critical thinking and scientific thinking

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What’s the Difference Between 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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Affiliations.

• 1 Department of Biology, Duke University, Durham, NC 27708 [email protected].
• 2 Department of Psychology and Neuroscience, Duke University, Durham, NC 27708.
• 3 Department of Microbiology and Immunology, University of Minnesota, Minneapolis, MN 55455.
• 4 Department of Biology, Duke University, Durham, NC 27708.
• PMID: 29326103
• PMCID: PMC6007780
• DOI: 10.1187/cbe.17-03-0052

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.

© 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).

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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.

The International Society for the Psychology of Science and Technology (ISPST). Available at http://www.ispstonline.org/

Ahn, W., Kalish, C. W., Medin, D. L., & Gelman, S. A. ( 1995 ). The role of covariation versus mechanism information in causal attribution.   Cognition , 54 , 299–352.

American Association for the Advancement of Science. ( 1993 ). Benchmarks for scientific literacy . New York: Oxford University Press.

Google Scholar

Google Preview

Atkins, L. J., Velez, L., Goudy, D., & Dunbar, K. N. ( 2009 ). The unintended effects of interactive objects and labels in the science museum.   Science Education , 54 , 161–184.

Azmitia, M. A., & Crowley, K. ( 2001 ). The rhythms of scientific thinking: A study of collaboration in an earthquake microworld. In K. Crowley, C. Schunn, & T. Okada (Eds.), Designing for science: Implications from everyday, classroom, and professional settings (pp. 45–72). Mahwah, NJ: Erlbaum.

Bacon, F. ( 1620 /1854). Novum organum (B. Monatgue, Trans.). Philadelphia, P A: Parry & McMillan.

Baillargeon, R. ( 2004 ). Infants' reasoning about hidden objects: Evidence for event-general and event-specific expectations (article with peer commentaries and response, listed below).   Developmental Science , 54 , 391–424.

Baker, L. M., & Dunbar, K. ( 2000 ). Experimental design heuristics for scientific discovery: The use of baseline and known controls.   International Journal of Human Computer Studies , 54 , 335–349.

Beilin, H. ( 1994 ). Jean Piaget's enduring contribution to developmental psychology. In R. D. Parke, P. A. Ornstein, J. J. Rieser, & C. Zahn-Waxler (Eds.), A century of developmental psychology (pp. 257–290). Washington, DC US: American Psychological Association.

Biswal, B. B., Mennes, M., Zuo, X.-N., Gohel, S., Kelly, C., Smith, S.M., et al. ( 2010 ). Toward discovery science of human brain function.   Proceedings of the National Academy of Sciences of the United States of America , 107, 4734–4739.

Brewer, W. F., & Samarapungavan, A. ( 1991 ). Children's theories vs. scientific theories: Differences in reasoning or differences in knowledge? In R. R. Hoffman & D. S. Palermo (Eds.), Cognition and the symbolic processes: Applied and ecological perspectives (pp. 209–232). Hillsdale, NJ: Erlbaum.

Bruner, J. S., Goodnow, J. J., & Austin, G. A. ( 1956 ). A study of thinking . New York: NY Science Editions.

Carey, S. ( 1985 ). Conceptual change in childhood . Cambridge, MA: MIT Press.

Carruthers, P., Stich, S., & Siegal, M. ( 2002 ). The cognitive basis of science . New York: Cambridge University Press.

Chi, M. ( 1992 ). Conceptual change within and across ontological categories: Examples from learning and discovery in science. In R. Giere (Ed.), Cognitive models of science (pp. 129–186). Minneapolis: University of Minnesota Press.

Chi, M. T. H., & Roscoe, R. D. ( 2002 ). The processes and challenges of conceptual change. In M. Limon & L. Mason (Eds.), Reconsidering conceptual change: Issues in theory and practice (pp 3–27). Amsterdam, Netherlands: Kluwer Academic Publishers.

Chen, Z., & Klahr, D. ( 1999 ). All other things being equal: Children's acquisition of the control of variables strategy.   Child Development , 54 (5), 1098–1120.

Clement, J. ( 1982 ). Students' preconceptions in introductory mechanics.   American Journal of Physics , 54 , 66–71.

Cohen, L. B., & Cashon, C. H. ( 2006 ). Infant cognition. In W. Damon & R. M. Lerner (Series Eds.) & D. Kuhn & R. S. Siegler (Vol. Eds.), Handbook of child psychology. Vol. 2: Cognition, perception, and language (6th ed., pp. 214–251). New York: Wiley.

National Commission on Excellence in Education. ( 1983 ). A nation at risk: The imperative for educational reform . Washington, DC: US Department of Education.

Crick, F. H. C. ( 1988 ). What mad pursuit: A personal view of science . New York: Basic Books.

Darden, L. ( 2002 ). Strategies for discovering mechanisms: Schema instantiation, modular subassembly, forward chaining/backtracking.   Philosophy of Science , 69, S354–S365.

Davenport, J. L., Yaron, D., Klahr, D., & Koedinger, K. ( 2008 ). Development of conceptual understanding and problem solving expertise in chemistry. In B. C. Love, K. McRae, & V. M. Sloutsky (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society (pp. 751–756). Austin, TX: Cognitive Science Society.

diSessa, A. A. ( 2004 ). Contextuality and coordination in conceptual change. In E. Redish & M. Vicentini (Eds.), Proceedings of the International School of Physics “Enrico Fermi:” Research on physics education (pp. 137–156). Amsterdam, Netherlands: ISO Press/Italian Physics Society

Dunbar, K. ( 1995 ). How scientists really reason: Scientific reasoning in real-world laboratories. In R. J. Sternberg, & J. Davidson (Eds.), Mechanisms of insight (pp. 365–395). Cambridge, MA: MIT press.

Dunbar, K. ( 1997 ). How scientists think: Online creativity and conceptual change in science. In T. B. Ward, S. M. Smith, & S. Vaid (Eds.), Conceptual structures and processes: Emergence, discovery and change (pp. 461–494). Washington, DC: American Psychological Association.

Dunbar, K. ( 1998 ). Problem solving. In W. Bechtel & G. Graham (Eds.), A companion to cognitive science (pp. 289–298). London: Blackwell

Dunbar, K. ( 1999 ). The scientist InVivo : How scientists think and reason in the laboratory. In L. Magnani, N. Nersessian, & P. Thagard (Eds.), Model-based reasoning in scientific discovery (pp. 85–100). New York: Plenum.

Dunbar, K. ( 2001 ). The analogical paradox: Why analogy is so easy in naturalistic settings, yet so difficult in the psychology laboratory. In D. Gentner, K. J. Holyoak, & B. Kokinov Analogy: Perspectives from cognitive science (pp. 313–334). Cambridge, MA: MIT press.

Dunbar, K. ( 2002 ). Science as category: Implications of InVivo science for theories of cognitive development, scientific discovery, and the nature of science. In P. Caruthers, S. Stich, & M. Siegel (Eds.) Cognitive models of science (pp. 154–170). New York: Cambridge University Press.

Dunbar, K. ( 2009 ). The biology of physics: What the brain reveals about our physical understanding of the world. In M. Sabella, C. Henderson, & C. Singh. (Eds.), Proceedings of the Physics Education Research Conference (pp. 15–18). Melville, NY: American Institute of Physics.

Dunbar, K., & Fugelsang, J. ( 2004 ). Causal thinking in science: How scientists and students interpret the unexpected. In M. E. Gorman, A. Kincannon, D. Gooding, & R. D. Tweney (Eds.), New directions in scientific and technical thinking (pp. 57–59). Mahway, NJ: Erlbaum.

Dunbar, K., Fugelsang, J., & Stein, C. ( 2007 ). Do naïve theories ever go away? In M. Lovett & P. Shah (Eds.), Thinking with Data: 33 rd Carnegie Symposium on Cognition (pp. 193–206). Mahwah, NJ: Erlbaum.

Dunbar, K., & Sussman, D. ( 1995 ). Toward a cognitive account of frontal lobe function: Simulating frontal lobe deficits in normal subjects.   Annals of the New York Academy of Sciences , 54 , 289–304.

Duschl, R. A., Schweingruber, H. A., & Shouse, A. W. (Eds.). ( 2007 ). Taking science to school: Learning and teaching science in grades K-8. Washington, DC: National Academies Press.

Einstein, A. ( 1950 ). Out of my later years . New York: Philosophical Library

Erdos, P., Fajtlowicz, S., & Staton, W. ( 1991 ). Degree sequences in the triangle-free graphs,   Discrete Mathematics , 54 (91), 85–88.

