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scientific hypothesis

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• National Center for Biotechnology Information - PubMed Central - On the scope of scientific hypotheses
• LiveScience - What is a scientific hypothesis?
• The Royal Society - Open Science - On the scope of scientific hypotheses

scientific hypothesis , an idea that proposes a tentative explanation about a phenomenon or a narrow set of phenomena observed in the natural world. The two primary features of a scientific hypothesis are falsifiability and testability, which are reflected in an “If…then” statement summarizing the idea and in the ability to be supported or refuted through observation and experimentation. The notion of the scientific hypothesis as both falsifiable and testable was advanced in the mid-20th century by Austrian-born British philosopher Karl Popper .

The formulation and testing of a hypothesis is part of the scientific method , the approach scientists use when attempting to understand and test ideas about natural phenomena. The generation of a hypothesis frequently is described as a creative process and is based on existing scientific knowledge, intuition , or experience. Therefore, although scientific hypotheses commonly are described as educated guesses, they actually are more informed than a guess. In addition, scientists generally strive to develop simple hypotheses, since these are easier to test relative to hypotheses that involve many different variables and potential outcomes. Such complex hypotheses may be developed as scientific models ( see scientific modeling ).

Depending on the results of scientific evaluation, a hypothesis typically is either rejected as false or accepted as true. However, because a hypothesis inherently is falsifiable, even hypotheses supported by scientific evidence and accepted as true are susceptible to rejection later, when new evidence has become available. In some instances, rather than rejecting a hypothesis because it has been falsified by new evidence, scientists simply adapt the existing idea to accommodate the new information. In this sense a hypothesis is never incorrect but only incomplete.

The investigation of scientific hypotheses is an important component in the development of scientific theory . Hence, hypotheses differ fundamentally from theories; whereas the former is a specific tentative explanation and serves as the main tool by which scientists gather data, the latter is a broad general explanation that incorporates data from many different scientific investigations undertaken to explore hypotheses.

Countless hypotheses have been developed and tested throughout the history of science . Several examples include the idea that living organisms develop from nonliving matter, which formed the basis of spontaneous generation , a hypothesis that ultimately was disproved (first in 1668, with the experiments of Italian physician Francesco Redi , and later in 1859, with the experiments of French chemist and microbiologist Louis Pasteur ); the concept proposed in the late 19th century that microorganisms cause certain diseases (now known as germ theory ); and the notion that oceanic crust forms along submarine mountain zones and spreads laterally away from them ( seafloor spreading hypothesis ).

What is a scientific hypothesis?

It's the initial building block in the scientific method.

Hypothesis basics

What makes a hypothesis testable.

• Types of hypotheses
• Hypothesis versus theory

Additional resources

Bibliography.

A scientific hypothesis is a tentative, testable explanation for a phenomenon in the natural world. It's the initial building block in the scientific method . Many describe it as an "educated guess" based on prior knowledge and observation. While this is true, a hypothesis is more informed than a guess. While an "educated guess" suggests a random prediction based on a person's expertise, developing a hypothesis requires active observation and background research. 

The basic idea of a hypothesis is that there is no predetermined outcome. For a solution to be termed a scientific hypothesis, it has to be an idea that can be supported or refuted through carefully crafted experimentation or observation. This concept, called falsifiability and testability, was advanced in the mid-20th century by Austrian-British philosopher Karl Popper in his famous book "The Logic of Scientific Discovery" (Routledge, 1959).

A key function of a hypothesis is to derive predictions about the results of future experiments and then perform those experiments to see whether they support the predictions.

A hypothesis is usually written in the form of an if-then statement, which gives a possibility (if) and explains what may happen because of the possibility (then). The statement could also include "may," according to California State University, Bakersfield .

Here are some examples of hypothesis statements:

• If garlic repels fleas, then a dog that is given garlic every day will not get fleas.
• If sugar causes cavities, then people who eat a lot of candy may be more prone to cavities.
• If ultraviolet light can damage the eyes, then maybe this light can cause blindness.

A useful hypothesis should be testable and falsifiable. That means that it should be possible to prove it wrong. A theory that can't be proved wrong is nonscientific, according to Karl Popper's 1963 book " Conjectures and Refutations ."

An example of an untestable statement is, "Dogs are better than cats." That's because the definition of "better" is vague and subjective. However, an untestable statement can be reworded to make it testable. For example, the previous statement could be changed to this: "Owning a dog is associated with higher levels of physical fitness than owning a cat." With this statement, the researcher can take measures of physical fitness from dog and cat owners and compare the two.

Types of scientific hypotheses

In an experiment, researchers generally state their hypotheses in two ways. The null hypothesis predicts that there will be no relationship between the variables tested, or no difference between the experimental groups. The alternative hypothesis predicts the opposite: that there will be a difference between the experimental groups. This is usually the hypothesis scientists are most interested in, according to the University of Miami .

For example, a null hypothesis might state, "There will be no difference in the rate of muscle growth between people who take a protein supplement and people who don't." The alternative hypothesis would state, "There will be a difference in the rate of muscle growth between people who take a protein supplement and people who don't."

If the results of the experiment show a relationship between the variables, then the null hypothesis has been rejected in favor of the alternative hypothesis, according to the book " Research Methods in Psychology " (​​BCcampus, 2015). 

There are other ways to describe an alternative hypothesis. The alternative hypothesis above does not specify a direction of the effect, only that there will be a difference between the two groups. That type of prediction is called a two-tailed hypothesis. If a hypothesis specifies a certain direction — for example, that people who take a protein supplement will gain more muscle than people who don't — it is called a one-tailed hypothesis, according to William M. K. Trochim , a professor of Policy Analysis and Management at Cornell University.

Sometimes, errors take place during an experiment. These errors can happen in one of two ways. A type I error is when the null hypothesis is rejected when it is true. This is also known as a false positive. A type II error occurs when the null hypothesis is not rejected when it is false. This is also known as a false negative, according to the University of California, Berkeley . 

A hypothesis can be rejected or modified, but it can never be proved correct 100% of the time. For example, a scientist can form a hypothesis stating that if a certain type of tomato has a gene for red pigment, that type of tomato will be red. During research, the scientist then finds that each tomato of this type is red. Though the findings confirm the hypothesis, there may be a tomato of that type somewhere in the world that isn't red. Thus, the hypothesis is true, but it may not be true 100% of the time.

Scientific theory vs. scientific hypothesis

The best hypotheses are simple. They deal with a relatively narrow set of phenomena. But theories are broader; they generally combine multiple hypotheses into a general explanation for a wide range of phenomena, according to the University of California, Berkeley . For example, a hypothesis might state, "If animals adapt to suit their environments, then birds that live on islands with lots of seeds to eat will have differently shaped beaks than birds that live on islands with lots of insects to eat." After testing many hypotheses like these, Charles Darwin formulated an overarching theory: the theory of evolution by natural selection.

"Theories are the ways that we make sense of what we observe in the natural world," Tanner said. "Theories are structures of ideas that explain and interpret facts." 

• Read more about writing a hypothesis, from the American Medical Writers Association.
• Find out why a hypothesis isn't always necessary in science, from The American Biology Teacher.
• Learn about null and alternative hypotheses, from Prof. Essa on YouTube .

Encyclopedia Britannica. Scientific Hypothesis. Jan. 13, 2022. https://www.britannica.com/science/scientific-hypothesis

Karl Popper, "The Logic of Scientific Discovery," Routledge, 1959.

California State University, Bakersfield, "Formatting a testable hypothesis." https://www.csub.edu/~ddodenhoff/Bio100/Bio100sp04/formattingahypothesis.htm  

Karl Popper, "Conjectures and Refutations," Routledge, 1963.

Price, P., Jhangiani, R., & Chiang, I., "Research Methods of Psychology — 2nd Canadian Edition," BCcampus, 2015.‌

University of Miami, "The Scientific Method" http://www.bio.miami.edu/dana/161/evolution/161app1_scimethod.pdf  

William M.K. Trochim, "Research Methods Knowledge Base," https://conjointly.com/kb/hypotheses-explained/  

University of California, Berkeley, "Multiple Hypothesis Testing and False Discovery Rate" https://www.stat.berkeley.edu/~hhuang/STAT141/Lecture-FDR.pdf  

University of California, Berkeley, "Science at multiple levels" https://undsci.berkeley.edu/article/0_0_0/howscienceworks_19

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Methodology

• How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

Published on May 6, 2022 by Shona McCombes . Revised on November 20, 2023.

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection .

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, other interesting articles, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more types of variables .

• An independent variable is something the researcher changes or controls.
• A dependent variable is something the researcher observes and measures.

If there are any control variables , extraneous variables , or confounding variables , be sure to jot those down as you go to minimize the chances that research bias  will affect your results.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

Prevent plagiarism. Run a free check.

Step 1. ask a question.

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2. Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to ensure that you’re embarking on a relevant topic . This can also help you identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalize more complex constructs.

Step 3. Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

4. Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables
• The specific group being studied
• The predicted outcome of the experiment or analysis

5. Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in  if…then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis . The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

• H 0 : The number of lectures attended by first-year students has no effect on their final exam scores.
• H 1 : The number of lectures attended by first-year students has a positive effect on their final exam scores.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

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A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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McCombes, S. (2023, November 20). How to Write a Strong Hypothesis | Steps & Examples. Scribbr. Retrieved August 5, 2024, from https://www.scribbr.com/methodology/hypothesis/

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.

Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

• Forming a question
• Performing background research
• Creating a hypothesis
• Designing an experiment
• Collecting data
• Analyzing the results
• Drawing conclusions
• Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.

Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore numerous factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment  do not  support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk adage that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

• Is your hypothesis based on your research on a topic?
• Can your hypothesis be tested?
• Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the  journal articles you read . Many authors will suggest questions that still need to be explored.

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

• Collect as many observations about a topic or problem as you can.
• Evaluate these observations and look for possible causes of the problem.
• Create a list of possible explanations that you might want to explore.
• After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method ,  falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that  if  something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

The Importance of Operational Definitions

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

One of the basic principles of any type of scientific research is that the results must be replicable.

Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. For example, how would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.

Hypothesis Checklist

• Does your hypothesis focus on something that you can actually test?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate the variables?
• Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

• Simple hypothesis : This type of hypothesis suggests there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
• Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
• Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
• Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative population sample and then generalizes the findings to the larger group.
• Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the  dependent variable  if you change the  independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

• "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
• "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
• "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."
• "Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."

Examples of a complex hypothesis include:

• "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
• "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

• "There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
• "There is no difference in scores on a memory recall task between children and adults."
• "There is no difference in aggression levels between children who play first-person shooter games and those who do not."

Examples of an alternative hypothesis:

• "People who take St. John's wort supplements will have less anxiety than those who do not."
• "Adults will perform better on a memory task than children."
• "Children who play first-person shooter games will show higher levels of aggression than children who do not." 

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as  case studies ,  naturalistic observations , and surveys are often used when  conducting an experiment is difficult or impossible. These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a  correlational study  can examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods  are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually  cause  another to change.

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Thompson WH, Skau S. On the scope of scientific hypotheses .  R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607

Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:].  Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z

Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004

Nosek BA, Errington TM. What is replication ?  PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691

Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies .  Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

1.2 The Process of Science

Learning objectives.

• Identify the shared characteristics of the natural sciences
• Understand the process of scientific inquiry
• Compare inductive reasoning with deductive reasoning
• Describe the goals of basic science and applied science

Like geology, physics, and chemistry, biology is a science that gathers knowledge about the natural world. Specifically, biology is the study of life. The discoveries of biology are made by a community of researchers who work individually and together using agreed-on methods. In this sense, biology, like all sciences is a social enterprise like politics or the arts. The methods of science include careful observation, record keeping, logical and mathematical reasoning, experimentation, and submitting conclusions to the scrutiny of others. Science also requires considerable imagination and creativity; a well-designed experiment is commonly described as elegant, or beautiful. Like politics, science has considerable practical implications and some science is dedicated to practical applications, such as the prevention of disease (see Figure 1.15 ). Other science proceeds largely motivated by curiosity. Whatever its goal, there is no doubt that science, including biology, has transformed human existence and will continue to do so.

The Nature of Science

Biology is a science, but what exactly is science? What does the study of biology share with other scientific disciplines? Science (from the Latin scientia, meaning "knowledge") can be defined as knowledge about the natural world.

Science is a very specific way of learning, or knowing, about the world. The history of the past 500 years demonstrates that science is a very powerful way of knowing about the world; it is largely responsible for the technological revolutions that have taken place during this time. There are however, areas of knowledge and human experience that the methods of science cannot be applied to. These include such things as answering purely moral questions, aesthetic questions, or what can be generally categorized as spiritual questions. Science cannot investigate these areas because they are outside the realm of material phenomena, the phenomena of matter and energy, and cannot be observed and measured.

The scientific method is a method of research with defined steps that include experiments and careful observation. The steps of the scientific method will be examined in detail later, but one of the most important aspects of this method is the testing of hypotheses. A hypothesis is a suggested explanation for an event, which can be tested. Hypotheses, or tentative explanations, are generally produced within the context of a scientific theory . A generally accepted scientific theory is thoroughly tested and confirmed explanation for a set of observations or phenomena. Scientific theory is the foundation of scientific knowledge. In addition, in many scientific disciplines (less so in biology) there are scientific laws , often expressed in mathematical formulas, which describe how elements of nature will behave under certain specific conditions. There is not an evolution of hypotheses through theories to laws as if they represented some increase in certainty about the world. Hypotheses are the day-to-day material that scientists work with and they are developed within the context of theories. Laws are concise descriptions of parts of the world that are amenable to formulaic or mathematical description.

Natural Sciences

What would you expect to see in a museum of natural sciences? Frogs? Plants? Dinosaur skeletons? Exhibits about how the brain functions? A planetarium? Gems and minerals? Or maybe all of the above? Science includes such diverse fields as astronomy, biology, computer sciences, geology, logic, physics, chemistry, and mathematics ( Figure 1.16 ). However, those fields of science related to the physical world and its phenomena and processes are considered natural sciences . Thus, a museum of natural sciences might contain any of the items listed above.

There is no complete agreement when it comes to defining what the natural sciences include. For some experts, the natural sciences are astronomy, biology, chemistry, earth science, and physics. Other scholars choose to divide natural sciences into life sciences , which study living things and include biology, and physical sciences , which study nonliving matter and include astronomy, physics, and chemistry. Some disciplines such as biophysics and biochemistry build on two sciences and are interdisciplinary.

Scientific Inquiry

One thing is common to all forms of science: an ultimate goal “to know.” Curiosity and inquiry are the driving forces for the development of science. Scientists seek to understand the world and the way it operates. Two methods of logical thinking are used: inductive reasoning and deductive reasoning.

Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. This type of reasoning is common in descriptive science. A life scientist such as a biologist makes observations and records them. These data can be qualitative (descriptive) or quantitative (consisting of numbers), and the raw data can be supplemented with drawings, pictures, photos, or videos. From many observations, the scientist can infer conclusions (inductions) based on evidence. Inductive reasoning involves formulating generalizations inferred from careful observation and the analysis of a large amount of data. Brain studies often work this way. Many brains are observed while people are doing a task. The part of the brain that lights up, indicating activity, is then demonstrated to be the part controlling the response to that task.

Deductive reasoning or deduction is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning. Deductive reasoning is a form of logical thinking that uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid. For example, a prediction would be that if the climate is becoming warmer in a region, the distribution of plants and animals should change. Comparisons have been made between distributions in the past and the present, and the many changes that have been found are consistent with a warming climate. Finding the change in distribution is evidence that the climate change conclusion is a valid one.

Both types of logical thinking are related to the two main pathways of scientific study: descriptive science and hypothesis-based science. Descriptive (or discovery) science aims to observe, explore, and discover, while hypothesis-based science begins with a specific question or problem and a potential answer or solution that can be tested. The boundary between these two forms of study is often blurred, because most scientific endeavors combine both approaches. Observations lead to questions, questions lead to forming a hypothesis as a possible answer to those questions, and then the hypothesis is tested. Thus, descriptive science and hypothesis-based science are in continuous dialogue.

Hypothesis Testing

Biologists study the living world by posing questions about it and seeking science-based responses. This approach is common to other sciences as well and is often referred to as the scientific method. The scientific method was used even in ancient times, but it was first documented by England’s Sir Francis Bacon (1561–1626) ( Figure 1.17 ), who set up inductive methods for scientific inquiry. The scientific method is not exclusively used by biologists but can be applied to almost anything as a logical problem-solving method.

The scientific process typically starts with an observation (often a problem to be solved) that leads to a question. Let’s think about a simple problem that starts with an observation and apply the scientific method to solve the problem. One Monday morning, a student arrives at class and quickly discovers that the classroom is too warm. That is an observation that also describes a problem: the classroom is too warm. The student then asks a question: “Why is the classroom so warm?”

Recall that a hypothesis is a suggested explanation that can be tested. To solve a problem, several hypotheses may be proposed. For example, one hypothesis might be, “The classroom is warm because no one turned on the air conditioning.” But there could be other responses to the question, and therefore other hypotheses may be proposed. A second hypothesis might be, “The classroom is warm because there is a power failure, and so the air conditioning doesn’t work.”

Once a hypothesis has been selected, a prediction may be made. A prediction is similar to a hypothesis but it typically has the format “If . . . then . . . .” For example, the prediction for the first hypothesis might be, “ If the student turns on the air conditioning, then the classroom will no longer be too warm.”

A hypothesis must be testable to ensure that it is valid. For example, a hypothesis that depends on what a bear thinks is not testable, because it can never be known what a bear thinks. It should also be falsifiable , meaning that it can be disproven by experimental results. An example of an unfalsifiable hypothesis is “Botticelli’s Birth of Venus is beautiful.” There is no experiment that might show this statement to be false. To test a hypothesis, a researcher will conduct one or more experiments designed to eliminate one or more of the hypotheses. This is important. A hypothesis can be disproven, or eliminated, but it can never be proven. Science does not deal in proofs like mathematics. If an experiment fails to disprove a hypothesis, then we find support for that explanation, but this is not to say that down the road a better explanation will not be found, or a more carefully designed experiment will be found to falsify the hypothesis.

Each experiment will have one or more variables and one or more controls. A variable is any part of the experiment that can vary or change during the experiment. A control is a part of the experiment that does not change. Look for the variables and controls in the example that follows. As a simple example, an experiment might be conducted to test the hypothesis that phosphate limits the growth of algae in freshwater ponds. A series of artificial ponds are filled with water and half of them are treated by adding phosphate each week, while the other half are treated by adding a salt that is known not to be used by algae. The variable here is the phosphate (or lack of phosphate), the experimental or treatment cases are the ponds with added phosphate and the control ponds are those with something inert added, such as the salt. Just adding something is also a control against the possibility that adding extra matter to the pond has an effect. If the treated ponds show lesser growth of algae, then we have found support for our hypothesis. If they do not, then we reject our hypothesis. Be aware that rejecting one hypothesis does not determine whether or not the other hypotheses can be accepted; it simply eliminates one hypothesis that is not valid ( Figure 1.18 ). Using the scientific method, the hypotheses that are inconsistent with experimental data are rejected.

In recent years a new approach of testing hypotheses has developed as a result of an exponential growth of data deposited in various databases. Using computer algorithms and statistical analyses of data in databases, a new field of so-called "data research" (also referred to as "in silico" research) provides new methods of data analyses and their interpretation. This will increase the demand for specialists in both biology and computer science, a promising career opportunity.

Visual Connection

In the example below, the scientific method is used to solve an everyday problem. Which part in the example below is the hypothesis? Which is the prediction? Based on the results of the experiment, is the hypothesis supported? If it is not supported, propose some alternative hypotheses.

• My toaster doesn’t toast my bread.
• Why doesn’t my toaster work?
• There is something wrong with the electrical outlet.
• If something is wrong with the outlet, my coffeemaker also won’t work when plugged into it.
• I plug my coffeemaker into the outlet.
• My coffeemaker works.

In practice, the scientific method is not as rigid and structured as it might at first appear. Sometimes an experiment leads to conclusions that favor a change in approach; often, an experiment brings entirely new scientific questions to the puzzle. Many times, science does not operate in a linear fashion; instead, scientists continually draw inferences and make generalizations, finding patterns as their research proceeds. Scientific reasoning is more complex than the scientific method alone suggests.

Basic and Applied Science

The scientific community has been debating for the last few decades about the value of different types of science. Is it valuable to pursue science for the sake of simply gaining knowledge, or does scientific knowledge only have worth if we can apply it to solving a specific problem or bettering our lives? This question focuses on the differences between two types of science: basic science and applied science.

Basic science or “pure” science seeks to expand knowledge regardless of the short-term application of that knowledge. It is not focused on developing a product or a service of immediate public or commercial value. The immediate goal of basic science is knowledge for knowledge’s sake, though this does not mean that in the end it may not result in an application.

In contrast, applied science or “technology,” aims to use science to solve real-world problems, making it possible, for example, to improve a crop yield, find a cure for a particular disease, or save animals threatened by a natural disaster. In applied science, the problem is usually defined for the researcher.

Some individuals may perceive applied science as “useful” and basic science as “useless.” A question these people might pose to a scientist advocating knowledge acquisition would be, “What for?” A careful look at the history of science, however, reveals that basic knowledge has resulted in many remarkable applications of great value. Many scientists think that a basic understanding of science is necessary before an application is developed; therefore, applied science relies on the results generated through basic science. Other scientists think that it is time to move on from basic science and instead to find solutions to actual problems. Both approaches are valid. It is true that there are problems that demand immediate attention; however, few solutions would be found without the help of the knowledge generated through basic science.

One example of how basic and applied science can work together to solve practical problems occurred after the discovery of DNA structure led to an understanding of the molecular mechanisms governing DNA replication. Strands of DNA, unique in every human, are found in our cells, where they provide the instructions necessary for life. During DNA replication, new copies of DNA are made, shortly before a cell divides to form new cells. Understanding the mechanisms of DNA replication enabled scientists to develop laboratory techniques that are now used to identify genetic diseases, pinpoint individuals who were at a crime scene, and determine paternity. Without basic science, it is unlikely that applied science could exist.

Another example of the link between basic and applied research is the Human Genome Project, a study in which each human chromosome was analyzed and mapped to determine the precise sequence of DNA subunits and the exact location of each gene. (The gene is the basic unit of heredity represented by a specific DNA segment that codes for a functional molecule.) Other organisms have also been studied as part of this project to gain a better understanding of human chromosomes. The Human Genome Project ( Figure 1.19 ) relied on basic research carried out with non-human organisms and, later, with the human genome. An important end goal eventually became using the data for applied research seeking cures for genetically related diseases.

While research efforts in both basic science and applied science are usually carefully planned, it is important to note that some discoveries are made by serendipity, that is, by means of a fortunate accident or a lucky surprise. Penicillin was discovered when biologist Alexander Fleming accidentally left a petri dish of Staphylococcus bacteria open. An unwanted mold grew, killing the bacteria. The mold turned out to be Penicillium , and a new critically important antibiotic was discovered. In a similar manner, Percy Lavon Julian was an established medicinal chemist working on a way to mass produce compounds with which to manufacture important drugs. He was focused on using soybean oil in the production of progesterone (a hormone important in the menstrual cycle and pregnancy), but it wasn't until water accidentally leaked into a large soybean oil storage tank that he found his method. Immediately recognizing the resulting substance as stigmasterol, a primary ingredient in progesterone and similar drugs, he began the process of replicating and industrializing the process in a manner that has helped millions of people. Even in the highly organized world of science, luck—when combined with an observant, curious mind focused on the types of reasoning discussed above—can lead to unexpected breakthroughs.

Reporting Scientific Work

Whether scientific research is basic science or applied science, scientists must share their findings for other researchers to expand and build upon their discoveries. Communication and collaboration within and between sub disciplines of science are key to the advancement of knowledge in science. For this reason, an important aspect of a scientist’s work is disseminating results and communicating with peers. Scientists can share results by presenting them at a scientific meeting or conference, but this approach can reach only the limited few who are present. Instead, most scientists present their results in peer-reviewed articles that are published in scientific journals. Peer-reviewed articles are scientific papers that are reviewed, usually anonymously by a scientist’s colleagues, or peers. These colleagues are qualified individuals, often experts in the same research area, who judge whether or not the scientist’s work is suitable for publication. The process of peer review helps to ensure that the research described in a scientific paper or grant proposal is original, significant, logical, and thorough. Grant proposals, which are requests for research funding, are also subject to peer review. Scientists publish their work so other scientists can reproduce their experiments under similar or different conditions to expand on the findings.

There are many journals and the popular press that do not use a peer-review system. A large number of online open-access journals, journals with articles available without cost, are now available many of which use rigorous peer-review systems, but some of which do not. Results of any studies published in these forums without peer review are not reliable and should not form the basis for other scientific work. In one exception, journals may allow a researcher to cite a personal communication from another researcher about unpublished results with the cited author’s permission.

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Biology archive

Course: biology archive   >   unit 1, the scientific method.

