Nature of Science FPLC Kincaid 2005-6

From CTLpedia

Teaching the Nature of Science in an Integrated Biology, Geology and Math Course for High School Teachers; Brad Kincaid -- Director, Center for Teaching and Learning and Facutly, Life Science Department, Mesa Community College. Contact me

Summary: This project was a small part of our overall FLC initiative to consider the questions, what is the nature of science and how should we teach it? Here I review our FLC goals and objectives and then document my FLC focus course project. The latter was a course I taught for high school teachers that are part of an ASU math and science program. The instruction modeled inquiry teaching and learning methods and included some explicit instruction on some aspects of the nature of science and math. Pre- ant post-testing of the class showed gains in understanding of some aspects of science. Although the gains were statistically not significant and the 95confidence level, small sample size and weak experimental design limit conclusions. The data at least may characterize NOS understanding among high school teachers in this community and provide some baseline for . future comparisons. Further evaluation may also help us determine the effectiveness of the assessment instrument used. Herein, I also describe current status of my understanding of the nature of science based on my class notes, our FLC readings, and discussions with colleagues from both MCC and ASU. I hope that these thoughts will eventually lead to some consensus regarding our scientific inquiry student outcomes.

Contents 1 Project Goal 2 Objectives 3 Focus Course Information 3.1 Weeks/Investigations 3.2 Course Goals for the Students (Teachers) 4 Focus Project 5 Assessment Procedure 6 Assessment Results 7 Focus Project Conclusions 8 Reflection on Nature of Science Outcomes 9 References

Project Goal

MCC has established scientific inquiry as one of our expected student outcomes. As is generally the case nationwide, MCC students have fallen short of our expectations in this area of our outcomes assessments. There is both a need and an opportunity to transform science education at MCC.

The goal of our Nature of Science faculty Learning community is to promote the understanding and appreciation of the nature of science, technology, engineering and math by all students to enable them to be productive citizens, professionals and scientists of tomorrow.

Objectives

1. Reach a consensus regarding the nature of science (and math).
2. Assessment of faculty understanding of the nature of science.
3. Determination of scientific inquiry student outcomes.
4. Improve our students' understanding of the nature of science.
5. Improve our students' appreciation of the nature of science.
6. Enable our students to be informed and productive citizens of tomorrow.
7. Enable our students to be informed and productive professionals of tomorrow.
8. Enable our students to be informed and productive scientists of tomorrow.
9. Formation of a respected interdisciplinary group of STEM educators.
10. Develop curriculum frameworks for achieving our NOS learning outcomes.
11. Implement, assessing and disseminating curriculum frameworks for achieving NOS learning outcomes.
12. Collaboration to transform STEM education at MCC.
13. Evaluation of our FLC strategy for enhancing teaching and learning.
14. Disseminate our FLC strategy especially to other community colleges.
15. Develop recommendations for assessing student understanding of NOS.
16. Pilot an assessment of student understanding of NOS.
17. Collection of baseline data on student understanding of NOS.

Focus Course Information

This course introduces selected topics in biology and geology using an inquiry-based instructional method called the learning cycle. The topics represent contexts in which you may apply the mathematical concepts and tools you learned in previous Project Pathways courses and construct and use new mathematical concepts and tools. The primary goal is for you to better understand how science works, how mathematics works, and how they connect. Some assignments will also help you apply your new knowledge to improve your classroom instruction.

The students were 12 math and science teachers from a local high school district participating in an NSF-funded Math Science Partnership project directed by Marilyn Carlson of the ASU Center for Research on Education in Science, Math, Engineering and Technology (CRESMET).

Weeks/Investigations

1. How do babies take in air and milk?
2. continued
3. How do students think?
4. What causes intra-specific variation?
5. continued
6. What happens to phenotypes and genotypes aross generations?
7. continued
8. What history can we read in the Earth?
9. continued
10. How old is the Earth?
11. continued
12. How do populations grow?
13. Participant projects and presentations
14. continued
15. continued

Course Goals for the Students (Teachers)

1. Gain a deeper understanding of the nature and goals of science and mathematics and their connections.
2. Become better scientific and mathematical thinkers.
3. Gain deeper understanding of specific concepts such as rate of change and function.
4. Improve your ability to model effective scientific and mathematical thinking behaviors when teaching.
5. Shift your instruction away from factual memorization toward inquiry and explanation generation and test.
6. Gain new understanding of student development and learning and apply that understanding to improve your teaching.
7. Improve the quality of your homework and your exams.
8. To become a critical consumer of supplemental materials and to be able to create and adapt them to enhance student development and learn.

