paranoid and schizotypal ideations ( Hofstadter, 1964 ; Darwin et al., 2011 ; Barron et al., 2014 ; Lobato et al., 2014 ; Georgiou et al., 2019 ; van Prooijen, 2019 ).

The (bi)directionality and the causality of these relationships is still unclear, given the limitations of the conducted studies (as explained in the discussion). On one hand, it seems plausible to assume that flawed heuristics and faulty reasoning, would result with increased tendency to believe in conspiratorial narratives. In this case, many strategies to improve analytic, critical and scientific thinking or reasoning, would serve to protect from such beliefs by enhancing observation, examination, checking, and rejection of unwarranted claims. On the other hand, the reverse causality also seems possible, where pronounced (pre)disposition toward conspiratorial beliefs, negatively affects information processing, decision making and problem solving, thus leading to faulty reasoning and flawed beliefs or knowledge systems.

We highlight findings that support the notion that analytic thinking reduces the tendency to engage in overly religious, paranormal ( Pennycook et al., 2012 ) and conspiratorial beliefs ( Swami et al., 2014 ). Overall, the link between the analytic thinking and the conspiratorial beliefs is negative, well-evidenced and robust ( Ståhl and van Prooijen, 2018 ; van der Wal et al., 2018 ; Georgiou et al., 2019 ; Wagner-Egger et al., 2019 ).

A number of studies have gone further, analyzing the link between conspiratorial beliefs and: (a) beliefs about the nature of knowledge i.e., epistemic beliefs ( Garrett and Weeks, 2017 ); (b) open-minded beliefs about the importance of evidence ( Pennycook et al., 2020a ); and (c) motivation to endorse beliefs that are calibrated with evidence i.e., epistemic rationality ( Ståhl and van Prooijen, 2018 ; Adam-Troian et al., 2019 ). Research on epistemic rationality, in particular, has shown that it moderates the relationship between conspiratorial beliefs and lower-level constructs in the following way: (a) it strengthens the negative relationship with general cognitive abilities ( Adam-Troian et al., 2019 ); and also (b) it strengthens the negative relationship with analytic thinking (Ståhl and van Prooijen, 2018 ). Overall, these studies have highlighted the pivotal role of the so-called “mindware” and various metacognitive strategies for the enhanced resistance toward conspiratorial narratives. In a recent study, Lantian et al. (2021) , the authors utilized Ennis-Weir critical thinking essay test ( Ennis, 1996 ) and the generic conspiracist beliefs scale ( Brotherton et al., 2013 ) to directly test the link, concluding that “conspiracy believers have less developed critical thinking ability.”

Lastly, research on the relationship between conspiracy theories and scientific reasoning (usually assessed via the scientific reasoning scale) Drummond and Fischhoff (2017) is still scarce. In fact, it remains limited to a single research group, reporting several findings over the last few years and confirming the negative correlation between this cognitive style and susceptibility toward cognitive biases ( Čavojová and Brezina, 2019 ) or COVID-19 related conspiratorial beliefs ( Čavojová and Brezina , 2019; Čavojová et al., 2020 ).

4. Discussing Implications and Future Perspectives

The framework proposed (in section 2) integrates theoretical considerations on three distinct cognitive styles (analytic thinking, critical thinking and scientific reasoning), into an organized system (with concise definitions of terms, clear description of meanings, and delineated mutual relationships). It is consistent with past psychological research, while at the same time providing fresh insights on the following aspects:

• The constructs: they can be thought of as nested within each other, with analytic thinking comprising the broadest set, and scientific reasoning as the narrowest and most specialized subset.

• The heuristics: analytic thinking relies on the dispositions for slow and conscious processing of information, critical thinking on the dispositions and the metacognitive strategies for reliable decision making, while scientific reasoning on the advanced cognitive skills and competencies for problem solving.

• The goals: analytic thinking is oriented toward unbiased and objective understanding of facts in daily situations, critical thinking toward reliable update of beliefs, while scientific reasoning toward updates of knowledge systems.

The overview on past research (in section 3) has revealed that the investigations have been: partial (because they explored the link between conspiratorial beliefs and separate cognitive styles in separate research contexts), sporadic (especially with regards to the research on the link with the critical thinking), or even accidental (especially with regards to the research on the link with the scientific reasoning). Furthermore, the investigations were predominantly cross-sectional and correlational, and therefore with limited ability to make conclusions on the causal inference. In addition, there has been little progress in standardizing methodology and empirical approaches across studies. For instance, most analyses in this area rely on self-reported measures (i.e., scales and questionnaires), and rarely on experimental designs (e.g., studies on cognitive biases and logical fallacies). While most of the studies employed quantitative analyses for assessment of results, the measurement of the variables (e.g., the conspiratorial beliefs) has been conducted on differing scales, and some of the scales had unknown psychometric properties.

Integrated theoretical considerations can serve as basis for a unified approach in empirical studies. Specifically, they can shape future psychological research to: (a) build models that will account for all presented variables; (b) conduct experiments with ecologic validity preferably outside of laboratory settings; (c) perform complex statistical analyses (e.g., hierarchical regressions and structural equation modeling) that explore mutual relations between all proposed variables and test the overall model. More realistic models and improved experimental designs can inspire future behavioral interventions in the fight against misinformation and conspiracy theories, by cultivating the capacity for analytic, critical and scientific thinking ( van der Linden et al., 2020 ; Lewandowsky et al., 2021 ). This is especially relevant in the context of the COVID-19 pandemic where millions across the globe are inundated by conspiracy theories that have been linked to engagement in non-normative prevention behaviors ( Marinthe et al., 2020 ), decreased trust in government and lack of compliance with official public health recommendations ( Freeman et al., 2020 ), or even engagement in risky and violent behaviors ( Jolley and Paterson, 2020 ).

Emerging evidence regarding pandemic-related conspiratorial beliefs and various cognitive markers, suggests that: (a) they are positively linked with a group of cognitive biases, marked by an increased tendency to make premature conclusions (delivered on basis of low subjective probability estimates, lack of sufficient evidence, or even in the face of disconfirmatory evidence) ( Kuhn et al., 2021 ); (b) they are negatively linked with scientific reasoning ( Čavojová et al., 2020 ); and most importantly (c) they can be reduced by nudging individuals to consider accuracy of presented statements ( Pennycook et al., 2020b ). In this respect, strategies that aim to enhance rationality seem to have potential in reducing prevalence of conspiratorial beliefs. For example, asking participants to judge the accuracy of a piece of information (in order to secure more reliable analysis and enhanced analytical thinking), or to judge subjective importance of an information (in order to secure more accurate interpretation and enhance critical thinking), or just providing digital literacy tips (for improved scientific reasoning) have been shown to reduce the spread of COVID-19 misinformation ( Epstein et al., 2021 ).

5. Conclusions

The present article offers a perspective on the current scientific consensus, and opens a perspective toward future investigations of the link between the conspiratorial beliefs and three cognitive styles: analytic thinking, critical thinking and scientific reasoning. It does so, by outlining a clear perspective on others' works, and conceptualizing the author's perspective in an integrated theoretical framework. The literature overview is given in a condensed format, to serve as a basis for future systematic reviews or meta-analyses. Also, the theoretical framework is quite broad, and could be further advanced in a study focused exclusively on theoretical improvements and hypothesis development. This study will hopefully inspire a dialogue between researchers from different disciplines seeking to develop unified and multidisciplinary approach in the fight against misinformation and conspiracy theories.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Author Contributions

BG has conceptualized the study, drafted the manuscript, and thoroughly revised the contents before submitting. The style and language were checked by native English speaker.

Conflict of Interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: conspiracy theories, conspiratorial beliefs, cognitive styles, analytic thinking, critical thinking, scientific reasoning

Citation: Gjoneska B (2021) Conspiratorial Beliefs and Cognitive Styles: An Integrated Look on Analytic Thinking, Critical Thinking, and Scientific Reasoning in Relation to (Dis)trust in Conspiracy Theories. Front. Psychol. 12:736838. doi: 10.3389/fpsyg.2021.736838

Received: 06 July 2021; Accepted: 10 September 2021; Published: 12 October 2021.

Reviewed by:

Copyright © 2021 Gjoneska. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Biljana Gjoneska, biljanagjoneska@manu.edu.mk

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

critical thinking and scientific thinking

3. Critical Thinking in Science: How to Foster Scientific Reasoning Skills

Critical thinking in science is important largely because a lot of students have developed expectations about science that can prove to be counter-productive. 

After various experiences — both in school and out — students often perceive science to be primarily about learning “authoritative” content knowledge: this is how the solar system works; that is how diffusion works; this is the right answer and that is not. 

This perception allows little room for critical thinking in science, in spite of the fact that argument, reasoning, and critical thinking lie at the very core of scientific practice.

Argument, reasoning, and critical thinking lie at the very core of scientific practice.

critical thinking and scientific thinking

In this article, we outline two of the best approaches to be most effective in fostering scientific reasoning. Both try to put students in a scientist’s frame of mind more than is typical in science education:

  • First, we look at  small-group inquiry , where students formulate questions and investigate them in small groups. This approach is geared more toward younger students but has applications at higher levels too.
  • We also look  science   labs . Too often, science labs too often involve students simply following recipes or replicating standard results. Here, we offer tips to turn labs into spaces for independent inquiry and scientific reasoning.

critical thinking and scientific thinking

I. Critical Thinking in Science and Scientific Inquiry

Even very young students can “think scientifically” under the right instructional support. A series of experiments , for instance, established that preschoolers can make statistically valid inferences about unknown variables. Through observation they are also capable of distinguishing actions that cause certain outcomes from actions that don’t. These innate capacities, however, have to be developed for students to grow up into rigorous scientific critical thinkers. 

Even very young students can “think scientifically” under the right instructional support.

Although there are many techniques to get young children involved in scientific inquiry — encouraging them to ask and answer “why” questions, for instance — teachers can provide structured scientific inquiry experiences that are deeper than students can experience on their own. 

Goals for Teaching Critical Thinking Through Scientific Inquiry

When it comes to teaching critical thinking via science, the learning goals may vary, but students should learn that:

  • Failure to agree is okay, as long as you have reasons for why you disagree about something.
  • The logic of scientific inquiry is iterative. Scientists always have to consider how they might improve your methods next time. This includes addressing sources of uncertainty.
  • Claims to knowledge usually require multiple lines of evidence and a “match” or “fit” between our explanations and the evidence we have.
  • Collaboration, argument, and discussion are central features of scientific reasoning.
  • Visualization, analysis, and presentation are central features of scientific reasoning.
  • Overarching concepts in scientific practice — such as uncertainty, measurement, and meaningful experimental contrasts — manifest themselves somewhat differently in different scientific domains.

