Scientific Research Expertise
from Middle School to Professional Practice
Wolff-Michael Roth (MROTH@UVIC.CA)
Lansdowne Professor
Applied Cognitive Science
Faculty of Education, University of Victoria, Victoria, BC, V8W
3N4 Canada
Symposium presentation at the 1999 annual meeting of the American
Educational Research Association, Montréal, Québec.
Abstract
In this paper, I report studies of experimenting among three different
populations: middle school students (Grade 7 and 8), university science
graduates in a science teacher education program, and scientists. The middle
school students and scientists were observed in `natural' situations where
individuals and small groups designed and conducted investigations, the
outcome of which they reported to a larger community. The university science
graduates were asked to design and conduct an investigation for which they
had 7 hours spread over two days. Unlike the other two population, the
science graduates did not engage in research as a regular part of their
daily activities. Our results show that the science graduates designed
experiments, operationalized variables, and represented scientific results
in ways that were not much different from what the much younger middle
school students had done. Because of the considerable competencies of the
Grade 8 students who had learned to conduct their own research, we therefore
have some indications that what we learned from our studies of scientists
can be transported into middle school classrooms.
Introduction
Ethnographic research in scientific laboratories and scientific field work
showed that designing investigations, collecting data, transforming data,
and interpreting the resulting representations are some of the quintessential
scientific practices (Latour, 1993; Roth & Bowen, 1999)--though, unlike
previously theorized, these practices cannot be thought separately from
each other and are highly contingent structured activities that are correlated
with a person's familiarity in the field of inquiry. Recent reform documents
have increasingly called for such "authentic" practices in mathematics
and science education which would allow students to engage in these subjects
in ways that correspond to everyday practices in these fields (AAAS, 1993;
NCTM, 1989). (I am including mathematics, for a considerable number of
scienfic practices are related to the representations they develop and
ultimately include in their publications.) For example, mathematics curricula
in Grades 5-8 should enable students to (NCTM, 1989):
* describe and represent relationships with tables, graphs, and rules;
(p. 98)
* analyze functional relationships to explain how a change in one quantity
results in a change in another; (p. 98)
* systematically collect, organize, and describe data; (p. 105)
* estimate, make, and use measurements to describe an dcompare phenomena;
(p. 116)
* construct, read, and interpret tables, charts, and graphs; (p. 105)
* make inferences and convincing arguments that are based on data analysis;
(p. 105)
* evaluate arguments that are based on data analysis; (p. 105)
* represent situations and number patterns with tables, graphs, verbal
rules, and equations and explore the interrelationships of these representations;
(p. 102) and
* analyze tables and graphs to identify properties and relationships.
(p. 102)
Because these competencies focus on data collection, analysis, and
presentation, they are easily integrated with science curriculum reform
at the same grade levels. In fact, the integration of mathematics and science
school activities may not only be interesting because children collect
their own data, but they may be essential for developing a thick layer
of experiential knowledge that underlies much of scientists' understandings
(Roth, 1996; Roth & Bowen, in press; Roth, Masciotra, & Bowen,
1998). Such integration of rich experiences with physical phenomena and
subsequent transformation and analysis of the data appears to lead to robust
mathematical and scientific understandings of phenomena (Greeno,
1988; Roth, Masciotra, & Bowen, 1998; Roth & McGinn, 1998).
To date, many science and mathematics teachers have not yet realized
the potential that lies in situating science and mathematics education
in students' self-directed inquiries about natural environments as a way
to implement standards such as those of NCTM. More so, there is some evidence
that science teachers may not enact competent data interpretation themselves
(Roth, McGinn, & Bowen, 1998) making it difficult for them to begin
such practices in their own classroom and even less to scaffold students
into these practices. In the past, all of our research studies in which
students developed competent research practices always included science
teachers who, at a minimum, had obtained a masters degree in a natural
science. But we were cognizant of the fact that our own curriculum innovations
may not scale to the regular classroom because of teachers' lack of experience
in scientific practices. My presentation today is therefore fundamentally
concerned with the question, "What are the practices enacted by Grade 8
students, B.Sc. graduates in a secondary science teacher education program,
and scientists during open-ended inquiry?"
Lab Science: In Schools and in Praxis
Traditional Science Teaching
When students do laboratory work, science comes out short: students do
learn something, but that often has more to do with turning a set of instructions
into an accounted-for course of action involving laboratory equipment (Amerine
& Bilmes, 1990; Lynch, Livingston, & Garfinkel, 1983) than it does
with actually learning any science. In fact, students often find themselves
in a double bind situation which prevents many of them to learn: To know
whether they have followed the laboratory instructions they need to know
that what they observed is what they should have observed; however, to
know whether what they observed is what they should have observed, they
need to know that what they have done is what they should have done (Roth,
McRobbie, Lucase, & Boutonné, 1997a). The practices tapped and
developed by such laboratory tasks are far less theoretical scientific
than practical (and possibly routoine) in nature. A vivid description of
current science teaching was provided by ethnographic studies of high school
science in Australia and the United States (Gallagher & Tobin, 1987;
Tobin & Gallagher, 1987).
