Teaching Chemistry to Non-Science Majors by Modeling
Research Activity
Francis J. (Frank) Schmidt,
Department of Biochemistry, University of Missouri-Columbia, Columbia MO 65212,
U.S.A.; schmidtf@missouri.edu
John E. Adams, Department of
Chemistry, University of Missouri-Columbia, Columbia MO 65211, U.S.A.; adamsje@missouri.edu
The Setting and the
Courses
“Second-tier” students (Tobias, 1990) are academically
capable of studying Science, Technology, Engineering and Mathematics (STEM) but
choose not to concentrate on them in college.
Studies of second-tier students, and our own experience, suggest that
they really are different from STEM majors: they are less mathematically
inclined and more focused on everyday experience, among other characteristics.
Our experience and the literature indicate that they are much more inclined
than STEM majors to reason by extrapolation from the particular to the
abstract, rather than by logical derivation from a known principle.
Teaching science to non-majors, however, is an essential
role for sciencist/educators. Second-tier students in later life will make
decisions that affect the future of science and technology. They will be
elementary teachers, journalists, school board presidents and legislators after
graduation. An understanding of science as it is practiced may make them more
supportive of it. Personally, as consumers, they will need to make informed
decisions on which medicines to take, whether to support a particular
development for environmental reasons, etc.
The University of Missouri-Columbia (MU) has been called
the quintessential state university. It admits about 4500 first-year students
each year and has a total of enrollment of 24,000 in all divisions. Along with
similar requirements in Humanities and Social Sciences, the University has a
General Education requirement for all undergraduates of three courses (9
semester hours) in Science or Mathematics. These courses must include both
physical and biological sciences; at least one course must have a laboratory.
We provide non-STEM students with a two-semester
interdisciplinary course sequence through the MU Honors College. These
students, primarily in their first year, are classic second tier. The Honors
College enrolls the top 10-15% of entering students, based on High School
grades and ACT scores. This percentage is typical of the number of
Honors-eligible students at large state universities. Like the entire
undergraduate population, our students have met the University’s admission
requirements of three units of Science and four of Mathematics during high
school. While they have been academically successful at it, they have no
interest in science as a career choice. About half of the students in our
courses are in pre-Journalism, with the remainder in Humanities, Social
Science, Education and Business concentrations. Commonly, they express their frustration at having “learned” (in
practice, “memorized”) the material in their pre-college courses without
mastering it.
We designed two courses
whose approach is to model science as it is practiced by researchers
(White and Fredriksen, 1998). The courses are entitled “The Warm Little Pond”
and “The Warm Little Planet.” They may be taken independently or in sequence,
and as a selling point, can be used to fill distribution requirements in either
Physical or Biological Sciences. The sequence operates with a small annual
budget for supplies and materials, less than or equal to the per-student budget
of a traditional laboratory-based introductory course. Tenured faculty and a
non-tenured coordinator teach the course as an overload and receive a small
supplement that can be used for books, meetings, etc. Enrollment is limited to
fifty students per semester due to two factors: first, the emphasis of the
Honors College on close student-faculty interaction, and, second, the
availability of the laboratory space, which is essentially donated by academic
departments.
The Material
What exactly does a second-tier student need to know
about Chemistry? Where might these students encounter Chemistry in their
post-college life? What principles can be applied to ordinary life? How can
they become intelligent consumers of scientific knowledge? It's pretty easy to
think of topics from an introductory course for STEM majors that are of little
interest to a nonscientist. (Some are
of little interest even to the STEM majors!)
The list includes many things that are important, near and dear to a
chemist or biochemist: quantum theory, Lewis acids, the chemiosmotic mechanism
of ATP synthesis, to name a few.
We consciously
adopted a “less is more” strategy regarding content. Each semester consists of
only six topics, taught in blocks of one and a half weeks. The blocks feature
an introductory lecture, a laboratory exercise, and a second lecture. The
balance of the semester is devoted to discussions of two popular science books
(e.g., Rachel Carson's Silent Spring or Walter Alvarez' T. Rex and
the Crater of Doom) where students are expected to find themes from the
course in the books. Finally, we have the students do a “capstone” laboratory
project. The topics covered in both courses proceed from the personally
observable to the molecular scale through the semester.