Focus Project

What causes students to achieve a better understanding of the nature of science and math (NOS)? I hypothesize that inquiry instruction, which is teaching science by doing science, causes improved understanding of NOS. If inquiry instruction causes improved understanding of NOS and I teach our integrated biology/geology/math class with inquiry methods, then students should show significant improvement in understanding of NOS on a NOS post-test relative to the pretest.

Assessment Procedure

The assessment instrument was developed by Professor A.E. Lawson of the ASU School of Life Science and Center for Research on Education in Science, Math, Engineering and Technology. The instrument comprised 30 questions. The first 20 questions were related to science, whereas the final 10 questions assessed the nature of math. Each question was a statement about science or math about which the respondents choose to strongly disagree, disagree, neither agree nor disagree, agree, or strongly agree. For most analyses, I pooled the agree and strongly agree and the disagree and strongly disagree to determine the appropriateness of their responses. It is also interesting to look at the strongly agree and disagree only responses because the statements were written such that these responses were usually the target responses.

The pretest was administered in class on the first day of the semester, and the post-test was given on the last day of class. all 12 students took both the pretest and post-test. Improvement between pre- and post-tests was tested with a paired t-test in an Excel spreadsheet.

Assessment Results

The mean pretest score was 16.9 (SE= ) or 56.3and the mean post-test score was 18.5 (SE= ) or 61.7 The gain in the post-test relative to the pretest is equal to a 9.5increase. To test the learning hypothesis, we must compare the results to the prediction of a significant improvement between pre- and post-test scores. I recognize that this is a weak test of this hypothesis. If a significant difference is found, it will only provide weak support for the hypothesis because there are other potential causes for the difference. Similarly the hypothesis could be true even if no significant difference is found because of the weak design. Addition of comparison groups and more replicates would help the design.

This comparison requires that we test the statistical hypothesis that the pre- and post-test scores were likely to have been drawn from the same sampling distribution. If they were likely to have been drawn from the same sampling distribution, then the test statistic will fall within some confidence limits of its sampling distribution and the learning hypothesis is not supported. If pre- and post-test scores were not likely to have been drawn from the same sampling distribution, then the test statistic will fall outside the confidence limit and the learning hypothesis. Thus, to support the learning hypothesis, you must not support the statistical hypothesis and vice versa.

The paired t-test statistic (t= ) fell within the 95confidence interval of the sampling distribution, so we should support the statistical hypothesis and not support the learning hypothesis. However, consistent with the nature of science, we can never totally disprove a hypothesis (but we can show that it is very unlikely to be true). In this case, the probability that these differences are from chance alone is only 0.09. Therefore, there is a 91chance that these are real gains in learning. This result is based on a very small sample size, which can limit the power of a test to distinguish true differences, so it could be fruitful to explore this learning hypothesis in a larger study.

The Figure above shows that increases in post-test scores were consistent across the science and the math questions. The increases are roughly proportional to the number of questions and and pretest means. Consideration of the individual questions revealed that only 6 of 30 questions exhibited a negative change in learning, whereas the number of appropriate responses increased on 17 questions. The largest negative change occurred on one question and indicated an increase in misconceptions about scientific conclusions. The largest positive gains were seen in several questions on different aspects of hypothesis testing. There were relatively small gains in the math questions for this class of whom 8 of 12 were math teachers.

Areas of NOS where at least 75 percent of students demonstrated good understanding included 7 items starting with those most understood.

1. The primary goal of science was to explain natural phenomena.
2. Science theories are evolutionary in nature (2).
3. Scientific hypotheses must be testable and involve creativity (2).
4. Predictions are necessary to test hypotheses.
5. Scientific discoveries are not based on luck.

Areas of NOS where fewer than 25 percent of the students demonstrated understanding included 4 items starting with those least understood.