Several problematic elements were identified with laboratory teaching.
First, laboratory investigations embody a "cookbook" approach. Students
(attempt to) follow recipes, gathering and recording data without a clear
sense of the purposes, the procedures, or the interconnections between
the two. Second, investigations provide low cognitive demands that preclude
reflective action by means of which students might learn to express themselves
about phenomena of scientific interest. Third, students engage in activities
not intended by the curriculum planners, spending much of their laboratory
time in off-task activity with short periods of attention to complete the
work. Time off-task is used for non-science related socialization with
peers. To make up for the time lost in these activities, teachers set a
high pace in regular classroom periods to transmit even more factual information.
Fourth, in common practice, laboratory activities are treated as if students
could see the conceptual structure of the discipline, that they could discover
the nature of nature; this despite the generally accepted understanding
that all observation is theory-laden and that therefore students will,
without knowing the science they are to learn, perceive phenomena in the
ways necessary to construct the concepts in the first place. (We documented
elsewhere the differences between students' perception of a science demonstration
and students' efforts in making their perception consistent with teacher
talk about the phenomena that appeared incongruent [Roth, McRobbie, Lucas,
& Boutonné, 1997b].) Thus, laboratory activities in high school
are all too often ritualized verification exercises in which the myths
of science as fact and sets of rational scientific process skills are perpetuated.
The results of such teaching are held accountable for the many students
that drop science in their early high school years: High school science
years have been described as "rites of passage" with the sole purpose of
selecting students for science programs at the university (Brookhart Costa,
1993). At the same time, those who are not successful, learn that science
is for a special breed of smart people, "geeks," and "geniuses." While
recent research in science and technology studies has largely debunked
the myth of scientists as a special breed, many ordinary people uncritically
accept the products of science and technology (Postman, 1992). However,
we would expect members of a scientifically and technologically advanced
society to make choices informed by a critical view of scientific and technological
knowledge. As part of this science- and technology-related literacy, one
would expect students to experience and learn how scientists and engineers
produce new (arti)facts. That is, students should experience not only ready-made
science (and technology) but science-in-the-making, to use the Latourian
(1987) notions of science as both product and process. We know from our
own work in school science that if such an experience is complemented with
critical reflection on the process of knowledge construction, students
are in a much better position to relativize scientific laboratories as
a special place, an enhanced environment in which natural order is improved
(Knorr-Cetina, 1992; Latour, 1983).
Science is Everyday Practice
The ontology and epistemology portrayed by traditional science teaching
are clearly at odds with recent field studies in scientific laboratories
(Knorr-Cetina, 1981; Latour & Woolgar, 1986; Law, 1994; Lynch, 1985;
Traweek, 1988). These studies show the tenuousness of the assumptions underlying
the conceptions of teaching as outlined in the previous section. Little
has changed in classrooms over the past two decades since the emergence
of an increasing number of studies that show the socially constructed nature
of scientific knowledge at various levels of analysis. Rather than being
pure, science is highly meshed with other aspects of daily life, politics,
economics, social relations, power, etc. Knowledge construction in scientific
laboratories therefore has a strong local, indexical, and serendipitous
character; at the same time, knowledge construction depends on relationships
with funding agencies, other researchers, those in charge at research sites,
and the public, thus illustrating its macrosocial nature.
One would expect that with a better understanding of the nature of
science, scientific facts and theories, and scientists' everyday work,
public policy makers and even just plain folks would be able to
make more informed decisions about the priorities for spending public moneys.
Schools are places where such better understandings can be fostered. To
bring about change, the above-cited aspects of everyday practice in science
and engineering should be shared with children. However, "telling children
how scientists do science does not necessarily lead to far-reaching changes
in how children do science; indeed, it cannot, as long as the school curriculum
is based on verbally expressed formal knowledge" (Papert, 1991, pp. 10-11).
Suzuki (1989) makes an explicit link between the quality of science education
and being able to function as responsible citizens in an increasingly sociotechnical
world. He thus suggests changes in science education: we do not need to
inculcate children with facts, but foster in them an excitement through
activities that engage them in exploring, discovering, and connecting.