The Warm Little Pond http://web.missouri.edu/~esiwww/GH161.html
uses a decorative pond on the campus as a focus. The overall theme of the
course relates to biological and chemical transformations and cycles. Key concepts
from Chemistry, Biology and Environmental Science include trophic relationships
and their foundation in Chemical Thermodynamics, biological adaptation and
evolution, population dynamics, biogeochemical cycles involving
oxidation-reduction reactions, and acid-base chemistry. In class sessions, we
make a continual effort to refer back to the material already covered. For
example, we point out that the concept of biogeochemical cycles explicitly
depends on the conservation of matter.
The Warm Little Pond starts with a simple exercise. We
provide the student groups with graph paper, string and tape measures and tell
them to measure the area of the pond. The answers are interesting: the
estimates of the area vary from less than 800 to more than 2000 square feet but
almost always are reported to four or more significant figures, i.e., to a
claimed precision on the order of a few square inches. The set of wildly
varying estimates furnishes the starting point for class discussion about
random and systematic error, which leads naturally to the concept of
significant figures. Given the varying estimates of the pond area, we can make
sense of these estimates only by assuming that the errors are randomly
distributed around the mean. Using the students’ experience and pre-existing
knowledge (for example, that more measurements are better than fewer), we
develop the equations for standard deviation and show how the mean cannot claim
to be more accurate than the least accurate single measurement.
This exercise reinforces several themes of the course:
1. Science as modeling. The students’ drawings and measurements are smaller
and simpler than the actual area of the pond, i.e., they are a model of the
real phenomenon. The mean and standard deviation of their measurements model
their actual data. Models, however, are always underdetermined--anything we say
about a system is less complex than is the system itself.
2. Data as an inexact
representation of a real situation. There
is always a “moment of truth” when one brave student asks “What is the area,
really?” and our answer is “We don't know,
but it's probably pretty close to the average measurement that the classes have
gotten through the years.” (This
response has, one at least one occasion, elicited an audible gasp from the
students.) This exchange exemplifies
the fact that measurement depends implicitly on the means of measurement, as
illustrated by Mandelbrot’s (1983) classic question “How long is the coastline
of Britain?” and its answer “ The answer you get depends on the length of your
ruler.”
3. Mathematics from the
bottom up rather than the top down. We develop the idea of standard deviation as a way to
describe (model) the uncertainty in their data instead of providing a formula
and asking students to compute the answer. The idea of significant figures
flows naturally from the study of errors. All of our students have heard of
significant figures and some can apply the rules, but none can state what the
concept means or is good for. (In this respect they don’t differ from
first-year science majors whom we have taught in other contexts.)
4. The need for
concrete experience. Data that students have argued over,
identified the weaknesses in, and presented to their peers is more meaningful
than any “better” measurement, such as a map of the pond derived from aerial
photographs.
As we developed this exercise, it became apparent that
this measurement mirrors the process that research scientists often use when
approaching a problem: Identify an objective (hypothesis or measurement), get
the data (experiment), and analyze what you get (conclusion). We have pushed this concept through the
course and now explicitly model research activity as a way of teaching
concepts.
The Laboratory as Model
Research Experience
1. Hypothesis testing. We tell our students that we expect them to use the
laboratory for hypothesis testing. We want them to carry out the same
activities as do professional scientists, albeit within a defined context:
brainstorming, hypothesis development, proposal submission, peer review,
experiment, appropriate record-keeping (i.e., in a “professional” notebook
format). We start by presenting laboratory exercises in the format of a
“mini-journal” rather than as a set of steps to follow. Students are expected
to carry out an experiment that builds on the paper they have read.
For
most experiments there is a lot of trial and error involved in devising the
right procedure to answer the question that is being asked. Because this is
usually very time-consuming, student science labs have most of the trial and
error worked out of them in advance. The only real unknown is whether the
students will be able to follow the directions carefully enough to get the
expected results... We have put some of the trial and error back into the
process.
The students aren't graded on
the results they get. Instead, we grade them on their ability to evaluate the
data they do obtain, identify sources of error and write up a coherent report.
Often, the entire class pools data; the students write individual reports using
the pooled data (thus diminishing the problem of outliers).