Evans, J., & Rzhetsky, A. ( 2010 ). Machine science.   Science , 54 , 399–400.

Fay, A., & Klahr, D. ( 1996 ). Knowing about guessing and guessing about knowing: Preschoolers' understanding of indeterminacy.   Child Development , 54 , 689–716.

Fischler, H., & Lichtfeldt, M. ( 1992 ). Modern physics and students conceptions.   International Journal of Science Education , 54 , 181–190.

Fitzpatrick, S. M. (in press). Functional brain imaging: Neuro-turn or wrong turn? In M. M., Littlefield & J.M., Johnson (Eds.), The neuroscientific turn: Transdisciplinarity in the age of the brain. Ann Arbor: University of Michigan Press.

Fox-Keller, E. ( 1985 ). Reflections on gender and science . New Haven, CT: Yale University Press.

Fugelsang, J., & Dunbar, K. ( 2005 ). Brain-based mechanisms underlying complex causal thinking.   Neuropsychologia , 54 , 1204–1213.

Fugelsang, J., & Dunbar, K. ( 2009 ). Brain-based mechanisms underlying causal reasoning. In E. Kraft (Ed.), Neural correlates of thinking (pp. 269–279). Berlin, Germany: Springer

Fugelsang, J., Stein, C., Green, A., & Dunbar, K. ( 2004 ). Theory and data interactions of the scientific mind: Evidence from the molecular and the cognitive laboratory.   Canadian Journal of Experimental Psychology , 54 , 132–141

Galilei, G. ( 1638 /1991). Dialogues concerning two new sciences (A. de Salvio & H. Crew, Trans.). Amherst, NY: Prometheus Books.

Galison, P. ( 2003 ). Einstein's clocks, Poincaré's maps: Empires of time . New York: W. W. Norton.

Gelman, R., & Baillargeon, R. ( 1983 ). A review of Piagetian concepts. In P. H. Mussen (Series Ed.) & J. H. Flavell & E. M. Markman (Vol. Eds.), Handbook of child psychology (4th ed., Vol. 3, pp. 167–230). New York: Wiley.

Gelman, S. A., & Kalish, C. W. ( 2006 ). Conceptual development. In D. Kuhn & R. Siegler (Eds.), Handbook of child psychology. Vol. 2: Cognition, perception and language (pp. 687–733). New York: Wiley.

Gelman, S., & Wellman, H. ( 1991 ). Insides and essences.   Cognition , 54 , 214–244.

Gentner, D. ( 2010 ). Bootstrapping the mind: Analogical processes and symbol systems.   Cognitive Science , 54 , 752–775.

Gentner, D., Brem, S., Ferguson, R. W., Markman, A. B., Levidow, B. B., Wolff, P., & Forbus, K. D. ( 1997 ). Analogical reasoning and conceptual change: A case study of Johannes Kepler.   The Journal of the Learning Sciences , 54 (1), 3–40.

Gentner, D., Holyoak, K. J., & Kokinov, B. ( 2001 ). The analogical mind: Perspectives from cognitive science . Cambridge, MA: MIT Press.

Gentner, D., & Jeziorski, M. ( 1993 ). The shift from metaphor to analogy in western science. In A. Ortony (Ed.), Metaphor and thought (2nd ed., pp. 447–480). Cambridge, England: Cambridge University Press.

Gianfelici, F. ( 2010 ). Machine science: Truly machine-aided science.   Science , 54 , 317–319.

Giere, R. ( 1993 ). Cognitive models of science . Minneapolis: University of Minnesota Press.

Gopnik, A. N., Meltzoff, A. N., & Kuhl, P. K. ( 1999 ). The scientist in the crib: Minds, brains and how children learn . New York: Harper Collins

Gorman, M. E. ( 1989 ). Error, falsification and scientific inference: An experimental investigation.   Quarterly Journal of Experimental Psychology: Human Experimental Psychology , 41A , 385–412

Gorman, M. E., Kincannon, A., Gooding, D., & Tweney, R. D. ( 2004 ). New directions in scientific and technical thinking . Mahwah, NJ: Erlbaum.

Gupta, A., Hammer, D., & Redish, E. F. ( 2010 ). The case for dynamic models of learners' ontologies in physics.   Journal of the Learning Sciences , 54 (3), 285–321.

Haufe, C., Elliott, K. C., Burian, R., & O'Malley, M. A. ( 2010 ). Machine science: What's missing.   Science , 54 , 318–320.

Hecht, E. ( 2011 ). On defining mass.   The Physics Teacher , 54 , 40–43.

Heit, E. ( 2000 ). Properties of inductive reasoning.   Psychonomic Bulletin and Review , 54 , 569–592.

Holyoak, K. J., & Thagard, P. ( 1995 ). Mental leaps . Cambridge, MA: MIT Press.

Karmiloff-Smith, A. ( 1988 ) The child is a theoretician, not an inductivist.   Mind and Language , 54 , 183–195.

Keil, F. C. ( 1999 ). Conceptual change. In R. Wilson & F. Keil (Eds.), The MIT encyclopedia of cognitive science . (pp. 179–182) Cambridge, MA: MIT press.

Kern, L. H., Mirels, H. L., & Hinshaw, V. G. ( 1983 ). Scientists' understanding of propositional logic: An experimental investigation.   Social Studies of Science , 54 , 131–146.

King, R. D. ( 2011 ). Rise of the robo scientists.   Scientific American , 54 (1), 73–77.

King, R. D., Rowland, J., Oliver, S. G., Young, M., Aubrey, W., Byrne, E., et al. ( 2009 ). The automation of science.   Science , 54 , 85–89.

Kirschner, P. A., Sweller, J., & Clark, R. ( 2006 ) Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching.   Educational Psychologist , 54 , 75–86

Klahr, D. ( 2000 ). Exploring science: The cognition and development of discovery processes . Cambridge, MA: MIT Press.

Klahr, D. ( 2010 ). Coming up for air: But is it oxygen or phlogiston? A response to Taber's review of constructivist instruction: Success or failure?   Education Review , 54 (13), 1–6.

Klahr, D., & Dunbar, K. ( 1988 ). Dual space search during scientific reasoning.   Cognitive Science , 54 , 1–48.

Klahr, D., & Nigam, M. ( 2004 ). The equivalence of learning paths in early science instruction: effects of direct instruction and discovery learning.   Psychological Science , 54 (10), 661–667.

Klahr, D. & Masnick, A. M. ( 2002 ). Explaining, but not discovering, abduction. Review of L. Magnani (2001) abduction, reason, and science: Processes of discovery and explanation.   Contemporary Psychology , 47, 740–741.

Klahr, D., & Simon, H. ( 1999 ). Studies of scientific discovery: Complementary approaches and convergent findings.   Psychological Bulletin , 54 , 524–543.

Klayman, J., & Ha, Y. ( 1987 ). Confirmation, disconfirmation, and information in hypothesis testing.   Psychological Review , 54 , 211–228.

Kozhevnikov, M., & Hegarty, M. ( 2001 ). Impetus beliefs as default heuristic: Dissociation between explicit and implicit knowledge about motion.   Psychonomic Bulletin and Review , 54 , 439–453.

Kuhn, T. ( 1962 ). The structure of scientific revolutions . Chicago, IL: University of Chicago Press.

Kuhn, D., Amsel, E., & O'Laughlin, M. ( 1988 ). The development of scientific thinking skills . Orlando, FL: Academic Press.

Kulkarni, D., & Simon, H. A. ( 1988 ). The processes of scientific discovery: The strategy of experimentation.   Cognitive Science , 54 , 139–176.

Langley, P. ( 2000 ). Computational support of scientific discovery.   International Journal of Human-Computer Studies , 54 , 393–410.

Langley, P. ( 2002 ). Lessons for the computational discovery of scientific knowledge. In Proceedings of the First International Workshop on Data Mining Lessons Learned (pp. 9–12).

Langley, P., Simon, H. A., Bradshaw, G. L., & Zytkow, J. M. ( 1987 ). Scientific discovery: Computational explorations of the creative processes . Cambridge, MA: MIT Press.

Lorch, R. F., Jr., Lorch, E. P., Calderhead, W. J., Dunlap, E. E., Hodell, E. C., & Freer, B. D. ( 2010 ). Learning the control of variables strategy in higher and lower achieving classrooms: Contributions of explicit instruction and experimentation.   Journal of Educational Psychology , 54 (1), 90–101.

Magnani, L., Carnielli, W., & Pizzi, C., (Eds.) ( 2010 ). Model-based reasoning in science and technology: Abduction, logic,and computational discovery. Series Studies in Computational Intelligence (Vol. 314). Heidelberg/Berlin: Springer.