Background
Over the past decade, I conducted (sometimes together with my teacher colleague
and later graduate student Michael Bowen, others with university colleagues)
a series of studies on the experimental and representational practices
of middle and high school students (e.g., Roth, 1994, 1995; Roth &
Bowen, 1993, 1994, 1995; Roth, McRobbie, Lucas, & Boutonné,
1997; Roth & Roychoudhury, 1993), B.Sc. graduates who were enrolled
in a fifth-year secondary science teacher preparation program (Bowen &
Roth, 1999; Roth, McGinn, & Bowen, 1998), and scientists (doctoral
students, postdocs) (Bowen & Roth, 1998; Roth & Bowen, 1998; Roth,
Masciotra, & Bowen, 1998). Here, I want to focus on those studies involving:
* Grade 8 students involved in an innovative science program that allowed
them to construct their own research questions, and design the studies
to answer these questions. Their only requirements were that the reports
they constructed about and from their research should be convincing as
judged by their peers who conducted similar or other research on the 50
acre property of the school. The students attended a private school in
its first year of transition to a coeducational institution. By Grade 8,
students of this school did not yet academically distinguish themselves
from those who attended the surrounding publish schools (other than that,
on the average, they came from more well-to-do homes). These students conducted
their own research for a 10 week period in the course of which they became
increasingly competent in the different aspects of doing research. The
data from this part involves a continuous video record of the 10-week session
from 2 Grade 8 sections, all written work, the results of experimental
design research to test research hypotheses about scientific representation
practices students had developed, and interviews.
* Preservice secondary science teachers all of whom had previously
obtained undergraduate or graduate degrees, most of them in the biological
sciences. As part of a "methods" course on the teaching of secondary science,
these students conducted, over the course of 10 days, a research of their
own design in a designated 1-acre area of mixed vegetation on the university
campus. The data collected in this case consists of extensive written reports
submitted at the end of the project. Furthermore, additional data were
obtained in several sessions asking students to interpret data sets, and
transform data, and to construct scientific representations.
* Together with Michael Bowen, I conducted a 18-month ethnography among
ecologists beginning one spring (preparation), covering two summers, and
into the second fall. As research assistants, we helped in capturing reptiles
(lizards, skinks, rubber boas, garter snakes) and thereby apprenticed to
the research of a herpetologist. We also attended local, national and international
conferences with our participants, seminar presentations, conducted formal
interviews with one participant (10 hours), and kept field notes on the
many informal meetings with members of the ecology community. Videotapes,
copies of papers, audiotapes, copies of field notes, etc. are part of our
data base.
Grade 8 Students Enacting Science
Learning Process
Traditional forms of science education are based on the assumption that
language, tools, instructions for an experiment, textbook problems, and
other items relevant to teaching have singular meanings. Consequently,
instructions should be converted into actions, tools used in canonical
ways, unitary meanings learned by definitions: if there is trouble, students
are usually blamed for not trying hard enough, malvolition, or cognitive
deficiencies. From the same perspective, it is assumed that the laws of
nature, concepts, and rules can be read from relevant documents (e.g.,
lab experiments). Our investigations in open-inquiry settings revealed
the problematic nature of these traditional assumptions. (The settings
include open ended inquiry, among others, in Grade 11 and 12 physics, Grade
4-5 engineering, Grade 6 and 7 physical science. For book length reports
see Roth [1995, 1998].)
a. the events students observed underdetermined the laws of nature
to such an extent that the convergence of their language (games) with accepted
science discourse was only one of several possibilities;
b. tools did not embed unique cultural meanings that could be unproblematically
appropriated by students;
c. textbook and teacher "problems" had multiple meanings that gave
rise to different solution-related activities; and
d. the interpretive flexibility of objects, events, and semiotic representations
was the very phenomenon which allowed shifts in language games, and thus
learning, to occur.
Over a period of time dealing with emergent problems, students learned
to cope with the ill-structured nature of the tasks, and in fact, they
began to capitalize on interpretive flexibility that is, the fact that
language, tools, materials could be interpreted in multiple rather than
singular ways--students, because they have not yet been sufficiently disciplined,
appear to attribute more different forms of meaning. This resulted in what
we described as their ability to tinker, to convert objects, materials,
or tools in ways to bring them closer to their answers; objects, materials,
or tools were often used in ways for which they were not designed. In fact,
one could say that students in these classrooms learned to live with greater
indeterminacy and developed greater flexibility in terms of meaning. Therefore,
boxes with tools and a steady supply of glue, various types of adhesive
tape, cardboard, thermometers, soil corers, soil acidity meters, soil testing
kits, rulers, measuring tape, and materials to repair equipment "on the
fly" became central to the learning environment.