2. Source material All of this activity flows from the original
presentation of the experiment and concept in a mini-paper format. The chief
difficulty then is in finding the source material for the students to read for
background in planning their experiments. One possibility would be to use the
current chemical literature; however, that approach fails for several reasons.
Usually the techniques are not readily adaptable to a beginning undergraduate
laboratory, the questions and hypotheses build on rather than explain the
introductory concepts, and the language is often opaque. Even teaching classic
experiments by providing students with the original papers can lead to problems
because non-STEM students seldom have the mathematical and other background
knowledge to follow the logic. Additionally, the concepts that we teach often
are not explicitly stated in the original source. For example, Rumford's
original paper on boring cannons states that work and heat are equivalent, and
that heat is a form of motion but omits that the energy change at equilibrium
is zero, although it is possible for a technically trained person to make that
induction.
We needed to find a rapid and accessible means of
developing the mini-papers. With the large number of well-established
laboratory exercises available, there was no need to re-invent specific topics;
rather, we present the experiments in a format that explicitly models the
scientific literature: Title, Abstract, Introduction, Experimental Procedures,
Results, Discussion and References. The
example
"An Estimate of the Molar Heat of Reaction for the Decomposition of Hydrogen Peroxide" is simply a
measurement of the heat released by the Fe(NO3)3-catalyzed
decomposition of hydrogen peroxide to water and molecular oxygen. The material
is written up in the mini-paper format, and, in contrast to the professional
literature, we present the students with a leading question for their
experiments.
3. Final Laboratory
Project. The students also carry
out a longer-term project. In the Warm Little Pond, for example, they set up a
Winogradsky plate (http://www.microscopy-uk.org.uk/mag/indexmag.html?http://www.microscopy-uk.org.uk/mag/artaug02/gchabitat.html).
Winogradsky plates are an adaptation of the Winogradsky column, an aquatic
microbial ecosystem which segregates into layers with photosynthetic organisms on top, oxygen-using metabolizers below and finally organisms on the
bottom that metabolize sulfate to sulfur to support a chemosynthetic
lifestyle(Charlton et al., 1997). Students write and present a research
proposal based on a plausible (i.e, mechanism-based) hypothesis that predicts
the distribution of the organisms (kind and number) depending on the chemical
composition of the water in which they grow. They review, revise and carry out
the experiments over a period of about six weeks. They then write a final paper
and present their results in a poster session. This sequence follows that
carried out by professional scientists and, we believe, increases students'
understanding of science as process.
Evaluation - lessons
learned
As the course has developed, we have obviously learned a
few things about our audience and how to reach them. At this stage we can draw
a few conclusions:
1. Less is more. We try to let the students see how concepts from
various branches of science carry through to other areas. This means that we
have to prune the coverage of many subjects. For example, we only treat acids
as proton donors, omitting the Lewis concept of acids and bases. Doing this,
however, allows us to treat the concept of acid rain more fully, emphasizing
that it follows from oxidation
reactions during metabolism or burning.
2. Group is good. All of the laboratory work and the final paper are
group projects. We find that our students, many of whom perceive themselves as
“not good in Chemistry,” are much more comfortable in group settings. A good
group experience (measured by scores on peer evaluations) was also correlated
with higher class scores (r = 0.435, p = 0.003), though it is impossible to
tell whether good group dynamics led to better grades or better grades left students
more satisfied with their group experience.
3. Math from the bottom
up. Mathematics is essential in
science, yet second-tier students are often intimidated by it. We try to
overcome this difficulty by starting with results and data, usually from experiments
that the students carry out. We try to get them to identify the “problem” e.g.,
these points do not fall on a straight
line; how do we find the best line for the points we found? The students
brainstorm various possibilities for solving the problem. For example, they
might decide to minimize the sum of the differences between the line and the
experimental points; however, this leads to a possibly false result because the
differences cancel each other out. They them might try to take the absolute values
of the differences, and, finally, for computational simplicity, the squares of
the distances of the experimental points and the constructed line. Then they
can apply the least squares formula on their calculators or spreadsheets. This
contrasts with the more common approach of starting with a formula and applying
it to the data. Such an approach often causes non-STEM students to tune out
completely, since they are not supposed to be “good at Math.”