Mandler, J.M. ( 2004 ). The foundations of mind: Origins of conceptual thought . Oxford, England: Oxford University Press.

Macpherson, R., & Stanovich, K. E. ( 2007 ). Cognitive ability, thinking dispositions, and instructional set as predictors of critical thinking.   Learning and Individual Differences , 54 , 115–127.

McCloskey, M., Caramazza, A., & Green, B. ( 1980 ). Curvilinear motion in the absence of external forces: Naive beliefs about the motion of objects.   Science , 54 , 1139–1141.

McDermott, L. C., & Redish, L. ( 1999 ). Research letter on physics education research.   American Journal of Psychics , 54 , 755.

Mestre, J. P. ( 1991 ). Learning and instruction in pre-college physical science.   Physics Today , 54 , 56–62.

Metz, K. E. ( 1995 ). Reassessment of developmental constraints on children's science instruction.   Review of Educational Research , 54 (2), 93–127.

Minner, D. D., Levy, A. J., & Century, J. ( 2010 ). Inquiry-based science instruction—what is it and does it matter? Results from a research synthesis years 1984 to 2002.   Journal of Research in Science Teaching , 54 (4), 474–496.

Mitchell, T. M. ( 2009 ). Mining our reality.   Science , 54 , 1644–1645.

Mitroff, I. ( 1974 ). The subjective side of science . Amsterdam, Netherlands: Elsevier.

Munakata, Y., Casey, B. J., & Diamond, A. ( 2004 ). Developmental cognitive neuroscience: Progress and potential.   Trends in Cognitive Sciences , 54 , 122–128.

Mynatt, C. R., Doherty, M. E., & Tweney, R. D. ( 1977 ) Confirmation bias in a simulated research environment: An experimental study of scientific inference.   Quarterly Journal of Experimental Psychology , 54 , 89–95.

Nersessian, N. ( 1998 ). Conceptual change. In W. Bechtel, & G. Graham (Eds.), A companion to cognitive science (pp. 157–166). London, England: Blackwell.

Nersessian, N. ( 1999 ). Models, mental models, and representations: Model-based reasoning in conceptual change. In L. Magnani, N. Nersessian, & P. Thagard (Eds.), Model-based reasoning in scientific discovery (pp. 5–22). New York: Plenum.

Nersessian, N. J. ( 2002 ). The cognitive basis of model-based reasoning in science In. P. Carruthers, S. Stich, & M. Siegal (Eds.), The cognitive basis of science (pp. 133–152). New York: Cambridge University Press.

Nersessian, N. J. ( 2008 ) Creating scientific concepts . Cambridge, MA: MIT Press.

O' Malley, M. A. ( 2011 ). Exploration, iterativity and kludging in synthetic biology.   Comptes Rendus Chimie , 54 (4), 406–412 .

Papert, S. ( 1980 ) Mindstorms: Children computers and powerful ideas. New York: Basic Books.

Penner, D. E., & Klahr, D. ( 1996 ). When to trust the data: Further investigations of system error in a scientific reasoning task.   Memory and Cognition , 54 (5), 655–668.

Petitto, L. A., & Dunbar, K. ( 2004 ). New findings from educational neuroscience on bilingual brains, scientific brains, and the educated mind. In K. Fischer & T. Katzir (Eds.), Building usable knowledge in mind, brain, and education Cambridge, England: Cambridge University Press.

Popper, K. R. ( 1959 ). The logic of scientific discovery . London, England: Hutchinson.

Qin, Y., & Simon, H.A. ( 1990 ). Laboratory replication of scientific discovery processes.   Cognitive Science , 54 , 281–312.

Reiser, B. J., Tabak, I., Sandoval, W. A., Smith, B., Steinmuller, F., & Leone, T. J., ( 2001 ). BGuILE: Stategic and conceptual scaffolds for scientific inquiry in biology classrooms. In S. M. Carver & D. Klahr (Eds.), Cognition and instruction: Twenty-five years of progress (pp. 263–306). Mahwah, NJ: Erlbaum

Riordan, M., Rowson, P. C., & Wu, S. L. ( 2001 ). The search for the higgs boson.   Science , 54 , 259–260.

Rutherford, F. J., & Ahlgren, A. ( 1991 ). Science for all Americans. New York: Oxford University Press.

Samarapungavan, A. ( 1992 ). Children's judgments in theory choice tasks: Scientifc rationality in childhood.   Cognition , 54 , 1–32.

Schauble, L., & Glaser, R. ( 1990 ). Scientific thinking in children and adults. In D. Kuhn (Ed.), Developmental perspectives on teaching and learning thinking skills. Contributions to Human Development , (Vol. 21, pp. 9–26). Basel, Switzerland: Karger.

Schunn, C. D., & Klahr, D. ( 1995 ). A 4-space model of scientific discovery. In Proceedings of the 17th Annual Conference of the Cognitive Science Society (pp. 106–111). Mahwah, NJ: Erlbaum.

Schunn, C. D., & Klahr, D. ( 1996 ). The problem of problem spaces: When and how to go beyond a 2-space model of scientific discovery. Part of symposium on Building a theory of problem solving and scientific discovery: How big is N in N-space search? In Proceedings of the 18th Annual Conference of the Cognitive Science Society (pp. 25–26). Mahwah, NJ: Erlbaum.

Shrager, J., & Langley, P. ( 1990 ). Computational models of scientific discovery and theory formation . San Mateo, CA: Morgan Kaufmann.

Siegler, R. S., & Liebert, R. M. ( 1975 ). Acquisition of formal scientific reasoning by 10- and 13-year-olds: Designing a factorial experiment.   Developmental Psychology , 54 , 401–412.

Simon, H. A. ( 1977 ). Models of discovery . Dordrecht, Netherlands: D. Reidel Publishing.

Simon, H. A., Langley, P., & Bradshaw, G. L. ( 1981 ). Scientific discovery as problem solving.   Synthese , 54 , 1–27.

Simon, H. A., & Lea, G. ( 1974 ). Problem solving and rule induction. In H. Simon (Ed.), Models of thought (pp. 329–346). New Haven, CT: Yale University Press.

Smith, E. E., Shafir, E., & Osherson, D. ( 1993 ). Similarity, plausibility, and judgments of probability.   Cognition. Special Issue: Reasoning and decision making , 54 , 67–96.

Sodian, B., Zaitchik, D., & Carey, S. ( 1991 ). Young children's differentiation of hypothetical beliefs from evidence.   Child Development , 54 , 753–766.

Taber, K. S. ( 2009 ). Constructivism and the crisis in U.S. science education: An essay review.   Education Review , 54 (12), 1–26.

Thagard, P. ( 1992 ). Conceptual revolutions . Cambridge, MA: MIT Press.

Thagard, P. ( 1999 ). How scientists explain disease . Princeton, NJ: Princeton University Press.

Thagard, P., & Croft, D. ( 1999 ). Scientific discovery and technological innovation: Ulcers, dinosaur extinction, and the programming language Java. In L. Magnani, N. Nersessian, & P. Thagard (Eds.), Model-based reasoning in scientific discovery (pp. 125–138). New York: Plenum.

Tobias, S., & Duffy, T. M. (Eds.). ( 2009 ). Constructivist instruction: Success or failure? New York: Routledge.

Toth, E. E., Klahr, D., & Chen, Z. ( 2000 ) Bridging research and practice: A cognitively-based classroom intervention for teaching experimentation skills to elementary school children.   Cognition and Instruction , 54 (4), 423–459.

Tweney, R. D. ( 1989 ). A framework for the cognitive psychology of science. In B. Gholson, A. Houts, R. A. Neimeyer, & W. Shadish (Eds.), Psychology of science: Contributions to metascience (pp. 342–366). Cambridge, England: Cambridge University Press.

Tweney, R. D., Doherty, M. E., & Mynatt, C. R. ( 1981 ). On scientific thinking . New York: Columbia University Press.

Valdes-Perez, R. E. ( 1994 ). Conjecturing hidden entities via simplicity and conservation laws: Machine discovery in chemistry.   Artificial Intelligence , 54 (2), 247–280.

Von Hofsten, C. ( 1980 ). Predictive reaching for moving objects by human infants.   Journal of Experimental Child Psychology , 54 , 369–382.

Von Hofsten, C., Feng, Q., & Spelke, E. S. ( 2000 ). Object representation and predictive action in infancy.   Developmental Science , 54 , 193–205.

Vosnaidou, S. (Ed.). ( 2008 ). International handbook of research on conceptual change . New York: Taylor & Francis.