1. The primary goal of science is NOT to describe natural phenomena.
2. A theory that gains support does NOT become a law.
3. A hypothesis that gains support does NOT become a theory.
4. A hypothesis is NOT an educated guess of what will be observed under certain conditions.

Areas of the nature of math where at least 75 percent of students demonstrated good understanding included 3 items starting with those most understood. There were no items for which less than 25 percent gave correct responses (recall that these were mostly math teachers).

1. A theorem is a consequence of logical argument built from axioms or other theorems.
2. Constructing an accepted mathematical proof means constructing a convincing argument.
3. Postulates and axioms are the terms that name mathematical assumptions.

Focus Project Conclusions

The students (8 of 12 were math teachers) in my small study started with a relatively poor understanding of the nature of science and only a slightly better understanding of math. The inquiry instruction, which teaches science by having the students do science, produced some small, non-significant increases in their understanding. Inquiry instruction has been shown elsewhere to increase scientific reasoning skills in students, but it may be that more explicit instruction is needed to promote significant gains in NOS understanding. Another factor that may have affected gains in this study is that students were not graded on their NOS understanding. Students generally will learn what they are evaluated on.

Use of this NOS assessment instrument has made it clear that it needs some revisions for future use. In some questions, a single word changed the appropriate response. We also need to ensure that all of the important aspects of NOS are covered in the instrument.

Reflection on Nature of Science Outcomes

What is the nature of science to you, and how do you teach it in your discipline?

1. How do I think about how I teach the NOS for Biology?

I have been thinking about NOS in teaching for many years. In about 1990, I adopted an inquiry approach to teaching based on the philosophy that we should teach science by having students do science. The core of inquiry instruction is H-D reasoning to address preconceptions and build scientific reasoning skills.

NOS was also a significant topic of consideration in my graduate work because ecology was accused of being a soft science and we wanted to be sure that we were taking a rigorous approach. Moreover, evolutionary theory, which was central to our work, was accused of being teleological. Thus, it was critical for us to examine our reasoning to be able to defend it.

2. What Terminology do I use and how do I use it? What is NOS? (NOS terms bolded in text below.)

I believe that Science is both our accumulated knowledge about nature and a process for developing scientific knowledge. It also includes a variety of assumptions and limitations.

The process is the scientific method, which although not totally rigid, appears to consistently employ hypothetico-deductive (HD) reasoning across all scientific disciplines. The process can be described as a sequence, but not all elements of the sequence are always part of a scientific investigation. The sequence begins with a puzzling observation about nature, which promotes a question. The most informative question is "what caused the phenomenon?" This is a causal question, but there can also be descriptive questions, which seek to develop accurate descriptions of variations in nature as an intermediary to developing an explanation of the natural phenomenon or without immediate concern for the causes.

Many people have said that not all scientists use HD reasoning (i.e., test hypotheses). Some have said that induction is more central to the nature of science. I agree that induction along with analogical reasoning (abduction) and retroduction help us identify reasonable hypotheses and theories. However, I think that testing hypotheses with HD reasoning is what sets science apart from other ways of knowing.

I would agree that that scientists do not do science all the time, and maybe some trained as scientists never actually do science. However, I would argue that those scientists who do not do HD reasoning are unlikely to be among those at the forefront contructing theoretical knowledge that produces successful explanations of nature. And if they are constructing theories, then they will have the hardest time convincing their peers of the validity of thier untested theories. I would further ask that if HD reasoning does not characterize science, then what distinguishes non-HD science from other ways of explaining nature?

(Zull 2002 suggests that testing hypotheses is central to deep learning, so maybe HD reasoning is fundamental to all learning.)

Hypotheses (or more general theories) are posed as tentative answers to the causal question. With the hypotheses in mind, a potential planned test of the hypotheses is imagined. The test may be a formal experiment, a search for correlation,circumstantial evidence, or an examination of existing data (retrodiction ),but it must produce different predictions or expected outcomes for each of the competing hypotheses. If not, the planned test must be redeisgned. Conducting the test produces the actual outcome or results, which are compared to the predictions to draw a conclusion. If the results match the prediction for a hypothesis, then the hypothesis is supported. If the results do not match the prediction, then the hypothesis is not supported. Thus, hypotheses are either supported or not supported. (see below about our inability to prove or disprove hypotheses)

The scientific method is affected by social influences. First the hypotheses considered are affected by individual and cultural biases, which may limit the range of causal factors considered. Fortunately, the scientific collaborations, scientific replication, and strict peer review processes tend to correct limited views. Moreover, the collaborative construction of our knowledge about nature is fundamental to our dramatic successes in understanding nature and applying that knowledge to solve human problems.