• Controlled experiments
• The scientific method and experimental design

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Video transcript

Taking Science to School: Learning and Teaching Science in Grades K-8 (2007)

Chapter: 5 generating and evaluating scientific evidence and explanations, 5 generating and evaluating scientific evidence and explanations.

Major Findings in the Chapter:

Children are far more competent in their scientific reasoning than first suspected and adults are less so. Furthermore, there is great variation in the sophistication of reasoning strategies across individuals of the same age.

In general, children are less sophisticated than adults in their scientific reasoning. However, experience plays a critical role in facilitating the development of many aspects of reasoning, often trumping age.

Scientific reasoning is intimately intertwined with conceptual knowledge of the natural phenomena under investigation. This conceptual knowledge sometimes acts as an obstacle to reasoning, but often facilitates it.

Many aspects of scientific reasoning require experience and instruction to develop. For example, distinguishing between theory and evidence and many aspects of modeling do not emerge without explicit instruction and opportunities for practice.

In this chapter, we discuss the various lines of research related to Strand 2—generate and evaluate evidence and explanations. 1 The ways in which

scientists generate and evaluate scientific evidence and explanations have long been the focus of study in philosophy, history, anthropology, and sociology. More recently, psychologists and learning scientists have begun to study the cognitive and social processes involved in building scientific knowledge. For our discussion, we draw primarily from the past 20 years of research in developmental and cognitive psychology that investigates how children’s scientific thinking develops across the K-8 years.

We begin by developing a broad sketch of how key aspects of scientific thinking develop across the K-8 years, contrasting children’s abilities with those of adults. This contrast allows us to illustrate both how children’s knowledge and skill can develop over time and situations in which adults’ and children’s scientific thinking are similar. Where age differences exist, we comment on what underlying mechanisms might be responsible for them. In this research literature, two broad themes emerge, which we take up in detail in subsequent sections of the chapter. The first is the role of prior knowledge in scientific thinking at all ages. The second is the importance of experience and instruction.

Scientific investigation, broadly defined, includes numerous procedural and conceptual activities, such as asking questions, hypothesizing, designing experiments, making predictions, using apparatus, observing, measuring, being concerned with accuracy, precision, and error, recording and interpreting data, consulting data records, evaluating evidence, verification, reacting to contradictions or anomalous data, presenting and assessing arguments, constructing explanations (to oneself and others), constructing various representations of the data (graphs, maps, three-dimensional models), coordinating theory and evidence, performing statistical calculations, making inferences, and formulating and revising theories or models (e.g., Carey et al., 1989; Chi et al., 1994; Chinn and Malhotra, 2001; Keys, 1994; McNay and Melville, 1993; Schauble et al., 1995; Slowiaczek et al., 1992; Zachos et al., 2000). As noted in Chapter 2 , over the past 20 to 30 years, the image of “doing science” emerging from across multiple lines of research has shifted from depictions of lone scientists conducting experiments in isolated laboratories to the image of science as both an individual and a deeply social enterprise that involves problem solving and the building and testing of models and theories.

Across this same period, the psychological study of science has evolved from a focus on scientific reasoning as a highly developed form of logical thinking that cuts across scientific domains to the study of scientific thinking as the interplay of general reasoning strategies, knowledge of the natural phenomena being studied, and a sense of how scientific evidence and explanations are generated. Much early research on scientific thinking and inquiry tended to focus primarily either on conceptual development or on the development of reasoning strategies and processes, often using very

simplified reasoning tasks. In contrast, many recent studies have attempted to describe a larger number of the complex processes that are deployed in the context of scientific inquiry and to describe their coordination. These studies often engage children in firsthand investigations in which they actively explore multivariable systems. In such tasks, participants initiate all phases of scientific discovery with varying amounts of guidance provided by the researcher. These studies have revealed that, in the context of inquiry, reasoning processes and conceptual knowledge are interdependent and in fact facilitate each other (Schauble, 1996; Lehrer et al. 2001).

It is important to note that, across the studies reviewed in this chapter, researchers have made different assumptions about what scientific reasoning entails and which aspects of scientific practice are most important to study. For example, some emphasize the design of well-controlled experiments, while others emphasize building and critiquing models of natural phenomena. In addition, some researchers study scientific reasoning in stripped down, laboratory-based tasks, while others examine how children approach complex inquiry tasks in the context of the classroom. As a result, the research base is difficult to integrate and does not offer a complete picture of students’ skills and knowledge related to generating and evaluating evidence and explanations. Nor does the underlying view of scientific practice guiding much of the research fully reflect the image of science and scientific understanding we developed in Chapter 2 .

TRENDS ACROSS THE K-8 YEARS

Generating evidence.

The evidence-gathering phase of inquiry includes designing the investigation as well as carrying out the steps required to collect the data. Generating evidence entails asking questions, deciding what to measure, developing measures, collecting data from the measures, structuring the data, systematically documenting outcomes of the investigations, interpreting and evaluating the data, and using the empirical results to develop and refine arguments, models, and theories.

Asking Questions and Formulating Hypotheses

Asking questions and formulating hypotheses is often seen as the first step in the scientific method; however, it can better be viewed as one of several phases in an iterative cycle of investigation. In an exploratory study, for example, work might start with structured observation of the natural world, which would lead to formulation of specific questions and hypotheses. Further data might then be collected, which lead to new questions,

revised hypotheses, and yet another round of data collection. The phase of asking questions also includes formulating the goals of the activity and generating hypotheses and predictions (Kuhn, 2002).

Children differ from adults in their strategies for formulating hypotheses and in the appropriateness of the hypotheses they generate. Children often propose different hypotheses from adults (Klahr, 2000), and younger children (age 10) often conduct experiments without explicit hypotheses, unlike 12- to 14-year-olds (Penner and Klahr, 1996a). In self-directed experimental tasks, children tend to focus on plausible hypotheses and often get stuck focusing on a single hypothesis (e.g., Klahr, Fay, and Dunbar, 1993). Adults are more likely to consider multiple hypotheses (e.g., Dunbar and Klahr, 1989; Klahr, Fay, and Dunbar, 1993). For both children and adults, the ability to consider many alternative hypotheses is a factor contributing to success.

At all ages, prior knowledge of the domain under investigation plays an important role in the formulation of questions and hypotheses (Echevarria, 2003; Klahr, Fay, and Dunbar, 1993; Penner and Klahr, 1996b; Schauble, 1990, 1996; Zimmerman, Raghavan, and Sartoris, 2003). For example, both children and adults are more likely to focus initially on variables they believe to be causal (Kanari and Millar, 2004; Schauble, 1990, 1996). Hypotheses that predict expected results are proposed more frequently than hypotheses that predict unexpected results (Echevarria, 2003). The role of prior knowledge in hypothesis formulation is discussed in greater detail later in the chapter.

Designing Experiments

The design of experiments has received extensive attention in the research literature, with an emphasis on developmental changes in children’s ability to build experiments that allow them to identify causal variables. Experimentation can serve to generate observations in order to induce a hypothesis to account for the pattern of data produced (discovery context) or to test the tenability of an existing hypothesis under consideration (confirmation/ verification context) (Klahr and Dunbar, 1988). At a minimum, one must recognize that the process of experimentation involves generating observations that will serve as evidence that will be related to hypotheses.

Ideally, experimentation should produce evidence or observations that are interpretable in order to make the process of evidence evaluation uncomplicated. One aspect of experimentation skill is to isolate variables in such a way as to rule out competing hypotheses. The control of variables is a basic strategy that allows valid inferences and narrows the number of possible experiments to consider (Klahr, 2000). Confounded experiments, those in which variables have not been isolated correctly, yield indetermi-

nate evidence, thereby making valid inferences and subsequent knowledge gain difficult, if not impossible.

Early approaches to examining experimentation skills involved minimizing the role of prior knowledge in order to focus on the strategies that participants used. That is, the goal was to examine the domain-general strategies that apply regardless of the content to which they are applied. For example, building on the research tradition of Piaget (e.g., Inhelder and Piaget, 1958), Siegler and Liebert (1975) examined the acquisition of experimental design skills by fifth and eighth graders. The problem involved determining how to make an electric train run. The train was connected to a set of four switches, and the children needed to determine the particular on/off configuration required. The train was in reality controlled by a secret switch, so that the discovery of the correct solution was postponed until all 16 combinations were generated. In this task, there was no principled reason why any one of the combinations would be more or less likely, and success was achieved by systematically testing all combinations of a set of four switches. Thus the task involved no domain-specific knowledge that would constrain the hypotheses about which configuration was most likely. A similarly knowledge-lean task was used by Kuhn and Phelps (1982), similar to a task originally used by Inhelder and Piaget (1958), involving identifying reaction properties of a set of colorless fluids. Success on the task was dependent on the ability to isolate and control variables in the set of all possible fluid combinations in order to determine which was causally related to the outcome. The study extended over several weeks with variations in the fluids used and the difficulty of the problem.

In both studies, the importance of practice and instructional support was apparent. Siegler and Liebert’s study included two experimental groups of children who received different kinds of instructional support. Both groups were taught about factors, levels, and tree diagrams. One group received additional, more elaborate support that included practice and help representing all possible solutions with a tree diagram. For fifth graders, the more elaborate instructional support improved their performance compared with a control group that did not receive any support. For eighth graders, both kinds of instructional support led to improved performance. In the Kuhn and Phelps task, some students improved over the course of the study, although an abrupt change from invalid to valid strategies was not common. Instead, the more typical pattern was one in which valid and invalid strategies coexisted both within and across sessions, with a pattern of gradual attainment of stable valid strategies by some students (the stabilization point varied but was typically around weeks 5-7).

Since this early work, researchers have tended to investigate children’s and adults’ performance on experimental design tasks that are more knowledge rich and less constrained. Results from these studies indicate that, in

general, adults are more proficient than children at designing informative experiments. In a study comparing adults with third and sixth graders, adults were more likely to focus on experiments that would be informative (Klahr, Fay, and Dunbar, 1993). Similarly, Schauble (1996) found that during the initial 3 weeks of exploring a domain, children and adults considered about the same number of possible experiments. However, when they began experimentation of another domain in the second 3 weeks of the study, adults considered a greater range of possible experiments. Over the full 6 weeks, children and adults conducted approximately the same number of experiments. Thus, children were more likely to conduct unintended duplicate or triplicate experiments, making their experimentation efforts less informative relative to the adults, who were selecting a broader range of experiments. Similarly, children are more likely to devote multiple experimental trials to variables that were already well understood, whereas adults move on to exploring variables they did not understand as well (Klahr, Fay, and Dunbar, 1993; Schauble, 1996). Evidence also indicates, however, that dimensions of the task often have a greater influence on performance than age (Linn, 1978, 1980; Linn, Chen, and Their, 1977; Linn and Levine, 1978).

With respect to attending to one feature at a time, children are less likely to control one variable at a time than adults. For example, Schauble (1996) found that across two task domains, children used controlled comparisons about a third of the time. In contrast, adults improved from 50 percent usage on the first task to 63 percent on the second task. Children usually begin by designing confounded experiments (often as a means to produce a desired outcome), but with repeated practice begin to use a strategy of changing one variable at time (e.g., Kuhn, Schauble, and Garcia-Mila, 1992; Kuhn et al. 1995; Schauble, 1990).

Reminiscent of the results of the earlier study by Kuhn and Phelps, both children and adults display intraindividual variability in strategy usage. That is, multiple strategy usage is not unique to childhood or periods of developmental transition (Kuhn et al., 1995). A robust finding is the coexistence of valid and invalid strategies (e.g., Kuhn, Schuable, and Garcia-Mila, 1992; Garcia-Mila and Andersen, 2005; Gleason and Schauble, 2000; Schauble, 1990; Siegler and Crowley, 1991; Siegler and Shipley, 1995). That is, participants may progress to the use of a valid strategy, but then return to an inefficient or invalid strategy. Similar use of multiple strategies has been found in research on the development of other academic skills, such as mathematics (e.g., Bisanz and LeFevre, 1990; Siegler and Crowley, 1991), reading (e.g., Perfetti, 1992), and spelling (e.g., Varnhagen, 1995). With respect to experimentation strategies, an individual may begin with an invalid strategy, but once the usefulness of changing one variable at a time is discovered, it is not immediately used exclusively. The newly discovered, effective strategy is only slowly incorporated into an individual’s set of strategies.

An individual’s perception of the goals of an investigation also has an important effect on the hypotheses they generate and their approach to experimentation. Individuals tend to differ in whether they see the overarching goal of an inquiry task as seeking to identify which factors make a difference (scientific) or seeking to produce a desired effect (engineering). It is a question for further research if these different approaches characterize an individual, or if they are invoked by task demand or implicit assumptions.

In a direct exploration of the effect of adopting scientific versus engineering goals, Schauble, Klopfer, and Raghavan (1991) provided fifth and sixth graders with an “engineering context” and a “science context.” When the children were working as scientists, their goal was to determine which factors made a difference and which ones did not. When the children were working as engineers, their goal was optimization, that is, to produce a desired effect (i.e., the fastest boat in the canal task). When working in the science context, the children worked more systematically, by establishing the effect of each variable, alone and in combination. There was an effort to make inclusion inferences (i.e., an inference that a factor is causal) and exclusion inferences (i.e., an inference that a factor is not causal). In the engineering context, children selected highly contrastive combinations and focused on factors believed to be causal while overlooking factors believed or demonstrated to be noncausal. Typically, children took a “try-and-see” approach to experimentation while acting as engineers, but they took a theory-driven approach to experimentation when acting as scientists. Schauble et al. (1991) found that children who received the engineering instructions first, followed by the scientist instructions, made the greatest improvements. Similarly, Sneider et al. (1984) found that students’ ability to plan and critique experiments improved when they first engaged in an engineering task of designing rockets.

Another pair of contrasting approaches to scientific investigation is the theorist versus the experimentalist (Klahr and Dunbar, 1998; Schauble, 1990). Similar variation in strategies for problem solving have been observed for chess, puzzles, physics problems, science reasoning, and even elementary arithmetic (Chase and Simon, 1973; Klahr and Robinson, 1981; Klayman and Ha, 1989; Kuhn et al., 1995; Larkin et al., 1980; Lovett and Anderson, 1995, 1996; Simon, 1975; Siegler, 1987; Siegler and Jenkins, 1989). Individuals who take a theory-driven approach tend to generate hypotheses and then test the predictions of the hypotheses. Experimenters tend to make data-driven discoveries, by generating data and finding the hypothesis that best summarizes or explains that data. For example, Penner and Klahr (1996a) asked 10-to 14-year-olds to conduct experiments to determine how the shape, size, material, and weight of an object influence sinking times. Students’ approaches to the task could be classified as either “prediction oriented” (i.e., a theorist: “I believe that weight makes a difference) or “hypothesis oriented” (i.e., an

experimenter: “I wonder if …”). The 10-year-olds were more likely to take a prediction (or demonstration) approach, whereas the 14-year-olds were more likely to explicitly test a hypothesis about an attribute without a strong belief or need to demonstrate that belief. Although these patterns may characterize approaches to any given task, it has yet to be determined if such styles are idiosyncratic to the individual and likely to remain stable across varying tasks, or if different styles might emerge for the same person depending on task demands or the domain under investigation.

Observing and Recording

Record keeping is an important component of scientific investigation in general, and of self-directed experimental tasks especially, because access to and consulting of cumulative records are often important in interpreting evidence. Early studies of experimentation demonstrated that children are often not aware of their own memory limitations, and this plays a role in whether they document their work during an investigation (e.g., Siegler and Liebert, 1975). Recent studies corroborate the importance of an awareness of one’s own memory limitations while engaged in scientific inquiry tasks, regardless of age. Spontaneous note-taking or other documentation of experimental designs and results may be a factor contributing to the observed developmental differences in performance on both experimental design tasks and in evaluation of evidence. Carey et al. (1989) reported that, prior to instruction, seventh graders did not spontaneously keep records when trying to determine and keep track of which substance was responsible for producing a bubbling reaction in a mixture of yeast, flour, sugar, salt, and warm water. Nevertheless, even though preschoolers are likely to produce inadequate and uninformative notations, they can distinguish between the two when asked to choose between them (Triona and Klahr, in press). Dunbar and Klahr (1988) also noted that children (grades 3-6) were unlikely to check if a current hypothesis was or was not consistent with previous experimental results. In a study by Trafton and Trickett (2001), undergraduates solving scientific reasoning problems in a computer environment were more likely to achieve correct performance when using the notebook function (78 percent) than were nonusers (49 percent), showing that this issue is not unique to childhood.

In a study of fourth graders’ and adults’ spontaneous use of notebooks during a 10-week investigation of multivariable systems, all but one of the adults took notes, whereas only half of the children took notes. Moreover, despite variability in the amount of notebook usage in both groups, on average adults made three times more notebook entries than children did. Adults’ note-taking remained stable across the 10 weeks, but children’s frequency of use decreased over time, dropping to about half of their initial

usage. Children rarely reviewed their notes, which typically consisted of conclusions, but not the variables used or the outcomes of the experimental tests (i.e., the evidence for the conclusion was not recorded) (Garcia-Mila and Andersen, 2005).

Children may differentially record the results of experiments, depending on familiarity or strength of prior theories. For example, 10- to 14-year-olds recorded more data points when experimenting with factors affecting force produced by the weight and surface area of boxes than when they were experimenting with pendulums (Kanari and Millar, 2004). Overall, it is a fairly robust finding that children are less likely than adults to record experimental designs and outcomes or to review what notes they do keep, despite task demands that clearly necessitate a reliance on external memory aids.

Given the increasing attention to the importance of metacognition for proficient performance on such tasks (e.g., Kuhn and Pearsall, 1998, 2000), it is important to determine at what point children and early adolescents recognize their own memory limitations as they navigate through a complex task. Some studies show that children’s understanding of how their own memories work continues to develop across the elementary and middle school grades (Siegler and Alibali, 2005). The implication is that there is no particular age or grade level when memory and limited understanding of one’s own memory are no longer a consideration. As such, knowledge of how one’s own memory works may represent an important moderating variable in understanding the development of scientific reasoning (Kuhn, 2001). For example, if a student is aware that it will be difficult for her to remember the results of multiple trials, she may be more likely to carefully record each outcome. However, it may also be the case that children, like adult scientists, need to be inducted into the practice of record keeping and the use of records. They are likely to need support to understand the important role of records in generating scientific evidence and supporting scientific arguments.

Evaluating Evidence

The important role of evidence evaluation in the process of scientific activity has long been recognized. Kuhn (1989), for example, has argued that the defining feature of scientific thinking is the set of skills involved in differentiating and coordinating theory and evidence. Various strands of research provide insight on how children learn to engage in this phase of scientific inquiry. There is an extensive literature on the evaluation of evidence, beginning with early research on identifying patterns of covariation and cause that used highly structured experimental tasks. More recently researchers have studied how children evaluate evidence in the context of self-directed experimental tasks. In real-world contexts (in contrast to highly controlled laboratory tasks) the process of evidence evaluation is very messy

and requires an understanding of error and variation. As was the case for hypothesis generation and the design of experiments, the role of prior knowledge and beliefs has emerged as an important influence on how individuals evaluate evidence.

Covariation Evidence

A number of early studies on the development of evidence evaluation skills used knowledge-lean tasks that asked participants to evaluate existing data. These data were typically in the form of covariation evidence—that is, the frequency with which two events do or do not occur together. Evaluation of covariation evidence is potentially important in regard to scientific thinking because covariation is one potential cue that two events are causally related. Deanna Kuhn and her colleagues carried out pioneering work on children’s and adults’ evaluation of covariation evidence, with a focus on how participants coordinate their prior beliefs about the phenomenon with the data presented to them (see Box 5-1 ).

Results across a series of studies revealed continuous improvement of the skills involved in differentiating and coordinating theory and evidence, as well as bracketing prior belief while evaluating evidence, from middle childhood (grades 3 and 6) to adolescence (grade 9) to adulthood (Kuhn, Amsel, and O’Loughlin, 1988). These skills, however, did not appear to develop to an optimal level even among adults. Even adults had a tendency to meld theory and evidence into a single mental representation of “the way things are.”

Participants had a variety of strategies for keeping theory and evidence in alignment with one another when they were in fact discrepant. One tendency was to ignore, distort, or selectively attend to evidence that was inconsistent with a favored theory. For example, the protocol from one ninth grader demonstrated that upon repeated instances of covariation between type of breakfast roll and catching colds, he would not acknowledge this relationship: “They just taste different … the breakfast roll to me don’t cause so much colds because they have pretty much the same thing inside” (Kuhn, Amsel, and O’Loughlin, 1998, p. 73).

Another tendency was to adjust a theory to fit the evidence, a process that was most often outside an individual’s conscious awareness and control. For example, when asked to recall their original beliefs, participants would often report a theory consistent with the evidence that was presented, and not the theory as originally stated. Take the case of one ninth grader who did not believe that type of condiment (mustard versus ketchup) was causally related to catching colds. With each presentation of an instance of covariation evidence, he acknowledged the evidence and elaborated a theory based on the amount of ingredients or vitamins and the temperature of the

food the condiment was served with to make sense of the data (Kuhn, Amsel, and O’Loughlin, 1988, p. 83). Kuhn argued that this tendency suggests that the student’s theory does not exist as an object of cognition. That is, a theory and the evidence for that theory are undifferentiated—they do not exist as separate cognitive entities. If they do not exist as separate entities, it is not possible to flexibly and consciously reflect on the relation of one to the other.

A number of researchers have criticized Kuhn’s findings on both methodological and theoretical grounds. Sodian, Zaitchik, and Carey (1991), for example, questioned the finding that third and sixth grade children cannot distinguish between their beliefs and the evidence, pointing to the complex-

ity of the tasks Kuhn used as problematic. They chose to employ simpler tasks that involved story problems about phenomena for which children did not hold strong beliefs. Children’s performance on these tasks demonstrated that even first and second graders could differentiate a hypothesis from the evidence. Likewise, Ruffman et al. (1993) used a simplified task and showed that 6-year-olds were able to form a causal hypothesis based on a pattern of covariation evidence. A study of children and adults (Amsel and Brock, 1996) indicated an important role of prior beliefs, especially for children. When presented with evidence that disconfirmed prior beliefs, children from both grade levels tended to make causal judgments consistent with their prior beliefs. When confronted with confirming evidence, however, both groups of children and adults made similar judgments. Looking across these studies provides insight into the conditions under which children are more or less proficient at coordinating theory and evidence. In some situations, children are better at distinguishing prior beliefs from evidence than the results of Kuhn et al. suggest.

Koslowksi (1996) criticized Kuhn et al.’s work on more theoretical grounds. She argued that reliance on knowledge-lean tasks in which participants are asked to suppress their prior knowledge may lead to an incomplete or distorted picture of the reasoning abilities of children and adults. Instead, Koslowski suggested that using prior knowledge when gathering and evaluating evidence is a valid strategy. She developed a series of experiments to support her thesis and to explore the ways in which prior knowledge might play a role in evaluating evidence. The results of these investigations are described in detail in the later section of this chapter on the role of prior knowledge.

Evidence in the Context of Investigations

Researchers have also looked at reasoning about cause in the context of full investigations of causal systems. Two main types of multivariable systems are used in these studies. In the first type of system, participants are involved in a hands-on manipulation of a physical system, such as a ramp (e.g., Chen and Klahr, 1999; Masnick and Klahr, 2003) or a canal (e.g., Gleason and Schauble, 2000; Kuhn, Schauble, and Garcia-Mila, 1992). The second type of system is a computer simulation, such as the Daytona microworld in which participants discover the factors affecting the speed of race cars (Schauble, 1990). A variety of virtual environments have been created in domains such as electric circuits (Schauble et al., 1992), genetics (Echevarria, 2003), earthquake risk, and flooding risk (e.g., Keselman, 2003).

The inferences that are made based on self-generated experimental evidence are typically classified as either causal (or inclusion), noncausal (or exclusion), indeterminate, or false inclusion. All inference types can be fur-

ther classified as valid or invalid. Invalid inclusion, by definition, is of particular interest because in self-directed experimental contexts, both children and adults often infer based on prior beliefs that a variable is causal, when in reality it is not.

Children tend to focus on making causal inferences during their initial explorations of a causal system. In a study in which children worked to discover the causal structure of a computerized microworld, fifth and sixth graders began by producing confounded experiments and relied on prior knowledge or expectations (Schauble, 1990). As a result, in their early explorations of the causal system, they were more likely to make incorrect causal inferences. In a direct comparison of adults and children (Schauble, 1996), adults also focused on making causal inferences, but they made more valid inferences because their experimentation was more often done using a control-of-variables strategy. Overall, children’s inferences were valid 44 percent of the time, compared with 72 percent for adults. The fifth and sixth graders improved over the course of six sessions, starting at 25 percent but improving to almost 60 percent valid inferences (Schauble, 1996). Adults were more likely than children to make inferences about which variables were noncausal or inferences of indeterminacy (80 and 30 percent, respectively) (Schauble, 1996).