Vosniadou, S., & Brewer, W. F. ( 1992 ). Mental models of the earth: A study of conceptual change in childhood.   Cognitive Psychology , 54 , 535–585.

Wason, P. C. ( 1968 ). Reasoning about a rule.   Quarterly Journal of Experimental Psychology , 54 , 273–281.

Wertheimer, M. ( 1945 ). Productive thinking . New York: Harper.

Yang, Y. ( 2009 ). Target discovery from data mining approaches.   Drug Discovery Today , 54 (3–4), 147–154.

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

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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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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Critical Thinking in Science: Fostering Scientific Reasoning Skills in Students

ALI Staff | Published  July 13, 2023

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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A Crash Course in Critical Thinking

What you need to know—and read—about one of the essential skills needed today..

Posted April 8, 2024 | Reviewed by Michelle Quirk

• In research for "A More Beautiful Question," I did a deep dive into the current crisis in critical thinking.
• Many people may think of themselves as critical thinkers, but they actually are not.
• Here is a series of questions you can ask yourself to try to ensure that you are thinking critically.

Conspiracy theories. Inability to distinguish facts from falsehoods. Widespread confusion about who and what to believe.

These are some of the hallmarks of the current crisis in critical thinking—which just might be the issue of our times. Because if people aren’t willing or able to think critically as they choose potential leaders, they’re apt to choose bad ones. And if they can’t judge whether the information they’re receiving is sound, they may follow faulty advice while ignoring recommendations that are science-based and solid (and perhaps life-saving).

Moreover, as a society, if we can’t think critically about the many serious challenges we face, it becomes more difficult to agree on what those challenges are—much less solve them.

On a personal level, critical thinking can enable you to make better everyday decisions. It can help you make sense of an increasingly complex and confusing world.

In the new expanded edition of my book A More Beautiful Question ( AMBQ ), I took a deep dive into critical thinking. Here are a few key things I learned.

First off, before you can get better at critical thinking, you should understand what it is. It’s not just about being a skeptic. When thinking critically, we are thoughtfully reasoning, evaluating, and making decisions based on evidence and logic. And—perhaps most important—while doing this, a critical thinker always strives to be open-minded and fair-minded . That’s not easy: It demands that you constantly question your assumptions and biases and that you always remain open to considering opposing views.

In today’s polarized environment, many people think of themselves as critical thinkers simply because they ask skeptical questions—often directed at, say, certain government policies or ideas espoused by those on the “other side” of the political divide. The problem is, they may not be asking these questions with an open mind or a willingness to fairly consider opposing views.

When people do this, they’re engaging in “weak-sense critical thinking”—a term popularized by the late Richard Paul, a co-founder of The Foundation for Critical Thinking . “Weak-sense critical thinking” means applying the tools and practices of critical thinking—questioning, investigating, evaluating—but with the sole purpose of confirming one’s own bias or serving an agenda.

In AMBQ , I lay out a series of questions you can ask yourself to try to ensure that you’re thinking critically. Here are some of the questions to consider:

• Why do I believe what I believe?
• Are my views based on evidence?
• Have I fairly and thoughtfully considered differing viewpoints?
• Am I truly open to changing my mind?

Of course, becoming a better critical thinker is not as simple as just asking yourself a few questions. Critical thinking is a habit of mind that must be developed and strengthened over time. In effect, you must train yourself to think in a manner that is more effortful, aware, grounded, and balanced.

For those interested in giving themselves a crash course in critical thinking—something I did myself, as I was working on my book—I thought it might be helpful to share a list of some of the books that have shaped my own thinking on this subject. As a self-interested author, I naturally would suggest that you start with the new 10th-anniversary edition of A More Beautiful Question , but beyond that, here are the top eight critical-thinking books I’d recommend.

The Demon-Haunted World: Science as a Candle in the Dark , by Carl Sagan

This book simply must top the list, because the late scientist and author Carl Sagan continues to be such a bright shining light in the critical thinking universe. Chapter 12 includes the details on Sagan’s famous “baloney detection kit,” a collection of lessons and tips on how to deal with bogus arguments and logical fallacies.

Clear Thinking: Turning Ordinary Moments Into Extraordinary Results , by Shane Parrish

The creator of the Farnham Street website and host of the “Knowledge Project” podcast explains how to contend with biases and unconscious reactions so you can make better everyday decisions. It contains insights from many of the brilliant thinkers Shane has studied.

Good Thinking: Why Flawed Logic Puts Us All at Risk and How Critical Thinking Can Save the World , by David Robert Grimes

A brilliant, comprehensive 2021 book on critical thinking that, to my mind, hasn’t received nearly enough attention . The scientist Grimes dissects bad thinking, shows why it persists, and offers the tools to defeat it.

Think Again: The Power of Knowing What You Don't Know , by Adam Grant

Intellectual humility—being willing to admit that you might be wrong—is what this book is primarily about. But Adam, the renowned Wharton psychology professor and bestselling author, takes the reader on a mind-opening journey with colorful stories and characters.

Think Like a Detective: A Kid's Guide to Critical Thinking , by David Pakman

The popular YouTuber and podcast host Pakman—normally known for talking politics —has written a terrific primer on critical thinking for children. The illustrated book presents critical thinking as a “superpower” that enables kids to unlock mysteries and dig for truth. (I also recommend Pakman’s second kids’ book called Think Like a Scientist .)

Rationality: What It Is, Why It Seems Scarce, Why It Matters , by Steven Pinker

The Harvard psychology professor Pinker tackles conspiracy theories head-on but also explores concepts involving risk/reward, probability and randomness, and correlation/causation. And if that strikes you as daunting, be assured that Pinker makes it lively and accessible.

How Minds Change: The Surprising Science of Belief, Opinion and Persuasion , by David McRaney

David is a science writer who hosts the popular podcast “You Are Not So Smart” (and his ideas are featured in A More Beautiful Question ). His well-written book looks at ways you can actually get through to people who see the world very differently than you (hint: bludgeoning them with facts definitely won’t work).

A Healthy Democracy's Best Hope: Building the Critical Thinking Habit , by M Neil Browne and Chelsea Kulhanek

Neil Browne, author of the seminal Asking the Right Questions: A Guide to Critical Thinking, has been a pioneer in presenting critical thinking as a question-based approach to making sense of the world around us. His newest book, co-authored with Chelsea Kulhanek, breaks down critical thinking into “11 explosive questions”—including the “priors question” (which challenges us to question assumptions), the “evidence question” (focusing on how to evaluate and weigh evidence), and the “humility question” (which reminds us that a critical thinker must be humble enough to consider the possibility of being wrong).

Warren Berger is a longtime journalist and author of A More Beautiful Question .

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

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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.

