In a typical K12 science class, the nature of science lesson — often with a misleading title like “The Scientific Method” — sits distinct and isolated at the beginning. Teachers, some with no formal scientific training, may fumble through the lesson simply repeating what is in the textbook. Most teachers get some of the nature of science wrong, promoting, for example, the misunderstanding that a hypothesis is an educated guess and no different from a prediction.1 Any attempt to further expose students to the nature of science is then abandoned, left behind in late August or early September at the expense of content and canned ‘labs’ where students simply follow written procedures.
This was how I was taught in my science classes and thus how I taught biology for my first eight years: mostly content, little science.
I do love teaching the content of biology. I love teaching students about the conversion of light energy to chemical energy and trophic cascades and the mechanisms of evolution, but the content of biology is not the most important thing I teach my students. Indeed, science content in general, while important, is not the most important thing we teach students in our science courses.
The most important thing we can teach our students is how to think like scientists. What this means for our classrooms is doing science every day.
The National Academy of Sciences (NAS) defines science as, “the use of evidence to construct testable explanations and predictions of natural phenomena as well as the knowledge generated through this process.”2 But my favorite informal definition is from “Faith vs. Fact” by Jerry Coyne: Science is “the set of methods we cite when we’re asked ‘How do you know that?’ after making claims such as, ‘Birds evolved from dinosaurs,’ or ‘The genetic material is not a protein, but a nucleic acid.’”3
Therefore, science is literally all of the tasks, including the trial and error, unique to the problem at hand, that we must carefully complete as we investigate patterns, test claims, and ultimately generate new knowledge.
Getting students to think like scientists and successfully perform these tasks is not easy; it means designing curriculum around content that gives students opportunities to:
- Know and embrace failure;
- Understand how to use statistics to quantify uncertainty and make inferences about populations from sample measurements;
- Know that science isn’t just content — a body of facts about the natural world — but also the methods we use to generate and confirm new knowledge;
- Creatively invent methods to test both new and old patterns and explanations; and
- Describe and test patterns in nature and generate testable explanations for those patterns.
This list is a tall order for any teacher. But one method of moving students in the direction of thinking like scientists is to develop inquiry experiences that incorporate one or more of these components because the inquiry is designed to generate messy data. The classroom data my students often generate can be messy enough that one class may find statistically significant support for a hypothesis while another class will not.
I provide my students many open-ended inquiry experiences where they design their own methods around some limited set of materials and equipment — and failure is common. As inexperienced scientists, their methods are often faulty due to a lack of controlling variables — indeed even our best scientists can be haunted by uncontrolled variables coupled with false assumptions (auxiliary hypotheses about the world that are not necessarily true). With each experience, students become more thoughtful about their own methodology and control, and move a little closer to what scientists actually do.
But training in experimental design is not enough.
We live today with overwhelming access to online information. Any citizen with a Web-connected device and a question about the world can ‘just Google it’ and be flooded with possible answers. But few citizens have the training, and more specifically, the scientific literacy to distinguish evidence-based answers from opinions, or legitimate scientific facts from pseudoscience and nonsense. No amount of scientific course content or training in experimental design can protect people with little knowledge of the nature of science and scientific reasoning from the lure of pseudoscience and nonsense. This mistake is especially likely if the answer they get is the one they were hoping to find — classic confirmation bias.
To that end, we must encourage our students to be thoughtfully skeptical of both their own and others’ claims, evidence, and reasoning; use logic to assess those claims; and be willing to have their own claims, evidence, and reasoning critiqued by their peers.
There are two strategies for critically assessing claims that I have found useful over the years. Both strategies are based on the claim-evidence-reasoning (CER) model. The first strategy comes from the textbook we use at my school in our first-year advanced biology courses, “Biology: The Unity and Diversity of Life,” 12th Ed. by Staar, et al.4
When confronted with a claim, the strategy prompts the students to take the following steps when asking themselves about the claim:
Step 1: What claim am I being asked to accept?
Step 2: What evidence supports the claim? Is the evidence valid?
Step 3: Is there another way to interpret the evidence?
Step 4: What other evidence would help me evaluate the alternatives?
Step 5: Is the claim the most reasonable one based on the evidence?
A more formal version of this kind of strategy is provided in the book, “Understanding Scientific Reasoning,” by University of Minnesota Philosophy of Science professor Ronald Giere and his colleagues. Giere and colleagues have described what they call a “program” for evaluating theoretical hypotheses (claims/explanations). The strategy goes roughly as follows (Giere, et al. 2006):5
Step 1: Identify the aspect of the real world that is the focus of the claim.
Step 2: Identify and describe a theoretical model that should fit with the real world if the claim is valid.
Step 3: Identify a prediction, based on the model and experimental setup identified, that says what data should be obtained as evidence if the model actually provides a good fit to the real world.
Step 4: Identify the data that have actually been obtained by observation or experimentation involving the real-world objects of study.
Step 5: Reasoning: Ask if the data agree with the prediction.
- If ‘No:’ Conclude that the data provide good evidence that the model does not fit the real world.
- If ‘Yes:’ First ask if there are there other plausible models that would yield the same prediction about the data. If the answer is ‘No,’ conclude that the data do provide good evidence that the model does fit the real world. If the answer is “Yes,” conclude that the data are inconclusive regarding a fit to the real world.
Getting students to think like scientists by using logical strategies like the ones outlined above can go a long way. This is especially true when students are faced with scientific claims that don’t fit comfortably with their own worldviews. Indeed, all of our students, regardless of whether or not they will pursue careers in science, are and will continue to be faced with issues, questions, and claims that may conflict with their worldviews, or are suspect and require careful and methodical analysis.
Getting students to think like scientists is critical. If for no other reason, the public must understand how science works because the most important goal of science education in a democracy is to produce a future consensus of public policy makers and an informed electorate who have a scientific understanding of the natural world. Thus, the most important thing we can teach our students is how to think like scientists.
1. Strode, P. K. (2015). Hypothesis generation in biology: A science teaching challenge & potential solution. The American Biology Teacher 77:17-23.↩ 2. National Academy of Sciences. (2008). Science, Evolution and Creationism. National Academies Press.↩ 3. Coyne, J. A. (2015). Faith Versus Fact: Why Science and Religion and Incompatible. New York, Viking.↩ 4. Starr, C., R. Taggart, C. Evers, & L. Starr. (2009). Biology: The unity and diversity of life, 12th Ed. Cengage Learning.↩ 5. Giere, R. N., J. Bickle, and R. F. Mauldin. (2006). Understanding Scientific Reasoning, 5th Ed. Wadsworth, Cengage Learning.↩
Paul K. Strode is a biology teacher at Fairview High School in Boulder, CO. Except for five years completing a doctorate in ecology and evolution at the University of Illinois at Urbana-Champaign, Strode has been teaching high school science since 1991. Strode has a master’s in science education from the University of Washington (Seattle), has authored several journal papers on the effects of climate change on bird migration ecology, coauthored the book, “Why Evolution Works (and Creationism Fails)” with Matt Young, and has recently published a Nature of Science paper in The American Biology Teacher. Paul Strode also blogs as Mr. Dr. Science Teacher.