Children’s difficulty with inferences of noncausality also emerged in a study of 10- to 14-year-olds who explored factors influencing the swing of a pendulum or the force needed to pull a box along a level surface (Kanari and Millar, 2004). Only half of the students were able draw correct conclusions about factors that did not covary with outcome. Students were likely to either selectively record data, selectively attend to data, distort or reinterpret the data, or state that noncovariation experimental trials were “inconclusive.” Such tendencies are reminiscent of other findings that some individuals selectively attend to or distort data in order to preserve a prior theory or belief (Kuhn, Amsel, and O’Loughlin, 1988; Zimmerman, Raghavan, and Sartoris, 2003).

Some researchers suggest children’s difficulty with noncausal or indeterminate inferences may be due both to experience and to the inherent complexity of the problem. In terms of experience, in the science classroom it is typical to focus on variables that “make a difference,” and therefore students struggle when testing variables that do not covary with the outcome (e.g., the weight of a pendulum does not affect the time of swing or the vertical height of a weight does not affect balance) (Kanari and Millar, 2004). Also, valid exclusion and indeterminacy inferences may be conceptually more complex, because they require one to consider a pattern of evidence produced from several experimental trials (Kuhn et al., 1995; Schauble, 1996). Looking across several trials may require one to review cumulative records of previous outcomes. As has been suggested previously, children do not

often have the memory skills to either record information, record sufficient information, or consult such information when it has been recorded.

The importance of experience is highlighted by the results of studies conducted over several weeks with fifth and sixth graders. After several weeks with a task, children started making more exclusion inferences (that factors are not causal) and indeterminacy inferences (that one cannot make a conclusive judgment about a confounded comparison) and did not focus solely on causal inferences (e.g., Keselman, 2003; Schauble, 1996). They also began to distinguish between an informative and an uninformative experiment by attending to or controlling other factors leading to an improved ability to make valid inferences. Through repeated exposure, invalid inferences, such as invalid inclusions, dropped in frequency. The tendency to begin to make inferences of indeterminacy suggests that students developed more awareness of the adequacy or inadequacy of their experimentation strategies for generating sufficient and interpretable evidence.

Children and adults also differ in generating sufficient evidence to support inferences. In contexts in which it is possible, children often terminate their search early, believing that they have determined a solution to the problem (e.g., Dunbar and Klahr, 1989). In studies over several weeks in which children must continue their investigation (e.g., Schauble et al., 1991), this is less likely because of the task requirements. Children are also more likely to refer to the most recently generated evidence. They may jump to a conclusion after a single experiment, whereas adults typically need to see the results of several experiments (e.g., Gleason and Schauble, 2000).

As was found with experimentation, children and adults display intraindividual variability in strategy usage with respect to inference types. Likewise, the existence of multiple inference strategies is not unique to childhood (Kuhn et al., 1995). In general, early in an investigation, individuals focus primarily on identifying factors that are causal and are less likely to consider definitely ruling out factors that are not causal. However, a mix of valid and invalid inference strategies co-occur during the course of exploring a causal system. As with experimentation, the addition of a valid inference strategy to an individual’s repertoire does not mean that they immediately give up the others. Early in investigations, there is a focus on causal hypotheses and inferences, whether they are warranted or not. Only with additional exposure do children start to make inferences of noncausality and indeterminacy. Knowledge change and experience—gaining a better understanding of the causal system via experimentation—was associated with the use of valid experimentation and inference strategies.

THE ROLE OF PRIOR KNOWLEDGE

In the previous section we reviewed evidence on developmental differences in using scientific strategies. Across multiple studies, prior knowledge

emerged as an important influence on several parts of the process of generating and evaluating evidence. In this section we look more closely at the specific ways that prior knowledge may shape part of the process. Prior knowledge includes conceptual knowledge, that is, knowledge of the natural world and specifically of the domain under investigation, as well as prior knowledge and beliefs about the purpose of an investigation and the goals of science more generally. This latter kind of prior knowledge is touched on here and discussed in greater detail in the next chapter.

Beliefs About Causal Mechanism and Plausibility

In response to research on evaluation of covariation evidence that used knowledge-lean tasks or even required participants to suppress prior knowledge, Koslowski (1996) argued that it is legitimate and even helpful to consider prior knowledge when gathering and evaluating evidence. The world is full of correlations, and consideration of plausibility, causal mechanism, and alternative causes can help to determine which correlations between events should be taken seriously and which should be viewed as spurious. For example, the identification of the E. coli bacterium allows a causal relationship between hamburger consumption and certain types of illness or mortality. Because of the absence of a causal mechanism, one does not consider seriously the correlation between ice cream consumption and violent crime rate as causal, but one looks for other covarying quantities (such as high temperatures) that may be causal for both behaviors and thus explain the correlation.

Koslowski (1996) presented a series of experiments that demonstrate the interdependence of theory and evidence in legitimate scientific reasoning (see Box 5-2 for an example). In most of these studies, all participants (sixth graders, ninth graders, and adults) did take mechanism into consideration when evaluating evidence in relation to a hypothesis about a causal relationship. Even sixth graders considered more than patterns of covariation when making causal judgments (Koslowksi and Okagaki, 1986; Koslowski et al., 1989). In fact, as discussed in the previous chapter, results of studies by Koslowski (1996) and others (Ahn et al., 1995) indicate that children and adults have naïve theories about the world that incorporate information about both covariation and causal mechanism.

The plausibility of a mechanism also plays a role in reasoning about cause. In some situations, scientific progress occurs by taking seemingly implausible correlations seriously (Wolpert, 1993). Similarly, Koslowski argued that if people rely on covariation and mechanism information in an interdependent and judicious manner, then they should pay attention to implausible correlations (i.e., those with no apparent mechanism) when the implausible correlation occurs repeatedly. For example, discovering the cause of Kawasaki’s syndrome depended on taking seriously the implausible cor-

relation between the illness and having recently cleaned carpets. Similarly, Thagard (1998a, 1998b) describes the case of researchers Warren and Marshall, who proposed that peptic ulcers could be caused by a bacterium, and their efforts to have their theory accepted by the medical community. The bacterial theory of ulcers was initially rejected as implausible, given the assumption that the stomach is too acidic to allow bacteria to survive.

Studies with both children and adults reveal links between reasoning about mechanism and the plausibility of that mechanism (Koslowski, 1996). When presented with an implausible covariation (e.g., improved gas mileage and color of car), participants rated the causal status of the implausible cause (color) before and after learning about a possible way that the cause could bring about the effect (improved gas mileage). In this example, par-

ticipants learned that the color of the car affects the driver’s alertness (which affects driving quality, which in turn affects gas mileage). At all ages, participants increased their causal ratings after learning about a possible mediating mechanism. The presence of a possible mechanism in addition to a large number of covariations (four or more) was taken to indicate the possibility of a causal relationship for both plausible and implausible covariations. When either generating or assessing mechanisms for plausible covariations, all age groups (sixth and ninth graders and adults) were comparable. When the covariation was implausible, sixth graders were more likely to generate dubious mechanisms to account for the correlation.

The role of prior knowledge, especially beliefs about causal mechanism and plausibility, is also evident in hypothesis formation and the design of investigations. Individuals’ prior beliefs influence the choice of which hypotheses to test, including which hypotheses are tested first, repeatedly, or receive the most time and attention (e.g., Echevarria, 2003; Klahr, Fay, and Dunbar, 1993; Penner and Klahr, 1996b; Schauble, 1990, 1996; Zimmerman, Raghavan, and Sartoris, 2003). For example, children’s favored theories sometimes result in the selection of invalid experimentation and evidence evaluation heuristics (e.g., Dunbar and Klahr, 1989; Schauble, 1990). Plausibility of a hypothesis may serve as a guide for which experiments to pursue. Klahr, Fay, and Dunbar (1993) provided third and sixth grade children and adults with hypotheses to test that were incorrect but either plausible or implausible. For plausible hypotheses, children and adults tended to go about demonstrating the correctness of the hypothesis rather than setting up experiments to decide between rival hypotheses. For implausible hypotheses, adults and some sixth graders proposed a plausible rival hypothesis and set up an experiment that would discriminate between the two. Third graders tended to propose a plausible hypothesis but then ignore or forget the initial implausible hypothesis, getting sidetracked in an attempt to demonstrate that the plausible hypothesis was correct.

Recognizing the interdependence of theory and data in the evaluation of evidence and explanations, Chinn and Brewer (2001) proposed that people evaluate evidence by building a mental model of the interrelationships between theories and data. These models integrate patterns of data, procedural details, and the theoretical explanation of the observed findings (which may include unobservable mechanisms, such as molecules, electrons, enzymes, or intentions and desires). The information and events can be linked by different kinds of connections, including causal, contrastive, analogical, and inductive links. The mental model may then be evaluated by considering the plausibility of these links. In addition to considering the links between, for example, data and theory, the model might also be evaluated by appealing to alternate causal mechanisms or alternate explanations. Essentially, an individual seeks to “undermine one or more of the links in the

model” (p. 337). If no reasons to be critical can be identified, the individual may accept the new evidence or theoretical interpretation.

Some studies suggest that the strength of prior beliefs, as well as the personal relevance of those beliefs, may influence the evaluation of the mental model (Chinn and Malhotra, 2002; Klaczynski, 2000; Klaczynski and Narasimham, 1998). For example, when individuals have reason to disbelieve evidence (e.g., because it is inconsistent with prior belief), they will search harder for flaws in the data (Kunda, 1990). As a result, individuals may not find the evidence compelling enough to reassess their cognitive model. In contrast, beliefs about simple empirical regularities may not be held with such conviction (e.g., the falling speed of heavy versus light objects), making it easier to change a belief in response to evidence.

Evaluating Evidence That Contradicts Prior Beliefs

Anomalous data or evidence refers to results that do not fit with one’s current beliefs. Anomalous data are considered very important by scientists because of their role in theory change, and they have been used by science educators to promote conceptual change. The idea that anomalous evidence promotes conceptual change (in the scientist or the student) rests on a number of assumptions, including that individuals have beliefs or theories about natural or social phenomena, that they are capable of noticing that some evidence is inconsistent with those theories, that such evidence calls into question those theories, and, in some cases, that a belief or theory will be altered or changed in response to the new (anomalous) evidence (Chinn and Brewer, 1998). Chinn and Brewer propose that there are eight possible responses to anomalous data. Individuals can (1) ignore the data; (2) reject the data (e.g., because of methodological error, measurement error, bias); (3) acknowledge uncertainty about the validity of the data; (4) exclude the data as being irrelevant to the current theory; (5) hold the data in abeyance (i.e., withhold a judgment about the relation of the data to the initial theory); (6) reinterpret the data as consistent with the initial theory; (7) accept the data and make peripheral change or minor modification to the theory; or (8) accept the data and change the theory. Examples of all of these responses were found in undergraduates’ responses to data that contradicted theories to explain the mass extinction of dinosaurs and theories about whether dinosaurs were warm-blooded or cold-blooded.

In a series of studies, Chinn and Malhotra (2002) examined how fourth, fifth, and sixth graders responded to experimental data that were inconsistent with their existing beliefs. Experiments from physical science domains were selected in which the outcomes produced either ambiguous or unambiguous data, and for which the findings were counterintuitive for most children. For example, most children assume that a heavy object falls faster

than a light object. When the two objects are dropped simultaneously, there is some ambiguity because it is difficult to observe both objects. An example of a topic that is counterintuitive but results in unambiguous evidence is the reaction temperature of baking soda added to vinegar. Children believe that either no change in temperature will occur, or that the fizzing causes an increase in temperature. Thermometers unambiguously show a temperature drop of about 4 degrees centigrade.

When examining the anomalous evidence produced by these experiments, children’s difficulties seemed to occur in one of four cognitive processes: observation, interpretation, generalization, or retention (Chinn and Malhotra, 2002). For example, prior belief may influence what is “observed,” especially in the case of data that are ambiguous, and children may not perceive the two objects as landing simultaneously. Inferences based on this faulty observation will then be incorrect. At the level of interpretation, even if individuals accurately observed the outcome, they might not shift their theory to align with the evidence. They can fail to do so in many ways, such as ignoring or distorting the data or discounting the data because they are considered flawed. At the level of generalization, an individual may accept, for example, that these particular heavy and light objects fell at the same rate but insist that the same rule may not hold for other situations or objects. Finally, even when children appeared to change their beliefs about an observed phenomenon in the immediate context of the experiment, their prior beliefs reemerged later, indicating a lack of long-term retention of the change.

Penner and Klahr (1996a) investigated the extent to which children’s prior beliefs affect their ability to design and interpret experiments. They used a domain in which most children hold a strong belief that heavier objects sink in fluid faster than light objects, and they examined children’s ability to design unconfounded experiments to test that belief. In this study, for objects of a given composition and shape, sink times for heavy and light objects are nearly indistinguishable to an observer. For example, the sink times for the stainless steel spheres weighing 65 gm and 19 gm were .58 sec and .62 sec, respectively. Only one of the eight children (out of 30) who chose to directly contrast these two objects continued to explore the reason for the unexpected finding that the large and small spheres had equivalent sink times. The process of knowledge change was not straightforward. For example, some children suggested that the size of the smaller steel ball offset the fact that it weighed less because it was able to move through the water as fast as the larger, heavier steel ball. Others concluded that both weight and shape make a difference. That is, there was an attempt to reconcile the evidence with prior knowledge and expectations by appealing to causal mechanisms, alternate causes, or enabling conditions.

What is also important to note about the children in the Penner and Klahr study is that they did in fact notice the surprising finding, rather than

ignore or misrepresent the data. They tried to make sense of the outcome by acting as a theorist who conjectures about the causal mechanisms, boundary conditions, or other ad hoc explanations (e.g., shape) to account for the results of an experiment. In Chinn and Malhotra’s (2002) study of students’ evaluation of observed evidence (e.g., watching two objects fall simultaneously), the process of noticing was found to be an important mediator of conceptual change.

Echevarria (2003) examined seventh graders’ reactions to anomalous data in the domain of genetics and whether they served as a catalyst for knowledge construction during the course of self-directed experimentation. Students in the study completed a 3-week unit on genetics that involved genetics simulation software and observing plant growth. In both the software and the plants, students investigated or observed the transmission of one trait. Anomalies in the data were defined as outcomes that were not readily explainable on the basis of the appearance of the parents.

In general, the number of hypotheses generated, the number of tests conducted, and the number of explanations generated were a function of students’ ability to encounter, notice, and take seriously an anomalous finding. The majority of students (80 percent) developed some explanation for the pattern of anomalous data. For those who were unable to generate an explanation, it was suggested that the initial knowledge was insufficient and therefore could not undergo change as a result of the encounter with “anomalous” evidence. Analogous to case studies in the history of science (e.g., Simon, 2001), these students’ ability to notice and explore anomalies was related to their level of domain-specific knowledge (as suggested by Pasteur’s oft quoted maxim “serendipity favors the prepared mind”). Surprising findings were associated with an increase in hypotheses and experiments to test these potential explanations, but without the domain knowledge to “notice,” anomalies could not be exploited.

There is some evidence that, with instruction, students’ ability to evaluate anomalous data improves (Chinn and Malhotra, 2002). In a study of fourth, fifth, and sixth graders, one group of students was instructed to predict the outcomes of three experiments that produce counterintuitive but unambiguous data (e.g., reaction temperature). A second group answered questions that were designed to promote unbiased observations and interpretations by reflecting on the data. A third group was provided with an explanation of what scientists expected to find and why. All students reported their prediction of the outcome, what they observed, and their interpretation of the experiment. They were then tested for generalizations, and a retention test followed 9-10 days later. Fifth and sixth graders performed better than did fourth graders. Students who heard an explanation of what scientists expected to find and why did best. Further analyses suggest that the explanation-based intervention worked by influencing students’ initial

predictions. This correct prediction then influenced what was observed. A correct observation then led to correct interpretations and generalizations, which resulted in conceptual change that was retained. A similar pattern of results was found using interventions employing either full or reduced explanations prior to the evaluation of evidence.

Thus, it appears that children were able to change their beliefs on the basis of anomalous or unexpected evidence, but only when they were capable of making the correct observations. Difficulty in making observations was found to be the main cognitive process responsible for impeding conceptual change (i.e., rather than interpretation, generalization, or retention). Certain interventions, in particular those involving an explanation of what scientists expected to happen and why, were very effective in mediating conceptual change when encountering counterintuitive evidence. With particular scaffolds, children made observations independent of theory, and they changed their beliefs based on observed evidence.

THE IMPORTANCE OF EXPERIENCE AND INSTRUCTION

There is increasing evidence that, as in the case of intellectual skills in general, the development of the component skills of scientific reasoning “cannot be counted on to routinely develop” (Kuhn and Franklin, 2006, p. 47). That is, young children have many requisite skills needed to engage in scientific thinking, but there are also ways in which even adults do not show full proficiency in investigative and inference tasks. Recent research efforts have therefore been focused on how such skills can be promoted by determining which types of educational interventions (e.g., amount of structure, amount of support, emphasis on strategic or metastrategic skills) will contribute most to learning, retention, and transfer, and which types of interventions are best suited to different students. There is a developing picture of what children are capable of with minimal support, and research is moving in the direction of ascertaining what children are capable of, and when, under conditions of practice, instruction, and scaffolding. It may one day be possible to tailor educational opportunities that neither under- or overestimate children’s ability to extract meaningful experiences from inquiry-based science classes.

Very few of the early studies focusing on the development of experimentation and evidence evaluation skills explicitly addressed issues of instruction and experience. Those that did, however, indicated an important role of experience and instruction in supporting scientific thinking. For example, Siegler and Liebert (1975) incorporated instructional manipulations aimed at teaching children about variables and variable levels with or without practice on analogous tasks. In the absence of both instruction and

extended practice, no fifth graders and a small minority of eighth graders were successful. Kuhn and Phelps (1982) reported that, in the absence of explicit instruction, extended practice over several weeks was sufficient for the development and modification of experimentation and inference strategies. Later studies of self-directed experimentation also indicate that frequent engagement with the inquiry environment alone can lead to the development and modification of cognitive strategies (e.g., Kuhn, Schauble, and Garcia-Mila, 1992; Schauble et al., 1991).

Some researchers have suggested that even simple prompts, which are often used in studies of students’ investigation skills, may provide a subtle form of instruction intervention (Klahr and Carver, 1995). Such prompts may cue the strategic requirements of the task, or they may promote explanation or the type of reflection that could induce a metacognitive or metastrategic awareness of task demands. Because of their role in many studies of revealing students’ thinking generation, it may be very difficult to tease apart the relative contributions of practice from the scaffolding provided by researcher prompts.

In the absence of instruction or prompts, students may not routinely ask questions of themselves, such as “What are you going to do next?” “What outcome do you predict?” “What did you learn?” and “How do you know?” Questions such as these may promote self-explanation, which has been shown to enhance understanding in part because it facilitates the integration of newly learned material with existing knowledge (Chi et al., 1994). Questions such as the prompts used by researchers may serve to promote such integration. Chinn and Malhotra (2002) incorporated different kinds of interventions, aimed at promoting conceptual change in response to anomalous experimental evidence. Interventions included practice at making predictions, reflecting on data, and explanation. The explanation-based interventions were most successful at promoting conceptual change, retention, and generalization. The prompts used in some studies of self-directed experimentation are very likely to serve the same function as the prompts used by Chi et al. (1994). Incorporating such prompts in classroom-based inquiry activities could serve as a powerful teaching tool, given that the use of self-explanation in tutoring systems (human and computer interface) has been shown to be quite effective (e.g., Chi, 1996; Hausmann and Chi, 2002).

Studies that compare the effects of different kinds of instruction and practice opportunities have been conducted in the laboratory, with some translation to the classroom. For example, Chen and Klahr (1999) examined the effects of direct and indirect instruction of the control of variables strategy on students’ (grades 2-4) experimentation and knowledge acquisition. The instructional intervention involved didactic teaching of the control-of-variables strategy, along with examples and probes. Indirect (or implicit) training involved the use of systematic probes during the course of children’s

experimentation. A control group did not receive instruction or probes. No group received instruction on domain knowledge for any task used (springs, ramps, sinking objects). For the students who received instruction, use of the control-of-variables strategy increased from 34 percent prior to instruction to 65 percent after, with 61-64 percent usage maintained on transfer tasks that followed after 1 day and again after 7 months, respectively. No such gains were evident for the implicit training or control groups.

Instruction about control of variables improved children’s ability to design informative experiments, which in turn facilitated conceptual change in a number of domains. They were able to design unconfounded experiments, which facilitated valid causal and noncausal inferences, resulting in a change in knowledge about how various multivariable causal systems worked. Significant gains in domain knowledge were evident only for the instruction group. Fourth graders showed better skill retention at long-term assessment than second or third graders.

The positive impact of instruction on control of variables also appears to translate to the classroom (Toth, Klahr, and Chen, 2000; Klahr, Chen and Toth, 2001). Fourth graders who received instruction in the control-of-variables strategy in their classroom increased their use of the strategy, and their domain knowledge improved. The percentage of students who were able to correctly evaluate others’ research increased from 28 to 76 percent.

Instruction also appears to promote longer term use of the control-of-variables strategy and transfer of the strategy to a new task (Klahr and Nigam, 2004). Third and fourth graders who received instruction were more likely to master the control-of-variables strategy than students who explored a multivariable system on their own. Interestingly, although the group that received instruction performed better overall, a quarter of the students who explored the system on their own also mastered the strategy. These results raise questions about the kinds of individual differences that may allow for some students to benefit from the discovery context, but not others. That is, which learner traits are associated with the success of different learning experiences?

Similar effects of experience and instruction have been demonstrated for improving students’ ability to use evidence from multiple records and make correct inferences from noncausal variables (Keselman, 2003). In many cases, students show some improvement when they are given the opportunity for practice, but greater improvement when they receive instruction (Kuhn and Dean, 2005).

Long-term studies of students’ learning in the classroom with instructional support and structured experiences over months and years reveal children’s potential to engage in sophisticated investigations given the appropriate experiences (Metz, 2004; Lehrer and Schauble, 2005). For example, in one classroom-based study, second and fourth and fifth graders took part

in a curriculum unit on animal behavior that emphasized domain knowledge, whole-class collaboration, scaffolded instruction, and discussions about the kinds of questions that can and cannot be answered by observational records (Metz, 2004). Pairs or triads of students then developed a research question, designed an experiment, collected and analyzed data, and presented their findings on a research poster. Such studies have demonstrated that, with appropriate support, students in grades K-8 and students from a variety of socioeconomic, cultural, and linguistic backgrounds can be successful in generating and evaluating scientific evidence and explanations (Kuhn and Dean, 2005; Lehrer and Schauble, 2005; Metz, 2004; Warren, Rosebery, and Conant, 1994).

KNOWLEDGE AND SKILL IN MODELING

The picture that emerges from developmental and cognitive research on scientific thinking is one of a complex intertwining of knowledge of the natural world, general reasoning processes, and an understanding of how scientific knowledge is generated and evaluated. Science and scientific thinking are not only about logical thinking or conducting carefully controlled experiments. Instead, building knowledge in science is a complex process of building and testing models and theories, in which knowledge of the natural world and strategies for generating and evaluating evidence are closely intertwined. Working from this image of science, a few researchers have begun to investigate the development of children’s knowledge and skills in modeling.

The kinds of models that scientists construct vary widely, both within and across disciplines. Nevertheless, the rhetoric and practice of science are governed by efforts to invent, revise, and contest models. By modeling, we refer to the construction and test of representations that serve as analogues to systems in the real world (Lehrer and Schauble, 2006). These representations can be of many forms, including physical models, computer programs, mathematical equations, or propositions. Objects and relations in the model are interpreted as representing theoretically important objects and relations in the represented world. Models are useful in summarizing known features and predicting outcomes—that is, they can become elements of or representations of theories. A key hurdle for students is to understand that models are not copies; they are deliberate simplifications. Error is a component of all models, and the precision required of a model depends on the purpose for its current use.

The forms of thinking required for modeling do not progress very far without explicit instruction and fostering (Lehrer and Schauble, 2000). For this reason, studies of modeling have most often taken place in classrooms over sustained periods of time, often years. These studies provide a pro-

vocative picture of the sophisticated scientific thinking that can be supported in classrooms if students are provided with the right kinds of experiences over extended periods of time. The instructional approaches used in studies of students’ modeling, as well as the approach to curriculum that may be required to support the development of modeling skills over multiple years of schooling, are discussed in the chapters in Part III .

Lehrer and Schauble (2000, 2003, 2006) reported observing characteristic shifts in the understanding of modeling over the span of the elementary school grades, from an early emphasis on literal depictional forms, to representations that are progressively more symbolic and mathematically powerful. Diversity in representational and mathematical resources both accompanied and produced conceptual change. As children developed and used new mathematical means for characterizing growth, they understood biological change in increasingly dynamic ways. For example, once students understood the mathematics of ratio and changing ratios, they began to conceive of growth not as simple linear increase, but as a patterned rate of change. These transitions in conception and representation appeared to support each other, and they opened up new lines of inquiry. Children wondered whether plant growth was like animal growth, and whether the growth of yeast and bacteria on a Petri dish would show a pattern like the growth of a single plant. These forms of conceptual development required a context in which teachers systematically supported a restricted set of central ideas, building successively on earlier concepts over the grades of schooling.