• American Association for the Advancement of Science . ( 2011 ). Vision and change in undergraduate biology education: A call to action . Washington, DC Retrieved September 26, 2017, from https://visionandchange.org/files/2013/11/aaas-VISchange-web1113.pdf . Google Scholar
• August, D. ( 2016 ). California Critical Thinking Skills Test user manual and resource guide . San Jose: Insight Assessment/California Academic Press. Google Scholar
• Beyer, C. H., Taylor, E., & Gillmore, G. M. ( 2013 ). Inside the undergraduate teaching experience: The University of Washington’s growth in faculty teaching study . Albany, NY: SUNY Press. Google Scholar
• Bissell, A. N., & Lemons, P. P. ( 2006 ). A new method for assessing critical thinking in the classroom . BioScience , 56 (1), 66–72. https://doi.org/10.1641/0006-3568(2006)056[0066:ANMFAC]2.0.CO;2 . Google Scholar
• Blattner, N. H., & Frazier, C. L. ( 2002 ). Developing a performance-based assessment of students’ critical thinking skills . Assessing Writing , 8 (1), 47–64. Google Scholar
• Clase, K. L., Gundlach, E., & Pelaez, N. J. ( 2010 ). Calibrated peer review for computer-assisted learning of biological research competencies . Biochemistry and Molecular Biology Education , 38 (5), 290–295. Medline ,  Google Scholar
• Condon, W., & Kelly-Riley, D. ( 2004 ). Assessing and teaching what we value: The relationship between college-level writing and critical thinking abilities . Assessing Writing , 9 (1), 56–75. https://doi.org/10.1016/j.asw.2004.01.003 . Google Scholar
• Ding, L., Wei, X., & Liu, X. ( 2016 ). Variations in university students’ scientific reasoning skills across majors, years, and types of institutions . Research in Science Education , 46 (5), 613–632. https://doi.org/10.1007/s11165-015-9473-y . Google Scholar
• Dowd, J. E., Connolly, M. P., Thompson, R. J.Jr., & Reynolds, J. A. ( 2015a ). Improved reasoning in undergraduate writing through structured workshops . Journal of Economic Education , 46 (1), 14–27. https://doi.org/10.1080/00220485.2014.978924 . Google Scholar
• Dowd, J. E., Roy, C. P., Thompson, R. J.Jr., & Reynolds, J. A. ( 2015b ). “On course” for supporting expanded participation and improving scientific reasoning in undergraduate thesis writing . Journal of Chemical Education , 92 (1), 39–45. https://doi.org/10.1021/ed500298r . Google Scholar
• Dowd, J. E., Thompson, R. J.Jr., & Reynolds, J. A. ( 2016 ). Quantitative genre analysis of undergraduate theses: Uncovering different ways of writing and thinking in science disciplines . WAC Journal , 27 , 36–51. Google Scholar
• Facione, P. A. ( 1990 ). Critical thinking: a statement of expert consensus for purposes of educational assessment and instruction. Research findings and recommendations . Newark, DE: American Philosophical Association. Retrieved September 26, 2017, from https://philpapers.org/archive/FACCTA.pdf . Google Scholar
• Gerdeman, R. D., Russell, A. A., Worden, K. J., Gerdeman, R. D., Russell, A. A., & Worden, K. J. ( 2007 ). Web-based student writing and reviewing in a large biology lecture course . Journal of College Science Teaching , 36 (5), 46–52. Google Scholar
• Greenhoot, A. F., Semb, G., Colombo, J., & Schreiber, T. ( 2004 ). Prior beliefs and methodological concepts in scientific reasoning . Applied Cognitive Psychology , 18 (2), 203–221. https://doi.org/10.1002/acp.959 . Google Scholar
• Haaga, D. A. F. ( 1993 ). Peer review of term papers in graduate psychology courses . Teaching of Psychology , 20 (1), 28–32. https://doi.org/10.1207/s15328023top2001_5 . Google Scholar
• Halonen, J. S., Bosack, T., Clay, S., McCarthy, M., Dunn, D. S., Hill, G. W., … Whitlock, K. ( 2003 ). A rubric for learning, teaching, and assessing scientific inquiry in psychology . Teaching of Psychology , 30 (3), 196–208. https://doi.org/10.1207/S15328023TOP3003_01 . Google Scholar
• Hand, B., & Keys, C. W. ( 1999 ). Inquiry investigation . Science Teacher , 66 (4), 27–29. Google Scholar
• Holm, S. ( 1979 ). A simple sequentially rejective multiple test procedure . Scandinavian Journal of Statistics , 6 (2), 65–70. Google Scholar
• Holyoak, K. J., & Morrison, R. G. ( 2005 ). The Cambridge handbook of thinking and reasoning . New York: Cambridge University Press. Google Scholar
• Insight Assessment . ( 2016a ). California Critical Thinking Skills Test (CCTST) Retrieved September 26, 2017, from www.insightassessment.com/Products/Products-Summary/Critical-Thinking-Skills-Tests/California-Critical-Thinking-Skills-Test-CCTST . Google Scholar
• Insight Assessment . ( 2016b ). Sample thinking skills questions. Retrieved September 26, 2017, from www.insightassessment.com/Resources/Teaching-Training-and-Learning-Tools/node_1487 . Google Scholar
• Kelly, G. J., & Takao, A. ( 2002 ). Epistemic levels in argument: An analysis of university oceanography students’ use of evidence in writing . Science Education , 86 (3), 314–342. https://doi.org/10.1002/sce.10024 . Google Scholar
• Kuhn, D., & Dean, D.Jr. ( 2004 ). Connecting scientific reasoning and causal inference . Journal of Cognition and Development , 5 (2), 261–288. https://doi.org/10.1207/s15327647jcd0502_5 . Google Scholar
• Kuhn, D., Iordanou, K., Pease, M., & Wirkala, C. ( 2008 ). Beyond control of variables: What needs to develop to achieve skilled scientific thinking? . Cognitive Development , 23 (4), 435–451. https://doi.org/10.1016/j.cogdev.2008.09.006 . Google Scholar
• Lawson, A. E. ( 2010 ). Basic inferences of scientific reasoning, argumentation, and discovery . Science Education , 94 (2), 336–364. https://doi.org/­10.1002/sce.20357 . Google Scholar
• Meizlish, D., LaVaque-Manty, D., Silver, N., & Kaplan, M. ( 2013 ). Think like/write like: Metacognitive strategies to foster students’ development as disciplinary thinkers and writers . In Thompson, R. J. (Ed.), Changing the conversation about higher education (pp. 53–73). Lanham, MD: Rowman & Littlefield. Google Scholar
• Miri, B., David, B.-C., & Uri, Z. ( 2007 ). Purposely teaching for the promotion of higher-order thinking skills: A case of critical thinking . Research in Science Education , 37 (4), 353–369. https://doi.org/10.1007/s11165-006-9029-2 . Google Scholar
• Moshman, D. ( 2015 ). Epistemic cognition and development: The psychology of justification and truth . New York: Psychology Press. Google Scholar
• National Research Council . ( 2000 ). How people learn: Brain, mind, experience, and school . Expanded ed.. Washington, DC: National Academies Press. Google Scholar
• Pukkila, P. J. ( 2004 ). Introducing student inquiry in large introductory genetics classes . Genetics , 166 (1), 11–18. https://doi.org/10.1534/genetics.166.1.11 . Medline ,  Google Scholar
• Quitadamo, I. J., Faiola, C. L., Johnson, J. E., & Kurtz, M. J. ( 2008 ). Community-based inquiry improves critical thinking in general education biology . CBE—Life Sciences Education , 7 (3), 327–337. https://doi.org/10.1187/cbe.07-11-0097 . Link ,  Google Scholar
• Quitadamo, I. J., & Kurtz, M. J. ( 2007 ). Learning to improve: Using writing to increase critical thinking performance in general education biology . CBE—Life Sciences Education , 6 (2), 140–154. https://doi.org/10.1187/cbe.06-11-0203 . Link ,  Google Scholar
• Reynolds, J. A., Smith, R., Moskovitz, C., & Sayle, A. ( 2009 ). BioTAP: A systematic approach to teaching scientific writing and evaluating undergraduate theses . BioScience , 59 (10), 896–903. https://doi.org/10.1525/bio.2009.59.10.11 . Google Scholar
• Reynolds, J. A., Thaiss, C., Katkin, W., & Thompson, R. J. ( 2012 ). Writing-to-learn in undergraduate science education: A community-based, conceptually driven approach . CBE—Life Sciences Education , 11 (1), 17–25. https://doi.org/10.1187/cbe.11-08-0064 . Link ,  Google Scholar
• Reynolds, J. A., & Thompson, R. J. ( 2011 ). Want to improve undergraduate thesis writing? Engage students and their faculty readers in scientific peer review . CBE—Life Sciences Education , 10 (2), 209–215. https://doi.org/­10.1187/cbe.10-10-0127 . Link ,  Google Scholar
• Rhemtulla, M., Brosseau-Liard, P. E., & Savalei, V. ( 2012 ). When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions . Psychological Methods , 17 (3), 354–373. https://doi.org/­10.1037/a0029315 . Medline ,  Google Scholar
• Stephenson, N. S., & Sadler-McKnight, N. P. ( 2016 ). Developing critical thinking skills using the science writing heuristic in the chemistry laboratory . Chemistry Education Research and Practice , 17 (1), 72–79. https://doi.org/­10.1039/C5RP00102A . Google Scholar
• Tariq, V. N., Stefani, L. A. J., Butcher, A. C., & Heylings, D. J. A. ( 1998 ). Developing a new approach to the assessment of project work . Assessment and Evaluation in Higher Education , 23 (3), 221–240. https://doi.org/­10.1080/0260293980230301 . Google Scholar
• Timmerman, B. E. C., Strickland, D. C., Johnson, R. L., & Payne, J. R. ( 2011 ). Development of a “universal” rubric for assessing undergraduates’ scientific reasoning skills using scientific writing . Assessment and Evaluation in Higher Education , 36 (5), 509–547. https://doi.org/10.1080/­02602930903540991 . Google Scholar
• Topping, K. J., Smith, E. F., Swanson, I., & Elliot, A. ( 2000 ). Formative peer assessment of academic writing between postgraduate students . Assessment and Evaluation in Higher Education , 25 (2), 149–169. https://doi.org/10.1080/713611428 . Google Scholar
• Willison, J., & O’Regan, K. ( 2007 ). Commonly known, commonly not known, totally unknown: A framework for students becoming researchers . Higher Education Research and Development , 26 (4), 393–409. https://doi.org/10.1080/07294360701658609 . Google Scholar
• Woodin, T., Carter, V. C., & Fletcher, L. ( 2010 ). Vision and Change in Biology Undergraduate Education: A Call for Action—Initial responses . CBE—Life Sciences Education , 9 (2), 71–73. https://doi.org/10.1187/cbe.10-03-0044 . Link ,  Google Scholar
• Zeineddin, A., & Abd-El-Khalick, F. ( 2010 ). Scientific reasoning and epistemological commitments: Coordination of theory and evidence among college science students . Journal of Research in Science Teaching , 47 (9), 1064–1093. https://doi.org/10.1002/tea.20368 . Google Scholar
• Zimmerman, C. ( 2000 ). The development of scientific reasoning skills . Developmental Review , 20 (1), 99–149. https://doi.org/10.1006/drev.1999.0497 . Google Scholar
• Zimmerman, C. ( 2007 ). The development of scientific thinking skills in elementary and middle school . Developmental Review , 27 (2), 172–223. https://doi.org/10.1016/j.dr.2006.12.001 . Google Scholar
• Gender, Equity, and Science Writing: Examining Differences in Undergraduate Life Science Majors’ Attitudes toward Writing Lab Reports 6 March 2024 | Education Sciences, Vol. 14, No. 3
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• Jason E. Dowd ,
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Submitted: 17 March 2017 Revised: 19 October 2017 Accepted: 20 October 2017