How to Teaching Critical Thinking in Science Via Inquiry

Sometimes we think of science education as being either a “direct” approach, where we tell students about a concept, or an “inquiry-based” approach, where students explore a concept themselves.  

But, especially, at the earliest grades, integrating both approaches can inform students of their options (i.e., generate and extend their ideas), while also letting students make decisions about what to do.

Like a lot of projects targeting critical thinking, limited classroom time is a challenge. Although the latest content standards, such as the Next Generation Science Standards , emphasize teaching scientific practices, many standardized tests still emphasize assessing scientific content knowledge.

The concept of uncertainty comes up in every scientific domain.

Creating a lesson that targets the right content is also an important aspect of developing authentic scientific experiences. It’s now more  widely acknowledged  that effective science instruction involves the interaction between domain-specific knowledge and domain-general knowledge, and that linking an inquiry experience to appropriate target content is vital.

For instance, the concept of uncertainty  comes up  in every scientific domain. But the sources of uncertainty coming from any given measurement vary tremendously by discipline. It requires content knowledge to know how to wisely apply the concept of uncertainty.

Tips and Challenges for teaching critical thinking in science

Teachers need to grapple with student misconceptions. Student intuition about how the world works — the way living things grow and behave, the way that objects fall and interact — often conflicts with scientific explanations. As part of the inquiry experience, teachers can help students to articulate these intuitions and revise them through argument and evidence.

Group composition is another challenge. Teachers will want to avoid situations where one member of the group will simply “take charge” of the decision-making, while other member(s) disengage. In some cases, grouping students by current ability level can make the group work more productive. 

Another approach is to establish group norms that help prevent unproductive group interactions. A third tactic is to have each group member learn an essential piece of the puzzle prior to the group work, so that each member is bringing something valuable to the table (which other group members don’t yet know).

It’s critical to ask students about how certain they are in their observations and explanations and what they could do better next time. When disagreements arise about what to do next or how to interpret evidence, the instructor should model good scientific practice by, for instance, getting students to think about what kind of evidence would help resolve the disagreement or whether there’s a compromise that might satisfy both groups.

The subjects of the inquiry experience and the tools at students’ disposal will depend upon the class and the grade level. Older students may be asked to create mathematical models, more sophisticated visualizations, and give fuller presentations of their results.

Lesson Plan Outline

This lesson plan takes a small-group inquiry approach to critical thinking in science. It asks students to collaboratively explore a scientific question, or perhaps a series of related questions, within a scientific domain.

Suppose students are exploring insect behavior. Groups may decide what questions to ask about insect behavior; how to observe, define, and record insect behavior; how to design an experiment that generates evidence related to their research questions; and how to interpret and present their results.

An in-depth inquiry experience usually takes place over the course of several classroom sessions, and includes classroom-wide instruction, small-group work, and potentially some individual work as well.

Students, especially younger students, will typically need some background knowledge that can inform more independent decision-making. So providing classroom-wide instruction and discussion before individual group work is a good idea.

For instance, Kathleen Metz had students observe insect behavior, explore the anatomy of insects, draw habitat maps, and collaboratively formulate (and categorize) research questions before students began to work more independently.

The subjects of a science inquiry experience can vary tremendously: local weather patterns, plant growth, pollution, bridge-building. The point is to engage students in multiple aspects of scientific practice: observing, formulating research questions, making predictions, gathering data, analyzing and interpreting data, refining and iterating the process.

As student groups take responsibility for their own investigation, teachers act as facilitators. They can circulate around the room, providing advice and guidance to individual groups. If classroom-wide misconceptions arise, they can pause group work to address those misconceptions directly and re-orient the class toward a more productive way of thinking.

Throughout the process, teachers can also ask questions like:

  • What are your assumptions about what’s going on? How can you check your assumptions?
  • Suppose that your results show X, what would you conclude?
  • If you had to do the process over again, what would you change? Why?

critical thinking and scientific thinking

II. Rethinking Science Labs

Beyond changing how students approach scientific inquiry, we also need to rethink science labs. After all, science lab activities are ubiquitous in science classrooms and they are a great opportunity to teach critical thinking skills.

Often, however, science labs are merely recipes that students follow to verify standard values (such as the force of acceleration due to gravity) or relationships between variables (such as the relationship between force, mass, and acceleration) known to the students beforehand. 

This approach does not usually involve critical thinking: students are not making many decisions during the process, and they do not reflect on what they’ve done except to see whether their experimental data matches the expected values.

With some small tweaks, however, science labs can involve more critical thinking. Science lab activities that give students not only the opportunity to design, analyze, and interpret the experiment, but re -design, re -analyze, and re -interpret the experiment provides ample opportunity for grappling with evidence and evidence-model relationships, particularly if students don’t know what answer they should be expecting beforehand.

Such activities improve scientific reasoning skills, such as: 

  • Evaluating quantitative data
  • Plausible scientific explanations for observed patterns

And also broader critical thinking skills, like:

  • Comparing models to data, and comparing models to each other
  • Thinking about what kind of evidence supports one model or another
  • Being open to changing your beliefs based on evidence

Traditional science lab experiences bear little resemblance to actual scientific practice. Actual practice  involves  decision-making under uncertainty, trial-and-error, tweaking experimental methods over time, testing instruments, and resolving conflicts among different kinds of evidence. Traditional in-school science labs rarely involve these things.

Traditional science lab experiences bear little resemblance to actual scientific practice.

When teachers use science labs as opportunities to engage students in the kinds of dilemmas that scientists actually face during research, students make more decisions and exhibit more sophisticated reasoning.

In the lesson plan below, students are asked to evaluate two models of drag forces on a falling object. One model assumes that drag increases linearly with the velocity of the falling object. Another model assumes that drag increases quadratically (e.g., with the square of the velocity).  Students use a motion detector and computer software to create a plot of the position of a disposable paper coffee filter as it falls to the ground. Among other variables, students can vary the number of coffee filters they drop at once, the height at which they drop them, how they drop  them, and how they clean their data. This is an approach to scaffolding critical thinking: a way to get students to ask the right kinds of questions and think in the way that scientists tend to think.

Design an experiment to test which model best characterizes the motion of the coffee filters. 

Things to think about in your design:

  • What are the relevant variables to control and which ones do you need to explore?
  • What are some logistical issues associated with the data collection that may cause unnecessary variability (either random or systematic) or mistakes?
  • How can you control or measure these?
  • What ways can you graph your data and which ones will help you figure out which model better describes your data?

Discuss your design with other groups and modify as you see fit.

Initial data collection

Conduct a quick trial-run of your experiment so that you can evaluate your methods.

  • Do your graphs provide evidence of which model is the best?
  • What ways can you improve your methods, data, or graphs to make your case more convincing?
  • Do you need to change how you’re collecting data?
  • Do you need to take data at different regions?
  • Do you just need more data?
  • Do you need to reduce your uncertainty?

After this initial evaluation of your data and methods, conduct the desired improvements, changes, or additions and re-evaluate at the end.

In your lab notes, make sure to keep track of your progress and process as you go. As always, your final product is less important than how you get there.

How to Make Science Labs Run Smoothly

Managing student expectations . As with many other lesson plans that incorporate critical thinking, students are not used to having so much freedom. As with the example lesson plan above, it’s important to scaffold student decision-making by pointing out what decisions have to be made, especially as students are transitioning to this approach.

Supporting student reasoning . Another challenge is to provide guidance to student groups without telling them how to do something. Too much “telling” diminishes student decision-making, but not enough support may leave students simply not knowing what to do. 

There are several key strategies teachers can try out here: 

  • Point out an issue with their data collection process without specifying exactly how to solve it.
  • Ask a lab group how they would improve their approach.
  • Ask two groups with conflicting results to compare their results, methods, and analyses.

Download our Teachers’ Guide

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Sources and Resources

Lehrer, R., & Schauble, L. (2007). Scientific thinking and scientific literacy . Handbook of child psychology , Vol. 4. Wiley. A review of research on scientific thinking and experiments on teaching scientific thinking in the classroom.

Metz, K. (2004). Children’s understanding of scientific inquiry: Their conceptualizations of uncertainty in investigations of their own design . Cognition and Instruction 22(2). An example of a scientific inquiry experience for elementary school students.

The Next Generation Science Standards . The latest U.S. science content standards.

Concepts of Evidence A collection of important concepts related to evidence that cut across scientific disciplines.

Scienceblind A book about children’s science misconceptions and how to correct them.

Holmes, N. G., Keep, B., & Wieman, C. E. (2020). Developing scientific decision making by structuring and supporting student agency. Physical Review Physics Education Research , 16 (1), 010109. A research study on minimally altering traditional lab approaches to incorporate more critical thinking. The drag example was taken from this piece.

ISLE , led by E. Etkina.  A platform that helps teachers incorporate more critical thinking in physics labs.

Holmes, N. G., Wieman, C. E., & Bonn, D. A. (2015). Teaching critical thinking . Proceedings of the National Academy of Sciences , 112 (36), 11199-11204. An approach to improving critical thinking and reflection in science labs. Walker, J. P., Sampson, V., Grooms, J., Anderson, B., & Zimmerman, C. O. (2012). Argument-driven inquiry in undergraduate chemistry labs: The impact on students’ conceptual understanding, argument skills, and attitudes toward science . Journal of College Science Teaching , 41 (4), 74-81. A large-scale research study on transforming chemistry labs to be more inquiry-based.

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Fostering Scientific Literacy and Critical Thinking in Elementary Science Education

  • Published: 30 December 2014
  • Volume 14 , pages 659–680, ( 2016 )

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  • Rui Marques Vieira 1 , 2 &
  • Celina Tenreiro-Vieira 2  

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Scientific literacy (SL) and critical thinking (CT) are key components of science education aiming to prepare students to think and to function as responsible citizens in a world increasingly affected by science and technology (S&T). Therefore, students should be given opportunities in their science classes to be engaged in learning experiences that promote SL and CT, which may trigger the need to build and develop knowledge, attitudes/values, thinking abilities, and standards/criteria in an integrated way, resulting in their ability to know how to take responsible action in contexts and situations of personal and social relevance. This paper reports on a study to design, implement, and assess science learning experiences focused on CT toward SL goal. Results support the conclusion that the learning experiences developed and implemented in a grade 6 science classroom had a significant influence on the students’ CT and SL. Within this elementary school context, the theoretical framework used appears to be a relevant and practical aid for developing learning experiences that promote CT/SL and in supporting teaching practices that are more in line with the goals of critical scientific literacy.