Representational Systems That Support Modeling

The development of specific representational forms and notations, such as graphs, tables, computer programs, and mathematical expressions, is a critical part of engaging in mature forms of modeling. Mathematics, data and scale models, diagrams, and maps are particularly important for supporting science learning in grades K-8.

Mathematics

Mathematics and science are, of course, separate disciplines. Nevertheless, for the past 200 years, the steady press in science has been toward increasing quantification, visualization, and precision (Kline, 1980). Mathematics in all its forms is a symbol system that is fundamental to both expressing and understanding science. Often, expressing an idea mathematically results in noticing new patterns or relationships that otherwise would not be grasped. For example, elementary students studying the growth of organisms (plants, tobacco hornworms, populations of bacteria) noted that when they graphed changes in heights over the life span, all the organisms

studied produced an emergent S-shaped curve. However, such seeing depended on developing a “disciplined perception” (Stevens and Hall, 1998), a firm grounding in a Cartesian system. Moreover, the shape of the curve was determined in light of variation, accounted for by selecting and connecting midpoints of intervals that defined piece-wise linear segments. This way of representing typical growth was contentious, because some midpoints did not correspond to any particular case value. This debate was therefore a pathway toward the idealization and imagined qualities of the world necessary for adopting a modeling stance. The form of the growth curve was eventually tested in other systems, and its replications inspired new questions. For example, why would bacteria populations and plants be describable by the same growth curve? In this case and in others, explanatory models and data models mutually bootstrapped conceptual development (Lehrer and Schauble, 2002).

It is not feasible in this report to summarize the extensive body of research in mathematics education, but one point is especially critical for science education: the need to expand elementary school mathematics beyond arithmetic to include space and geometry, measurement, and data/ uncertainty. The National Council of Teachers of Mathematics standards (2000) has strongly supported this extension of early mathematics, based on their judgment that arithmetic alone does not constitute a sufficient mathematics education. Moreover, if mathematics is to be used as a resource for science, the resource base widens considerably with a broader mathematical base, affording students a greater repertoire for making sense of the natural world.

For example, consider the role of geometry and visualization in comparing crystalline structures or evaluating the relationship between the body weights and body structures of different animals. Measurement is a ubiquitous part of the scientific enterprise, although its subtleties are almost always overlooked. Students are usually taught procedures for measuring but are rarely taught a theory of measure. Educators often overestimate children’s understanding of measurement because measuring tools—like rulers or scales—resolve many of the conceptual challenges of measurement for children, so that they may fail to grasp the idea that measurement entails the iteration of constant units, and that these units can be partitioned. It is reasonably common, for example, for even upper elementary students who seem proficient at measuring lengths with rulers to tacitly hold the theory that measuring merely entails the counting of units between boundaries. If these students are given unconnected units (say, tiles of a constant length) and asked to demonstrate how to measure a length, some of them almost always place the units against the object being measured in such a way that the first and last tile are lined up flush with the end of the object measured. This arrangement often requires leaving spaces between units. Diagnosti-

cally, these spaces do not trouble a student who holds this “boundary-filling” conception of measurement (Lehrer, 2003; McClain et al., 1999).

Researchers agree that scientific thinking entails the coordination of theory with evidence (Klahr and Dunbar, 1988; Kuhn, Amsel, and O’Loughlin, 1988), but there are many ways in which evidence may vary in both form and complexity. Achieving this coordination therefore requires tools for structuring and interpreting data and error. Otherwise, students’ interpretation of evidence cannot be accountable. There have been many studies of students’ reasoning about data, variation, and uncertainty, conducted both by psychologists (Kahneman, Solvic, and Tversky, 1982; Konold, 1989; Nisbett et al., 1983) and by educators (Mokros and Russell, 1995; Pollatsek, Lima, and Well, 1981; Strauss and Bichler, 1988). Particularly pertinent here are studies that focus on data modeling (Lehrer and Romberg, 1996), that is, how reasoning with data is recruited as a way of investigating genuine questions about the world.

Data modeling is, in fact, what professionals do when they reason with data and statistics. It is central to a variety of enterprises, including engineering, medicine, and natural science. Scientific models are generated with acute awareness of their entailments for data, and data are recorded and structured as a way of making progress in articulating a scientific model or adjudicating among rival models. The tight relationship between model and data holds generally in domains in which inquiry is conducted by inscribing, representing, and mathematizing key aspects of the world (Goodwin, 2000; Kline, 1980; Latour, 1990).

Understanding the qualities and meaning of data may be enhanced if students spend as much attention on its generation as on its analysis. First and foremost, students need to grasp the notion that data are constructed to answer questions (Lehrer, Giles, and Schauble, 2002). The National Council of Teachers of Mathematics (2000) emphasizes that the study of data should be firmly anchored in students’ inquiry, so that they “address what is involved in gathering and using the data wisely” (p. 48). Questions motivate the collection of certain types of information and not others, and many aspects of data coding and structuring also depend on the question that motivated their collection. Defining the variables involved in addressing a research question, considering the methods and timing to collect data, and finding efficient ways to record it are all involved in the initial phases of data modeling. Debates about the meaning of an attribute often provoke questions that are more precise.

For example, a group of first graders who wanted to learn which student’s pumpkin was the largest eventually understood that they needed to agree

whether they were interested in the heights of the pumpkins, their circumferences, or their weights (Lehrer et al., 2001). Deciding what to measure is bound up with deciding how to measure. As the students went on to count the seeds in their pumpkins (they were pursuing a question about whether there might be relationship between pumpkin size and number of seeds), they had to make decisions about whether they would include seeds that were not full grown and what criteria would be used to decide whether any particular seed should be considered mature.

Data are inherently a form of abstraction: an event is replaced by a video recording, a sensation of heat is replaced by a pointer reading on a thermometer, and so on. Here again, the tacit complexity of tools may need to be explained. Students often have a fragile grasp of the relationship between the event of interest and the operation (hence, the output) of a tool, whether that tool is a microscope, a pan balance, or a “simple” ruler. Some students, for example, do not initially consider measurement to be a form of comparison and may find a balance a very confusing tool. In their mind, the number displayed on a scale is the weight of the object. If no number is displayed, weight cannot be found.

Once the data are recorded, making sense of them requires that they be structured. At this point, students sometimes discover that their data require further abstraction. For example, as they categorized features of self-portraits drawn by other students, a group of fourth graders realized that it would not be wise to follow their original plan of creating 23 categories of “eye type” for the 25 portraits that they wished to categorize (DiPerna, 2002). Data do not come with an inherent structure; rather, structure must be imposed (Lehrer, Giles, and Schauble, 2002). The only structure for a set of data comes from the inquirers’ prior and developing understanding of the phenomenon under investigation. He imposes structure by selecting categories around which to describe and organize the data.

Students also need to mentally back away from the objects or events under study to attend to the data as objects in their own right, by counting them, manipulating them to discover relationships, and asking new questions of already collected data. Students often believe that new questions can be addressed only with new data; they rarely think of querying existing data sets to explore questions that were not initially conceived when the data were collected (Lehrer and Romberg, 1996).

Finally, data are represented in various ways in order to see or understand general trends. Different kinds of displays highlight certain aspects of the data and hide others. An important educational agenda for students, one that extends over several years, is to come to understand the conventions and properties of different kinds of data displays. We do not review here the extensive literature on students’ understanding of different kinds of representational displays (tables, graphs of various kinds, distributions), but, for

purposes of science, students should not only understand the procedures for generating and reading displays, but they should also be able to critique them and to grasp the communicative advantages and disadvantages of alternative forms for a given purpose (diSessa, 2004; Greeno and Hall, 1997). The structure of the data will affect the interpretation. Data interpretation often entails seeking and confirming relationships in the data, which may be at varying levels of complexity. For example, simple linear relationships are easier to spot than inverse relationships or interactions (Schauble, 1990), and students often fail to entertain the possibility that more than one relationship may be operating.

The desire to interpret data may further inspire the creation of statistics, such as measures of center and spread. These measures are a further step of abstraction beyond the objects and events originally observed. Even primary grade students can learn to consider the overall shape of data displays to make interpretations based on the “clumps” and “holes” in the data. Students often employ multiple criteria when trying to identify a “typical value” for a set of data. Many young students tend to favor the mode and justify their choice on the basis of repetition—if more than one student obtained this value, perhaps it is to be trusted. However, students tend to be less satisfied with modes if they do not appear near the center of the data, and they also shy away from measures of center that do not have several other values clustered near them (“part of a clump”). Understanding the mean requires an understanding of ratio, and if students are merely taught to “average” data in a procedural way without having a well-developed sense of ratio, their performance notoriously tends to degrade into “average stew”—eccentric procedures for adding and dividing things that make no sense (Strauss and Bichler, 1988). With good instruction, middle and upper elementary students can simultaneously consider the center and the spread of the data. Students can also generate various forms of mathematical descriptions of error, especially in contexts of measurement, where they can readily grasp the relationships between their own participation in the act of measuring and the resulting variation in measures (Petrosino, Lehrer, and Schauble, 2003).

Scale Models, Diagrams, and Maps

Although data representations are central to science, they are not, of course, the only representations students need to use and understand. Perhaps the most easily interpretable form of representation widely used in science is scale models. Physical models of this kind are used in science education to make it possible for students to visualize objects or processes that are at a scale that makes their direct perception impossible or, alternatively, that permits them to directly manipulate something that otherwise

they could not handle. The ease or difficulty with which students understand these models depends on the complexity of the relationships being communicated. Even preschoolers can understand scale models used to depict location in a room (DeLoache, 2004). Primary grade students can pretty readily overcome the influence of the appearance of the model to focus on and investigate the way it functions (Penner et al., 1997), but middle school students (and some adults) struggle to work out the positional relationships of the earth, the sun, and the moon, which involves not only reconciling different perspectives with respect to perspective and frame (what one sees standing on the earth, what one would see from a hypothetical point in space), but also visualizing how these perspectives would change over days and months (see, for example, the detailed curricular suggestions at the web site http://www.wcer.wisc.edu/ncisla/muse/ ).

Frequently, students are expected to read or produce diagrams, often integrating the information from the diagram with information from accompanying text (Hegarty and Just, 1993; Mayer, 1993). The comprehensibility of diagrams seems to be governed less by domain-general principles than by the specifics of the diagram and its viewer. Comprehensibility seems to vary with the complexity of what is portrayed, the particular diagrammatic details and features, and the prior knowledge of the user.

Diagrams can be difficult to understand for a host of reasons. Sometimes the desired information is missing in the first place; sometimes, features of the diagram unwittingly play into an incorrect preconception. For example, it has been suggested that the common student misconception that the earth is closer to the sun in the summer than in the winter may be due in part to the fact that two-dimensional representations of the three-dimensional orbit make it appear as if the foreshortened orbit is indeed closer to the sun at some points than at others.

Mayer (1993) proposes three common reasons why diagrams mis-communicate: some do not include explanatory information (they are illustrative or decorative rather than explanatory), some lack a causal chain, and some fail to map the explanation to a familiar or recognizable context. It is not clear that school students misperceive diagrams in ways that are fundamentally different from the perceptions of adults. There may be some diagrammatic conventions that are less familiar to children, and children may well have less knowledge about the phenomena being portrayed, but there is no reason to expect that adult novices would respond in fundamentally different ways. Although they have been studied for a much briefer period of time, the same is probably true of complex computer displays.

Finally, there is a growing developmental literature on students’ understanding of maps. Maps can be particularly confusing because they preserve some analog qualities of the space being represented (e.g., relative position and distance) but also omit or alter features of the landscape in ways that

require understanding of mapping conventions. Young children often initially confuse maps of the landscape with pictures of objects in the landscape. It is much easier for youngsters to represent objects than to represent large-scale space (which is the absence of or frame for objects). Students also may struggle with orientation, perspective (the traditional bird’s eye view), and mathematical descriptions of space, such as polar coordinate representations (Lehrer and Pritchard, 2002; Liben and Downs, 1993).

CONCLUSIONS

There is a common thread throughout the observations of this chapter that has deep implications for what one expects from children in grades K-8 and how their science learning should be structured. In almost all cases, the studies converge to the position that the skills under study develop with age, but also that this development is significantly enhanced by prior knowledge, experience, and instruction.

One of the continuing themes evident from studies on the development of scientific thinking is that children are far more competent than first suspected, and likewise that adults are less so. Young children experiment, but their experimentation is generally not systematic, and their observations as well as their inferences may be flawed. The progression of ability is seen with age, but it is not uniform, either across individuals or for a given individual. There is variation across individuals at the same age, as well as variation within single individuals in the strategies they use. Any given individual uses a collection of strategies, some more valid than others. Discovering a valid strategy does not mean that an individual, whether a child or an adult, will use the strategy consistently across all contexts. As Schauble (1996, p. 118) noted:

The complex and multifaceted nature of the skills involved in solving these problems, and the variability in performance, even among the adults, suggest that the developmental trajectory of the strategies and processes associated with scientific reasoning is likely to be a very long one, perhaps even lifelong . Previous research has established the existence of both early precursors and competencies … and errors and biases that persist regardless of maturation, training, and expertise.

One aspect of cognition that appears to be particularly important for supporting scientific thinking is awareness of one’s own thinking. Children may be less aware of their own memory limitations and therefore may be unsystematic in recording plans, designs, and outcomes, and they may fail to consult such records. Self-awareness of the cognitive strategies available is also important in order to determine when and why to employ various strategies. Finally, awareness of the status of one’s own knowledge, such as

recognizing the distinctions between theory and evidence, is important for reasoning in the context of scientific investigations. This last aspect of cognition is discussed in detail in the next chapter.

Prior knowledge, particularly beliefs about causality and plausibility, shape the approach to investigations in multiple ways. These beliefs influence which hypotheses are tested, how experiments are designed, and how evidence is evaluated. Characteristics of prior knowledge, such as its type, strength, and relevance, are potential determinants of how new evidence is evaluated and whether anomalies are noticed. Knowledge change occurs as a result of the encounter.

Finally, we conclude that experience and instruction are crucial mediators of the development of a broad range of scientific skills and of the degree of sophistication that children exhibit in applying these skills in new contexts. This means that time spent doing science in appropriately structured instructional frames is a crucial part of science education. It affects not only the level of skills that children develop, but also their ability to think about the quality of evidence and to interpret evidence presented to them. Students need instructional support and practice in order to become better at coordinating their prior theories and the evidence generated in investigations. Instructional support is also critical for developing skills for experimental design, record keeping during investigations, dealing with anomalous data, and modeling.

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What is science for a child? How do children learn about science and how to do science? Drawing on a vast array of work from neuroscience to classroom observation, Taking Science to School provides a comprehensive picture of what we know about teaching and learning science from kindergarten through eighth grade. By looking at a broad range of questions, this book provides a basic foundation for guiding science teaching and supporting students in their learning. Taking Science to School answers such questions as:

• When do children begin to learn about science? Are there critical stages in a child's development of such scientific concepts as mass or animate objects?
• What role does nonschool learning play in children's knowledge of science?
• How can science education capitalize on children's natural curiosity?
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• How can teachers be taught to teach science?

The book also provides a detailed examination of how we know what we know about children's learning of science—about the role of research and evidence. This book will be an essential resource for everyone involved in K-8 science education—teachers, principals, boards of education, teacher education providers and accreditors, education researchers, federal education agencies, and state and federal policy makers. It will also be a useful guide for parents and others interested in how children learn.

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Scientific Reasoning

Scientific reasoning is the foundation supporting the entire structure of logic underpinning scientific research.

This article is a part of the guide:

• Falsifiability
• Inductive Reasoning
• Deductive Reasoning
• Hypothetico-Deductive Method
• Testability

Browse Full Outline

• 1 Scientific Reasoning
• 2.1 Falsifiability
• 2.2 Verification Error
• 2.3 Testability
• 2.4 Post Hoc Reasoning
• 3 Deductive Reasoning
• 4.1 Raven Paradox
• 5 Causal Reasoning
• 6 Abductive Reasoning
• 7 Defeasible Reasoning

It is impossible to explore the entire process, in any detail, because the exact nature varies between the various scientific disciplines.

Despite these differences, there are four basic foundations that underlie the idea, pulling together the cycle of scientific reasoning.

Observation

Most research has real world observation as its initial foundation. Looking at natural phenomena is what leads a researcher to question what is going on, and begin to formulate scientific questions and hypotheses .

Any theory, and prediction, will need to be tested against observable data.

Theories and Hypotheses

This is where the scientist proposes the possible reasons behind the phenomenon, the laws of nature governing the behavior.

Scientific research uses various scientific reasoning processes to arrive at a viable research problem and hypothesis. A theory is generally broken down into individual hypotheses, or problems, and tested gradually.

Predictions

A good researcher has to predict the results of their research, stating their idea about the outcome of the experiment, often in the form of an alternative hypothesis .

Scientists usually test the predictions of a theory or hypothesis, rather than the theory itself. If the predictions are found to be incorrect, then the theory is incorrect, or in need of refinement.

Data is the applied part of science, and the results of real world observations are tested against the predictions.

If the observations match the predictions, the theory is strengthened. If not, the theory needs to be changed. A range of statistical tests is used to test predictions, although many observation based scientific disciplines cannot use statistics .

The Virtuous Cycle

This process is cyclical: as experimental results accept or refute hypotheses, these are applied to the real world observations, and future scientists can build upon these observations to generate further theories.

Differences

Whilst the scientific reasoning process is a solid foundation to the scientific method , there are variations between various disciplines.

For example, social science, with its reliance on case studies , tends to emphasis the observation phase, using this to define research problems and questions.

Physical sciences, on the other hand, tend to start at the theory stage, building on previous studies, and observation is probably the least important stage of the cycle.

Many theoretical physicists spend their entire career building theories, without leaving their office. Observation is, however, always used as the final proof.

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Martyn Shuttleworth (May 7, 2008). Scientific Reasoning. Retrieved Aug 08, 2024 from Explorable.com: https://explorable.com/scientific-reasoning

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The Nature and Development of Scientific Reasoning: A Synthetic View

• Published: September 2004
• Volume 2 , pages 307–338, ( 2004 )

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• Antone E. Lawson 1  

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This paper presents a synthesis of what is currently known about the nature and development of scientific reasoning and why it plays a central role in acquiring scientific literacy. Science is viewed as a hypothetico-deductive (HD) enterprise engaging in the generation and test of alternative explanations. Explanation generation and test requires the use of several key reasoning patterns and sub-patterns. Reasoning at the highest level is complicated by the fact that scientific explanations generally involve the postulation of non-perceptible entities, thus arguments used in their test require sub-arguments to link the postulate under test with its deduced consequence. Science is HD in nature because this is how the brain spontaneously processes information whether it basic visual recognition, every-day descriptive and causal hypothesis testing, or advanced theory testing. The key point in terms of complex HD arguments is that if sufficient chunking of concepts and/or reasoning sub-patterns have not occurred, then one’s attempt to construct and maintain such arguments in working memory and use them to draw conclusions and construct concepts will “fall apart.” Thus, the conclusions and concepts will be “lost.” Consequently, teachers must know what students bring with them in terms of their stages of intellectual development (i.e., preoperational, concrete, formal, or post-formal) and subject-specific declarative knowledge. Effective instruction mirrors the practice of science where students confront puzzling observations and then personally participate in the explanation generation and testing process – a process in which some of their ideas are contradicted by the evidence and by the arguments of others.

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Lawson, A.E. The Nature and Development of Scientific Reasoning: A Synthetic View. Int J Sci Math Educ 2 , 307–338 (2004). https://doi.org/10.1007/s10763-004-3224-2

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Home > Books > Current Topics in Children's Learning and Cognition

The Emergence of Scientific Reasoning

Submitted: 31 March 2012 Published: 14 November 2012

DOI: 10.5772/53885

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Bradley j. morris.

• Kent State University, USA

Steve Croker

• Illinois State University, USA

Corinne Zimmerman

Amy m. masnick.

• Hofstra University, USA

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1. Introduction

Scientific reasoning encompasses the reasoning and problem-solving skills involved in generating, testing and revising hypotheses or theories, and in the case of fully developed skills, reflecting on the process of knowledge acquisition and knowledge change that results from such inquiry activities. Science, as a cultural institution, represents a “hallmark intellectual achievement of the human species” and these achievements are driven by both individual reasoning and collaborative cognition ( Feist, 2006 , p. ix).

Our goal in this chapter is to describe how young children build from their natural curiosity about their world to having the skills for systematically observing, predicting, and understanding that world. We suggest that scientific reasoning is a specific type of intentional information seeking, one that shares basic reasoning mechanisms and motivation with other types of information seeking ( Kuhn, 2011a ). For example, curiosity is a critical motivational component that underlies information seeking ( Jirout & Klahr, 2012 ), yet only in scientific reasoning is curiosity sated by deliberate data collection and formal analysis of evidence. In this way, scientific reasoning differs from other types of information seeking in that it requires additional cognitive resources as well as an integration of cultural tools. To that end, we provide an overview of how scientific reasoning emerges from the interaction between internal factors (e.g., cognitive and metacognitive development) and cultural and contextual factors.

The current state of empirical research on scientific reasoning presents seemingly contradictory conclusions. Young children are sometimes deemed “little scientists” because they appear to have abilities that are used in formal scientific reasoning (e.g., causal reasoning; Gopnik et al., 2004 ). At the same time, many studies show that older children (and sometimes adults) have difficulties with scientific reasoning. For example, children have difficulty in systematically designing controlled experiments, in drawing appropriate conclusions based on evidence, and in interpreting evidence (e.g., Croker, 2012 ; Chen & Klahr, 1999 ; Kuhn, 1989 ; Zimmerman, 2007 ).

In the following account, we suggest that despite the early emergence of many of the precursors of skilled scientific reasoning, its developmental trajectory is slow and requires instruction, support, and practice. In Section 2of the chapter, we discuss cognitive and metacognitive factors. We focus on two mechanisms that play a critical role in all cognitive processes (i.e., encoding and strategy acquisition/selection). Encoding involves attention to relevant information; it is foundational in all reasoning. Strategy use involves intentional approaches to seeking new knowledge and synthesizing existing knowledge. These two mechanisms are key components for any type of intentional information seeking yet follow a slightly different development trajectory in the development of scientific reasoning skills. We then discuss the analogous development of metacognitive awareness of what is being encoded, and metastrategic skills for choosing and deploying hypothesis testing and inference strategies. In Section 3, we describe the role of contextual factors such as direct and scaffolded instruction, and the cultural tools that support the development of the cognitive and metacognitive skills required for the emergence of scientific thinking.

2. The development of scientific reasoning

Effective scientific reasoning requires both deductive and inductive skills. Individuals must understand how to assess what is currently known or believed, develop testable questions, test hypotheses, and draw appropriate conclusions by coordinating empirical evidence and theory. Such reasoning also requires the ability to attend to information systematically and draw reasonable inferences from patterns that are observed. Further, it requires the ability to assess one’s reasoning at each stage in the process. Here, we describe some of the key issues in developing these cognitive and metacognitive scientific reasoning skills.

2.1. Cognitive processes and mechanisms

The main task for developmental researchers is to explain how children build on their intuitive curiosity about the world to become skilled scientific reasoners. Curiosity , defined as “the threshold of desired uncertainty in the environment that leads to exploratory behavior” ( Jirout & Klahr, 2012 , p. 150), will lead to information seeking. Information seeking activates a number of basic cognitive mechanisms that are used to extract (encode) information from the environment and then children (and adults) can act on this information in order to achieve a goal (i.e., use a strategy; Klahr, 2001; Kuhn, 2010 ). We turn our discussion to two such mechanisms and discuss how these mechanisms underlie the development of a specific type of information seeking: scientific reasoning.

A mechanistic account of the development of scientific reasoning includes information about the processes by which this change occurs, and how these processes lead to change over time (Klahr, 2001). Mechanisms can be described at varying levels (e.g., neurological, cognitive, interpersonal) and over different time scales. For example, neurological mechanisms (e.g., inhibition) operate at millisecond time scales (Burlea, Vidala, Tandonneta, & Hasbroucq, 2004) while learning mechanisms may operate over the course of minutes (e.g., inhibiting irrelevant information during problem solving; Becker, 2010 ). Many of the cognitive processes and mechanisms that account for learning and for problem solving across a variety of domains are important to the development of scientific reasoning skills and science knowledge acquisition. Many cognitive mechanisms have been identified as underlying scientific reasoning and other high-level cognition (e.g., analogy, statistical learning, categorization, imitation, inhibition; Goswami, 2008 ). However, due to space limitations we focus on what we argue are the two most critical mechanisms – encoding and strategy development –to illustrate the importance of individual level cognitive abilities.