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Redefining Scientific Thinking for Higher Education pp 79–103 Cite as

Evidenced-Based Thinking for Scientific Thinking

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This chapter presents a framework for teaching evidenced-based thinking as an integral component of critical thinking, which is a critical foundation of scientific thinking. A qualitative case study is described during which the instructors implemented a typology of five types of evidence: Statistical, qualitative, anecdotal, legal, and expert opinion. By using this typology, students effectively described types of evidence, used evidence to support their arguments, combined them to promote their claims, and became more sceptical of information. These findings provide insights into students’ process of evidenced-based thinking. Ultimately, this research provides insight and guidance for instructors who wish to improve students’ scientific thinking.

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The Tennessee STAR project was a large-scale research study that examined the correlation between reducing class sizes and student achievement. It found that there were positive effects in reducing class size. The intervention occurred in the 1980s in the American state of Tennessee and this longitudinal study measured students’ progress through the end of their secondary education (in US terms, twelfth grade).

Alwehaibi, H. U. (2012). Novel programs to promote critical thinking among higher education students: Empirical study from Saudi Arabia. Asian Social Science, 8 (11), 193–204. https://doi.org/10.5539/ass.v8n11p193 .

Article   Google Scholar  

Arum, R., & Roksa, J. (2011). Academically adrift: Limited learning on college campuses . Chicago, IL: University of Chicago Press.

Google Scholar  

Association of American Colleges and Universities (AAC&U). (2011). The leap vision for learning: Outcomes, practices, impact, and employers’ view . Washington, DC: Association of American Colleges and Universities.

Biddle, B. J., & Berliner, D. (2002). Small class size and its effects. Educational Leadership, 59 (5), 12–23. Retrieved from http://www.ascd.org/publications/educational-leadership/feb02/vol59/num05/Small-Class-Size-and-Its-Effects.aspx .

Bok, D. (2006). Our underachieving colleges: A candid look at how much students learn and why they should be learning more . Princeton, NJ: Princeton University Press.

Condon, W., & Kelly-Riley, D. (2004). Assessing and teaching what we value: The relationship between college-level writing and critical thinking abilities. Assessing Writing, 9 (1), 56–75. https://doi.org/10.1016/j.asw.2004.01.003 .

Fisher, A. (2007). Critical thinking: An introduction . Cambridge, UK: Cambridge University Press.

Hyytinen, H., Holma, K., Toom, A., Shavelson, R. J., & Lindblom-Ylänne, S. (2014). The complex relationship between students’ critical thinking and epistemological beliefs in the context of problem solving. Frontline Learning Research, 2 (5), 1–25. https://doi.org/10.14786/flr.v2i4.124 .

Lai, E. R. (2011). Critical thinking: A literature review . New York: Pearson Education. Retrieved from http://images.pearsonassessments.com/images/tmrs/CriticalThinkingReviewFINAL.pdf .

Lemons, P. P., Reynolds, J. A., Curtin-Soyden, A. J., & Bissell, A. N. (2013). Improving critical-thinking skills in introductory biology through quality practice and metacognition. In M. Kaplan, N. Silver, D. Lavaque-Manty, & D. Meizlish (Eds.), Using reflection and metacognition to improve student learning (pp. 53–77). Sterling, VA: Stylus.

Liu, O. L., Mao, L. K., Frankel, L., & Xu, J. (2016). Assessing critical thinking in higher education: The HEIghten approach and preliminary validity evidence. Assessment & Evaluation in Higher Education, 41 (5), 677–694. https://doi.org/10.1080/02602938.2016.1168358 .

Lucariello, J. (n.d.). How do my students think: Diagnosing student thinking. American Psychological Association. Retrieved from https://www.apa.org/education/k12/student-thinking.aspx .

Merriam, S. B., & Tisdell, E. J. (2016). Qualitative research: A guide to design and implementation . San Francisco, CA: Jossey-Bass.

Pascarella, E. T., Blaich, C., Martin, G. L., & Hanson, J. M. (2011). How robust are the findings of academically adrift? Change, 43 (3), 20–24. https://doi.org/10.1080/00091383.2011.568898 .

Pascarella, E. T., & Terenzini, P. (2005). How college affects students: A third decade of research (Vol. 2). San Francisco, CA: Jossey-Bass.

Paul, R., & Elder, E. (2006). Critical thinking: Learn the tools the best thinkers use . Columbus, OH: Pearson Prentice Hall.

Schmaltz, R. M., Jansen, E., & Wenckowski, N. (2017). Redefining critical thinking: Teaching students to think like scientists. Frontiers in Psychology, 8 (459). https://doi.org/10.3389/fpsyg.2017.00459 .

Shargel, R., & Laster, B. (2016). Partner learning (Havruta) for close reading comprehension. English Journal, 105 (3), 63–68. Retrieved from http://www2.ncte.org/resources/journals/english-journal/ .

Stevens, C., & Witkow, M. R. (2014). Training scientific thinking skills: Evidence from an MCAT2015-aligned classroom module. Teaching of Psychology, 41 (2), 115–121. https://doi.org/10.1177/0098628314530341 .

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Supplement to Critical Thinking

Educational methods.

Experiments have shown that educational interventions can improve critical thinking abilities and dispositions, as measured by standardized tests. Glaser (1941) developed teaching materials suitable for senior primary school, high school and college students. To test their effectiveness, he developed with his sponsor Goodwin Watson the Watson-Glaser Tests of Critical Thinking, whose descendants are in widespread global use under the name “Watson-Glaser Critical Thinking Appraisal” (Watson & Glaser 1980a, 1980b, 1994). He found that senior secondary school students receiving 10 weeks of instruction using these materials improved their scores on these tests more than other such students receiving the standard English curriculum during the 10 weeks, to a degree that was statistically significant (i.e., probably not due to chance). More recently, Abrami et al. (2015) summarized in a meta-analysis the best available evidence on the effectiveness of various strategies for teaching students to think critically. The meta-analysis used as a measure of effectiveness a modified version of a statistical measure known as “Cohen’s d”: the ratio of a difference in mean score to the statistical deviation (SD) of the scores in a reference group. A difference of 0.2 SD is a small effect, a difference of 0.5 SD is a moderate effect, and a difference of 0.8 is a large effect (Cohen 1988: 25–27). Abrami et al. (2015) found a weighted mean effect size of 0.30 among 341 effect sizes, with effect sizes ranging from −1 to +2. This methodologically careful meta-analysis provides strong statistical evidence that explicit instruction for critical thinking can improve critical thinking abilities and dispositions, as measured by standardized tests.