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The authors thank Dr. Larry Yore and Mrs. Sharyl Yore for their mentoring assistance.

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Vieira, R.M., Tenreiro-Vieira, C. Fostering Scientific Literacy and Critical Thinking in Elementary Science Education. Int J of Sci and Math Educ 14 , 659–680 (2016). https://doi.org/10.1007/s10763-014-9605-2

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Critical Thinking in Science: Fostering Scientific Reasoning Skills in Students

Thinking like a scientist is a central goal of all science curricula.

As students learn facts, methodologies, and methods, what matters most is that all their learning happens through the lens of scientific reasoning what matters most is that it’s all through the lens of scientific reasoning.

That way, when it comes time for them to take on a little science themselves, either in the lab or by theoretically thinking through a solution, they understand how to do it in the right context.

One component of this type of thinking is being critical. Based on facts and evidence, critical thinking in science isn’t exactly the same as critical thinking in other subjects.

Students have to doubt the information they’re given until they can prove it’s right.

They have to truly understand what’s true and what’s hearsay. It’s complex, but with the right tools and plenty of practice, students can get it right.

What is critical thinking?

This particular style of thinking stands out because it requires reflection and analysis. Based on what's logical and rational, thinking critically is all about digging deep and going beyond the surface of a question to establish the quality of the question itself.

It ensures students put their brains to work when confronted with a question rather than taking every piece of information they’re given at face value.

It’s engaged, higher-level thinking that will serve them well in school and throughout their lives.

Why is critical thinking important?

Critical thinking is important when it comes to making good decisions.

It gives us the tools to think through a choice rather than quickly picking an option — and probably guessing wrong. Think of it as the all-important ‘why.’

Why is that true? Why is that right? Why is this the only option?

Finding answers to questions like these requires critical thinking. They require you to really analyze both the question itself and the possible solutions to establish validity.

Will that choice work for me? Does this feel right based on the evidence?

How does critical thinking in science impact students?

Critical thinking is essential in science.

It’s what naturally takes students in the direction of scientific reasoning since evidence is a key component of this style of thought.

It’s not just about whether evidence is available to support a particular answer but how valid that evidence is.

It’s about whether the information the student has fits together to create a strong argument and how to use verifiable facts to get a proper response.

Critical thinking in science helps students:

  • Actively evaluate information
  • Identify bias
  • Separate the logic within arguments
  • Analyze evidence

4 Ways to promote critical thinking

Figuring out how to develop critical thinking skills in science means looking at multiple strategies and deciding what will work best at your school and in your class.

Based on your student population, their needs and abilities, not every option will be a home run.

These particular examples are all based on the idea that for students to really learn how to think critically, they have to practice doing it. 

Each focuses on engaging students with science in a way that will motivate them to work independently as they hone their scientific reasoning skills.

Project-Based Learning

Project-based learning centers on critical thinking.

Teachers can shape a project around the thinking style to give students practice with evaluating evidence or other critical thinking skills.

Critical thinking also happens during collaboration, evidence-based thought, and reflection.

For example, setting students up for a research project is not only a great way to get them to think critically, but it also helps motivate them to learn.

Allowing them to pick the topic (that isn’t easy to look up online), develop their own research questions, and establish a process to collect data to find an answer lets students personally connect to science while using critical thinking at each stage of the assignment.

They’ll have to evaluate the quality of the research they find and make evidence-based decisions.

Self-Reflection

Adding a question or two to any lab practicum or activity requiring students to pause and reflect on what they did or learned also helps them practice critical thinking.

At this point in an assignment, they’ll pause and assess independently. 

You can ask students to reflect on the conclusions they came up with for a completed activity, which really makes them think about whether there's any bias in their answer.

Addressing Assumptions

One way critical thinking aligns so perfectly with scientific reasoning is that it encourages students to challenge all assumptions. 

Evidence is king in the science classroom, but even when students work with hard facts, there comes the risk of a little assumptive thinking.

Working with students to identify assumptions in existing research or asking them to address an issue where they suspend their own judgment and simply look at established facts polishes their that critical eye.

They’re getting practice without tossing out opinions, unproven hypotheses, and speculation in exchange for real data and real results, just like a scientist has to do.

Lab Activities With Trial-And-Error

Another component of critical thinking (as well as thinking like a scientist) is figuring out what to do when you get something wrong.

Backtracking can mean you have to rethink a process, redesign an experiment, or reevaluate data because the outcomes don’t make sense, but it’s okay.

The ability to get something wrong and recover is not only a valuable life skill, but it’s where most scientific breakthroughs start. Reminding students of this is always a valuable lesson.

Labs that include comparative activities are one way to increase critical thinking skills, especially when introducing new evidence that might cause students to change their conclusions once the lab has begun.

For example, you provide students with two distinct data sets and ask them to compare them.

With only two choices, there are a finite amount of conclusions to draw, but then what happens when you bring in a third data set? Will it void certain conclusions? Will it allow students to make new conclusions, ones even more deeply rooted in evidence?

Thinking like a scientist

When students get the opportunity to think critically, they’re learning to trust the data over their ‘gut,’ to approach problems systematically and make informed decisions using ‘good’ evidence.

When practiced enough, this ability will engage students in science in a whole new way, providing them with opportunities to dig deeper and learn more.

It can help enrich science and motivate students to approach the subject just like a professional would.

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The Relationship Between Critical Thinking and Critical Theory

Comparing approaches..

Posted February 15, 2024 | Reviewed by Gary Drevitch

  • Critical theory is a way of identifying, critiquing, and challenging social dynamics and power structures.
  • Modern critical theory seems to skip a lot of steps associated with logic and mechanisms of good thinking.
  • Human beings think in hierarchically structured fashion, and they develop social groups in a similar manner.

I recently asked a fellow academic, in conversation, how they try to integrate critical thinking into their classroom, and they replied that they don’t have "much time for that kind of thing." I quickly realised that they didn’t know what I was talking about and likely confused it for something else. This shouldn’t have been entirely surprising to me, given research by Lloyd and Bahr (2010) indicates that, unfortunately, many educators are not au fait with what critical thinking actually is. Following further conversation, I came to understand what this academic was referring to: critical theory. This was neither the first time I’ve encountered such confusion of terms, nor was it the first time I heard criticism of the field.

What Critical Theory Is

I recognise that the phrasing "critical lens" one often hears in educational contexts might be a bit ambiguous and could be perceived in various ways. Critical thinking is many things, but one thing it is not is critical theory. Critical theory is an arts and humanities approach to identifying, critiquing, and challenging social dynamics and power structures within society (e.g., see Tyson 2023, Marcuse, 1968). Simply, it’s a critique of society; hence, the name—though some in the field would argue this and uphold the belief that it’s an association with our beloved critical thinking. I would argue that such people would fit in well with the aforementioned cohort of people who don’t really understand what critical thinking is.

The critical theory approach developed out of post-World War II German social climates as a means of exploring how Germany and, indeed, Europe got to where they were at that point in time. This is reasonable; indeed, psychology was interested in these implications as well (e.g., consider the work of Milgram and Asch). Critical theory grew from there into other socially aware applications. Despite methodological concerns, there is some good work done through critical theory. However, there is also considerable poor research done in this area. I would argue that the core reason for this is that the approach is often founded in bias . That is, unfortunately, a lot of modern critical theory starts with the proposition that some dynamic is "bad." Now, I’m not saying that many of the dynamics often under investigation aren’t bad, but starting research on the basis of a biased perspective doesn’t sound like a particularly promising rationale. Where’s the critical thinking? Where’s the evaluation? If you truly care about the topic, apply critical thinking from an unbiased perspective. Modern critical theory seems to skip a lot of steps associated with logic and the mechanisms of good thinking.

The purpose of this brief discussion on critical theory is two-fold. First, it’s argued that there has been "considerable" growth in the field in recent years (e.g., critical theory student numbers, growing presence in popular society, and growing inclusion in educational curricula), which is concerning given the rationale above, and, second, consistent with my observation in the introduction, its name is unfortunately similar to "critical thinking" and, thus, the two are often confused for one another. Please, don’t make this mistake.

Power Structures

Similar to the aforementioned negative social dynamics, I’m not saying that power structures don’t exist either. Look at families: Parents hold "power" over their children. Look at jobs: Employees are under the power of their managers, who are under the power of other managers, and so on. Indeed, depending on what country you live in, your government has varying levels of power over those it governs (e.g., with respect to law and policy-making). Some will argue that it’s the people who should be governing themselves: voting in law- and policy-makers as representatives, which is reasonable to me, but not all governments are like this— that’s politics for you (e.g., largely belief-led) , so what can you do? "Think critically about it" would be a reasonable response in the context of this page, and that is notably distinct from engaging in critical theory.

The point is that such "structures" are naturally occurring. Human beings think in hierarchically structured fashion (e.g., through schema construction, classification, categorisation) and they develop social groups in a similar manner. That’s not to say that we should accept such structures in all situations, but no amount of academia is likely to change human nature; believe me, we’ve been trying to get people to think critically for a long time. Another important consideration for recognising this commonality is our expectance of these structures. Unfortunately, because we expect to see them everywhere, we wind up creating many of them, through our interpretations, when they might not even exist.

So, if you are approaching your research from the perspective that because something (e.g., some group) experiences, for example, a less-than-desirable event or condition, it’s very easy—without the application of critical thinking—that such negative outcomes should be attributed to some other group, in a sort of causal relationship. The problem is, as opposed to this being a conclusion ( a leads to b ), it is often the starting point of research, which then biases the methodology and its outcomes. For example, in an effort not to single out any particular group, let’s say I’m studying some topic from a Zuggist perspective (I made-up the word/group "Zug"). Considering the fact that I side with Zuggists—I might even be a Zug myself—the chances of me reporting something that is biased in favour of Zugs is more likely than not. To me, that’s not good research.

Again, I’m not saying that all research from a critical theory approach is like this, but, unfortunately, a noticeable amount of it is. Sure, every field has its barriers and "crises" from time to time: Psychology has been battling a replicability crisis in recent years. However, at least psychology (for the most part) recognises the importance of replicability and other research mechanisms associated with good methodology. I have concerns about that with respect to critical theory.