2.1.1. Encoding

Encoding is the process of representing information and its context in memory as a result of attention to stimuli ( Chen, 2007 ; Siegler, 1989 ). As such, it is a central mechanism in scientific reasoning because we must represent information before we can reason about it, and the quality and process of representation can affect reasoning. Importantly, there are significant developmental changes in the ability to encode the relevant features that will lead to sound reasoning and problem solving ( Siegler, 1983 ; 1985 ). Encoding abilities improve with the acquisition of encoding strategies and with increases in children’s domain knowledge ( Siegler, 1989 ). Young children often encode irrelevant features due to limited domain knowledge (Gentner, Loewenstein, & Thompson, 2003). For example, when solving problems to make predictions about the state of a two-arm balance beam (i.e., tip left, tip right, or balance), children often erroneously encode distance to the fulcrum and amount of weight as a single factor, decreasing the likelihood of producing a correct solution (which requires weight and distance to be encoded and considered separately as causal factors, while recognizing non-causal factors such as color; Amsel, Goodman, Savoie, & Clark, 1996; Siegler, 1983 ). Increased domain knowledge helps children assess more effectively what information is and is not necessary to encode. Further, children’s encoding often improves with the acquisition of encoding strategies. For example, if a child is attempting to recall the location of an item in a complex environment, she may err in encoding only the features of the object itself without encoding its relative position. With experience, she may encode the relations between the target item and other objects (e.g., the star is in front of the box), a strategy known as cue learning. Encoding object position and relative position increases the likelihood of later recall and is an example of how encoding better information is more important than simply encoding more information ( Chen, 2007 ; Newcombe & Huttenlocher, 2000 ).

Effective encoding is dependent on directing attention to relevant information, which in turn leads to accurate representations that can guide reasoning. Across a variety of tasks, experts are more likely to attend to critical elements in problem solving, and less likely to attend to irrelevant information, compared to novices ( Gobet, 2005 ). Domain knowledge plays an important role in helping to guide attention to important features. Parents often direct a child’s attention to critical problem features during problem solving. For example, a parent may keep track of which items have been counted in order to help a child organize counting ( Saxe, Guberman, & Gearhart, 1987 ). Instructional interventions in which children were directed towards critical elements in problem solving improved their attention to these features ( Kloos & VanOrden, 2005 ). Although domain knowledge is helpful in directing attention to critical features, it may sometimes limit novel reasoning in a domain and limit the extent to which attention is paid to disconfirming evidence ( Li & Klahr, 2006 ). Finally, self-generated activity improves encoding. Self-generation of information from memory, rather than passive attention, is associated with more effective encoding because it recruits greater attentional resources than passive encoding ( Chi, 2009 ).

2.1.2. Strategy development

Strategies are sequences of procedural actions used to achieve a goal ( Siegler, 1996 ). In the context of scientific reasoning, strategies are the steps that guide children from their initial state (e.g., a question about the effects of weight and distance in balancing a scale) to a goal state (e.g., understanding the nature of the relationship between variables). We will briefly examine two components of strategy development: strategy acquisition and strategy selection . Strategies are particularly important in the development of scientific reasoning. Children often actively explore objects in a manner that is like hypothesis testing; however, these exploration strategies are not systematic investigations in which variables are manipulated and controlled as in formal hypothesis-testing strategies ( Klahr, 2000 ). The acquisition of increasingly optimal strategies for hypothesis testing, inference, and evidence evaluation leads to more effective scientific reasoning that allows children to construct more veridical knowledge.

New strategies are added to the repertoire of possible strategies through discovery, instruction, or other social interactions ( Chen, 2007 ; Gauvain, 2001 ; Siegler, 1996 ). There is evidence that children can discover strategies on their own ( Chen, 2007 ). Children often discover new strategies when they experience an insight into a new way of solving a familiar problem. For example, 10- and 11-year-olds discovered new strategies for evaluating causal relations between variables in a computerized task only after creating different cars (e.g., comparing the effects of engine size) and testing them ( Schauble, 1990 ). Similarly, when asked to determine the cause of a chemical reaction, children discovered new experimentation strategies only after several weeks ( Kuhn & Phelps, 1982 ). Over time, existing strategies may be modified to reduce time and complexity of implementation (e.g., eliminating redundant steps in a problem solving sequence; Klahr, 1984 ). For example, determining causal relations among variables requires more time when experimentation is unsystematic. In order to identify which variables resulted in the fastest car, children often constructed up to 25 cars, whereas an adult scientist identified the fastest car after constructing only seven cars ( Schauble, 1990 ).

Children also gain new strategies through social interaction, by being explicitly taught a strategy, imitating a strategy, or by collaborating in problem solving ( Gauvain, 2001 ). For example, when a parent asks a child questions about events in a photograph, the parent evokes memories of the event and helps to structure the child’s understanding of the depicted event, a process called conversational remembering ( Middleton, 1997 ). Conversational remembering improves children’s recall of events and often leads to children spontaneously using this strategy. Parent conversations about event structures improved children’s memory for these structures; for example, questions about a child’s day at school help to structure this event and improved recall ( Nelson, 1996 ). Children also learn new strategies by solving problems cooperatively with adults. In a sorting task, preschool children were more likely to improve their classification strategies after working with their mothers ( Freund, 1990 ). Further, children who worked with their parents on a hypothesis-testing task were more likely to identify causal variables than children who worked alone because parents helped children construct valid experiments, keep data records, and repeat experiments ( Gleason & Schauble, 2000 ).

Children also acquire strategies by interacting with an adult modeling a novel strategy. Middle-school children acquired a reading comprehension strategy (e.g., anticipating the ending of a story) after seeing it modeled by their teacher ( Palinscar, Brown, & Campione, 1993 ). Additionally, children can acquire new strategies from interactions with other children. Monitoring other children during problem solving improves a child’s understanding of the task and appears to improve how they evaluate their own performance ( Brownell & Carriger, 1991 ). Elementary school children who collaborated with other students to solve the balance-scale task outperformed students who worked alone ( Pine & Messer, 1998 ). Ten-year-olds working in dyads were more likely to discuss their strategies than children working alone and these discussions were associated with generating better hypotheses than children working alone ( Teasley, 1995 ).

More than one strategy may be useful for solving a problem, which requires a means to select among candidate strategies. One suggestion is that this process occurs by adaptive selection. In adaptive selection, strategies that match features of the problem are candidates for selection. One component of selection is that newer strategies tend to have a slightly higher priority for use when compared to older strategies ( Siegler, 1996 ). Successful selection is made on the basis of the effectiveness of the strategy and its cost (e.g., speed), and children tend to choose the fastest, most accurate strategy available (i.e., the most adaptive strategy).

Cognitive mechanisms provide the basic investigation and inferential tools used in scientific reasoning. The ability to reason about knowledge and the means for obtaining and evaluating knowledge provide powerful tools that augment children’s reasoning. Metacognitive abilities such as these may help explain some of the discrepancies between early scientific reasoning abilities and limitations in older children, as well as some of the developmental changes in encoding and strategy use.

2.2. Metacognitive and metastrategic processes

Sodian, Zaitchik, and Carey (1991 ) argue that two basic skills related to early metacognitive acquisitions are needed for scientific reasoning. First, children need to understand that inferences can be drawn from evidence. The theory of mind literature (e.g., Wellman, Cross, & Watson, 2001 ) suggests that it is not until the age of 4 that children understand that beliefs and knowledge are based on perceptual experience (i.e., evidence). As noted earlier, experimental work demonstrates that preschoolers can use evidence to make judgments about simple causal relationships (Gopnik, Sobel, Schulz, & Glymour, 2001; Schulz & Bonawitz, 2007; Schulz & Gopnik, 2004 ; Schulz, Gopnik,& Glymour, 2007). Similarly, several classic studies show that children as young as 6 can succeed in simple scientific reasoning tasks. Children between 6 and 9 can discriminate between a conclusive and an inclusive test of a simple hypothesis ( Sodian et al., 1991 ). Children as young as 5 can form a causal hypothesis based on a pattern of evidence, and even 4-year-olds seem to understand some of the principles of causal reasoning (Ruffman, Perner, Olson, & Doherty, 1993).

Second, according to Sodian et al. (1991 ), children need to understand that inference is itself a mechanism with which further knowledge can be acquired. Four-year-olds base their knowledge on perceptual experiences, whereas 6-year-olds understand that the testimony of others can also be used in making inferences ( Sodian & Wimmer, 1987 ). Other research suggests that children younger than 6 can make inferences based on testimony, but in very limited circumstances ( Koenig, Clément, & Harris, 2004 ). These findings may explain why, by the age of 6, children are able to succeed on simple causal reasoning, hypothesis testing, and evidence evaluation tasks.

Research with older children, however, has revealed that 8- to 12-year-olds have limitations in their abilities to (a) generate unconfounded experiments, (b) disconfirm hypotheses, (c) keep accurate and systematic records, and (d) evaluate evidence ( Klahr, Fay, & Dunbar, 1993 ; Kuhn, Garcia-Mila, Zohar, & Andersen, 1995; Schauble, 1990 , 1996 ; Zimmerman, Raghavan, & Sartoris, 2003 ). For example, Schauble (1990 ) presented children aged 9-11 with a computerized task in which they had to determine which of five factors affect the speed of racing cars. Children often varied several factors at once (only 22% of the experiments were classified as valid) and they often drew conclusions consistent with belief rather than the evidence generated. They used a positive test strategy, testing variables believed to influence speed (e.g., engine size) and not testing those believed to be non-causal (e.g., color). Some children recorded features without outcomes, or outcomes without features, but most wrote down nothing at all, relying on memory for details of experiments carried out over an eight-week period.

Although the performance differences between younger and older children may be interpreted as potentially contradictory, the differing cognitive and metacognitive demands of tasks used to study scientific reasoning at different ages may account for some of the disconnect in conclusions. Even though the simple tasks given to preschoolers and young children require them to understand evidence as a source of knowledge, such tasks require the cognitive abilities of induction and pattern recognition, but only limited metacognitive abilities. In contrast, the tasks used to study the development of scientific reasoning in older children (and adults) are more demanding and focused on hypothetico-deductive reasoning; they include more variables, involve more complex causal structures, require varying levels of domain knowledge, and are negotiated across much longer time scales. Moreover, the tasks given to older children and adults involve the acquisition, selection, and coordination of investigation strategies, combining background knowledge with empirical evidence. The results of investigation activities are then used in the acquisition, selection, and coordinationof evidence evaluation and inference strategies. With respect to encoding, increases in task complexity require attending to more information and making judgments about which features are relevant. This encoding happens in the context of prior knowledge and, in many cases, it is also necessary to inhibit prior knowledge (Zimmerman & Croker, in press).

Sodian and Bullock (2008 ) also argue that mature scientific reasoning involves the metastrategic process of being able to think explicitly about hypotheses and evidence, and that this skill is not fully mastered until adolescence at the very earliest. According to Amsel et al. (2008 ), metacognitive competence is important for hypothetical reasoning. These conclusions are consistent with Kuhn’s (1989 , 2005 , 2 011a ) argument that the defining feature of scientific thinking is the set of cognitive and metacognitive skills involved in differentiating and coordinating theory and evidence. Kuhn argues that the effective coordination of theory and evidence depends on three metacognitive abilities: (a) The ability to encode and represent evidence and theory separately, so that relations between them can be recognized; (b) the ability to treat theories as independent objects of thought (i.e., rather than a representation of “the way things are”); and (c) the ability to recognize that theories can be false, setting aside the acceptance of a theory so evidence can be assessed to determine the veridicality of a theory. When we consider these cognitive and metacognitive abilities in the larger social context, it is clear that skills that are highly valued by the scientific community may be at odds with the cultural and intuitive views of the individual reasoner ( Lemke, 2001 ). Thus, it often takes time for conceptual change to occur; evidence is not just evaluated in the context of the science investigation and science classroom, but within personal and community values. Conceptual change also takes place in the context of an individual’s personal epistemology, which can undergo developmental transitions (e.g., Sandoval, 2005 ).

2.2.1. Encoding and strategy use

Returning to the encoding and retrieval of information relevant to scientific reasoning tasks, many studies demonstrate that both children and adults are not always aware of their memory limitations while engaged in investigation tasks (e.g., Carey, Evans, Honda, Jay, & Unger, 1989; Dunbar & Klahr, 1989 ; Garcia-Mila & Andersen, 2007 ; Gleason & Schauble, 2000 ; Siegler & Liebert, 1975 ; Trafton & Trickett, 2001 ). Kanari and Millar (2004 ) found that children differentially recorded the results of experiments, depending on familiarity or strength of prior beliefs. For example, 10- to 14-year-olds recorded more data points when experimenting with unfamiliar items (e.g., using a force-meter to determine the factors affecting the force produced by the weight and surface area of boxes) than with familiar items (e.g., using a stopwatch to experiment with pendulums). Overall, children are less likely than adults to record experimental designs and outcomes, or to review notes they do keep, despite task demands that clearly necessitate a reliance on external memory aids.

Children are often asked to judge their memory abilities, and memory plays an important role in scientific reasoning. Children’s understanding of memory as a fallible process develops over middle childhood ( Jaswal & Dodson, 2009 ; Kreuzer, Leonard, & Flavell, 1975). Young children view all strategies on memory tasks as equally effective, whereas 8- to 10-year-olds start to discriminate between strategies, and 12-year-olds know which strategies work best ( Justice, 1986 ; Schneider, 1986 ). The development of metamemory continues through adolescence ( Schneider, 2008 ), so there may not be a particular age that memory and metamemory limitations are no longer a consideration for children and adolescents engaged in complex scientific reasoning tasks. However, it seems likely that metamemory limitations are more profound for children under 10-12 years.

Likewise, the acquisition of other metacognitive and metastrategic skills is a gradual process. Early strategies for coordinating theory and evidence are replaced with better ones, but there is not a stage-like change from using an older strategy to a newer one. Multiple strategies are concurrently available so the process of change is very much like Siegler’s (1996 ) overlapping waves model ( Kuhn et al., 1995 ). However, metastrategic competence does not appear to routinely develop in the absence of instruction. Kuhn and her colleagues have incorporated the use of specific practice opportunities and prompts to help children develop these types of competencies. For example, Kuhn, Black, Keselman, and Kaplan (2000) incorporated performance-level practice and metastrategic-level practice for sixth- to eighth-grade students. Performance-level exercise consisted of standard exploration of the task environment, whereas metalevel practice consisted of scenarios in which two individuals disagreed about the effect of a particular feature in a multivariable situation. Students then evaluated different strategies that could be used to resolve the disagreement. Such scenarios were provided twice a week during the course of ten weeks. Although no performance differences were found between the two types of practice with respect to the number of valid inferences, there were more sizeable differences in measures of understanding of task objectives and strategies (i.e., metastrategic understanding).

Similarly, Zohar and Peled (2008 ) focused instruction in the control-of-variables strategy (CVS) on metastrategic competence. Fifth-graders were given a computerized task in which they had to determine the effects of five variables on seed germination. Students in the control group were taught about seed germination, and students in the experimental group were given a metastrategic knowledge intervention over several sessions. The intervention consisted of describing CVS, discussing when it should be used, and discussing what features of a task indicate that CVS should be used. A second computerized task on potato growth was used to assess near transfer. A physical task in which participants had to determine which factors affect the distance a ball will roll was used to assess far transfer. The experimental group showed gains on both the strategic and the metastrategic level. The latter was measured by asking participants to explain what they had done. These gains were still apparent on the near and far transfer tasks when they were administered three months later. Moreover, low-academic achievers showed the largest gains. It is clear from these studies that although meta-level competencies may not develop routinely, they can certainly be learned via explicit instruction.

Metacognitive abilities are necessary precursors to sophisticated scientific thinking, and represent one of the ways in which children, adults, and professional scientists differ. In order for children’s behavior to go beyond demonstrating the correctness of one’s existing beliefs (e.g., Dunbar & Klahr, 1989 ) it is necessary for meta-level competencies to be developed and practiced ( Kuhn, 2005 ). With metacognitive control over the processes involved, children (and adults) can change what they believe based on evidence and, in doing so, are aware not only that they are changing a belief, but also know why they are changing a belief. Thus, sophisticated reasoning involves both the use of various strategies involved in hypothesis testing, induction, inference, and evidence evaluation, and a meta-level awareness of when, how, and why one should engage in these strategies.

3. Scientific reasoning in context

Much of the existing laboratory work on the development of scientific thinking has not overtly acknowledged the role of contextual factors. Although internal cognitive and metacognitive processes have been a primary focus of past work, and have helped us learn tremendously about the processes of scientific thinking, we argue that many of these studies focused on individual cognition have, in fact, included both social factors (in the form of, for example, collaborations with other students, or scaffolds by parents or teachers) and cultural tools that support scientific reasoning.

3.1. Instructional and peer support: The role of others in supporting cognitive development

Our goal in this section is to re-examine our two focal mechanisms (i.e., encoding and strategy) and show how the development of these cognitive acquisitions and metastrategic control of them are facilitated by both the social and physical environment.

3.1.1. Encoding

Children must learn to encode effectively, by knowing what information is critical to pay attention to. They do so in part with the aid of their teachers, parents, and peers. Once school begins, teachers play a clear role in children’s cognitive development. An ongoing debate in the field of science education concerns the relative value of having children learn and discover how the world works on their own (often called “discovery learning”) and having an instructor guide the learning more directly (often called “direct instruction”). Different researchers interpret these labels in divergent ways, which adds fuel to the debate (see e.g., Bonawitz et al., 2011 ; Hmelo-Silver, Duncan, & Chinn, 2007 ; Kirshner, Sweller, & Clark, 2006; Klahr, 2010 ; Mayer, 2004 ; Schmidt, Loyens, van Gog, & Paas, 2007 ). Regardless of definitions, though, this issue illustrates the core idea that learning takes place in a social context, with guidance that varies from minimal to didactic.

Specifically, this debate is about the ideal role for adults in helping children to encode information. In direct instruction, there is a clear role for a teacher, often actively pointing out effective examples as compared to ineffective ones, or directly teaching a strategy to apply to new examples. And, indeed, there is evidence that more direct guidance to test variables systematically can help students in learning, particularly in the ability to apply their knowledge to new contexts (e.g., Klahr & Nigam, 2004 ; Lorch et al., 2010 ; Strand-Cary & Klahr, 2008 ). There is also evidence that scaffolded discovery learning can be effective (e.g., Alfieri, Brooks, Adrich, & Tenenbaum, 2011). Those who argue for discovery learning often do so because they note that pedagogical approaches commonly labeled as “discovery learning,” such as problem-based learning and inquiry learning, are in fact highly scaffolded, providing students with a structure in which to explore ( Alfieri et al., 2011 ; Hmelo-Silver et al., 2007 ; Schmidt et al., 2007 ). Even in microgenetic studies in which children are described as engaged in “self-directed learning,” researchers ask participants questions along that way that serve as prompts, hints, dialogue, and scaffolds that facilitate learning ( Klahr & Carver, 1995 ). What there appears to be little evidence for is “pure discovery learning” in which students are given little or no guidance and expected to discover rules of problem solving or other skills on their own ( Alfieri et al., 2011 ; Mayer, 2004 ). Thus, it is clear that formal education includes a critical role for a teacher to scaffold children’s scientific reasoning.

A common goal in science education is to correct the many misconceptions students bring to the classroom. Chinn and Malhotra (2002 ) examined the role of encoding evidence, interpreting evidence, generalization, and retention as possible impediments to correcting misconceptions. Over four experiments, they concluded that the key difficulty faced by children is in making accurate observations or properly encoding evidence that does not match prior beliefs. However, interventions involving an explanation of what scientists expected to happen (and why) were very effective in mediating conceptual change when encountering counterintuitive evidence. That is, with scaffolds, children made observations independent of theory, and changed their beliefs based on observed evidence. For example, the initial belief that a thermometer placed inside a sweater would display a higher temperature than a thermometer outside a sweater was revised after seeing evidence that disconfirmed this belief and hearing a scientist’s explanation that the temperature would be the same unless there was something warm inside the sweater. Instructional supports can play a crucial role in improving the encoding and observational skills required for reasoning about science.

In laboratory studies of reasoning, there is direct evidence of the role of adult scaffolding. Butler and Markman (2012a ) demonstrate that in complex tasks in which children need to find and use evidence, causal verbal framing (i.e., asking whether one event caused another) led young children to more effectively extract patterns from scenes they observed, which in turn led to more effective reasoning. In further work demonstrating the value of adult scaffolding in children’s encoding, Butler and Markman (2012b ) found that by age 4, children are much more likely to explore and make inductive inferences when adults intentionally try to teach something than when they are shown an “accidental” effect.

3.1.2. Strategy development and use

As discussed earlier in this chapter, learning which strategies are available and useful is a fundamental part of developing scientific thinking skills. Much research has looked at the role of adults in teaching strategies to children in both formal (i.e., school) and informal settings (e.g., museums, home; Fender & Crowley, 2007 ; Tenenbaum, Rappolt-Schlichtmann, & Zanger, 2004).

A central task in scientific reasoning involves the ability to design controlled experiments. Chen and Klahr (1999 ) found that directly instructing 7- to 10-year-old children in the strategies for designing unconfounded experiments led to learning in a short time frame. More impressively, the effectiveness of the training was shown seven months later, when older students given the strategy training were much better at correctly distinguishing confounded and unconfounded designs than those not explicitly trained in the strategy. In another study exploring the role of scaffolded strategy instruction, Kuhn and Dean (2005 ) worked with sixth graders on a task to evaluate the contribution of different factors to earthquake risk. All students given the suggestion to focus attention on just one variable were able to design unconfounded experiments, compared to only 11% in the control group given their typical science instruction. This ability to design unconfounded experiments increased the number of valid inferences in the intervention group, both immediately and three months later. Extended engagement alone resulted in minimal progress, confirming that even minor prompts and suggestions represent potentially powerful scaffolds. In yet another example, when taught to control variables either with or without metacognitive supports, 11-year-old children learned more when guided in thinking about how to approach each problem and evaluate the outcome ( Dejonckheere, Van de Keere, & Tallir, 2011 ). Slightly younger children did not benefit from the same manipulation, but 4- to 6-year-olds given an adapted version of the metacognitive instruction were able to reason more effectively about simpler physical science tasks than those who had no metacognitive supports (Dejonckheere, Van de Keere, & Mestdagh, 2010).

3.2. Cultural tools that support scientific reasoning

Clearly, even with the number of studies that have focused on individual cognition, a picture is beginning to emerge to illustrate the importance of social and cultural factors in the development of scientific reasoning. Many of the studies we describe highlight that even “controlled laboratory studies” are actually scientific reasoning in context. To illustrate, early work by Siegler and Liebert (1975 ) includes both an instructional context (a control condition plus two types of instruction: conceptual framework , and conceptual framework plus analogs ) and the role of cultural supports. In addition to traditional instruction about variables (factors, levels, tree diagrams), one type of instruction included practice with analogous problems. Moreover, 10- and 13-year-olds were provided with paper and pencil to keep track of their results. A key finding was that record keeping was an important mediating factor in success. Children who had the metacognitive awareness of memory limitations and therefore used the provided paper for record keeping were more successful at producing all possible combinations necessary to manipulate and isolate variables to test hypotheses.

3.2.1. Cultural resources to facilitate encoding and strategy use

The sociocultural perspective highlights the role that language, speech, symbols, signs, number systems, objects, and tools play in individual cognitive development ( Lemke, 2001 ). As highlighted in previous examples, adult and peer collaboration, dialogue, and other elements of the social environment are important mediators. In this section, we highlight some of the verbal, visual, and numerical elements of the physical context that support the emergence of scientific reasoning.

Most studies of scientific reasoning include some type of verbal and pictorial representation as an aid to reasoning. As encoding is the first step in solving problems and reasoning, the use of such supports reduces cognitive load. In studies of hypothesis testing strategies with children (e.g., Croker & Buchanan, 2011 ; Tschirgi, 1980 ), for example, multivariable situations are described both verbally and with the help of pictures that represent variables (e.g., type of beverage), levels of the variable (e.g., cola vs. milk), and hypothesis-testing strategies (see Figure 1 , panel A). In classic work by Kuhn, Amsel, and O’Loughlin (1988 ), a picture is provided that includes the outcomes (children depicted as healthy or sick) along with the levels of four dichotomous variables (e.g., orange/apple, baked potato/French fries, see Kuhn et al., 1988 , pp. 40-41). In fact, most studies that include children as participants provide pictorial supports (e.g., Ruffman et al., 1993 ; Koerber, Sodian, Thoermer, & Nett, 2005). Even at levels of increasing cognitive development and expertise, diagrams and visual aids are regularly used to support reasoning (e.g., Schunn & Dunbar, 1996 ; Trafton & Trickett, 2001 ; Veermans, van Joolingen, & de Jong, 2006).

Panel A illustrates the type of pictorial support that accompanies the verbal description of a hypothesis-testing task (from Croker & Buchanan, 2011 ). Panel B shows an example of a physical apparatus (from Triona & Klahr, 2007 ). Panel C shows a screenshot from an intelligent tutor designed to teach how to control variables in experimental design ( Siler & Klahr, 2012 ; see http://tedserver.psy.cmu.edu/demo/ted4.html, for a demonstration of the tutor).