3. KISS (Keep it
simple, Sherlock). We
intentionally go low-tech wherever possible: thermometers rather than probes,
measuring ammonia, nitric oxide and nitrous oxide with the test strips used for
aquariums, etc. We found that when we used more sophisticated apparatus, e.g.,
temperature probes and PC's, the students spent much more time trying to
understand the apparatus than they did doing the experiments. We try to use the
most direct apparatus wherever possible.
4. Lab and Lecture are
connected. There are six
conceptual units, and each has a lab with it that depends on an understanding
of the key concept of the unit. Initially we thought simply associating the
unit and the lab in time would be enough for students to see the connections.
However, we have gradually had to increase the amount of lecture time used to
explain the lab and how it fits into the overall picture. This tradeoff is
necessary because we are introducing more uncertainty into the lab experience
and seeking a fairly high level of conceptual understanding.
5. This works. We tested the student for their ability to reason
scientifically using a content-neutral test of scientific reasoning ( ). Students achieved a statistically
significant increase in their reasoning ability over the semester. We are currently
doing comparative studies with a similar population of students in a
conventional course.
6. This is NOT for
everyone. Non-STEM students
really are different from science majors. When STEM majors take the course,
they commonly are frustrated with the slow pace and emphasis on the underlying
bases for current knowledge.
Where we are and where
we're going
So far, the curriculum
appears to be working according to plan: the students are active participants
in the course, they seem to be learning something, and we have had a few
students take more science even when doing so was not required (one journalism
major even did an independent research course in one of our laboratories). In
the future we want to:
1. Expand the course
approach to new audiences.
Perhaps the most important scientific educators are also those who are the most
neglected: elementary school teachers. Most of what children learn about
inquiry is introduced in the primary
grades. Typically, though, Education majors take very few science courses, and
they often express negative attitudes about the ones they do take. We
hypothesize that an inquiry-driven course would help overcome these
perceptions, which can often be communicated to students in subtle but
unfortunately effective ways.
2. Make it
instructor-friendly. Trial and
error is the lot of a scientist. Even so, we cannot ignore the fact that an
instructor in a large class often has neither the time nor the resources to
convert existing materials from the standard protocols to a mini-paper. We are
currently identifying the roadblocks to converting these cookbook labs into the
inquiry format.
3. Semester lab
projects. Is the final project
cost-effective? Does it lead to new knowledge on the part of the students?
Given the time and effort involved, one would hope so, but that assumption has
not been validated.
4. Find out if the
course makes a difference long-term.
Are students who take this course different from their peers? Do they
eventually have different attitudes toward science and Chemistry than do their
peers who take conventional courses? Follow-up studies are notoriously
difficult with a mobile population; however, this kind of evaluation seems to
be a national need given the emphasis over the last ten years on inquiry-based
learning.
Charlton, P. J., McGrath, J.E.,
Harfoot, C.G.. 1997. The Winogradsky Plate, a Convenient and Efficient
Method for the Enrichment of Anoxygenic Phototrophic Bacteria. Journal of
Microbiological Methods 30, 161-163.http://www.woodrow.org/teachers/esi/1999/princeton/projects/microbe/micro_succession.html
Lawson, A. E. 2000. Classroom
Test of Scientific Reasoning (Multiple Choice Version)
Based on Lawson, A. E. 1978.
Development and validation of the classroom test of
formal reasoning. Journal
of Research in Science Teaching, 15, 11-24.
Mandelbrot, B.B. 1983. The
Fractal Geometry of Nature. New York: Freeman and Company
Tobias, S. 1990 They're
Not Dumb, They're Different: Stalking the Second Tier. Tucson,
AZ.: Research. Corporation.
White B.Y.; Frederiksen J.R..
1998. Inquiry, Modeling and Metacognition: Making Science
Accessible to All Students. Cognition
and Instruction 16:3-118
Acknowledgments
We are grateful to our faculty colleagues who have been involved in this exercise, especially Jan Weaver, Sandi Abell, Jim Carrel and Ray Ethington. We are also grateful to a number of dedicated Teaching Assistants, John Burkhardt, Stacy James, Suzy Otto, Angela Sell, Ken Stensrud and Tony Thorpe, and, finally, to the MU Honors students who helped us plan the course and showed remarkable forbearance as subjects for this ongoing educational experiment. This work was supported by NSF CCLI grant 0230779.