All in all, critical theory doesn’t mean much to me, but, for now, like my fellow academic said in the introduction, "I don’t have much time for that kind of thing." So, why bother talking about it here? This page is focused on critical thinking and good decision-making . These are the outcomes in which I and readers of this blog are interested, alongside learning more about how we can enhance them. It’s difficult enough conceptualising and describing critical thinking without having something similarly named adding further confusion. I’m not putting blame on anyone for the manner in which they coined the term "critical theory"; however, I think it important that people from all walks of life know the differences between them, because those differences are many and important.

Lloyd, M., & Bahr, N. (2010). Thinking critically about critical thinking in higher education. International Journal for the Scholarship of Teaching and Learning, 4, 2, 1–16.

Marcuse, Herbert. "Philosophy and Critical Theory," in Negations: Essays in Critical Theory , with translations from the German by Jeremy J. Shapiro (Boston: Beacon Press, 1968), pp. 134–158.

Tyson, L. (2023). Critical Theory Today: A User-Friendly Guide . Taylor & Francis.

Christopher Dwyer Ph.D.

Christopher Dwyer, Ph.D., is a lecturer at the Technological University of the Shannon in Athlone, Ireland.

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Chapter 6 clinical reasoning, decisionmaking, and action: thinking critically and clinically.

Patricia Benner ; Ronda G. Hughes ; Molly Sutphen .

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This chapter examines multiple thinking strategies that are needed for high-quality clinical practice. Clinical reasoning and judgment are examined in relation to other modes of thinking used by clinical nurses in providing quality health care to patients that avoids adverse events and patient harm. The clinician’s ability to provide safe, high-quality care can be dependent upon their ability to reason, think, and judge, which can be limited by lack of experience. The expert performance of nurses is dependent upon continual learning and evaluation of performance.

  • Critical Thinking

Nursing education has emphasized critical thinking as an essential nursing skill for more than 50 years. 1 The definitions of critical thinking have evolved over the years. There are several key definitions for critical thinking to consider. The American Philosophical Association (APA) defined critical thinking as purposeful, self-regulatory judgment that uses cognitive tools such as interpretation, analysis, evaluation, inference, and explanation of the evidential, conceptual, methodological, criteriological, or contextual considerations on which judgment is based. 2 A more expansive general definition of critical thinking is

. . . in short, self-directed, self-disciplined, self-monitored, and self-corrective thinking. It presupposes assent to rigorous standards of excellence and mindful command of their use. It entails effective communication and problem solving abilities and a commitment to overcome our native egocentrism and sociocentrism. Every clinician must develop rigorous habits of critical thinking, but they cannot escape completely the situatedness and structures of the clinical traditions and practices in which they must make decisions and act quickly in specific clinical situations. 3

There are three key definitions for nursing, which differ slightly. Bittner and Tobin defined critical thinking as being “influenced by knowledge and experience, using strategies such as reflective thinking as a part of learning to identify the issues and opportunities, and holistically synthesize the information in nursing practice” 4 (p. 268). Scheffer and Rubenfeld 5 expanded on the APA definition for nurses through a consensus process, resulting in the following definition:

Critical thinking in nursing is an essential component of professional accountability and quality nursing care. Critical thinkers in nursing exhibit these habits of the mind: confidence, contextual perspective, creativity, flexibility, inquisitiveness, intellectual integrity, intuition, openmindedness, perseverance, and reflection. Critical thinkers in nursing practice the cognitive skills of analyzing, applying standards, discriminating, information seeking, logical reasoning, predicting, and transforming knowledge 6 (Scheffer & Rubenfeld, p. 357).

The National League for Nursing Accreditation Commission (NLNAC) defined critical thinking as:

the deliberate nonlinear process of collecting, interpreting, analyzing, drawing conclusions about, presenting, and evaluating information that is both factually and belief based. This is demonstrated in nursing by clinical judgment, which includes ethical, diagnostic, and therapeutic dimensions and research 7 (p. 8).

These concepts are furthered by the American Association of Colleges of Nurses’ definition of critical thinking in their Essentials of Baccalaureate Nursing :

Critical thinking underlies independent and interdependent decision making. Critical thinking includes questioning, analysis, synthesis, interpretation, inference, inductive and deductive reasoning, intuition, application, and creativity 8 (p. 9).
Course work or ethical experiences should provide the graduate with the knowledge and skills to:
  • Use nursing and other appropriate theories and models, and an appropriate ethical framework;
  • Apply research-based knowledge from nursing and the sciences as the basis for practice;
  • Use clinical judgment and decision-making skills;
  • Engage in self-reflective and collegial dialogue about professional practice;
  • Evaluate nursing care outcomes through the acquisition of data and the questioning of inconsistencies, allowing for the revision of actions and goals;
  • Engage in creative problem solving 8 (p. 10).

Taken together, these definitions of critical thinking set forth the scope and key elements of thought processes involved in providing clinical care. Exactly how critical thinking is defined will influence how it is taught and to what standard of care nurses will be held accountable.

Professional and regulatory bodies in nursing education have required that critical thinking be central to all nursing curricula, but they have not adequately distinguished critical reflection from ethical, clinical, or even creative thinking for decisionmaking or actions required by the clinician. Other essential modes of thought such as clinical reasoning, evaluation of evidence, creative thinking, or the application of well-established standards of practice—all distinct from critical reflection—have been subsumed under the rubric of critical thinking. In the nursing education literature, clinical reasoning and judgment are often conflated with critical thinking. The accrediting bodies and nursing scholars have included decisionmaking and action-oriented, practical, ethical, and clinical reasoning in the rubric of critical reflection and thinking. One might say that this harmless semantic confusion is corrected by actual practices, except that students need to understand the distinctions between critical reflection and clinical reasoning, and they need to learn to discern when each is better suited, just as students need to also engage in applying standards, evidence-based practices, and creative thinking.

The growing body of research, patient acuity, and complexity of care demand higher-order thinking skills. Critical thinking involves the application of knowledge and experience to identify patient problems and to direct clinical judgments and actions that result in positive patient outcomes. These skills can be cultivated by educators who display the virtues of critical thinking, including independence of thought, intellectual curiosity, courage, humility, empathy, integrity, perseverance, and fair-mindedness. 9

The process of critical thinking is stimulated by integrating the essential knowledge, experiences, and clinical reasoning that support professional practice. The emerging paradigm for clinical thinking and cognition is that it is social and dialogical rather than monological and individual. 10–12 Clinicians pool their wisdom and multiple perspectives, yet some clinical knowledge can be demonstrated only in the situation (e.g., how to suction an extremely fragile patient whose oxygen saturations sink too low). Early warnings of problematic situations are made possible by clinicians comparing their observations to that of other providers. Clinicians form practice communities that create styles of practice, including ways of doing things, communication styles and mechanisms, and shared expectations about performance and expertise of team members.

By holding up critical thinking as a large umbrella for different modes of thinking, students can easily misconstrue the logic and purposes of different modes of thinking. Clinicians and scientists alike need multiple thinking strategies, such as critical thinking, clinical judgment, diagnostic reasoning, deliberative rationality, scientific reasoning, dialogue, argument, creative thinking, and so on. In particular, clinicians need forethought and an ongoing grasp of a patient’s health status and care needs trajectory, which requires an assessment of their own clarity and understanding of the situation at hand, critical reflection, critical reasoning, and clinical judgment.

Critical Reflection, Critical Reasoning, and Judgment

Critical reflection requires that the thinker examine the underlying assumptions and radically question or doubt the validity of arguments, assertions, and even facts of the case. Critical reflective skills are essential for clinicians; however, these skills are not sufficient for the clinician who must decide how to act in particular situations and avoid patient injury. For example, in everyday practice, clinicians cannot afford to critically reflect on the well-established tenets of “normal” or “typical” human circulatory systems when trying to figure out a particular patient’s alterations from that typical, well-grounded understanding that has existed since Harvey’s work in 1628. 13 Yet critical reflection can generate new scientifically based ideas. For example, there is a lack of adequate research on the differences between women’s and men’s circulatory systems and the typical pathophysiology related to heart attacks. Available research is based upon multiple, taken-for-granted starting points about the general nature of the circulatory system. As such, critical reflection may not provide what is needed for a clinician to act in a situation. This idea can be considered reasonable since critical reflective thinking is not sufficient for good clinical reasoning and judgment. The clinician’s development of skillful critical reflection depends upon being taught what to pay attention to, and thus gaining a sense of salience that informs the powers of perceptual grasp. The powers of noticing or perceptual grasp depend upon noticing what is salient and the capacity to respond to the situation.

Critical reflection is a crucial professional skill, but it is not the only reasoning skill or logic clinicians require. The ability to think critically uses reflection, induction, deduction, analysis, challenging assumptions, and evaluation of data and information to guide decisionmaking. 9 , 14 , 15 Critical reasoning is a process whereby knowledge and experience are applied in considering multiple possibilities to achieve the desired goals, 16 while considering the patient’s situation. 14 It is a process where both inductive and deductive cognitive skills are used. 17 Sometimes clinical reasoning is presented as a form of evaluating scientific knowledge, sometimes even as a form of scientific reasoning. Critical thinking is inherent in making sound clinical reasoning. 18

An essential point of tension and confusion exists in practice traditions such as nursing and medicine when clinical reasoning and critical reflection become entangled, because the clinician must have some established bases that are not questioned when engaging in clinical decisions and actions, such as standing orders. The clinician must act in the particular situation and time with the best clinical and scientific knowledge available. The clinician cannot afford to indulge in either ritualistic unexamined knowledge or diagnostic or therapeutic nihilism caused by radical doubt, as in critical reflection, because they must find an intelligent and effective way to think and act in particular clinical situations. Critical reflection skills are essential to assist practitioners to rethink outmoded or even wrong-headed approaches to health care, health promotion, and prevention of illness and complications, especially when new evidence is available. Breakdowns in practice, high failure rates in particular therapies, new diseases, new scientific discoveries, and societal changes call for critical reflection about past assumptions and no-longer-tenable beliefs.

Clinical reasoning stands out as a situated, practice-based form of reasoning that requires a background of scientific and technological research-based knowledge about general cases, more so than any particular instance. It also requires practical ability to discern the relevance of the evidence behind general scientific and technical knowledge and how it applies to a particular patient. In dong so, the clinician considers the patient’s particular clinical trajectory, their concerns and preferences, and their particular vulnerabilities (e.g., having multiple comorbidities) and sensitivities to care interventions (e.g., known drug allergies, other conflicting comorbid conditions, incompatible therapies, and past responses to therapies) when forming clinical decisions or conclusions.