Various elements of number and number systems are extremely important in science. Sophisticated scientific reasoning requires an understanding of data and the evaluation of numerical data. Early work on evidence evaluation (e.g., Shaklee, Holt, Elek, & Hall, 1988 ) included 2 x 2 contingency tables to examine the types of strategies children and adults used (e.g., comparing numbers in particular cells, the “sums of diagonals” strategy). Masnick and Morris (2008 ) used data tables to present evidence to be evaluated, and varied features of the presentation (e.g., sample size, variability of data). When asked to make decisions without the use of statistical tools, even third- and sixth-graders had rudimentary skills in detecting trends, overlapping data points, and the magnitude of differences. By sixth grade, participants had developing ideas about the importance of variability and the presence of outliers for drawing conclusions from numerical data.

Although language, symbols, and number systems are used as canonical examples of cultural tools and resources within the socio-cultural tradition ( Lemke, 2001 ), recent advances in computing and computer simulation are having a huge impact on the development and teaching of scientific reasoning. Although many studies have incorporated the use of physical systems ( Figure 1 , panel B) such as the canal task ( Gleason & Schauble, 2000 ), the ramps task (e.g., Masnick & Klahr, 2003 ), mixing chemicals ( Kuhn & Ho, 1980 ), and globes (Vosniadou, Skopeliti, & Ikospentaki, 2005), there is an increase in the use of interactive computer simulations (see Figure 1 , panel C). Simulations have been developed for electric circuits (Schauble, Glaser, Raghavan, & Reiner, 1992), genetics ( Echevarria, 2003 ), earthquakes ( Azmitia & Crowley, 2001 ), flooding risk ( Keselman, 2003 ), human memory ( Schunn & Anderson, 1999 ), and visual search (Métrailler, Reijnen, Kneser, & Opwis, 2008). Non-traditional science domains have also been used to develop inquiry skills. Examples include factors that affect TV enjoyment ( Kuhn et al., 1995 ), CD catalog sales ( Dean & Kuhn, 2007 ), athletic performance ( Lazonder, Wilhelm, & Van Lieburg, 2009 ), and shoe store sales ( Lazonder, Hagemans, & de Jong, 2010 ).

Computer simulations allow visualization of phenomena that are not directly observable in the classroom (e.g., atomic structure, planetary motion). Other advantages include that they are less prone to measurement error in apparatus set up, and that they can be programmed to record all actions taken (and their latencies). Moreover, many systems include a scaffolded method for participants to keep and consult records and notes. Importantly, there is evidence that simulated environments provide the same advantages as isomorphic “hands on” apparatus ( Klahr, Triona, & Williams, 2007 ; Triona & Klahr, 2007 ).

New lines of research are taking advantage of advances in computing and intelligent computer systems. Kuhn (2011b ) recently examined how to facilitate reasoning about multivariable causality, and the problems associated with the visualization of outcomes resulting from multiple causes (e.g., the causes for different cancer rates by geographical area). Participants had access to software that produces a visual display of data points that represent main effects and their interactions. Similarly, Klahr and colleagues (Siler, Mowery, Magaro, Willows, & Klahr, 2010 ) have developed an intelligent tutor to teach experimentation strategies (see Figure 1 , panel C). The use of intelligent tutors provides the unique opportunity of personally tailored learning and feedback experiences, dependent on each student’s pattern of errors. This immediate feedback can be particularly useful in helping develop metacognitive skills (e.g., Roll, Alaven, McLaren, & Koedinger, 2011) and facilitate effective student collaboration (Diziol, Walker, Rummel, & Koedinger, 2010).

Tweney, Doherty, and Mynatt (1981 ) noted some time ago that most tasks used to study scientific thinking were artificial because real investigations require aided cognition. However, as can be seen by several exemplars, even lab studies include support and assistance for many of the known cognitive limitations faced by both children and adults.

4. Summary and conclusions

Determining the developmental trajectory of scientific reasoning has been challenging, in part because scientific reasoning is not a unitary construct. Our goal was to outline how the investigation, evidence evaluation, and inference skills that constitute scientific reasoning emerge from intuitive information seeking via the interaction of individual and contextual factors. We describe the importance of (a) cognitive processes and mechanisms, (b) metacognitive and metastrategic skills, (c) the role of direct and scaffolded instruction, and (d) a context in which scientific activity is supported and which includes cultural tools (literacy, numeracy, technology) that facilitate the emergence of scientific reasoning. At the outset, we intended to keep section boundaries clean and neat. What was apparent to us, and may now be apparent to the reader, is that these elements are highly intertwined. It was difficult to discuss pure encoding in early childhood without noting the role that parents play. Likewise, it was difficult to discuss individual discovery of strategies, without noting such discovery takes place in the presence of peers, parents, and teachers. Similarly, discussing the teaching and learning of strategies is difficult without noting the role of cultural tools such as language, number, and symbol systems.

There is far more to a complete account of scientific reasoning than has been discussed here, including other cognitive mechanisms such as formal hypothesis testing, retrieval, and other reasoning processes. There are also relevant non-cognitive factors such as motivation, disposition, personality, argumentation skills, and personal epistemology, to name a few (see Feist, 2006 ). These additional considerations do not detract from our assertion that encoding and strategy use are critical to the development of scientific reasoning, and that we must consider cognitive and metacognitive skills within a social and physical context when seeking to understand the development of scientific reasoning. Scientific knowledge acquisition and, importantly, scientific knowledge change is the result of individual and social cognition that is mediated by education and cultural tools. The cultural institution of science has taken hundreds of years to develop. As individuals, we may start out with the curiosity and disposition to be little scientists, but it is a long journey from information seeking to skilled scientific reasoning, with the help of many scaffolds along the way.

Acknowledgements

All authors contributed equally to the manuscript. The authors thank Eric Amsel, Deanna Kuhn, and Jamie Jirout for comments on a previous version of this chapter.

© 2012 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3.0 License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Scientific Hypothesis, Model, Theory, and Law

Understanding the Difference Between Basic Scientific Terms

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Words have precise meanings in science. For example, "theory," "law," and "hypothesis" don't all mean the same thing. Outside of science, you might say something is "just a theory," meaning it's a supposition that may or may not be true. In science, however, a theory is an explanation that generally is accepted to be true. Here's a closer look at these important, commonly misused terms.

A hypothesis is an educated guess, based on observation. It's a prediction of cause and effect. Usually, a hypothesis can be supported or refuted through experimentation or more observation. A hypothesis can be disproven but not proven to be true.

Example: If you see no difference in the cleaning ability of various laundry detergents, you might hypothesize that cleaning effectiveness is not affected by which detergent you use. This hypothesis can be disproven if you observe a stain is removed by one detergent and not another. On the other hand, you cannot prove the hypothesis. Even if you never see a difference in the cleanliness of your clothes after trying 1,000 detergents, there might be one more you haven't tried that could be different.

Scientists often construct models to help explain complex concepts. These can be physical models like a model volcano or atom  or conceptual models like predictive weather algorithms. A model doesn't contain all the details of the real deal, but it should include observations known to be valid.

Example: The  Bohr model shows electrons orbiting the atomic nucleus, much the same way as the way planets revolve around the sun. In reality, the movement of electrons is complicated but the model makes it clear that protons and neutrons form a nucleus and electrons tend to move around outside the nucleus.

A scientific theory summarizes a hypothesis or group of hypotheses that have been supported with repeated testing. A theory is valid as long as there is no evidence to dispute it. Therefore, theories can be disproven. Basically, if evidence accumulates to support a hypothesis, then the hypothesis can become accepted as a good explanation of a phenomenon. One definition of a theory is to say that it's an accepted hypothesis.

Example: It is known that on June 30, 1908, in Tunguska, Siberia, there was an explosion equivalent to the detonation of about 15 million tons of TNT. Many hypotheses have been proposed for what caused the explosion. It was theorized that the explosion was caused by a natural extraterrestrial phenomenon , and was not caused by man. Is this theory a fact? No. The event is a recorded fact. Is this theory, generally accepted to be true, based on evidence to-date? Yes. Can this theory be shown to be false and be discarded? Yes.

A scientific law generalizes a body of observations. At the time it's made, no exceptions have been found to a law. Scientific laws explain things but they do not describe them. One way to tell a law and a theory apart is to ask if the description gives you the means to explain "why." The word "law" is used less and less in science, as many laws are only true under limited circumstances.

Example: Consider Newton's Law of Gravity . Newton could use this law to predict the behavior of a dropped object but he couldn't explain why it happened.

As you can see, there is no "proof" or absolute "truth" in science. The closest we get are facts, which are indisputable observations. Note, however, if you define proof as arriving at a logical conclusion, based on the evidence, then there is "proof" in science. Some work under the definition that to prove something implies it can never be wrong, which is different. If you're asked to define the terms hypothesis, theory, and law, keep in mind the definitions of proof and of these words can vary slightly depending on the scientific discipline. What's important is to realize they don't all mean the same thing and cannot be used interchangeably.

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The development of scientific reasoning in medical education: a psychological perspective

Scientific reasoning has been studied from a variety of theoretical perspectives, which have tried to identify the underlying mechanisms responsible for the development of this particular cognitive process. Scientific reasoning has been defined as a problem-solving process that involves critical thinking in relation to content, procedural, and epistemic knowledge. The development of scientific reasoning in medical education was influenced by current paradigmatic trends, it could be traced along educational curriculum and followed cognitive processes.

The purpose of the present review is to discuss the role of scientific reasoning in medical education and outline educational methods for its development.

Current evidence suggests that medical education should foster a new ways of development of scientific reasoning, which include exploration of the complexity of scientific inquiry, and also take into consideration the heterogeneity of clinical cases found in practice.

Introduction

Scientific reasoning has been studied from a variety of theoretical perspectives, which have tried to identify the underlying mechanisms that are responsible for the development of this particular cognitive process. Scientific reasoning has been defined as a problem-solving process that involves critical thinking in relation to content, procedural, and epistemic knowledge [ 1 , 2 ].

One specific approach to the study of scientific reasoning has focused on the development of this cognitive skill throughout medical education. Scientific reasoning has become an essential skill to develop throughout medical education due to the recent emphasis on evidence-based medicine (EBM). The patient-centered approach to clinical practice and the “best evidence paradigm” have had an impact on academic content, teaching methods and curricular structure of the medical education. Several studies into medical pedagogy have discussed the major types of medical curricula in relation to their capacity for nurturing essential skills for clinical expertise [ 3 ].

In order to provide an up-to-date review on the role of scientific reasoning in medical education, we comprehensively searched PubMed, ScienceDirect and APA electronic databases for relevant articles, without publication year restrictions. A combination of search terms such as scientific reasoning, medical education, evidence-based medicine, clinical reasoning revealed 87 articles. With appropriate selection based on relevancy and overall impact, 25 articles were considered.

The aim of this review is to discuss evidence that reveales the role of scientific reasoning in medical education and outline educational methods for its development development.

Scientific reasoning

Scientific reasoning has been studied across several distinct domains – cognitive sciences, education, developmental psychology, even artificial intelligence – which have tried to identify its underlying mechanisms. Although research has defined scientific reasoning through different rationales, a common approach refers to the mental processes used when reasoning about scientific facts or when engaged in scientific research. There is no doubt with regard to the involvement of several general cognitive processes in the emergence and development of scientific reasoning, such as inductive reasoning, deductive reasoning, problem-solving and causal reasoning [ 4 ].

Although mental processes involved in science have intrigued researchers since the 1620, it was not until Simon and Newell (1971) that an actual theory of scientific reasoning was proposed. Simon and Newell defined scientific reasoning as a problem-solving process, which included a problem space consisting of the initial state, the goal state, and all the possible states in between, and operators, which were the actions that can be taken in order to move from one state to the other. According to the problem space theory , by investigating the types of representations people had and the actions that they took to get from one state to another, one could understand scientific reasoning [ 1 ].

Over time, a contrasting approach emerged which focused on studying the concepts that people hold about scientific phenomena. This approach brought forth the argument referring to the aspect of knowledge-dependency of reasoning. According to the domain-specific approach, a scientific reasoning task required participants to use their conceptual knowledge of a particular scientific phenomenon. In opposition, the domain-general reasoning approach focused on problem-solving strategies and cognitive processes that transcended specific domains and were applied in scientific discovery, the development of theories, experimentation and evidence evaluation [ 5 ]. Therefore, these two distinct approaches emphasized either conceptual knowledge or experimentation strategies.

Klahr and Dunbar’s Model of Scientific Discovery (1988, SDDS – Scientific Discovery as Dual Search ) has been the most preeminent attempt to integrate both knowledge acquisition, as well as cognitive mechanisms in order to provide a framework for the development of scientific reasoning [ 6 ]. The SDDS Model, influenced by Simon and Newell’s theory of problem-solving, described scientific reasoning as a guided search within two related problem spaces – the hypothesis space and the experiment space. Klahr and Dunbar found that this search was bidirectional, one could move from the hypothesis space to the experiment space or vice versa, the primary goal being the discovery of either a hypothesis or a theory.

This conceptualization has recently been complemented by the scientific argumentation approach. This recent approach has focused on science pedagogy and it postulated the use of scientific discourse as “the new focus for scientific reasoning activity” [ 2 ]. By shifting the focus from the classical scientific experimentation approach to the socially-constructed scientific knowledge approach, scientific reasoning has broadened its conceptual framework. This view on scientific reasoning has emphasized the importance of evidence evaluation and coordination, for the advancement of scientific knowledge.

The current trend moved toward a unified approach for the study of scientific reasoning, which brought together both the psychological and the philosophical perspective. According to the philosophical perspective, scientific reasoning was essentially critical thinking in relation to content, procedural, and epistemic knowledge [ 2 ]. The unified approach proposed by Kind referred to a combination of Giere et al’s work with the SDDS model through which we could explain how “expert scientists are better at scientific reasoning because they have superior understanding of these three knowledge types”. Therefore, scientific reasoning refers to both the cognitive process, which, based on Kind’s approach, involved hypothesizing, experimenting, and evidence evaluation, as well as content, procedural, and epistemic knowledge.

Educational perspectives on the development of scientific reasoning

Developing scientific reasoning across education has raised many viewpoints with regard to early developmental processes, specific teaching methods in science education, evaluation of scientific literacy and so on. In this section, we will only be revising the most relevant educational theories with regard to the development of scientific reasoning in medical education, with special attention given to cognitive learning approaches.

The first major educational approach has focused on the interaction between different aspects of scientific thinking in a collaborative setting, rather than analyzing “the product of one person thinking alone” [ 4 ]. This view regarding the development of scientific reasoning was specific to the constructivist theory of education, according to which learning was an active process rather than an independent mechanism of knowledge acquisition. According to the constructivist learning theory, developing scientific research skills in students involved, primarily, changing their beliefs in line with the scientific principles shouldered by the community. Constructivism provided the philosophical bases for the conceptual change toward “building skills”, perceived also in medical education [ 7 , 8 ].

The second educational approach has placed authentic learning environments as the foundation for developing reasoning skills in students. The “Situated learning theory” or “Situative learning theory” [ 9 ] offered an instructional approach according to which students were more inclined to learn by actively participating in the learning experience. This perspective claimed that procedural knowledge acquisition took place through problem-solving; the novice was immersed in a context that involved other people who were experienced at solving similar problems [ 3 ].

One other main theoretical direction, which attempted to provide foundation for the development of scientific reasoning throughout education, related to the cognitive perspective. The Adaptive Character of Thought (ACT-R) theory, developed by Anderson [ 10 ], proposed that cognition arose from the interaction of procedural and declarative knowledge. Procedural knowledge consisted of production rules , which represented the “how to”, while declarative knowledge consists of facts, organized in units called chunks , which represented the “what”. The individual units were created by encodings of objects in chunks or encoding of transformations in production rules. According to the ACT-R theory, “human cognition depends on the amount of knowledge encoded and the effective deployment of the encoded knowledge” [ 10 , 11 ].

As an attempt to integrate the role of memory into educational theory and practice, Sweller and Chandler [ 12 ] proposed the Cognitive Load Theory (CLT). CLT suggested that effective instructional resources would facilitate learning through relevant directed activities and an ineffective instructional design required learners to integrate disparate, split-source information, which might generate heavy cognitive load. Based on Sweller and Chandler’s research findings, in areas where complex knowledge acquisition was necessary in order to assimilate more than two sources of information, conventional teaching should be replaced by integrated instructional designs.

To summarize, the development of scientific reasoning in medical education could be traced along these two major influences: cognitive learning theories , which focused on individual cognitive processes, and constructivist learning theories , which focused on interaction within an educational setting; both perspectives provided valuable support for the design and implementation of educational methods applied in medicine [ 9 ].

Based on these findings, medical curriculum has integrated academic content, teaching methods and curricular structure to adapt to the students’ needs. Research into medical pedagogy has revealed two major types of medical curricula: the conventional approach and the problem-based learning approach (PBL) [ 9 ].

The conventional approach referred to the classical division of stages: preclinical (1 st and 2 nd year) and clinical (from the 3 rd year to final year). The division was based on the difference between educational objectives between fundamental sciences, which provided basic scientific knowledge, and clinical sciences, which provided clinical knowledge. According to the conventional curricular approach to medical education in Romanian universities, a model for the development of scientific reasoning is presented in Figure 1 .

Conventional model for the development of scientific reasoning in medical education.

Problem-based learning curriculum focused on a blended approach to knowledge acquisition, which combined scientific content with practical problem-solving tasks. Teaching methods aimed at facilitating self-directed and collaborative learning, while developing critical thinking skills. PBL has emerged as a solution for the ineffectiveness of scientific content taught abstractly for skills development in clinical practice [ 9 ]. However, research findings have not been conclusive regarding the differences in clinical skills or even critical thinking, between students from conventional and PBL programs. Pardamean [ 13 ] has investigated change in critical thinking skills of dental students educated in a PBL curriculum, using the Health Sciences Reasoning Test (HSRT – psychometric measure for critical thinking, analysis, inference, evaluation, deductive reasoning, and inductive reasoning). Results showed no significant differences in critical thinking scores throughout education on a PBL curriculum. While results were contradictory, some researchers strongly believed in a medical curriculum based on problem solving principles of applying new knowledge in practical tasks and “promoting learner responsibility” [ 7 ].

Scientific reasoning and clinical expertise

Studies on clinical expertise revealed contradictory findings regarding domain-specific versus domain-general competency and it was perceived in relation to current paradigmatic trends. Evidence-based medicine has been defined as “the conscientious, explicit, and judicious use of the best evidence in making decisions about the care of individual patients” [ 14 ]. Although EBM has been around for centuries, the focus on using the best evidence in medical research to treat patients began in the late 1980s in Canada and the United Kingdom [ 15 ]. It has been termed as a paradigm shift , however debates were continuing over the philosophical underpinnings and the practical nature of EBM. Sehon and Stanley [ 16 ] strenuously challenged the notion of ‘paradigm shift’, arguing that definitions of EBM were vacuous because they merely emphasized the use of the best evidence available, “as if any alternative to EBM means doing medicine based on something other than evidence”. In turn, Sehon and Stanley suggested that advocates of EBM should have emphasized the essence of what separated EBM from the other approaches, which was the priority it gave to certain forms of evidence, such as randomized clinical trials (RCTs), systematic reviews and meta-analyses of RCTs.

Apart from the limitations discussed by Sehon and Stanley, EBM has ignited reluctance due to its mechanical and reductionist approach to medical practice. Miles and Loughlin discussed the potential harmful effects of EBM and the possible new benefits found with Person-Centred Medicine (PCM) [ 17 ]. They referred to Tournier, a medical practitioner, who firstly mentioned the dangers of depersonalized clinical practice, “lacking the integration of body, mind and spirit necessary for health and wholeness and overlooking the healing potential of the therapeutic relationship”. They also argue that, from a person-centred perspective, EBM fails to incorporate patients’ “values and preferences into clinical decision making, when these are in conflict with EBM’s ‘evidence’ ”. Thus, the methodological challenge would be to integrate biomedical findings and technological advances “within a humanistic framework”, without undermining applied science into medical education.

Studies on clinical expertise revealed that differences between experts and novices were more complex when it came to biomedical knowledge. Several studies on medical students’ research skills aimed at exploring scientific reasoning and critical thinking skills [ 18 , 19 , 20 ]. Msaouel et al. [ 19 ] aimed at researching familiarity of medical residents with statistical concepts, evaluated their ability to integrate these concepts in clinical scenarios and investigated cognitive biases in particular judgment tasks. They used a multi-institutional, cross-sectional survey, which focused on basic statistical concepts, biostatistics in clinical settings and cognitive biases. Results showed that out of 153 respondents, only 2 were able to answer all biostatistics knowledge questions correctly, while 29 residents gave incorrect answers to all questions (the majority were susceptible to cognitive bias tasks).

Schunn and Anderson [ 21 ] performed a study on the domain-general or domain-specific nature of scientific reasoning skills. They used think aloud protocols to investigate differences in designing experiments between domain experts, experts skilled in other domains and undergraduate students. Results showed that domain experts and “other experts” differed in terms of domain-specific skills, while “other experts” and undergraduate students differed with respect to domain-general skills. By analyzing verbal protocols, Schunn and Anderson were able to identify domain-general skills that seem more important in scientific reasoning than domain-specific skills.

Another such study used memory probes to identify possible differences between students, residents and internists, in diagnostic competency. Participants were asked to read case histories, assign diagnoses before tasks of free recall, cued recall, and recognition tests. Students’ performance was superior to that of internists, while residents’ performance was more variable [ 22 ]. These results suggested that students focused more on the details of a clinical case, their procedural knowledge was closely tied to the amount of information they possessed, while clinicians took on a larger perspective when assessing diagnosis. This idea has been explored in-depth by Schmidt and Boshuizen [ 23 , 24 ] who have proposed the concept of “knowledge encapsulation”. “Knowledge encapsulation is the subsumption, or “packaging,” of lower level detailed propositions, concepts, and their interrelations in an associative net under a smaller number of higher level propositions with the same explanatory power.” According to this theory, novices processed, in a bottom-up manner, detailed knowledge with regard to a clinical case, leading to increases in free-recall, “as a function of the growth of the knowledge base”. Experts, being exposed to more and more similar clinical cases, used certain shortcuts in their diagnoses, facilitated by “encapsulated knowledge”. However, Schmidt et al. have suggested that clinical expertise is a process of reaching three kinds of mental representations: basic mechanisms of disease, illness scripts, and exemplars derived from prior experience [ 24 ].

In his chronological review of research into clinical reasoning, Norman concludes that there is no clinical reasoning as a stand-alone concept, but rather experts used multiple knowledge representation and the cognitive flexibility and skill adaptation allowed the expert to perform successfully [ 25 ]. Four levels of expertise in medical education have been proposed [ 9 ]: (1) novice (possesses the prerequisites or basic knowledge required, like the 1 st year student); (2) intermediate (possesses above the beginner level but below the sub-expert level, like the 2 nd year student; (3) sub-expert (possesses general knowledge but insufficient specialized domain knowledge, like a resident; and (4) expert (possesses sufficient specialized domain knowledge, like a consultant physician). According to Norman’s review, any type of expertise should be viewed as a developmental path from novice to expert, which can be facilitated through efficient instructional designs, but which also has many intermediate phases.

Conclusions and future directions

From a psychological perspective, educational theories can provide the foundation for teaching methods and curricular improvements, in terms of advancing scientific reasoning throughout medical education. Constructivism provides the philosophical bases for the conceptual change needed in science education, present in medical education as well. Clinical reasoning, diagnostic reasoning, and clinical decision-making are terms that have been used in a growing body of literature that examines how clinicians assign diagnoses and make medical decisions. However, clinical reasoning is being shaped starting with undergraduate medical education. Medical educators’ definitions of superior clinical reasoning will invariably influence their choices in shaping the thought processes of future doctors. In addition, principles of ACT-R can provide medical education with support through cognitive tutors, which are computer-based instructional systems that simulate student behavior. Implementation of a cognitive tutors program within clinical practice would provide medical students with essential knowledge-based support for efficient learning and skills training [ 6 , 9 , 11 ]. On the other hand, medical education involves acquisition of a large amount of both procedural and declarative knowledge. Instructional designs within medical education should also take into consideration research findings regarding the role of memory and cognitive processes in relation to short-term and long-term acquisition [ 12 ].

From a medical standpoint, medical education and clinical practice are influenced by two contrasting approaches: EBM and PCM or PM (Personalized Medicine). Theorists and practitioners both have discussed issues which arise from this segregation. First of all, the most important issue is to develop scientific methodologies which would integrate both EBM and PM, that would involve personalizing clinical and research guidelines, but also offering a rigorous framework for the person-centered approach. Secondly, medical education should foster a new way of scientific reasoning that includes exploration of the complexity of scientific inquiry, but also appreciation for the heterogeneity found in clinical practice. Clinical cases should be examined in problem-solving terms and placed under scrutiny through self-directed search and discovery. Trainees should be presented at all times with both possible outcomes – effect vs. no-effect; this way, both alternatives become legitimate conclusions to be reached and students can understand the more complex nature of scientific discovery. Future directions regarding the development of scientific reasoning in medical education should also focus on mapping clinical expertise. Although studies into expertise have encountered inherent limitations, building on a gradual developmental expertise acquisition and identifying novices’ and experts’ mental representations in clinical scenarios can provide valuable insight for future research.