Although contemporary meta-analysis provides a more justified verdict on claims of causal effectiveness than other methods of investigation, it does not give the reader an intuitive grasp of what difference a particular intervention makes to the lives of those who receive it. To get an appreciation of this difference, it helps to read the testimony of the teachers and students in the Laboratory School of Chicago where Dewey’s ideas obtained concreteness. The history of the school, written by two of its former teachers in collaboration with Dewey, makes the following claim for the effects of its approach:

As a result of this guarding and direction of their freedom, the children retained the power of initiative naturally present in young children through their inquisitive interests. This spirit of inquiry was given plenty of opportunity and developed with most of the children into the habit of trying a thing out for themselves. Thus, they gradually became familiar with, and to varying degrees skilled in, the use of the experimental method to solve problems in all areas of their experience. (Mayhew & Edwards 1936: 402–403)

A science teacher in the school wrote:

I think the children did get the scientific attitude of mind. They found out things for themselves. They worked out the simplest problems that may have involved a most commonplace and everyday fact in the manner that a really scientific investigator goes to work. (Mayhew & Edwards 1936: 403)

An alumna of the school summed up the character of its former students as follows:

It is difficult for me to be restrained about the character building results of the Dewey School. As the years have passed and as I have watched the lives of many Dewey School children, I have always been astonished at the ease which fits them into all sorts and conditions of emergencies. They do not vacillate and flounder under unstable emotions; they go ahead and work out the problem in hand, guided by their positively formed working habits. Discouragement to them is non-existent, almost ad absurdum. For that very fact, accomplishment in daily living is inevitable. Whoever has been given the working pattern of tackling problems has a courage born of self-confidence and achieves. (Mayhew & Edwards 1936: 406–407)

In the absence of control groups, of standardized tests, and of statistical methods of controlling for confounding variables, such testimonies are weak evidence of the effectiveness of educational interventions in developing the abilities and dispositions of a critical thinker—in Dewey’s conception, a scientific attitude. But they give a vivid impression of what might be accomplished in an educational system that takes the development of critical thinking as a goal.

Dewey established the Laboratory School explicitly as an experiment to test his theory of knowledge, which

emphasized the part in the development of thought of problems which originated in active situations and also the necessity of testing thought by action if thought was to pass over into knowledge. (Dewey 1936: 464)

Hence the curriculum of the school started from situations familiar to children from their home life (such as preparing food and making clothing) and posed problems that the children were to solve by doing things and noting the consequences. This curriculum was adjusted in the light of its observed results in the classroom.

The school’s continued experimentation with the subject matter of the elementary curriculum proved that classroom results were best when activities were in accord with the child’s changing interests, his growing consciousness of the relation of means and ends, and his increasing willingness to perfect means and to postpone satisfactions in order to arrive at better ends…. The important question for those guiding this process of growth, and of promoting the alignment and cooperation of interest and effort, is this. What specific subject-matter or mode of skill has such a vital connection with the child’s interest, existing powers, and capabilities as will extend the one [the interest–DH] and stimulate, exercise, and carry forward the others [the powers and capabilities–DH] in a progressive course of action? (Mayhew & Edwards 1936: 420–421)

In an appendix to the history of the Laboratory School, Dewey (1936: 468–469) acknowledges that the school did not solve the problem of finding things in the child’s present experience out of which would grow more elaborate, technical and organized knowledge. Passmore (1980: 91) notes one difficulty of starting from children’s out-of-school experiences: they differ a lot from one child to another. More fundamentally, the everyday out-of-school experiences of a child provide few links to the systematic knowledge of nature and of human history that humanity has developed and that schools should pass on to the next generation. If children are to acquire such knowledge through investigation of problems, teachers must first provide information as a basis for formulating problems that interest them (Passmore 1980: 93–94).

More than a century has passed since Dewey’s experiment. In the interim, researchers have refined the methodology of experimenting with human subjects, in educational research and elsewhere. They have also developed the methodology of meta-analysis for combining the results of various experiments to form a comprehensive picture of what has been discovered. Abrami et al. (2015) report the results of such a meta-analysis of all the experimental and quasi-experimental studies published or archived before 2010 that used as outcome variables standardized measures of critical thinking abilities or dispositions of the sort enumerated in Facione 1990a and described in sections 8 and 9 of the main entry. By an experimental study, they mean one in which participants are divided randomly into two groups, one of which receives the educational intervention designed to improve critical thinking and the other of which serves as a control; they found few such experiments, because of the difficulty of achieving randomization in the classrooms where the studies were conducted. By a quasi-experiment, they mean a study with an intervention group that receives an educational intervention designed to improve critical thinking and a control group, but without random allocation to the two groups. Initially, they included also what they called “pre-experiments”, with single-group pretest-posttest designs, but decided at the analysis stage not to include these studies. By a standardized measure, they mean a test with norms derived from previous administration of the test, as set out in the test’s manual, such as the Watson-Glaser Critical Thinking Appraisal (Watson & Glaser 1980a, 1980b, 1994), the Cornell Critical Thinking Tests (Ennis & Millman 1971; Ennis, Millman, & Tomko 1985; 2005), the California Critical Thinking Skills Test (Facione 1990b, 1992) and the California Critical Thinking Dispositions Inventory (Facione & Facione 1992; Facione, Facione, & Giancarlo 2001). They included all such studies in which the educational intervention lasted at least three hours and the participants were at least six years old.

In these studies they found 341 effect sizes. They rated each educational intervention according to the degree to which it involved dialogue, anchored instruction, and mentoring. They found that each of these factors increased the effectiveness of the educational intervention, and that they were most effective when combined. They explained the three factors as follows.

Dialogue : In critical dialogue, which historically goes back to Socrates, individuals discuss a problem together. The dialogue can be oral or written, and cooperative or adversarial. It can take the form of asking questions, discussion, or debate. Some curricula designed to promote critical thinking establish “communities of inquiry” among the students. Such communities were a prominent feature of Dewey’s Laboratory School, incorporated as a means of promoting the primary moral objective of fostering a spirit of social cooperation among the children.

An important aspect of this conditioning process by means of the school’s daily practices was to aid each child in forming a habit of thinking before doing in all of his various enterprises. The daily classroom procedure began with a face-to-face discussion of the work of the day and its relation to that of the previous period. The new problem was then faced, analyzed, and possible plans and resources for its solution suggested by members of the group. The children soon grew to like this method. It gave both individual and group a sense of power to be intelligent, to know what they wanted to do before they did it, and to realize the reasons why one plan was preferred to another. It also enlisted their best effort to prove the validity of their judgment by testing the plan in action. Each member of the group thus acquired a habit of observing, criticizing, and integrating values in thought, in order that they should guide the action that would integrate them in fact. The value of thus previsioning consequences of action before they became fixed as fact was emphasized in the school’s philosophy. The social implication is evident. The conscious direction of his actions toward considered social ends became an unfailing index of the child’s progress toward maturity. (Mayhew & Edwards 1936: 423–424)

Communities of inquiry are also a feature of the Montessori method described by Thayer-Bacon (2000) and of the Philosophy for Children program developed by Matthew Lipman (Splitter 1987). Lipman (2003) examines theoretically what is involved in creating communities of inquiry. Hitchcock (2021) argues that the most obvious way for schools to develop critical thinking is to foster development of communities of inquiry.

Anchored instruction : In anchored instruction, whose advocacy goes back to Rousseau (1762) and Dewey (1910), there is an effort to present students with problems that make sense to them, engage them, and stimulate them to inquire. Simulations, role-playing and presentation of ethical or medical dilemmas are methods of anchoring.

Mentoring : Mentoring is a one-on-one relationship in which someone with more relevant expertise (the mentor) interacts with someone with less (the mentee). The mentor acts as a model and as a critic correcting errors by the mentee. Examples of mentoring are an advisor talking to a student, a physician modeling a procedure for a medical student, and an employee correcting an intern. Abrami et al. (2015) identified three kinds of mentoring in the studies that they analyzed: one-on-one teacher-student interaction, peer-led dyads, and internships.

Abrami et al. (2015) also compared educational interventions with respect to whether they were part of subject-matter instruction. For this purpose, they used a distinction among four types of intervention articulated by Ennis (1989). A general approach tries to teach critical thinking separately from subject-matter instruction. An infusion approach combines deep subject-matter instruction in which students are encouraged to think critically with explicit reference to critical thinking principles. An immersion approach provides deep subject-matter instruction with encouragement to think critically, but without explicit reference to critical thinking principles. A mixed approach combines the general approach with either the infusion or the immersion approach; students combine a separate thread or course aimed at teaching general critical thinking principles with deep subject-matter instruction in which they are encouraged to think critically about the subject-matter. Although the average effect size in the studies using a mixed intervention (+0.38) was greater than the average effect sizes in the studies using general (+0.26), infusion (+0.29) and immersion (+0.23) interventions, the difference was not statistically significant; in other words, it might have been due to chance.

Cleghorn (2021), Makaiau (2021), and Hiner (2021) make specific suggestions for fostering critical thinking respectively in elementary, secondary and post-secondary education. Vincent-Lancrin et al. (2019) report the results of a project of the Organization for Economic Cooperation and Development to develop with teachers and schools in 11 countries resources for fostering creativity and critical thinking in elementary and secondary schools.