Situated in a practice setting, clinical reasoning occurs within social relationships or situations involving patient, family, community, and a team of health care providers. The expert clinician situates themselves within a nexus of relationships, with concerns that are bounded by the situation. Expert clinical reasoning is socially engaged with the relationships and concerns of those who are affected by the caregiving situation, and when certain circumstances are present, the adverse event. Halpern 19 has called excellent clinical ethical reasoning “emotional reasoning” in that the clinicians have emotional access to the patient/family concerns and their understanding of the particular care needs. Expert clinicians also seek an optimal perceptual grasp, one based on understanding and as undistorted as possible, based on an attuned emotional engagement and expert clinical knowledge. 19 , 20

Clergy educators 21 and nursing and medical educators have begun to recognize the wisdom of broadening their narrow vision of rationality beyond simple rational calculation (exemplified by cost-benefit analysis) to reconsider the need for character development—including emotional engagement, perception, habits of thought, and skill acquisition—as essential to the development of expert clinical reasoning, judgment, and action. 10 , 22–24 Practitioners of engineering, law, medicine, and nursing, like the clergy, have to develop a place to stand in their discipline’s tradition of knowledge and science in order to recognize and evaluate salient evidence in the moment. Diagnostic confusion and disciplinary nihilism are both threats to the clinician’s ability to act in particular situations. However, the practice and practitioners will not be self-improving and vital if they cannot engage in critical reflection on what is not of value, what is outmoded, and what does not work. As evidence evolves and expands, so too must clinical thought.

Clinical judgment requires clinical reasoning across time about the particular, and because of the relevance of this immediate historical unfolding, clinical reasoning can be very different from the scientific reasoning used to formulate, conduct, and assess clinical experiments. While scientific reasoning is also socially embedded in a nexus of social relationships and concerns, the goal of detached, critical objectivity used to conduct scientific experiments minimizes the interactive influence of the research on the experiment once it has begun. Scientific research in the natural and clinical sciences typically uses formal criteria to develop “yes” and “no” judgments at prespecified times. The scientist is always situated in past and immediate scientific history, preferring to evaluate static and predetermined points in time (e.g., snapshot reasoning), in contrast to a clinician who must always reason about transitions over time. 25 , 26

Techne and Phronesis

Distinctions between the mere scientific making of things and practice was first explored by Aristotle as distinctions between techne and phronesis. 27 Learning to be a good practitioner requires developing the requisite moral imagination for good practice. If, for example, patients exercise their rights and refuse treatments, practitioners are required to have the moral imagination to understand the probable basis for the patient’s refusal. For example, was the refusal based upon catastrophic thinking, unrealistic fears, misunderstanding, or even clinical depression?

Techne, as defined by Aristotle, encompasses the notion of formation of character and habitus 28 as embodied beings. In Aristotle’s terms, techne refers to the making of things or producing outcomes. 11 Joseph Dunne defines techne as “the activity of producing outcomes,” and it “is governed by a means-ends rationality where the maker or producer governs the thing or outcomes produced or made through gaining mastery over the means of producing the outcomes, to the point of being able to separate means and ends” 11 (p. 54). While some aspects of medical and nursing practice fall into the category of techne, much of nursing and medical practice falls outside means-ends rationality and must be governed by concern for doing good or what is best for the patient in particular circumstances, where being in a relationship and discerning particular human concerns at stake guide action.

Phronesis, in contrast to techne, includes reasoning about the particular, across time, through changes or transitions in the patient’s and/or the clinician’s understanding. As noted by Dunne, phronesis is “characterized at least as much by a perceptiveness with regard to concrete particulars as by a knowledge of universal principles” 11 (p. 273). This type of practical reasoning often takes the form of puzzle solving or the evaluation of immediate past “hot” history of the patient’s situation. Such a particular clinical situation is necessarily particular, even though many commonalities and similarities with other disease syndromes can be recognized through signs and symptoms and laboratory tests. 11 , 29 , 30 Pointing to knowledge embedded in a practice makes no claim for infallibility or “correctness.” Individual practitioners can be mistaken in their judgments because practices such as medicine and nursing are inherently underdetermined. 31

While phronetic knowledge must remain open to correction and improvement, real events, and consequences, it cannot consistently transcend the institutional setting’s capacities and supports for good practice. Phronesis is also dependent on ongoing experiential learning of the practitioner, where knowledge is refined, corrected, or refuted. The Western tradition, with the notable exception of Aristotle, valued knowledge that could be made universal and devalued practical know-how and experiential learning. Descartes codified this preference for formal logic and rational calculation.

Aristotle recognized that when knowledge is underdetermined, changeable, and particular, it cannot be turned into the universal or standardized. It must be perceived, discerned, and judged, all of which require experiential learning. In nursing and medicine, perceptual acuity in physical assessment and clinical judgment (i.e., reasoning across time about changes in the particular patient or the clinician’s understanding of the patient’s condition) fall into the Greek Aristotelian category of phronesis. Dewey 32 sought to rescue knowledge gained by practical activity in the world. He identified three flaws in the understanding of experience in Greek philosophy: (1) empirical knowing is the opposite of experience with science; (2) practice is reduced to techne or the application of rational thought or technique; and (3) action and skilled know-how are considered temporary and capricious as compared to reason, which the Greeks considered as ultimate reality.

In practice, nursing and medicine require both techne and phronesis. The clinician standardizes and routinizes what can be standardized and routinized, as exemplified by standardized blood pressure measurements, diagnoses, and even charting about the patient’s condition and treatment. 27 Procedural and scientific knowledge can often be formalized and standardized (e.g., practice guidelines), or at least made explicit and certain in practice, except for the necessary timing and adjustments made for particular patients. 11 , 22

Rational calculations available to techne—population trends and statistics, algorithms—are created as decision support structures and can improve accuracy when used as a stance of inquiry in making clinical judgments about particular patients. Aggregated evidence from clinical trials and ongoing working knowledge of pathophysiology, biochemistry, and genomics are essential. In addition, the skills of phronesis (clinical judgment that reasons across time, taking into account the transitions of the particular patient/family/community and transitions in the clinician’s understanding of the clinical situation) will be required for nursing, medicine, or any helping profession.

Thinking Critically

Being able to think critically enables nurses to meet the needs of patients within their context and considering their preferences; meet the needs of patients within the context of uncertainty; consider alternatives, resulting in higher-quality care; 33 and think reflectively, rather than simply accepting statements and performing tasks without significant understanding and evaluation. 34 Skillful practitioners can think critically because they have the following cognitive skills: information seeking, discriminating, analyzing, transforming knowledge, predicating, applying standards, and logical reasoning. 5 One’s ability to think critically can be affected by age, length of education (e.g., an associate vs. a baccalaureate decree in nursing), and completion of philosophy or logic subjects. 35–37 The skillful practitioner can think critically because of having the following characteristics: motivation, perseverance, fair-mindedness, and deliberate and careful attention to thinking. 5 , 9

Thinking critically implies that one has a knowledge base from which to reason and the ability to analyze and evaluate evidence. 38 Knowledge can be manifest by the logic and rational implications of decisionmaking. Clinical decisionmaking is particularly influenced by interpersonal relationships with colleagues, 39 patient conditions, availability of resources, 40 knowledge, and experience. 41 Of these, experience has been shown to enhance nurses’ abilities to make quick decisions 42 and fewer decision errors, 43 support the identification of salient cues, and foster the recognition and action on patterns of information. 44 , 45

Clinicians must develop the character and relational skills that enable them to perceive and understand their patient’s needs and concerns. This requires accurate interpretation of patient data that is relevant to the specific patient and situation. In nursing, this formation of moral agency focuses on learning to be responsible in particular ways demanded by the practice, and to pay attention and intelligently discern changes in patients’ concerns and/or clinical condition that require action on the part of the nurse or other health care workers to avert potential compromises to quality care.

Formation of the clinician’s character, skills, and habits are developed in schools and particular practice communities within a larger practice tradition. As Dunne notes,

A practice is not just a surface on which one can display instant virtuosity. It grounds one in a tradition that has been formed through an elaborate development and that exists at any juncture only in the dispositions (slowly and perhaps painfully acquired) of its recognized practitioners. The question may of course be asked whether there are any such practices in the contemporary world, whether the wholesale encroachment of Technique has not obliterated them—and whether this is not the whole point of MacIntyre’s recipe of withdrawal, as well as of the post-modern story of dispossession 11 (p. 378).

Clearly Dunne is engaging in critical reflection about the conditions for developing character, skills, and habits for skillful and ethical comportment of practitioners, as well as to act as moral agents for patients so that they and their families receive safe, effective, and compassionate care.

Professional socialization or professional values, while necessary, do not adequately address character and skill formation that transform the way the practitioner exists in his or her world, what the practitioner is capable of noticing and responding to, based upon well-established patterns of emotional responses, skills, dispositions to act, and the skills to respond, decide, and act. 46 The need for character and skill formation of the clinician is what makes a practice stand out from a mere technical, repetitious manufacturing process. 11 , 30 , 47

In nursing and medicine, many have questioned whether current health care institutions are designed to promote or hinder enlightened, compassionate practice, or whether they have deteriorated into commercial institutional models that focus primarily on efficiency and profit. MacIntyre points out the links between the ongoing development and improvement of practice traditions and the institutions that house them:

Lack of justice, lack of truthfulness, lack of courage, lack of the relevant intellectual virtues—these corrupt traditions, just as they do those institutions and practices which derive their life from the traditions of which they are the contemporary embodiments. To recognize this is of course also to recognize the existence of an additional virtue, one whose importance is perhaps most obvious when it is least present, the virtue of having an adequate sense of the traditions to which one belongs or which confront one. This virtue is not to be confused with any form of conservative antiquarianism; I am not praising those who choose the conventional conservative role of laudator temporis acti. It is rather the case that an adequate sense of tradition manifests itself in a grasp of those future possibilities which the past has made available to the present. Living traditions, just because they continue a not-yet-completed narrative, confront a future whose determinate and determinable character, so far as it possesses any, derives from the past 30 (p. 207).

It would be impossible to capture all the situated and distributed knowledge outside of actual practice situations and particular patients. Simulations are powerful as teaching tools to enable nurses’ ability to think critically because they give students the opportunity to practice in a simplified environment. However, students can be limited in their inability to convey underdetermined situations where much of the information is based on perceptions of many aspects of the patient and changes that have occurred over time. Simulations cannot have the sub-cultures formed in practice settings that set the social mood of trust, distrust, competency, limited resources, or other forms of situated possibilities.