Acknowledgements

This paper was published under the frame of European Social Fund, Human Resources Development Operational programme 2007–2013, project no POSDRU/159/1.5/138776.

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The “scientific method” is traditionally presented in the first chapter of science textbooks as a simple, linear, five- or six-step procedure for performing scientific investigations. Although the Scientific Method captures the core logic of science (testing ideas with evidence), it misrepresents many other aspects of the true process of science — the dynamic, nonlinear, and creative ways in which science is actually done. In fact, the Scientific Method more accurately describes how science is summarized  after the fact  — in textbooks and journal articles — than how scientific research is actually performed. Teachers may ask that students use the format of the scientific method to write up the results of their investigations (e.g., by reporting their  question, background information, hypothesis, study design, data analysis,  and  conclusion ), even though the process that students went through in their investigations may have involved many iterations of questioning, background research, data collection, and data analysis and even though the students’ “conclusions” will always be tentative ones. To learn more about how science really works and to see a more accurate representation of this process, visit  The  real  process of science .

Why do scientists often seem tentative about their explanations?

Scientists often seem tentative about their explanations because they are aware that those explanations could change if new evidence or perspectives come to light. When scientists write about their ideas in journal articles, they are expected to carefully analyze the evidence for and against their ideas and to be explicit about alternative explanations for what they are observing. Because they are trained to do this for their scientific writing, scientist often do the same thing when talking to the press or a broader audience about their ideas. Unfortunately, this means that they are sometimes misinterpreted as being wishy-washy or unsure of their ideas. Even worse, ideas supported by masses of evidence are sometimes discounted by the public or the press because scientists talk about those ideas in tentative terms. It’s important for the public to recognize that, while provisionality is a fundamental characteristic of scientific knowledge, scientific ideas supported by evidence are trustworthy. To learn more about provisionality in science, visit our page describing  how science builds knowledge . To learn more about how this provisionality can be misinterpreted, visit a section of the  Science toolkit .

Why is peer review useful?

Peer review helps assure the quality of published scientific work: that the authors haven’t ignored key ideas or lines of evidence, that the study was fairly-designed, that the authors were objective in their assessment of their results, etc. This means that even if you are unfamiliar with the research presented in a particular peer-reviewed study, you can trust it to meet certain standards of scientific quality. This also saves scientists time in keeping up-to-date with advances in their fields by weeding out untrustworthy studies. Peer-reviewed work isn’t necessarily correct or conclusive, but it does meet the standards of science. To learn more, visit  Scrutinizing science .

What is the difference between independent and dependent variables?

In an experiment, the independent variables are the factors that the experimenter manipulates. The dependent variable is the outcome of interest—the outcome that depends on the experimental set-up. Experiments are set-up to learn more about how the independent variable does or does not affect the dependent variable. So, for example, if you were testing a new drug to treat Alzheimer’s disease, the independent variable might be whether or not the patient received the new drug, and the dependent variable might be how well participants perform on memory tests. On the other hand, to study how the temperature, volume, and pressure of a gas are related, you might set up an experiment in which you change the volume of a gas, while keeping the temperature constant, and see how this affects the gas’s pressure. In this case, the independent variable is the gas’s volume, and the dependent variable is the pressure of the gas. The temperature of the gas is a controlled variable. To learn more about experimental design, visit Fair tests: A do-it-yourself guide .

What is a control group?

In scientific testing, a control group is a group of individuals or cases that is treated in the same way as the experimental group, but that is not exposed to the experimental treatment or factor. Results from the experimental group and control group can be compared. If the control group is treated very similarly to the experimental group, it increases our confidence that any difference in outcome is caused by the presence of the experimental treatment in the experimental group. For an example, visit our side trip  Fair tests in the field of medicine .

What is the difference between a positive and a negative control group?

A negative control group is a control group that is not exposed to the experimental treatment or to any other treatment that is expected to have an effect. A positive control group is a control group that is not exposed to the experimental treatment but that is exposed to some other treatment that is known to produce the expected effect. These sorts of controls are particularly useful for validating the experimental procedure. For example, imagine that you wanted to know if some lettuce carried bacteria. You set up an experiment in which you wipe lettuce leaves with a swab, wipe the swab on a bacterial growth plate, incubate the plate, and see what grows on the plate. As a negative control, you might just wipe a sterile swab on the growth plate. You would not expect to see any bacterial growth on this plate, and if you do, it is an indication that your swabs, plates, or incubator are contaminated with bacteria that could interfere with the results of the experiment. As a positive control, you might swab an existing colony of bacteria and wipe it on the growth plate. In this case, you  would  expect to see bacterial growth on the plate, and if you do not, it is an indication that something in your experimental set-up is preventing the growth of bacteria. Perhaps the growth plates contain an antibiotic or the incubator is set to too high a temperature. If either the positive or negative control does not produce the expected result, it indicates that the investigator should reconsider his or her experimental procedure. To learn more about experimental design, visit  Fair tests: A do-it-yourself guide .

What is a correlational study, and how is it different from an experimental study?

In a correlational study, a scientist looks for associations between variables (e.g., are people who eat lots of vegetables less likely to suffer heart attacks than others?) without manipulating any variables (e.g., without asking a group of people to eat more or fewer vegetables than they usually would). In a correlational study, researchers may be interested in any sort of statistical association — a positive relationship among variables, a negative relationship among variables, or a more complex one. Correlational studies are used in many fields (e.g., ecology, epidemiology, astronomy, etc.), but the term is frequently associated with psychology. Correlational studies are often discussed in contrast to experimental studies. In experimental studies, researchers do manipulate a variable (e.g., by asking one group of people to eat more vegetables and asking a second group of people to eat as they usually do) and investigate the effect of that change. If an experimental study is well-designed, it can tell a researcher more about the cause of an association than a correlational study of the same system can. Despite this difference, correlational studies still generate important lines of evidence for testing ideas and often serve as the inspiration for new hypotheses. Both types of study are very important in science and rely on the same logic to relate evidence to ideas. To learn more about the basic logic of scientific arguments, visit  The core of science .

What is the difference between deductive and inductive reasoning?

Deductive reasoning involves logically extrapolating from a set of premises or hypotheses. You can think of this as logical “if-then” reasoning. For example, IF an asteroid strikes Earth, and IF iridium is more prevalent in asteroids than in Earth’s crust, and IF nothing else happens to the asteroid iridium afterwards, THEN there will be a spike in iridium levels at Earth’s surface. The THEN statement is the logical consequence of the IF statements. Another case of deductive reasoning involves reasoning from a general premise or hypothesis to a specific instance. For example, based on the idea that all living things are built from cells, we might  deduce  that a jellyfish (a specific example of a living thing) has cells. Inductive reasoning, on the other hand, involves making a generalization based on many individual observations. For example, a scientist who samples rock layers from the Cretaceous-Tertiary (KT) boundary in many different places all over the world and always observes a spike in iridium may  induce  that all KT boundary layers display an iridium spike. The logical leap from many individual observations to one all-inclusive statement isn’t always warranted. For example, it’s possible that, somewhere in the world, there is a KT boundary layer without the iridium spike. Nevertheless, many individual observations often make a strong case for a more general pattern. Deductive, inductive, and other modes of reasoning are all useful in science. It’s more important to understand the logic behind these different ways of reasoning than to worry about what they are called.

What is the difference between a theory and a hypothesis?

Scientific theories are broad explanations for a wide range of phenomena, whereas hypotheses are proposed explanations for a fairly narrow set of phenomena. The difference between the two is largely one of breadth. Theories have broader explanatory power than hypotheses do and often integrate and generalize many hypotheses. To be accepted by the scientific community, both theories and hypotheses must be supported by many different lines of evidence. However, both theories and hypotheses may be modified or overturned if warranted by new evidence and perspectives.

What is a null hypothesis?

A null hypothesis is usually a statement asserting that there is no difference or no association between variables. The null hypothesis is a tool that makes it possible to use certain statistical tests to figure out if another hypothesis of interest is likely to be accurate or not. For example, if you were testing the idea that sugar makes kids hyperactive, your null hypothesis might be that there is no difference in the amount of time that kids previously given a sugary drink and kids previously given a sugar-substitute drink are able to sit still. After making your observations, you would then perform a statistical test to determine whether or not there is a significant difference between the two groups of kids in time spent sitting still.

What is Ockhams's razor?

Ockham’s razor is an idea with a long philosophical history. Today, the term is frequently used to refer to the principle of parsimony — that, when two explanations fit the observations equally well, a simpler explanation should be preferred over a more convoluted and complex explanation. Stated another way, Ockham’s razor suggests that, all else being equal, a straightforward explanation should be preferred over an explanation requiring more assumptions and sub-hypotheses. Visit  Competing ideas: Other considerations  to read more about parsimony.

What does science have to say about ghosts, ESP, and astrology?

Rigorous and well controlled scientific investigations 1  have examined these topics and have found  no  evidence supporting their usual interpretations as natural phenomena (i.e., ghosts as apparitions of the dead, ESP as the ability to read minds, and astrology as the influence of celestial bodies on human personalities and affairs) — although, of course, different people interpret these topics in different ways. Science can investigate such phenomena and explanations only if they are thought to be part of the natural world. To learn more about the differences between science and astrology, visit  Astrology: Is it scientific?  To learn more about the natural world and the sorts of questions and phenomena that science can investigate, visit  What’s  natural ?  To learn more about how science approaches the topic of ESP, visit  ESP: What can science say?

Has science had any negative effects on people or the world in general?

Knowledge generated by science has had many effects that most would classify as positive (e.g., allowing humans to treat disease or communicate instantly with people half way around the world); it also has had some effects that are often considered negative (e.g., allowing humans to build nuclear weapons or pollute the environment with industrial processes). However, it’s important to remember that the process of science and scientific knowledge are distinct from the uses to which people put that knowledge. For example, through the process of science, we have learned a lot about deadly pathogens. That knowledge might be used to develop new medications for protecting people from those pathogens (which most would consider a positive outcome), or it might be used to build biological weapons (which many would consider a negative outcome). And sometimes, the same application of scientific knowledge can have effects that would be considered both positive and negative. For example, research in the first half of the 20th century allowed chemists to create pesticides and synthetic fertilizers. Supporters argue that the spread of these technologies prevented widespread famine. However, others argue that these technologies did more harm than good to global food security. Scientific knowledge itself is neither good nor bad; however, people can choose to use that knowledge in ways that have either positive or negative effects. Furthermore, different people may make different judgments about whether the overall impact of a particular piece of scientific knowledge is positive or negative. To learn more about the applications of scientific knowledge, visit  What has science done for you lately?

1 For examples, see:

• Milton, J., and R. Wiseman. 1999. Does psi exist? Lack of replication of an anomalous process of information transfer.  Psychological Bulletin  125:387-391.
• Carlson, S. 1985. A double-blind test of astrology.  Nature  318:419-425.
• Arzy, S., M. Seeck, S. Ortigue, L. Spinelli, and O. Blanke. 2006. Induction of an illusory shadow person.  Nature  443:287.
• Gassmann, G., and D. Glindemann. 1993. Phosphane (PH 3 ) in the biosphere.  Angewandte Chemie International Edition in English  32:761-763.

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Narrative Summary of Remarks on the Latest Form of the Development Theory

Overview:  

This text is a critical analysis of Darwin’s theory of evolution, specifically addressing the idea of cumulative variation and its role in the origin of species. Bowen, writing in 1860, argues against the theory by focusing on the lack of direct evidence, the statistical improbability of cumulative variation, and the role of natural selection in extinction. He also criticizes Darwin’s use of the word “tendency” to describe the frequency of variations. Bowen suggests that Darwin’s theory relies too heavily on rare exceptions, while ignoring the vast majority of cases where variations do not occur or are not inherited.

Main Parts:

• Cumulative Variation and the Origin of Species:  Bowen refutes the idea that cumulative variation can explain the origin of species, highlighting the lack of direct evidence and the improbability of successive variations occurring and being inherited. He uses mathematical calculations to illustrate the astronomical odds against such a process.
• The Struggle for Life and Conditions of Existence:  Bowen discusses the “struggle for life,” arguing that it merely reflects the fact that every species has specific conditions of existence. He points out that the intrusion of new species, changes in the environment, or the failure of any condition can lead to extinction but do not necessarily create new species.
• Natural Selection and the Role of Extinction:  Bowen analyzes natural selection, concluding that it is not a creative force but rather a selective force operating only after different species have already been established. He emphasizes the infrequency of species extinction and argues that it is an insufficient basis for a theory explaining the origin of all species. He also points out that natural selection can only operate on existing variations and cannot create new ones.

View on Life:  Bowen’s standpoint is rooted in a belief in a purposeful creation, where species have specific conditions of existence and are not subject to random chance. He emphasizes the importance of direct evidence and statistical probability in scientific reasoning, arguing against relying on rare exceptions or speculations about what might have happened over an infinite period. He sees Darwin’s theory as a “blank hypothesis,” lacking the grounding in evidence and logical reasoning that is necessary for a scientific theory.

• Norway rat replacing the common house rat:  This scenario illustrates how a more prolific or adaptable species can displace another.
• Human impact on animal populations:  This example shows how humans can exterminate species due to their physical capabilities and the use of reason.
• Coexistence of antelope species in South Africa:  This example illustrates how multiple species can coexist without one necessarily driving the others to extinction.

Challenges:

• Lack of direct evidence for cumulative variation:  Bowen highlights the absence of concrete evidence showing that two distinct species have descended from a common ancestor.
• Statistical improbability of cumulative variation:  Bowen uses mathematical calculations to demonstrate the extremely low probability of multiple successive variations occurring and being inherited.
• Incompleteness of the theory of natural selection:  Bowen argues that natural selection, while a valid force, only operates on existing variations and cannot explain the origin of those variations.
• The conflict is between Bowen’s view of a purposeful creation and Darwin’s theory of evolution through natural selection.  Bowen believes that species were created with specific characteristics and conditions of existence, while Darwin argues that species evolve through gradual changes driven by natural selection.
• This conflict is overcome by Bowen’s forceful critique of Darwin’s theory.  He argues that the theory lacks sufficient evidence and relies on improbable assumptions about cumulative variation and the role of natural selection in extinction.

Plot:  The text does not have a traditional plot but follows a logical progression of argumentation.

• Introduction:  Bowen presents Darwin’s theory and outlines his critique.
• Cumulative Variation:  Bowen addresses the central argument of cumulative variation, emphasizing the lack of evidence and statistical improbability.
• Struggle for Life and Conditions of Existence:  Bowen discusses the concept of “struggle for life” and argues that it does not support the creation of new species.
• Natural Selection and Extinction:  Bowen critiques natural selection, highlighting its limitations and emphasizing the infrequency of extinction.
• Conclusion:  Bowen summarizes his arguments, rejecting Darwin’s theory as a “blank hypothesis” lacking evidence and logical reasoning.

Point of View:

• Bowen writes from the perspective of a critic of Darwin’s theory.  He uses logical reasoning, statistical evidence, and examples to challenge the theory’s validity.
• This perspective is shaped by Bowen’s belief in a purposeful creation, where species have specific conditions of existence.

How It’s Written:

• The text is written in a formal and academic style, characterized by precise language, logical reasoning, and extensive citations.
• For example, Bowen uses mathematical calculations to illustrate the statistical improbability of cumulative variation.
• The tone is critical and skeptical, reflecting Bowen’s disagreement with Darwin’s theory.
• He uses strong language to express his disapproval, calling Darwin’s theory a “blank hypothesis” and arguing that it rests on “no evidence whatever.”

Life Choices:

• The text does not focus on specific life choices, but rather on the broader philosophical implications of Darwin’s theory.
• Bowen’s choice to criticize the theory reflects his commitment to a different scientific worldview.
• The text highlights the importance of direct evidence and logical reasoning in scientific inquiry.
• It cautions against relying on speculation or improbable assumptions in the pursuit of scientific knowledge.
• It emphasizes the value of a critical and skeptical approach to scientific theories.

Characters:

• Francis Bowen:  The author, a prominent philosopher and economist, is the central character in this text. He is presented as a critical thinker with a deep understanding of science and a strong commitment to evidence-based reasoning.
• Charles Darwin:  While not directly present, Darwin is the subject of Bowen’s critique, represented by his theory of evolution through natural selection.
• The nature of scientific evidence:  The text emphasizes the importance of direct evidence and the dangers of relying on speculation or improbable assumptions.
• The role of purpose and design in nature:  Bowen’s critique of Darwin’s theory reflects a belief in a purposeful creation, suggesting that natural phenomena are not simply the result of random chance.
• The importance of critical thinking:  Bowen’s analysis exemplifies the value of a critical and skeptical approach to scientific theories, encouraging readers to question assumptions and demand evidence-based reasoning.

Principles:

• The principle of parsimony:  Bowen advocates for theories that are based on the simplest and most direct explanations, rejecting Darwin’s theory as unnecessarily complex and improbable.
• The principle of evidence-based reasoning:  Bowen emphasizes the importance of scientific knowledge being grounded in direct observation and testable evidence, rejecting explanations that rely solely on speculation.

Intentions:

• Bowen’s intention is to challenge Darwin’s theory of evolution by natural selection, arguing that it lacks sufficient evidence and relies on improbable assumptions.
• He aims to convince readers of the limitations of the theory and to promote a different worldview based on a purposeful creation.

Unique Vocabulary:

• Cumulative Variation:  A term used by Bowen to describe the gradual accumulation of variations over time as proposed by Darwin.
• Struggle for Life:  A phrase used by Darwin to describe the competition for resources among living organisms.
• Natural Selection:  The process by which organisms better adapted to their environment tend to survive and reproduce more successfully.
• The Norway rat replacing the common house rat:  This anecdote illustrates the concept of a more prolific or adaptable species displacing another, highlighting the “struggle for life” but not the creation of new species.
• Human impact on animal populations:  This anecdote underscores the human ability to exterminate species, demonstrating the forcefulness of human action but not supporting Darwin’s theory of evolution.
• Coexistence of antelope species in South Africa:  This anecdote showcases the coexistence of multiple species without one necessarily driving the others to extinction, challenging the idea that natural selection always leads to extinction.
• The idea of a purposeful creation:  Bowen argues that species have specific conditions of existence and were designed with those conditions in mind, rejecting Darwin’s idea of random variation and natural selection.
• The idea of scientific evidence as a foundation for theory:  Bowen stresses the importance of direct evidence and logical reasoning in developing scientific theories, rejecting theories based on speculation or improbable assumptions.

Facts and Findings:

• The lack of direct evidence for cumulative variation:  Bowen highlights the absence of direct evidence showing two distinct species having descended from a common ancestor.
• The infrequency of species extinction:  Bowen points out that extinction is a rare event, undermining the idea that natural selection plays a significant role in driving extinction.

Statistics:

• The statistical improbability of cumulative variation:  Bowen uses mathematical calculations to illustrate the astronomically low probability of multiple successive variations occurring and being inherited.

Points of View:

• The text is written from Bowen’s perspective as a critic of Darwin’s theory.  This perspective shapes the tone and argumentation, emphasizing the limitations of the theory and promoting a different worldview based on a purposeful creation.

Perspective:

• Bowen’s perspective is shaped by his belief in a purposeful creation and his commitment to evidence-based reasoning.  He critiques Darwin’s theory not only for its lack of evidence but also for its reliance on improbable assumptions and its departure from the principle of parsimony.

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HYPOTHESIS AND THEORY article

Teaching abductive reasoning for use as a problem-solving tool in organic chemistry and beyond.

• 1 Chemistry Program, Department of Natural Sciences, Central College, Pella, IA, United States
• 2 Department of Chemistry, Augsburg University, Minneapolis, MN, United States
• 3 Department of Chemistry, University of Saint Joseph, West Hartford, CT, United States
• 4 Department of Chemical and Environmental Sciences, United States Coast Guard Academy, New London, CT, United States

The second-year undergraduate Organic Chemistry course sequence is often cited as one of the most, if not the most, challenging for students in the US. Thus, a persistent question remains: What is it about Organic Chemistry that makes the course so difficult for students? Herein, we put forward the hypothesis that a new mode of thinking and problem solving is expected of the students; these skills have not yet been developed in their prior scientific coursework and are often not deliberately taught in Organic Chemistry. This form of reasoning and problem solving, known as abductive reasoning, is highlighted for its connection to medical diagnosis and scientific thinking. We provide examples to showcase how instructors could explicitly foreground the reasoning process in their classroom. Ultimately, we argue that teaching how to reason using abduction may benefit students in both the short term (in the course) and the long term (in their careers as scientists and medical practitioners).

“What changes must be made in the kind of science that we teach and the way that we teach it so that the fundamental ideas of our discipline can be used outside the classroom?” – Herron & Greenbowe

1 Introduction

1.1 background.

Organic Chemistry, as traditionally taught in the US as a primarily second-year undergraduate course sequence, is often considered a course for “weeding out pre-meds” ( Moran, 2013 ) that “strik[es] fear in the hearts of students” ( Garg, 2019 ). This socially constructed barrier adds an additional level of pedagogical challenge for instructors. We, the authors, are instructors of Organic Chemistry and also write and review questions for standardized exams that are required for entrance into specialized medical programs; 1 thus, we are at a position in both the content delivery and assessment where we find ourselves continually asking the question: What do we want students to learn in the Organic Chemistry course sequence?

While some students may think the answer to this question is “to know, understand, and recite back the course material,” this is an unsatisfying response for a number of reasons. First, such a response would imply that only memorization and algorithmic problem-solving skills are necessary for success in Organic Chemistry ( Stowe and Cooper, 2017 ). 2 However, expert organic chemists recognize that the interconnected complexities within chemical systems means that simply following basic rules (i.e., deductive inference) will not necessarily lead to a set outcome (e.g., bulky bases do not always react via E2) ( Achet et al., 1986 ). Second, while the students enter our classrooms as novices, some of them will go on to become practicing, expert organic chemists. We owe it to them, and the future of scientific discovery, to build a sound foundation of both fundamental (e.g., understanding the aldol condensation) and higher order (e.g., performing retrosynthetic analysis) skills within the discipline. Third, most US health professions (e.g., MD, DO, PA, DDS, DMD, OD, PharmD) require this course to be taken as a prerequisite for admission into their graduate programs ( Kovac, 2002 ). These students should be presented, within their undergraduate education, the chance to improve their scientific reasoning and critical thinking skills. We think that these three features, which might not be clear to all students entering the course, illustrate that students are expected to learn and problem solve in new ways—essentially to begin to “think like a chemist” (e.g., Platt, 1964 ).

While certain ideas within this article were presented in a preceding paper ( Wackerly, 2021 ), we intend to flesh out and expand upon some of those initial assertions in this manuscript and craft a more detailed hypothesis that the use of abductive reasoning is critical in the learning of organic chemistry concepts. Herein we provide support for this hypothesis by viewing it from a few different conceptual angles. First, we provide a science education overview on why learning certain organic chemistry concepts is considered challenging for students. Then, we briefly summarize the medical education viewpoint on the teaching of diagnosis and why this is important to many students in Organic Chemistry. Finally, using the lens of the Organic Chemistry curriculum we provide problem-solving examples of how abductive reasoning can assist in the teaching and learning of organic chemistry.

1.2 Why is science difficult to learn?

Johnstone asked this titular question in his seminal 1991 paper ( Johnstone, 1991 ). One conclusion that he drew, which has since been supported by a variety of other work (e.g., Graulich, 2015 ; Tiettmeyer et al., 2017 ; Reid, 2020 ; Dood and Watts, 2022 ), is that the nature and complexity of scientific concepts strain the working memory of students. To assist instructors in conceptualizing the strain of a given concept, he created the “triangle model” which illustrated three levels of thought ( Figure 1 ). He argued that the more levels a concept included the more cognitive load was placed on students.

Figure 1 . Reproduction of Johnstone’s model: “Triangle of Levels of Thought”.