The scientific process uses logical arguments. Analogical reasoning(abduction) helps us successfully identify hypotheses that potentially answer our causal questions, but also introduces cultural biases into the process. Historically, induction was thought to be used in hypothesis generation, but apparently recent studies suggest that abduction serves this function. Then deduction is employed to connect logically hypotheses, planned tests, predictions, and results to draw conclusions. In this HD argument, it is useful to consider the phenomenon in question to be a dependent variable (y) and the potential causal factors (x’s) to be independent variables. Thus, if x causes y and we manipulate the values of x (or observe variation in x, or see situations in which there probably was variation in x), then there should be (or have been) concomitant variation in y. If results equal the expected variation in y, then we conclude that the evidence supports the hypothesis: x causes y. If the results are not equal to expected variation in y, then we conclude that the hypothesis is not supported.

Planning and designing tests of hypotheses is central to the process of science and requires considerable creativity. This is espeically true for controlled experiments, but also for correlational or circumstantial studies. Well stated hypotheses facilitate test design. For example, if you hypothesize that x causes variation in y, then you must design an experiment in which you manipulate values of x (holding all other independent variables constant) and measure values of y (or some surogate for it) under each condition to obtain your results. It is almost inconceivable to design any experiment without an explicit hypothesis in mind.

Hypothesis testing generally requires statistical analysis, but statistical hypotheses are distinct from explanatory or descriptive hypotheses. Statistical hypothesis testing is embedded within the logical argument of the explanatory hypothesis testing. Again, it is very difficult to do an appropriate statistical test without an explict hypothesis statement. Moreover, most statistical tests require a priori (before the experiment) hypothesis statements for accurate estimation of the signifiance level for a difference from the statistical null hypothesis. For example, consider the following.

1. If explanatory hypothesis/theory correct
2. And we imagine a planned test
3. Then we predict an expected outcome
4. Actual test = Results
5. Compare Results to Predictions to draw conclusion, but how do we know if similarity or difference could be from chance alone?
6. Need to test statistical hypothesis that results equal random expectation.
   1. If observed results are from random variation (statistical null hypothesis)
   2. And we build a sampling distribution for our measure of the difference between observed and expected when null hypothesis is true (statistical test),
   3. Then value of measure for observed data should fall within certain confidence limits of the sampling distribution (statistical prediction)
   4. Compute measure and generate/select sampling distribution (Could be parametric distribution (Normal, Poisson, X2, etc. or could be randomization)
   5. Compare measure to sampling distribution and calculate probability that measure could be obtained by chance alone
   6. Conclusion: support or reject statistical hypothesis
7. Conclusion: support/non support of explanatory hypothesis based on rejection/support of statistical hypothesis
   1. Notice for explanatory hypotheses statistical support = explanatory reject and statistical reject = explanatory support
   2. For descriptive hypotheses (e.g., for testing the fit of a model to observations), statistical support = explanatory support and statistical reject = explanatory reject.

This view of science makes several assumptions including: 1) an objective reality exists, 2) natural phenomena have natural causes, 3) nature is not totally chaotic and 4) the most parsimonious explanations are more likely than others to be correct (Ockham's razor). An unwritten assumption may be that the best explanations are not advanced by force of authority nor by democratic consensus, but rather by winning in a competition of ideas.

Science is also subject to several limitations, which have been said to actually promote the success of science. Science requires testable hypotheses, so it cannot deal with explanations for which no one can imagine a test that will give different predictions for alternative hypotheses. Thus, science cannot test supernatural causes. Science can never prove any hypothesis or theory to be true. Science employs deduction in which one can never discount the possibility that a result will sometime be observed that will contradict the hypothesis. Furthermore, science can never disprove any hypothesis. Although testable hypothesis must be potentially falsifiable, in practice, we can never be certain that contradictory evidence did not result from a flawed experiment or some biased interpretation of the results. Even though the scientific processes and conclusions are inherently tentative and uncertain, careful application of the processes can minimize uncertainty and produce conclusions that are extremely successful in predicting nature and that are rarely rejected inappropriately.