One of the hallmark studies in nursing providing keen insight into understanding the influence of experience was a qualitative study of adult, pediatric, and neonatal intensive care unit (ICU) nurses, where the nurses were clustered into advanced beginner, intermediate, and expert level of practice categories. The advanced beginner (having up to 6 months of work experience) used procedures and protocols to determine which clinical actions were needed. When confronted with a complex patient situation, the advanced beginner felt their practice was unsafe because of a knowledge deficit or because of a knowledge application confusion. The transition from advanced beginners to competent practitioners began when they first had experience with actual clinical situations and could benefit from the knowledge gained from the mistakes of their colleagues. Competent nurses continuously questioned what they saw and heard, feeling an obligation to know more about clinical situations. In doing do, they moved from only using care plans and following the physicians’ orders to analyzing and interpreting patient situations. Beyond that, the proficient nurse acknowledged the changing relevance of clinical situations requiring action beyond what was planned or anticipated. The proficient nurse learned to acknowledge the changing needs of patient care and situation, and could organize interventions “by the situation as it unfolds rather than by preset goals 48 (p. 24). Both competent and proficient nurses (that is, intermediate level of practice) had at least two years of ICU experience. 48 Finally, the expert nurse had a more fully developed grasp of a clinical situation, a sense of confidence in what is known about the situation, and could differentiate the precise clinical problem in little time. 48

Expertise is acquired through professional experience and is indicative of a nurse who has moved beyond mere proficiency. As Gadamer 29 points out, experience involves a turning around of preconceived notions, preunderstandings, and extends or adds nuances to understanding. Dewey 49 notes that experience requires a prepared “creature” and an enriched environment. The opportunity to reflect and narrate one’s experiential learning can clarify, extend, or even refute experiential learning.

Experiential learning requires time and nurturing, but time alone does not ensure experiential learning. Aristotle linked experiential learning to the development of character and moral sensitivities of a person learning a practice. 50 New nurses/new graduates have limited work experience and must experience continuing learning until they have reached an acceptable level of performance. 51 After that, further improvements are not predictable, and years of experience are an inadequate predictor of expertise. 52

The most effective knower and developer of practical knowledge creates an ongoing dialogue and connection between lessons of the day and experiential learning over time. Gadamer, in a late life interview, highlighted the open-endedness and ongoing nature of experiential learning in the following interview response:

Being experienced does not mean that one now knows something once and for all and becomes rigid in this knowledge; rather, one becomes more open to new experiences. A person who is experienced is undogmatic. Experience has the effect of freeing one to be open to new experience … In our experience we bring nothing to a close; we are constantly learning new things from our experience … this I call the interminability of all experience 32 (p. 403).

Practical endeavor, supported by scientific knowledge, requires experiential learning, the development of skilled know-how, and perceptual acuity in order to make the scientific knowledge relevant to the situation. Clinical perceptual and skilled know-how helps the practitioner discern when particular scientific findings might be relevant. 53

Often experience and knowledge, confirmed by experimentation, are treated as oppositions, an either-or choice. However, in practice it is readily acknowledged that experiential knowledge fuels scientific investigation, and scientific investigation fuels further experiential learning. Experiential learning from particular clinical cases can help the clinician recognize future similar cases and fuel new scientific questions and study. For example, less experienced nurses—and it could be argued experienced as well—can use nursing diagnoses practice guidelines as part of their professional advancement. Guidelines are used to reflect their interpretation of patients’ needs, responses, and situation, 54 a process that requires critical thinking and decisionmaking. 55 , 56 Using guidelines also reflects one’s problem identification and problem-solving abilities. 56 Conversely, the ability to proficiently conduct a series of tasks without nursing diagnoses is the hallmark of expertise. 39 , 57

Experience precedes expertise. As expertise develops from experience and gaining knowledge and transitions to the proficiency stage, the nurses’ thinking moves from steps and procedures (i.e., task-oriented care) toward “chunks” or patterns 39 (i.e., patient-specific care). In doing so, the nurse thinks reflectively, rather than merely accepting statements and performing procedures without significant understanding and evaluation. 34 Expert nurses do not rely on rules and logical thought processes in problem-solving and decisionmaking. 39 Instead, they use abstract principles, can see the situation as a complex whole, perceive situations comprehensively, and can be fully involved in the situation. 48 Expert nurses can perform high-level care without conscious awareness of the knowledge they are using, 39 , 58 and they are able to provide that care with flexibility and speed. Through a combination of knowledge and skills gained from a range of theoretical and experiential sources, expert nurses also provide holistic care. 39 Thus, the best care comes from the combination of theoretical, tacit, and experiential knowledge. 59 , 60

Experts are thought to eventually develop the ability to intuitively know what to do and to quickly recognize critical aspects of the situation. 22 Some have proposed that expert nurses provide high-quality patient care, 61 , 62 but that is not consistently documented—particularly in consideration of patient outcomes—and a full understanding between the differential impact of care rendered by an “expert” nurse is not fully understood. In fact, several studies have found that length of professional experience is often unrelated and even negatively related to performance measures and outcomes. 63 , 64

In a review of the literature on expertise in nursing, Ericsson and colleagues 65 found that focusing on challenging, less-frequent situations would reveal individual performance differences on tasks that require speed and flexibility, such as that experienced during a code or an adverse event. Superior performance was associated with extensive training and immediate feedback about outcomes, which can be obtained through continual training, simulation, and processes such as root-cause analysis following an adverse event. Therefore, efforts to improve performance benefited from continual monitoring, planning, and retrospective evaluation. Even then, the nurse’s ability to perform as an expert is dependent upon their ability to use intuition or insights gained through interactions with patients. 39

Intuition and Perception

Intuition is the instant understanding of knowledge without evidence of sensible thought. 66 According to Young, 67 intuition in clinical practice is a process whereby the nurse recognizes something about a patient that is difficult to verbalize. Intuition is characterized by factual knowledge, “immediate possession of knowledge, and knowledge independent of the linear reasoning process” 68 (p. 23). When intuition is used, one filters information initially triggered by the imagination, leading to the integration of all knowledge and information to problem solve. 69 Clinicians use their interactions with patients and intuition, drawing on tacit or experiential knowledge, 70 , 71 to apply the correct knowledge to make the correct decisions to address patient needs. Yet there is a “conflated belief in the nurses’ ability to know what is best for the patient” 72 (p. 251) because the nurses’ and patients’ identification of the patients’ needs can vary. 73

A review of research and rhetoric involving intuition by King and Appleton 62 found that all nurses, including students, used intuition (i.e., gut feelings). They found evidence, predominately in critical care units, that intuition was triggered in response to knowledge and as a trigger for action and/or reflection with a direct bearing on the analytical process involved in patient care. The challenge for nurses was that rigid adherence to checklists, guidelines, and standardized documentation, 62 ignored the benefits of intuition. This view was furthered by Rew and Barrow 68 , 74 in their reviews of the literature, where they found that intuition was imperative to complex decisionmaking, 68 difficult to measure and assess in a quantitative manner, and was not linked to physiologic measures. 74

Intuition is a way of explaining professional expertise. 75 Expert nurses rely on their intuitive judgment that has been developed over time. 39 , 76 Intuition is an informal, nonanalytically based, unstructured, deliberate calculation that facilitates problem solving, 77 a process of arriving at salient conclusions based on relatively small amounts of knowledge and/or information. 78 Experts can have rapid insight into a situation by using intuition to recognize patterns and similarities, achieve commonsense understanding, and sense the salient information combined with deliberative rationality. 10 Intuitive recognition of similarities and commonalities between patients are often the first diagnostic clue or early warning, which must then be followed up with critical evaluation of evidence among the competing conditions. This situation calls for intuitive judgment that can distinguish “expert human judgment from the decisions” made by a novice 79 (p. 23).

Shaw 80 equates intuition with direct perception. Direct perception is dependent upon being able to detect complex patterns and relationships that one has learned through experience are important. Recognizing these patterns and relationships generally occurs rapidly and is complex, making it difficult to articulate or describe. Perceptual skills, like those of the expert nurse, are essential to recognizing current and changing clinical conditions. Perception requires attentiveness and the development of a sense of what is salient. Often in nursing and medicine, means and ends are fused, as is the case for a “good enough” birth experience and a peaceful death.

  • Applying Practice Evidence

Research continues to find that using evidence-based guidelines in practice, informed through research evidence, improves patients’ outcomes. 81–83 Research-based guidelines are intended to provide guidance for specific areas of health care delivery. 84 The clinician—both the novice and expert—is expected to use the best available evidence for the most efficacious therapies and interventions in particular instances, to ensure the highest-quality care, especially when deviations from the evidence-based norm may heighten risks to patient safety. Otherwise, if nursing and medicine were exact sciences, or consisted only of techne, then a 1:1 relationship could be established between results of aggregated evidence-based research and the best path for all patients.

Evaluating Evidence

Before research should be used in practice, it must be evaluated. There are many complexities and nuances in evaluating the research evidence for clinical practice. Evaluation of research behind evidence-based medicine requires critical thinking and good clinical judgment. Sometimes the research findings are mixed or even conflicting. As such, the validity, reliability, and generalizability of available research are fundamental to evaluating whether evidence can be applied in practice. To do so, clinicians must select the best scientific evidence relevant to particular patients—a complex process that involves intuition to apply the evidence. Critical thinking is required for evaluating the best available scientific evidence for the treatment and care of a particular patient.

Good clinical judgment is required to select the most relevant research evidence. The best clinical judgment, that is, reasoning across time about the particular patient through changes in the patient’s concerns and condition and/or the clinician’s understanding, are also required. This type of judgment requires clinicians to make careful observations and evaluations of the patient over time, as well as know the patient’s concerns and social circumstances. To evolve to this level of judgment, additional education beyond clinical preparation if often required.

Sources of Evidence

Evidence that can be used in clinical practice has different sources and can be derived from research, patient’s preferences, and work-related experience. 85 , 86 Nurses have been found to obtain evidence from experienced colleagues believed to have clinical expertise and research-based knowledge 87 as well as other sources.