One feature that might make learning science difficult is that the instructor, or expert, may not be aware of the extent of cognitive load they are placing on students, or novices. When “multicomponent phenomena that are invisible, dynamic, and interdependent” are presented to students, a large demand is placed on the working memory of novices ( Hmelo-Silver et al., 2007 ). However, experts are able to easily connect two or more cognitive components by “chunking several pieces of information together” ( Overton and Potter, 2008 ) and through years of practice ( Randles and Overton, 2015 ). Specialization within a discipline that requires connecting multiple levels will lower cognitive load for such repetitive tasks over time ( Tiettmeyer et al., 2017 ; Price et al., 2021 ). However, students have typically not been exposed to such tasks, let alone have the opportunity to consistently repeat them, and thus instructors need to disentangle new concepts that might cause cognitive overload for students so they can process and incorporate new material starting from their present knowledge base and scientific models. 3

“[R]easoning [is the] knowledge of some facts [which] leads to a belief in others not directly observed.” – C. S. Peirce

1.3 Why is organic chemistry so difficult to learn?

Here we argue that it should come as no surprise when former and current students of organic chemistry cite that organic chemistry is difficult to learn, because they are asked to problem solve and reason in new ways utilizing new content without prior exposure to, or repetition of, these scientific tasks. 4 Naturally, when a student enters a course they are expected to be ignorant of the course content since they enroll to learn it. However, students might feel that a bait-and-switch has occurred in Organic Chemistry because not only is the content new, but the logical processes required to be successful are also typically new to the students as well.

In prior scientific courses, which for most pre-health ( vide infra ) US students are two courses in general biology and two in general chemistry, students are typically required to perform recall (memorization) or reason algorithmically on summative assessment items ( Raker and Towns, 2010 ). While these skills hold value in organic chemistry, current organic chemistry education research shows that skills such as multivariate ( Kraft et al., 2010 ; Christian and Talanquer, 2012 ) and mechanistic reasoning ( Bhattacharyya, 2013 ) are more important. 5 Thus, inspired by the work in chemistry education research, the philosophy of science, and Johnstone’s seminal triangle, here we propose a tetrahedron model of layered reasoning strategies that are important for consideration by instructors when teaching novice organic chemistry students.

The bottom-most point of the tetrahedron ( Figure 2 ) was chosen to be memorization because it is not a reasoning skill. However, terms and chemical facts still need to be learned by students, which is often not a problem because they have developed this skill during their general biology and chemistry coursework. Algorithmic reasoning is a skill many students leaving General Chemistry assume they will utilize in Organic Chemistry because it was employed so frequently in that course. For example, if a student knows the pressure, temperature, and number of moles of an ideal gas, these students will likely be able to provide the volume of the gas’s container. While these mathematical and deductive reasoning skills remain relevant in the laboratory portion of Organic Chemistry and even for the IUPAC naming of organic molecules (i.e., there is a definitive rule set), they start to break down when chemical systems become more complex and chemical formulas evolve to contain more meaning in the form of chemical structures.

Figure 2 . Tetrahedron model of problem-solving in Organic Chemistry.

The right corner of the tetrahedron is for the set of competencies required to interpret diagrams in organic chemistry, such as visualization ( Gilbert, 2005 ), visuo-spatial reasoning ( Pribyl and Bodner, 1987 ; Habraken, 1996 ), and representational competence ( Kozma and Russell, 1997 ). In lieu of individually listing these skills, we designate this corner as perceptual learning, which integrates conceptual knowledge with a broad set of skills, including those related to visualization and representational competence ( Van Dantzig et al., 2008 ; Kellman and Massey, 2013 ). Perceptual learning “refers, roughly, to the long-lasting changes in perception that result from practice or experience” ( Connolly, 2017 ), and is beginning to be more deeply explored in organic chemistry pedagogy (e.g., Kim et al., 2019 ).

We briefly illustrate how changes associated with perceptual learning might take place with students. Consider, for example, that in General Chemistry students might be asked to calculate the heat of combustion of hexane (denoted at C 6 H 14 ). For most students at that stage, the sole association they would have with the compound’s name is its molecular formula, whereas its “zig-zag” structure might represent nothing more than a crooked line. As these students progress into Organic Chemistry and learn about different representational systems and constitutional isomers, the verbal representation “hexane” changes, this is because the term is now associated with five unique isomers each with unique connectivity, properties, and reactivity (e.g., radical reaction with Br 2 ). Through this process, the students’ perception for the term “hexane” changes from representing a single molecular formula to representing a family of five constitutional isomers each with a unique bond-line structure. This process continues as students advance to more complex structures (e.g., stereochemistry) and learn additional concepts like three-dimensionality, IMFs, physical properties, etc. We propose that the three corners of the tetrahedron discussed thus far are often directly connected to abductive reasoning which focuses on solving problems by generating the most likely most likely outcome of a chemical situation.

Our hypothesis includes the postulation that abductive reasoning is a complex reasoning skill for students in Organic Chemistry and should explicitly be taught in the classroom. While this idea has been presented by us previously ( Wackerly, 2021 ), here we will just provide a brief overview so we can move on to discuss the relevance of this reasoning skill within the Organic Chemistry classroom and to highlight some examples. Firstly, the term “abduction” ( Douven, 2021 ) is often used interchangeably with the terms “inference to the best explanation” ( Lipton, 2017 ) and “scientific hypothesis”—and below we will argue “diagnosis.” All of these terms hold common ground in that they use reasoning that connects various (similar or dissimilar) pieces of evidence/observations together in a way where a plausible conclusion can causally describe the collection of phenomena. 6 For example, say you are inside of grain windmill by the grindstone, and then you begin to see the stone rotating and producing flour. You will abduce that the weather outside has become windy. While this is a simple example only requiring you to understand that outside wind turns the sails and the sails, via a series of machinery, turn the grindstone, it is similar to the reasoning employed by expert organic chemists. Leaving the windmill and heading into your synthetic laboratory, let us say you wish to publish a new compound in the Journal of Organic Chemistry . According to the journal, to conclude that you have made this new compound you must “establish both identity and degree of purity.” Minimally, this means you will need to obtain a 1 H NMR spectrum, 13 C NMR spectrum, and HRMS spectrum then interpret the data present in the spectra to abduce the molecular structure of your new compound. This exact same skill that is required of expert organic chemists, is typically required of students in Organic Chemistry ( Stowe and Cooper, 2019a ). Thus, these students should be taught how to reason like expert scientists in order for them to develop into scientists ( Cartrette and Bodner, 2010 ). Just as the spectroscopic analysis example highlights, instructors of Organic Chemistry often profess a goal is for students to develop critical thinking and scientific problem-solving skills: Our hypothesis presented here is that instructors must explicitly utilize the abductive reasoning process within their teaching and assessment.

Solving problems that require abductive reasoning will also require skills from the three other points of the tetrahedron, which will render them cognitively complex. Teaching abductive reasoning in the classroom should not require additional formal training for instructors/experts since abductive reasoning skills have already been developed over the course of their careers. Further, philosophers have long held ( Harman, 1965 ) that humans utilize abductive reasoning as a matter of course in their day-to-day lives. Paralleling human logic, abductive reasoning has likely been utilized ( Pareschi, 2023 ) and will continue to be ( Dai and Muggleton, 2021 ) an integral part of artificial intelligence. This reasoning skill is particularly important for students required to take Organic Chemistry. It might be obvious that future scientists will need the skills to create new hypotheses and design experiments that could potentially refute current hypotheses, but in our experience, it seems less obvious to pre-health students that using abductive reasoning for problem solving in Organic Chemistry will play a critical role in their desired careers.

2 Framing for pre-health students (diagnosis)

2.1 why is organic chemistry relevant for pre-health students.

In a post-COVID world where test-optional admissions are on the rise and the future of post-graduate education feels increasingly uncertain, convincing students of the importance of Organic Chemistry goes beyond just passing the course. This is especially true for the majority of students taking Organic Chemistry who are pre-health majors. Instructors need to show students the connection between organic chemistry and the health field.

Thus, problem solving in Organic Chemistry can be framed as a diagnostic problem-solving tool–similar to what medical practitioners do when making a diagnosis ( Stowe and Cooper, 2019b ). By overtly showing students the parallels between medical diagnosis and organic chemistry problem solving, instructors demonstrate that students are not just being taught a bunch of facts–they are developing critical thinking skills they can use in the real world. Bridging the gap between theory and practice helps students see the bigger picture and gives them the tools they need to succeed in both their studies and future careers.

The parallels between medical diagnosis and organic chemistry problem solving should be readily apparent ( Table 1 ). Both involve analyzing complex systems (human body/chemical reactions) to identify patterns and relationships, emphasizing the importance of critical thinking and logic-based problem-solving skills, as well as using evidence. Both fields rely on the use of abductive reasoning ( Wackerly, 2021 ; Martini, 2023 ), although typically neither field explicitly states it to students. Table 1 uses simplified language accessible to students that describes the abductive theory of method (ATOM) in clinical diagnosis ( Vertue and Haig, 2008 ), and its parallel to expert thinking in organic chemistry.

Table 1 . Comparison of medical diagnosis to skills developed in Organic Chemistry.

For example, to “diagnose” the product of an organic chemistry reaction, first the background information, including structure, reactivity, and stability of the starting materials and reagents must be analyzed, which is similar to how medical professionals take patient history. Abductive reasoning is then used to generate the most likely answer. Finally, the hypothesis is tested through gathering evidence such as utilizing spectroscopic analysis which is similar to a physician ordering lab work or imaging. This is an iterative process, wherein multiple pieces of spectroscopic evidence are needed to point to the same answer. Similarly, a physician may order additional studies or perform physical exams to support or refute their medical diagnosis. Although the goals appear different, the same skills are developed such as drawing hypothesis based on empirical evidence. By explicitly demonstrating how these thought processes are parallel, instructors of Organic Chemistry may help students to appreciate the mental training they are receiving in the course.

Organic Chemistry has been deemed essential as a prerequisite for medical school by a panel of medical school professors of biochemistry ( Buick, 1995 ). While many current medical students do not think that the material covered in Organic Chemistry was a valuable part of their undergraduate curriculum, the majority agree that the critical thinking skills learned in the course were valuable ( Dixson et al., 2022 ). While there are those in the field of medicine who think that Organic Chemistry should be de-emphasized in the pre-med curriculum, those that defend Organic Chemistry do so for some of the same reasons we discuss herein, namely that the critical thinking and problem-solving skills in the course directly align with patient diagnosis ( Higgins and Reed, 2007 ).

This process of abductive reasoning coupled with framing for the medical field may serve the students better in both the short term and long term. Students who employ more metacognitive strategies such as the type we are advocating for here are better able to solve problems in Organic Chemistry ( Blackford et al., 2023 ). Connecting course material to students’ future career aspirations also leads to better engagement and course performance ( Hulleman et al., 2010 ). Additional benefits of this diagnostic reasoning process include students’ ability to apply this metacognitive strategy in other courses in their majors, such as biology ( Morris Dye and Dangremond Stanton, 2017 ), and their future medical careers ( Friel and Chandar, 2021 ). Therefore, diagnostic reasoning should be explicitly modeled and assessed in Organic Chemistry courses.

2.2 Using “diagnosis” in examples for students

While there are a variety of ways to teach students how to approach organic chemistry problems like an expert, we would like to present how to do this through the lens of “diagnosis.” Other ways of describing argumentation and the process of problem solving have been discussed in the chemical education literature (e.g., Cruz-Ramírez De Arellano and Towns, 2014 ; Stowe and Cooper, 2019a ; Walker et al., 2019 ) as well as the philosophy of chemistry literature (e.g., Kovac, 2002 ; Goodwin, 2003 ). While they differ in the number of steps and what those steps are called, the processes have a similar logical flow. First, gather evidence and make observations ( What you see ), link this to previous knowledge ( What you know ), and finally make a reasoned conclusion ( Hypothesis ) which is a logical consequence—often via abductive inference.

The following examples ( Figures 3 – 6 ) are designed to highlight the use of these three steps to explicitly diagnose problems from across the two semester Organic Chemistry sequence. This process can be used in the classroom as a model to guide students through the abduction process and could be used to explicitly scaffold problems. Moreover, instructors can use this model to ascertain the complexity of their assessments including the required prerequisite factual knowledge and the multiple steps required. The complexity of organic chemistry questions is determined by the number of “subtasks” the student must complete ( Raker et al., 2013 ), factual knowledge required, and facets of perceptual learning ( vide supra ). A number of explicit decisions were made in formulating the below questions. The discussion points are certainly not exhaustive, and practitioners should adapt questions to their own students and situations. The amount of information provided or not provided, such as the exclusion of lone-pairs and inorganic by-products, was chosen to be consistent with the information provided by practicing organic chemists and one goal of teaching organic chemistry is to facilitate the development toward expert-level practice. We intentionally included one example of additional information, Figure 4 entry marked with a *, to highlight that there are many more subtasks that could be utilized to assist with arriving at a probable conclusion, but we tried to exclude all other non-essential explanations. We do not suggest that all students should solve each problem from top to bottom as outlined here; in reality expert chemists often take different routes, based on the same evidence and premises, to reach similar conclusions. Although these problems are multiple-choice, we have modeled how to solve them as either multiple-choice or open format. The complexity of these questions can also be adjusted, for example in Figure 4 the mechanistic arrows could be included in the distractors and answer instead of in the stem. This type of alteration can allow for the assessment of mechanistic thinking (e.g., Bodé et al., 2019 ; Finkenstaedt-Quinn et al., 2020 ; Watts et al., 2020 ; Dood and Watts, 2022 ). The following examples demonstrate that when the diagnosis/abduction process is utilized, students can develop and enhance their problem-solving skills.

Figure 3 . Diagnosis of an aromaticity problem.

Figure 4 . Diagnosis of a mechanism problem.

Figure 5 . Diagnosis of a substitution/elimination question.

Figure 6 . Diagnosis of a predict the product reaction.

The first example shown in Figure 3 is a case of aromaticity ( Jin et al., 2022 ). Students will typically memorize the requirements and check the structure for being cyclic, planar, containing Huckel’s number (4 n  + 2) electrons, and a p orbital at every vertex (i.e., conjugated). However, this problem does not ask for a simple definition of aromaticity, but an application of the ruleset to a structure students would not have typically encountered. The diagnosis requires observations about the structure including recognition of the implicit lone pairs on the nitrogen atoms and the carbon–carbon π bonds, recall of the requirements of aromaticity, and then application of abductive reasoning to the concepts learned (e.g., in class) and perceived by the structural representation. It is easy to see that the 1,4-dihydropyrazine is cyclic, has 8 π electrons, and a p orbital at each vertex. However, this simple analysis would result in the structure being anti-aromatic, so the student must recognize that in order for it to be non-aromatic as the problem states, planarity must be disrupted.

The second example shown in Figure 4 is a curved arrow mechanism problem for a reaction not typically covered in the Organic Chemistry course sequence ( Sarode et al., 2016 ). Students must apply the rules of curved arrows and properly atom map to diagnose the correct product. 7 The pre-existing conditions for a mechanism question with arrows shown include the nature of curved arrows and the examination of the scheme will require atom mapping and keeping track of which bonds are broken and formed.

The third example shown in Figure 5 is a substitution/elimination problem ( Brown et al., 1956 ). Students frequently find these reactions challenging and may employ a variety of heuristic models to approach them. Just as medical diagnosis begins with gathering information (taking patient information), solving this problem begins with direct observation and application of what is known about the structure and reactivity of these molecules. The alkyl halide has a good leaving group and has tertiary electrophilic carbon classification while the t -butoxide reagent is electron-rich, bulky, and reactive. Students must reason abductively how these characteristics interact with each other. This iterative process first eliminates S N 2 due to the nature of the alkyl halide, then identifies E2 as the mechanism with the bulky alkoxide. Next, an understanding of thermodynamics vs. kinetics to differentiate the two possible E2 pathways. Finally, a re-examination of the problem indicates the less stable product is formed preferentially; this is best explained by steric crowding in the transition state of the reaction between the alkyl halide and alkoxide.

The final, and most complex, example shown in Figure 6 is a predict the product, addition reaction problem ( Inoue and Murata, 1997 ) that is analogous to halohydrin formation. The problem requires separate diagnoses as it is layered where advancement to the second part is necessitated by the successful completion of the first addition step. Students would need to differentiate between the nucleophilicity of the alcohol and π bond after recognizing them as potential nucleophiles. After using abduction to recognize the higher reactivity of the π bond, students should then reason that selenium is electrophilic, akin to bromine in Br 2 due to being polarizable and bonded to a leaving group. This diagnosis is supported when taking into account the stereospecificity of the transformation, which precludes carbocation intermediates. The second diagnosis requires that students recall the regioselectivity of reactions with 3-membered cationic rings at the more substituted carbon. The remaining nucleophilic oxygen atom can now react with a higher energy seleniranium ion. However, conformational analysis of the transition state is needed to discern the pseudo axial/equatorial approach of the oxygen atom on the seleniranium ion ( Figure 6 , bottom). Students would then need to apply their knowledge of chair conformations and the lower energy state when having ring substituents equatorial. Thus, the trans -oxacyclohexane is formed.

3 Conclusion and future work

While Organic Chemistry is often regarded as the most challenging undergraduate course in the US, we argue it has gotten a “bad rap” because students are not always prepared for the challenges that lie ahead when they enter the course. Students generally perform better on assessments when they employ metacognitive strategies (i.e., “thinking about thinking”). This has been demonstrated in a variety of courses ( Arslantas et al., 2018 ), including Organic Chemistry (e.g., Graulich et al., 2021 ; Blackford et al., 2023 ). The consensus is that students who employ more metacognitive strategies in Organic Chemistry are more successful in problem-solving tasks and are better able to use those strategies when they are explicitly modeled and scaffolded. We have argued that instructors of Organic Chemistry should teach and demonstrate how to think and problem solve via “diagnosis” (i.e., abductive reasoning) in their classrooms. We hypothesize that students may score higher on metrics that assess scientific learning when these types of diagnostic models are utilized.

As constructors of nationally standardized exams, we fully acknowledge that a lot of growth on organic chemistry knowledge assessment still remains to be achieved. For Organic Chemistry course instructors, we hope the above insight into abductive reasoning can also be used on the assessment side of teaching requirements. Namely, that the cognitive load placed on students when solving each problem be carefully considered when constructing summative assessment items. Though this point has been frequently made previously e.g., (see Raker et al., 2013 ), we believe it is worthwhile for all writers of questions in Organic Chemistry to map out, step-by-step, the logic required to solve each question to determine the cognitive load. This can, in turn, help these instructors teach from a novice-focused perspective—as opposed to the “sage on the stage.” The prior section provided examples with varying levels of complexity and demonstrated that cognitive load can be approximated by the number of reasoning steps (subtasks) required when the assessment piece is broken down. Further, this process could potentially also help the exam writer identify if items require little to no scientific reasoning (e.g., pure memorization questions).

The above manuscript merely outlines a hypothesis that we have generated over the course of our time teaching Organic Chemistry with this “diagnosis” method of abduction. To fully explore its validity, educational research is needed. This will be a precarious endeavor, because measuring the efficacy of teaching abductive reasoning will require assessment of scientific thinking skills in Organic Chemistry, and, as we just pointed out, there are already strong arguments that we are still quite far away from such valid assessments. However, we can be sure that if you are teaching Organic Chemistry from the perspective of your experience and expertise as an organic chemist, then opening a window for your students into how you think and problem solve will benefit your students. Our position is that instructors of Organic Chemistry should not only be explicitly teaching students the abductive reasoning skills to tackle complex problems, but they should also frame it as “diagnosing” the chemical situation.

Data availability statement

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

Author contributions

JW: Writing – review & editing, Writing – original draft, Conceptualization. MW: Writing – review & editing, Writing – original draft. SZ: Writing – review & editing, Writing – original draft.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Acknowledgments

The authors would like to thank Gautam Bhattacharyya for helpful discussions during revisions of this manuscript, specifically regarding perceptual learning theory. JW would like to acknowledge his undergraduate Organic Chemistry professor, Thomas Nalli, on the recent occasion of his 65th birthday for teaching him scientific problem-solving skills and fostering his interest in the discipline.

Conflict of interest

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

Publisher’s note

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

1. ^ We wish to keep the focus of this manuscript on the relevant student population of the Organic Chemistry course sequence. Students intending to pursue medically relevant careers which require advanced degrees (e.g., medical, dental, optometry, pharmacy, etc.) are a large portion of this population. However, if the reader is curious, we specifically write for the dental and optometric admissions exams.

2. ^ In this manuscript we attempt to provide the reader a broad overview of important chemical education and philosophy of chemistry publications. Since this is not a review article and the scope is quite a bit smaller, all possible relevant literature has not been cited.

3. ^ Cognitive overload could also stem from misconceptions and oversimplified concepts, such as the oft-stated “breaking bonds in ATP releases energy” from introductory biology courses.

4. ^ This can be contrasted with General Chemistry which repeats some of the content of the high school chemistry.

5. ^ Multivariate and mechanistic reasoning are highlighted as examples because they often require combining features from all four points of the tetrahedron.

6. ^ The conclusion need not explain the entire collection of evidence as some may be irrelevant, and they are unrelated to the conclusion. However, the entire collection may not contain a piece of evidence that refutes the conclusion. Thus, abductive reasoning can be useful in differentiating science from non-science and pseudoscience.

7. ^ While one could argue that the diagnosis/answer to the problem presented in Figure 4 does not require abductive reasoning, we have included it because the skills required here can be applied to more complex problems that, for example, include mechanistic reasoning ( vide infra ).

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Keywords: abduction, abductive reasoning, organic chemistry, diagnosis, metacognition, problem solving, pre-health education

Citation: Wackerly JW, Wentzel MT and Zingales SK (2024) Teaching abductive reasoning for use as a problem-solving tool in organic chemistry and beyond. Front. Educ . 9:1412417. doi: 10.3389/feduc.2024.1412417

Received: 04 April 2024; Accepted: 05 July 2024; Published: 06 August 2024.

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Copyright © 2024 Wackerly, Wentzel and Zingales. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Jay Wm. Wackerly, [email protected]

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

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5 reasons deep fakes (and elon musk) won’t destroy democracy.

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Elon Musk's sharing of a deep fake video of Kamala Harris raises concerns among AI experts.

“I have always had an especially great desire to learn to distinguish the true from the false, in order to see my way clearly in my actions, and to go forward with confidence in this life.”

When René Descartes wrote this in 1637, he had never heard of deep fakes . And he certainly hadn't heard of Elon Musk.

Yet he seemed to struggle with the same issues that have been raised by the media since Musk last week shared a deep fake video of Kamala Harris.

Descartes wanted to find a method to "conduct one's reason well” and to “seek truth in the sciences." But although he was very successful in disseminating the latter and is today considered the founder of modern science, most people do not know his approach to ‘conducting one’s reason’ when surrounded by misinformation.

If they did, they would be less afraid of deep fakes destroying democracy.

Best High-Yield Savings Accounts Of 2024

Best 5% interest savings accounts of 2024, we know how to navigate misinformation.

Just like the experts quoted in the daily press today, Descartes was concerned about the power of fake information to mislead people. But unlike AI experts and government advisers, he did not warn against AI tools harming people and democracy. Instead, he warned against relying too much on the information we get from books, schools, and even our own senses.

Descartes’ ‘method for conducting one’s reason well’ was essentially about doubting everything. And he was not the first in the history of philosophy to question how much we can and should trust what appears before us. Nor was he the last.

In the allegory of the cave, Plato compared man to prisoners who are tied up in a cave and therefore do not see things as they truly are, but only as they are presented to them as shadows on a wall.

This sparked 2,400 years of philosophical reflection on how we can know if something is true and what enables us to act responsibly even if we can’t.

The short summary of these 2,400 years of reflection is that, although we humans have always had a great desire to learn to distinguish the true from the false, we have never succeeded in developing a scientific method to do so.

Instead, we have built up millennia of experience navigating responsibly without having a method. And this is also what we do when faced with new technologies and people who use them to prevent rather than promote our ability to see our way clearly.

5 Shadow Skills Help Us Resist Deception

Combining insights from Plato, Descartes and a number of other great philosophical thinkers, I have identified five ‘shadow skills’ that enable us to withstand the dangers of phenomena such as deep fakes.

I call them shadow skills because no one was ever taught when and how to use them (that's how it is when there's no method). And yet everyone regardless of gender, age, ethnicity, job, religious beliefs, etc. uses them daily.

The five shadow skills offer five reasons why deep fakes won’t destroy democracy. In short, we are able to withstand the dangers because:

1. We define what we (don’t) want

The fact that we have come up with a name for 'deep fakes' means we take it seriously as something to be aware of and actively decide how to deal with.

2. We distinguish true from false

As 2,500 years of philosophy shows, we have always had a desire to distinguish true from false. We don't always know WHAT the difference is, but we know THAT there is a difference and that is enough to maintain a sense of reality.

3. We doubt what we see and hear

Descartes' idea of ​​an evil genius, seen in countless variants, including Nick Bostrom's simulation hypothesis , is a reminder of our ability to doubt not only what seems foreign to us, but also what we most take for granted.

4. We discuss what’s important

We have countless ways and mechanisms to debate things. Personal and public, formal and informal, local and global. We base our actions on a multitude of interactions, and only a fraction of them take place through digital technologies.

5. We decide what to do

Although we may fear the opposite, we make our individual and collective decisions based on 1-4, not on single videos shared by single individuals.

So, am I saying we shouldn't be careful?

No. I’m saying we know how to deal with deep fakes and people like Musk. We've been finding ways to deal with things and people like them since the beginning of time. And as long as we define, distinguish, doubt, discuss and decide, we will find ways to deal with them to the end.

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