Ennis (2013, 2018) has made a detailed proposal for a mixed approach to teaching critical thinking across the curriculum of undergraduate education. Attempts at implementing such an approach have faced difficulties. Weinstein (2013: 209–213) describes the attempt at Montclair State University in Montclair, New Jersey, from 1987 through the 1990s. He reports that the university’s requirement to include critical thinking in all general education courses led to the use of the concept in identifying topics and tasks in course syllabi, but without a unifying theoretical basis. The committee that approved courses as satisfying a general education requirement ignored the relation of curricular outcomes to critical thinking, and focused instead on work requirements with a prima facie relation to reflective thought: term papers, projects, group work, and dialogue. Sheffield (2018) reports similar difficulties encountered in his position from 2012 to 2015 as the inaugural Eugene H. Fram Chair in Applied Critical Thinking at Rochester Institute of Technology (RIT) in Rochester, New York. A cross-disciplinary faculty advisory group was not ready to accept RIT’s approved definition of critical thinking, but never reached a consensus on an alternative. Payette and Ross (2016), on the other hand, report widespread acceptance of the Paul-Elder framework, which involves elements of thought, intellectual standards, and intellectual virtues (Paul & Elder 2006). Sheffield (2018) reports that many colleges and universities in the United States have received funding for so-called “Quality Enhancement Plans” (QEPs) devoted to critical thinking, many of them written by Paul and Elder or developed in consultation with them. He faults the plans for having a typical time frame of five years, which he argues is probably too short for meaningful results, since lasting institutional change is often extremely slow.

Copyright © 2022 by David Hitchcock < hitchckd @ mcmaster . ca >

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• Int J Exerc Sci

Research and Critical Thinking : An Important Link for Exercise Science Students Transitioning to Physical Therapy

Harvey w. wallmann.

1 Department of Physical Therapy, Western Kentucky University, Bowling Green, KY, USA

DONALD L. HOOVER

2 Department of Physical Therapy Education, Rockhurst University, Kansas City, MO, USA

Critical thinking skills are increasingly necessary for success in professional health care careers. Changes in the contemporary healthcare system in the United States arguably make these critical thinking skills more important than they have ever been, as clinicians are required on a daily basis to evaluate multiple bits of information about patients with multiple-systemic health concerns and make appropriate treatment decisions based on this information. We believe the IJES, with its emphasis on engaging undergraduate and graduate students in research and scholarly activity, is a valuable resource for promoting the higher-order critical thinking skills necessary for preparing exercise science students with an interest in professional healthcare careers such as physical therapy.

Higher-order critical thinking skills are necessary for students preparing for and/or enrolled in professional programs, especially the ability to evaluate and synthesize information, which are vital for problem-solving. Essentially, critical thinking is learning to think independently and to develop one’s own opinions supported by existing evidence. In learning scenarios that promote and foster problem-solving and critical thinking skills, it is much more difficult for the student to simply adhere to the role of the passive student; rather, this type of learning prompts the student to assume the role of a self-reliant thinker and researcher.

However, attaining critical thinking skills does not come without its challenges as students must be able to manage a vast array of resources within a series of complex network systems. This is especially true when students are asked to write a research paper, which is one of the most common methods for teaching critical thinking skills. Inherent within writing a research paper are various levels of reasoning with each level becoming progressively more abstract, complex, and effortful. This, according to Bloom’s taxonomy, promotes higher-order thinking skill and more critical thought in the form of synthesis-level thinking and builds upon the prior skill levels in a hierarchical fashion ( 1 ). However, when confronted with this seemingly daunting task, many college students shy away; presumably, because they lack these skills and therefore need to be taught how to learn and apply them ( 2 , 4 ).

Upon closer scrutiny, deficiencies in critical thinking skills among students may rest with the educational system itself, which often stresses memorization of voluminous amounts of material essentially unrelated to any type of application at all ( 2 ). The question then arises as to the extent which critical thinking is initiated during a student’s education in any given institution in higher education. As such, any focus on learning without critical thought becomes less meaningful, thereby disengaging students from any formal training and experience specifically as it relates to critically reviewing and evaluating research ( 3 ).

Arguably, an important component of critical thinking skills is the ability to critically examine and understand published research in one’s professional area of interest ( 7 ). Requiring students to critique published research is one way of addressing the goal of teaching students to critically evaluate research while gaining experience doing it ( 3 ). At its very essence, scientific research is a problem-based learning activity that sharpens critical thinking skills.

An even greater challenge, and one that provides a framework for differentiating between different levels of learning and thought by incorporating reasoning and critical thinking skills to a greater degree, is to actually engage students in the scientific method. Here, students actively participate in the formulation of a research question, data collection, and statistical analysis as a means of creating a learning environment that encourages or even forces them to engage in critical thinking and higher level reasoning. This process is arguably complete only when students are encouraged to complete the manuscript submission process in order to publish their research. Additionally, the manuscript submission process teaches students to be consumers of information while constantly examining, questioning, and evaluating the credibility of sources as they make sense of their own work ( 6 ).

Thus, we see the International Journal of Exercise Science (IJES), with its aim on engaging undergraduate and graduate students in scholarly activity, as a quite suitable vehicle for promoting critical thinking skills in exercise science students interested in entering professional programs such as physical therapy. For example, a very meaningful way to engage students is to enlist their support in a research effort of interest to them and for them to assist in the publication process. Given changes seen among Kinesiology majors on the undergraduate level in recent years, the IJES, with its emphasis on student involvement in the research process, is a great venue for disseminating research findings emphasizing this type of undergraduate student involvement ( 5 ). The research findings typically published in this journal are highly relevant to physical therapy given the central role of exercise within this healthcare profession.

We encourage all authors who work with undergraduate students interested in physical therapy to publish in this journal. Doing so will help to “raise the level” of critical thinking skills for all students involved. Among other things, doing so would also provide another valuable measure for evaluating applicants to physical therapy programs. We believe that student experiences of this nature are helpful when making admissions decisions for physical therapy programs, in part because evidence of prior research experiences provide some indication of a given student’s ability to handle the level of critical thinking necessary for success within a physical therapy education program.

In other words, while measures such as undergraduate GPA and exam scores on standardized aptitude tests are helpful in the selection process, they are certainly finite and incomplete measures for predicting which students are most capable of handling the rigors of these graduate professional programs. We believe that undergraduate research experiences provide an emphasis on higher-order critical thinking skills that are often hard to replicate in other parts of the typical undergraduate educational experience, and these experiences typically translate broadly into academic success when these students matriculate into graduate professional programs such as physical therapy.

When viewed from another vantage point, the IJES may also serve as a vehicle for further refining critical thinking skills once students are enrolled in graduate professional programs. In this same vein, we also encourage researchers working with students enrolled in Doctor of Physical Therapy (DPT) programs to publish in the IJES. Physical therapy curricula typically employ a research course sequence as part of the overall curriculum, as a means of fostering critical thinking skills for all students involved, and many projects completed in this manner are particularly suitable for publication in this journal. Many of the manuscripts published to date in the IJES are similarly highly generalizable to therapeutic exercise scenarios regularly encountered in physical therapy practice, providing a valuable resource for students and practicing clinicians alike.

The free, full-text format of the IJES further increases the attractiveness of this journal, as anecdotal evidence suggests that both students and practicing clinicians are mostly likely to use the resources they can access most easily. Thus, DPT faculty can confidently point to manuscripts in this journal as 1) resources for promoting evidence-based clinical practice as well as 2) an attainable target for publishing their own work. Realizing any of these aims on a consistent basis can contribute to stronger critical thinking skills and perhaps higher clinical outcomes for all involved.

In summary, higher-order critical thinking skills are increasingly necessary for success in professional health care careers. Changes in the contemporary healthcare system in the United States arguably make these critical thinking skills more important than they’ve ever been, as clinicians are required on a daily basis to evaluate multiple bits of information about patients with multiple-systemic health concerns and make appropriate treatment decisions based on this information.

We believe the IJES, with its emphasis on engaging undergraduate and graduate students in research and scholarly activity, is a valuable resource for promoting the higher-order critical thinking skills necessary for preparing exercise science students with an interest in professional healthcare careers such as physical therapy. This viewpoint is based not only upon our experience working with students who enter DPT programs possessing strong higher-order critical thinking skills honed through undergraduate research activities, but also partly upon the many research projects students complete in DPT programs that are highly suitable for dissemination in this journal. The IJES has much potential for strengthening the existing bonds between exercise science and physical therapy that benefit all involved.

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.

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.

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?

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

(please click here)

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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