For many years now, randomized controlled trials (RCTs) have often been considered the best standard for evaluating clinical practice. Yet, unless the common threats to the validity (e.g., representativeness of the study population) and reliability (e.g., consistency in interventions and responses of study participants) of RCTs are addressed, the meaningfulness and generalizability of the study outcomes are very limited. Relevant patient populations may be excluded, such as women, children, minorities, the elderly, and patients with multiple chronic illnesses. The dropout rate of the trial may confound the results. And it is easier to get positive results published than it is to get negative results published. Thus, RCTs are generalizable (i.e., applicable) only to the population studied—which may not reflect the needs of the patient under the clinicians care. In instances such as these, clinicians need to also consider applied research using prospective or retrospective populations with case control to guide decisionmaking, yet this too requires critical thinking and good clinical judgment.

Another source of available evidence may come from the gold standard of aggregated systematic evaluation of clinical trial outcomes for the therapy and clinical condition in question, be generated by basic and clinical science relevant to the patient’s particular pathophysiology or care need situation, or stem from personal clinical experience. The clinician then takes all of the available evidence and considers the particular patient’s known clinical responses to past therapies, their clinical condition and history, the progression or stages of the patient’s illness and recovery, and available resources.

In clinical practice, the particular is examined in relation to the established generalizations of science. With readily available summaries of scientific evidence (e.g., systematic reviews and practice guidelines) available to nurses and physicians, one might wonder whether deep background understanding is still advantageous. Might it not be expendable, since it is likely to be out of date given the current scientific evidence? But this assumption is a false opposition and false choice because without a deep background understanding, the clinician does not know how to best find and evaluate scientific evidence for the particular case in hand. The clinician’s sense of salience in any given situation depends on past clinical experience and current scientific evidence.

Evidence-Based Practice

The concept of evidence-based practice is dependent upon synthesizing evidence from the variety of sources and applying it appropriately to the care needs of populations and individuals. This implies that evidence-based practice, indicative of expertise in practice, appropriately applies evidence to the specific situations and unique needs of patients. 88 , 89 Unfortunately, even though providing evidence-based care is an essential component of health care quality, it is well known that evidence-based practices are not used consistently.

Conceptually, evidence used in practice advances clinical knowledge, and that knowledge supports independent clinical decisions in the best interest of the patient. 90 , 91 Decisions must prudently consider the factors not necessarily addressed in the guideline, such as the patient’s lifestyle, drug sensitivities and allergies, and comorbidities. Nurses who want to improve the quality and safety of care can do so though improving the consistency of data and information interpretation inherent in evidence-based practice.

Initially, before evidence-based practice can begin, there needs to be an accurate clinical judgment of patient responses and needs. In the course of providing care, with careful consideration of patient safety and quality care, clinicians must give attention to the patient’s condition, their responses to health care interventions, and potential adverse reactions or events that could harm the patient. Nonetheless, there is wide variation in the ability of nurses to accurately interpret patient responses 92 and their risks. 93 Even though variance in interpretation is expected, nurses are obligated to continually improve their skills to ensure that patients receive quality care safely. 94 Patients are vulnerable to the actions and experience of their clinicians, which are inextricably linked to the quality of care patients have access to and subsequently receive.

The judgment of the patient’s condition determines subsequent interventions and patient outcomes. Attaining accurate and consistent interpretations of patient data and information is difficult because each piece can have different meanings, and interpretations are influenced by previous experiences. 95 Nurses use knowledge from clinical experience 96 , 97 and—although infrequently—research. 98–100

Once a problem has been identified, using a process that utilizes critical thinking to recognize the problem, the clinician then searches for and evaluates the research evidence 101 and evaluates potential discrepancies. The process of using evidence in practice involves “a problem-solving approach that incorporates the best available scientific evidence, clinicians’ expertise, and patient’s preferences and values” 102 (p. 28). Yet many nurses do not perceive that they have the education, tools, or resources to use evidence appropriately in practice. 103

Reported barriers to using research in practice have included difficulty in understanding the applicability and the complexity of research findings, failure of researchers to put findings into the clinical context, lack of skills in how to use research in practice, 104 , 105 amount of time required to access information and determine practice implications, 105–107 lack of organizational support to make changes and/or use in practice, 104 , 97 , 105 , 107 and lack of confidence in one’s ability to critically evaluate clinical evidence. 108

When Evidence Is Missing

In many clinical situations, there may be no clear guidelines and few or even no relevant clinical trials to guide decisionmaking. In these cases, the latest basic science about cellular and genomic functioning may be the most relevant science, or by default, guestimation. Consequently, good patient care requires more than a straightforward, unequivocal application of scientific evidence. The clinician must be able to draw on a good understanding of basic sciences, as well as guidelines derived from aggregated data and information from research investigations.

Practical knowledge is shaped by one’s practice discipline and the science and technology relevant to the situation at hand. But scientific, formal, discipline-specific knowledge are not sufficient for good clinical practice, whether the discipline be law, medicine, nursing, teaching, or social work. Practitioners still have to learn how to discern generalizable scientific knowledge, know how to use scientific knowledge in practical situations, discern what scientific evidence/knowledge is relevant, assess how the particular patient’s situation differs from the general scientific understanding, and recognize the complexity of care delivery—a process that is complex, ongoing, and changing, as new evidence can overturn old.

Practice communities like individual practitioners may also be mistaken, as is illustrated by variability in practice styles and practice outcomes across hospitals and regions in the United States. This variability in practice is why practitioners must learn to critically evaluate their practice and continually improve their practice over time. The goal is to create a living self-improving tradition.

Within health care, students, scientists, and practitioners are challenged to learn and use different modes of thinking when they are conflated under one term or rubric, using the best-suited thinking strategies for taking into consideration the purposes and the ends of the reasoning. Learning to be an effective, safe nurse or physician requires not only technical expertise, but also the ability to form helping relationships and engage in practical ethical and clinical reasoning. 50 Good ethical comportment requires that both the clinician and the scientist take into account the notions of good inherent in clinical and scientific practices. The notions of good clinical practice must include the relevant significance and the human concerns involved in decisionmaking in particular situations, centered on clinical grasp and clinical forethought.

The Three Apprenticeships of Professional Education

We have much to learn in comparing the pedagogies of formation across the professions, such as is being done currently by the Carnegie Foundation for the Advancement of Teaching. The Carnegie Foundation’s broad research program on the educational preparation of the profession focuses on three essential apprenticeships:

To capture the full range of crucial dimensions in professional education, we developed the idea of a three-fold apprenticeship: (1) intellectual training to learn the academic knowledge base and the capacity to think in ways important to the profession; (2) a skill-based apprenticeship of practice; and (3) an apprenticeship to the ethical standards, social roles, and responsibilities of the profession, through which the novice is introduced to the meaning of an integrated practice of all dimensions of the profession, grounded in the profession’s fundamental purposes. 109

This framework has allowed the investigators to describe tensions and shortfalls as well as strengths of widespread teaching practices, especially at articulation points among these dimensions of professional training.

Research has demonstrated that these three apprenticeships are taught best when they are integrated so that the intellectual training includes skilled know-how, clinical judgment, and ethical comportment. In the study of nursing, exemplary classroom and clinical teachers were found who do integrate the three apprenticeships in all of their teaching, as exemplified by the following anonymous student’s comments:

With that as well, I enjoyed the class just because I do have clinical experience in my background and I enjoyed it because it took those practical applications and the knowledge from pathophysiology and pharmacology, and all the other classes, and it tied it into the actual aspects of like what is going to happen at work. For example, I work in the emergency room and question: Why am I doing this procedure for this particular patient? Beforehand, when I was just a tech and I wasn’t going to school, I’d be doing it because I was told to be doing it—or I’d be doing CPR because, you know, the doc said, start CPR. I really enjoy the Care and Illness because now I know the process, the pathophysiological process of why I’m doing it and the clinical reasons of why they’re making the decisions, and the prioritization that goes on behind it. I think that’s the biggest point. Clinical experience is good, but not everybody has it. Yet when these students transition from school and clinicals to their job as a nurse, they will understand what’s going on and why.

The three apprenticeships are equally relevant and intertwined. In the Carnegie National Study of Nursing Education and the companion study on medical education as well as in cross-professional comparisons, teaching that gives an integrated access to professional practice is being examined. Once the three apprenticeships are separated, it is difficult to reintegrate them. The investigators are encouraged by teaching strategies that integrate the latest scientific knowledge and relevant clinical evidence with clinical reasoning about particular patients in unfolding rather than static cases, while keeping the patient and family experience and concerns relevant to clinical concerns and reasoning.

Clinical judgment or phronesis is required to evaluate and integrate techne and scientific evidence.

Within nursing, professional practice is wise and effective usually to the extent that the professional creates relational and communication contexts where clients/patients can be open and trusting. Effectiveness depends upon mutual influence between patient and practitioner, student and learner. This is another way in which clinical knowledge is dialogical and socially distributed. The following articulation of practical reasoning in nursing illustrates the social, dialogical nature of clinical reasoning and addresses the centrality of perception and understanding to good clinical reasoning, judgment and intervention.

Clinical Grasp *

Clinical grasp describes clinical inquiry in action. Clinical grasp begins with perception and includes problem identification and clinical judgment across time about the particular transitions of particular patients. Garrett Chan 20 described the clinician’s attempt at finding an “optimal grasp” or vantage point of understanding. Four aspects of clinical grasp, which are described in the following paragraphs, include (1) making qualitative distinctions, (2) engaging in detective work, (3) recognizing changing relevance, and (4) developing clinical knowledge in specific patient populations.

Making Qualitative Distinctions

Qualitative distinctions refer to those distinctions that can be made only in a particular contextual or historical situation. The context and sequence of events are essential for making qualitative distinctions; therefore, the clinician must pay attention to transitions in the situation and judgment. Many qualitative distinctions can be made only by observing differences through touch, sound, or sight, such as the qualities of a wound, skin turgor, color, capillary refill, or the engagement and energy level of the patient. Another example is assessing whether the patient was more fatigued after ambulating to the bathroom or from lack of sleep. Likewise the quality of the clinician’s touch is distinct as in offering reassurance, putting pressure on a bleeding wound, and so on. 110

Engaging in Detective Work, Modus Operandi Thinking, and Clinical Puzzle Solving

Clinical situations are open ended and underdetermined. Modus operandi thinking keeps track of the particular patient, the way the illness unfolds, the meanings of the patient’s responses as they have occurred in the particular time sequence. Modus operandi thinking requires keeping track of what has been tried and what has or has not worked with the patient. In this kind of reasoning-in-transition, gains and losses of understanding are noticed and adjustments in the problem approach are made.