Science is also our accumulated knowledge about nature and includes descriptions of nature, which are collected as facts and laws, and general explanations of natural phenomena, which are called theories. The latter often subsume the former; that is, theories often incorporate facts and laws to explain general phenomena.

Theories are general explanations of natural phenomena and usually represent collections of hypotheses (which often are described as postulates of the theory). They can also be considered to be conceptual systems or models of natural phenomena. As models, they are not just physical, mathematical or heuristic representations of natural phenomena. Scientific models (theories) are more than mere descriptions; they are abstract, mental explanations for natural phenomena.

The term theory is itself a neutral term. Whereas our accumulated knowledge about nature is the collection of theories that have been supported by all credible scientific evidence, theories comprise untested theories, rejected theories, and embedded theories. Untested theories are just that, untested, and rejected theories are those theories posed but that have not withstood the rigors of the scientific process. While they may never be absolutely disproven, their liklihood of being true is vanishingly small given all the contradictory evidence. Embedded theories are those theories that have been supported by all credible scientific evidence and thus become embedded in our general understanding of nature.

A hallmark of embedded theories (usually lacking the adjective when the context is clear) is their predictive ability. Interestingly, many descriptive theories can make very precise predictions while doing little to advance understanding, whereas corresponding explanatory theories advance our understanding greatly but are often unable to make precise predictions because of their complexity.

Historically, scientific theories have been organized along content categories or disciplines. However, it seems more likely today that disciplinary boundaries are artificial and may actually impede our progress in understanding nature. Interdisciplinary studies are becoming the norm at the frontiers of science.

Because of the limitations of science, scientific knowledge must always be considered to be tentative and uncertain. Nevertheless, the predictive ability of theories makes it likely that these explanations of nature are stable and durable. Thus, they are unlikely to be radically changed very often. However, scientific theories do evolve as new discoveries and inventions are made. In the history of science, there have also been revolutionary shifts in our understanding of nature. These revolutions are brought about by paradigm shifts allowing the phenomena in question to be viewed from new perspectives.

Technology is closely associated with science, but is distinct in that it comprises applications of science to invent new processes and products that address societal needs. The distinction between applied and basic science is becoming blurred. Potential applications are considered in almost all scientific endeavors. It is in applied science and technology where ethical considerations are most critical. Technology also expands the horizons of science because it allows us to investigate aspects of nature that are not possible without it. Most experiments involve some technology to make measurements or to increase the precision and accuracy of measurement.

Science may be of limited application for political decisions because of the human biases and uncertainty inherent in science and because multiple disciplinary perspectives often do not provide a unified view of natural phenomena. For example, the systemic view of an ecologist may be at odds with the molecular view of an geneticist who are both investigating the effects of genetically modified crops.

Nature of math??

3. How is data collection done in my discipline?

Pretty much as I have described above.

4. What am I doing in my classroom to promote the understanding of NOS for my discipline? How do I teach NOS?

I employ activities like the Mystery Box and Water Rise labs to explicitly teach the nature of science.

I also teach using an inquiry instructional method called the learning cycle (LC). LC instruction follows a cycle comprising three phases: exploration, term introduction and concept application. Some descriptions of LC instruction include five phases wherein they add an engagement phase to begin and an evaluation phase at the end. The central three phases follow and are derived from the scientific process. A key principle of inquiry instruction is that the term introduction always follows exploration of the pattern that defines the term. Thus, the definition is developed and experienced before the term is introduced.

LC instruction was designed for lab instruction, but can be employed in lecture settings.

1. Exploration phase:
2. Term introduction phase:
3. Application phase

5. How would I do research in my specific field (is that different or the same as to how you teach it in your classroom)?

I am attempting to teach the same way that I would do research. I admit that I would probably be a much better researcher now that I have thought more deeply about the nature of science. Tony Lawson has carefully examined the research of some famous scientists making explicit the apparent scientific reasoning of their work. In a few cases, he shared his analysis with the researchers to get their feedback. On at least one occassion, the researcher reported back that his analysis helped them to clarify the next steps for their reseach program!

References

TBA