We found that teachers in a medical surgical unit at the University of Washington deliberately teach their students to engage in “detective work.” Students are given the daily clinical assignment of “sleuthing” for undetected drug incompatibilities, questionable drug dosages, and unnoticed signs and symptoms. For example, one student noted that an unusual dosage of a heart medication was being given to a patient who did not have heart disease. The student first asked her teacher about the unusually high dosage. The teacher, in turn, asked the student whether she had asked the nurse or the patient about the dosage. Upon the student’s questioning, the nurse did not know why the patient was receiving the high dosage and assumed the drug was for heart disease. The patient’s staff nurse had not questioned the order. When the student asked the patient, the student found that the medication was being given for tremors and that the patient and the doctor had titrated the dosage for control of the tremors. This deliberate approach to teaching detective work, or modus operandi thinking, has characteristics of “critical reflection,” but stays situated and engaged, ferreting out the immediate history and unfolding of events.

Recognizing Changing Clinical Relevance

The meanings of signs and symptoms are changed by sequencing and history. The patient’s mental status, color, or pain level may continue to deteriorate or get better. The direction, implication, and consequences for the changes alter the relevance of the particular facts in the situation. The changing relevance entailed in a patient transitioning from primarily curative care to primarily palliative care is a dramatic example, where symptoms literally take on new meanings and require new treatments.

Developing Clinical Knowledge in Specific Patient Populations

Extensive experience with a specific patient population or patients with particular injuries or diseases allows the clinician to develop comparisons, distinctions, and nuanced differences within the population. The comparisons between many specific patients create a matrix of comparisons for clinicians, as well as a tacit, background set of expectations that create population- and patient-specific detective work if a patient does not meet the usual, predictable transitions in recovery. What is in the background and foreground of the clinician’s attention shifts as predictable changes in the patient’s condition occurs, such as is seen in recovering from heart surgery or progressing through the predictable stages of labor and delivery. Over time, the clinician develops a deep background understanding that allows for expert diagnostic and interventions skills.

Clinical Forethought

Clinical forethought is intertwined with clinical grasp, but it is much more deliberate and even routinized than clinical grasp. Clinical forethought is a pervasive habit of thought and action in nursing practice, and also in medicine, as clinicians think about disease and recovery trajectories and the implications of these changes for treatment. Clinical forethought plays a role in clinical grasp because it structures the practical logic of clinicians. At least four habits of thought and action are evident in what we are calling clinical forethought: (1) future think, (2) clinical forethought about specific patient populations, (3) anticipation of risks for particular patients, and (4) seeing the unexpected.

Future think

Future think is the broadest category of this logic of practice. Anticipating likely immediate futures helps the clinician make good plans and decisions about preparing the environment so that responding rapidly to changes in the patient is possible. Without a sense of salience about anticipated signs and symptoms and preparing the environment, essential clinical judgments and timely interventions would be impossible in the typically fast pace of acute and intensive patient care. Future think governs the style and content of the nurse’s attentiveness to the patient. Whether in a fast-paced care environment or a slower-paced rehabilitation setting, thinking and acting with anticipated futures guide clinical thinking and judgment. Future think captures the way judgment is suspended in a predictive net of anticipation and preparing oneself and the environment for a range of potential events.

Clinical forethought about specific diagnoses and injuries

This habit of thought and action is so second nature to the experienced nurse that the new or inexperienced nurse may have difficulty finding out about what seems to other colleagues as “obvious” preparation for particular patients and situations. Clinical forethought involves much local specific knowledge about who is a good resource and how to marshal support services and equipment for particular patients.

Examples of preparing for specific patient populations are pervasive, such as anticipating the need for a pacemaker during surgery and having the equipment assembled ready for use to save essential time. Another example includes forecasting an accident victim’s potential injuries, and recognizing that intubation might be needed.

Anticipation of crises, risks, and vulnerabilities for particular patients

This aspect of clinical forethought is central to knowing the particular patient, family, or community. Nurses situate the patient’s problems almost like a topography of possibilities. This vital clinical knowledge needs to be communicated to other caregivers and across care borders. Clinical teaching could be improved by enriching curricula with narrative examples from actual practice, and by helping students recognize commonly occurring clinical situations in the simulation and clinical setting. For example, if a patient is hemodynamically unstable, then managing life-sustaining physiologic functions will be a main orienting goal. If the patient is agitated and uncomfortable, then attending to comfort needs in relation to hemodynamics will be a priority. Providing comfort measures turns out to be a central background practice for making clinical judgments and contains within it much judgment and experiential learning.

When clinical teaching is too removed from typical contingencies and strong clinical situations in practice, students will lack practice in active thinking-in-action in ambiguous clinical situations. In the following example, an anonymous student recounted her experiences of meeting a patient:

I was used to different equipment and didn’t know how things went, didn’t know their routine, really. You can explain all you want in class, this is how it’s going to be, but when you get there … . Kim was my first instructor and my patient that she assigned me to—I walked into the room and he had every tube imaginable. And so I was a little overwhelmed. It’s not necessarily even that he was that critical … . She asked what tubes here have you seen? Well, I know peripheral lines. You taught me PICC [peripherally inserted central catheter] lines, and we just had that, but I don’t really feel comfortable doing it by myself, without you watching to make sure that I’m flushing it right and how to assess it. He had a chest tube and I had seen chest tubes, but never really knew the depth of what you had to assess and how you make sure that it’s all kosher and whatever. So she went through the chest tube and explained, it’s just bubbling a little bit and that’s okay. The site, check the site. The site looked okay and that she’d say if it wasn’t okay, this is what it might look like … . He had a feeding tube. I had done feeding tubes but that was like a long time ago in my LPN experiences schooling. So I hadn’t really done too much with the feeding stuff either … . He had a [nasogastric] tube, and knew pretty much about that and I think at the time it was clamped. So there were no issues with the suction or whatever. He had a Foley catheter. He had a feeding tube, a chest tube. I can’t even remember but there were a lot.

As noted earlier, a central characteristic of a practice discipline is that a self-improving practice requires ongoing experiential learning. One way nurse educators can enhance clinical inquiry is by increasing pedagogies of experiential learning. Current pedagogies for experiential learning in nursing include extensive preclinical study, care planning, and shared postclinical debriefings where students share their experiential learning with their classmates. Experiential learning requires open learning climates where students can discuss and examine transitions in understanding, including their false starts, or their misconceptions in actual clinical situations. Nursing educators typically develop open and interactive clinical learning communities, so that students seem committed to helping their classmates learn from their experiences that may have been difficult or even unsafe. One anonymous nurse educator described how students extend their experiential learning to their classmates during a postclinical conference:

So for example, the patient had difficulty breathing and the student wanted to give the meds instead of addressing the difficulty of breathing. Well, while we were sharing information about their patients, what they did that day, I didn’t tell the student to say this, but she said, ‘I just want to tell you what I did today in clinical so you don’t do the same thing, and here’s what happened.’ Everybody’s listening very attentively and they were asking her some questions. But she shared that. She didn’t have to. I didn’t tell her, you must share that in postconference or anything like that, but she just went ahead and shared that, I guess, to reinforce what she had learned that day but also to benefit her fellow students in case that thing comes up with them.

The teacher’s response to this student’s honesty and generosity exemplifies her own approach to developing an open community of learning. Focusing only on performance and on “being correct” prevents learning from breakdown or error and can dampen students’ curiosity and courage to learn experientially.

Seeing the unexpected

One of the keys to becoming an expert practitioner lies in how the person holds past experiential learning and background habitual skills and practices. This is a skill of foregrounding attention accurately and effectively in response to the nature of situational demands. Bourdieu 29 calls the recognition of the situation central to practical reasoning. If nothing is routinized as a habitual response pattern, then practitioners will not function effectively in emergencies. Unexpected occurrences may be overlooked. However, if expectations are held rigidly, then subtle changes from the usual will be missed, and habitual, rote responses will inappropriately rule. The clinician must be flexible in shifting between what is in background and foreground. This is accomplished by staying curious and open. The clinical “certainty” associated with perceptual grasp is distinct from the kind of “certainty” achievable in scientific experiments and through measurements. Recognition of similar or paradigmatic clinical situations is similar to “face recognition” or recognition of “family resemblances.” This concept is subject to faulty memory, false associative memories, and mistaken identities; therefore, such perceptual grasp is the beginning of curiosity and inquiry and not the end. Assessment and validation are required. In rapidly moving clinical situations, perceptual grasp is the starting point for clarification, confirmation, and action. Having the clinician say out loud how he or she is understanding the situation gives an opportunity for confirmation and disconfirmation from other clinicians present. 111 The relationship between foreground and background of attention needs to be fluid, so that missed expectations allow the nurse to see the unexpected. For example, when the background rhythm of a cardiac monitor changes, the nurse notices, and what had been background tacit awareness becomes the foreground of attention. A hallmark of expertise is the ability to notice the unexpected. 20 Background expectations of usual patient trajectories form with experience. Tacit expectations for patient trajectories form that enable the nurse to notice subtle failed expectations and pay attention to early signs of unexpected changes in the patient's condition. Clinical expectations gained from caring for similar patient populations form a tacit clinical forethought that enable the experienced clinician to notice missed expectations. Alterations from implicit or explicit expectations set the stage for experiential learning, depending on the openness of the learner.

Learning to provide safe and quality health care requires technical expertise, the ability to think critically, experience, and clinical judgment. The high-performance expectation of nurses is dependent upon the nurses’ continual learning, professional accountability, independent and interdependent decisionmaking, and creative problem-solving abilities.

This section of the paper was condensed and paraphrased from Benner, Hooper-Kyriakidis, and Stannard. 23 Patricia Hooper-Kyriakidis wrote the section on clinical grasp, and Patricia Benner wrote the section on clinical forethought.

  • Cite this Page Benner P, Hughes RG, Sutphen M. Clinical Reasoning, Decisionmaking, and Action: Thinking Critically and Clinically. In: Hughes RG, editor. Patient Safety and Quality: An Evidence-Based Handbook for Nurses. Rockville (MD): Agency for Healthcare Research and Quality (US); 2008 Apr. Chapter 6.
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  25. 2023-2024 California State University General Education Requirements

    A3 Critical Thinking: COMM 311**, 315, 316; ENGRD 310; ENGWR 301, 302, 303, 482; PHIL 300**, 320, 325; SOC 305 ** Courses are listed in more than one section in an area or other areas but can only be used once to satisfy a requirement. B. Scientific Inquiry and Quantitative Reasoning