One of our descriptive metaphors for students was that of tinkerers
or bricoleurs whose practices are characterized by their indexical
logic, situationally contingent and circumstantial nature, and implied
opportunism (Knorr-Cetina, 1981; Papert, 1991). Tinkerers flexibly interpret
their settings to constitute problems and solutions as part of making things
work. This leads to a conception of problems and problem solving that differs
from traditional problem solving research in education and psychology (e.g.,
Roth & Bowen, 1993). Rather than being prefabricated with a definite
structure, problems unfold and emerge from engaged activity and are structured
by the dialectic between individuals, setting, and social context. Solution
processes cannot be divided into separate means and ends: the formulation
of problems and solutions are inseparable and may occur in any order. Because
goals are endogenous to the constitution of problems, problems are owned
by students rather than given in a normative, decontextualized form by
external agents. Students' most significant learning in this context may
have been to cope with the unstable and ever shifting characteristics of
their problems, solutions, and the setting in which they worked. Rather
than expecting a stable and structured world in which they seek "right"
answers, students learned to structure and make the most of available physical
and social resources, to find answers from a range of possible answers,
depending on how they solved the problem they framed. These
students learned to make sense of a world in flux rather than expecting
rigid bodies of knowledge.
In traditional laboratory exercises, students often go through the
activities without knowing what they are doing or why they are doing it;
they leave the interpretation until after they have finished and when they
have opportunities to "fix" data so they fit the theory.[1]
The students in the participating classes, on the other hand, engaged continuously
in interpretation and sense-making activities. Their ways of talking emerged
from the continuous interaction of measurement procedures, instruments,
and current language games in a "mangle of practice" (e.g., Pickering,
1995). With new language games, new ideas and topics became so important
that they overrode earlier knowledge claims from their laboratory and field
reports. Subsequently, individual claims were also modified to keep pace
with changes of the discourse. Our data collection procedures allowed us
to track some of these changes as they emerged in the course of students'
activities. For example, we showed how Miles, a Grade 8 student doing field
research, evovled his description of factors that influenced plant growth
in his research site. During a 10-day period and in the course of various
conversations with his teacher, research partner, peers, and a laboratory
assistant, we noted how a nearby lake first entered his discourse, then
progressively became a central referent in his effort to geographically
order his sampling sites, to finally becoming a factor that influenced
plant growth. At the end of this period of time, he constructed a graph
to support his contention that there was a correlation between distance
to the lake and plant growth. The following vignette (I am drawing on a
case published in Roth & McGinn, 1998) further underscores the way
students operated in this learning environment.
Jamie and Miles are two grade 8 students participating in an innovative
10-week science curriculum which asks them to formulate and answer research
questions about biotic and abiotic aspects of their own 40 square meter
ecozone on the school grounds. Jamie and Miles have chosen their ecozone
in a forested area. Earlier in the day they decided to investigate the
soil composition in three 1 square meter subplots that would later constitute
comparative sites for a series of studies on plant growth. From each of
the subplots, they collect samples of top soil. As Jamie and Miles return
to the laboratory, they decide to follow the recommendations in one of
their resource texts to float each soil sample in a large beaker. After
the soil has settled, Jamie and Miles observe three distinct layers of
materials; Miles instructs Jamie to make a drawing of the beaker and the
layers so that they have a record that will show the relative composition
of their samples in the three subplots. Miles measures the height of each
layer and Jamie records each measure next to his drawing. Prompted by their
teacher's comment to present their data in a way that permits comparison
with others who might investigate soil composition, the two boys decide
to find the relative contribution ("%-amount") of each layer to the total
sample. They encounter some difficulty finding relative amounts so they
ask the teacher who engages them in a conversation over and about little
sketches they drew in Jamie's field notebook. Jamie then measures the total
height of all layers allowing Miles to calculate the relative contribution
of each category. They find a chart tin their resource materials that classifies
soil according to the different contributions of three composite materials.
Miles suggests copying the chart and then marking the compositions of the
three samples onto the chart. [Roth & McGinn, 1998, p. xx]
Here, Jamie and Miles' research moved along a trajectory which began
with digging up soil and ended with points on a soil chart. Between these
beginning and endpoints, Jamie and Miles engaged in a number of practices
that transformed real soil into dots on a chart via drawings, height measurements,
and percentage calculations. They generated what Latour (1987, 1993) called
a cascade of inscriptions of increasingly general nature. Their initial
drawing and height measurements were very much a function of the amount
of soil they had collected. From this early representation in which the
signs were still soil-like, they literally abstracted (Lat. ab-,
away and strahere pull) a part which was then embodied in their
drawings, and, through continued abstraction ended in a series of calculated
numbers and ultimately as singular points on a grid. Their transformation
of the layer thickness into a relative height constituted a scaling of
raw into normalized measures. Once they had relative amounts of soil types,
their results could easily be compared with those of other students and
they could be mapped onto the published chart.
Science teachers and parents are often afraid that pupils in our learning
environments do not "cover the content" and will not compare well with
others on the basis of achievement tests. Our studies show that the diversity
of the projects allowed classes as a whole to cover more than the required
curriculum. As students began to talk about and defend their products (design
artifacts, research and results), they had to elaborate and therefore clarify
many ideas (as embodied in the conversations about their work and objects
therein) for themselves before they were actually ready to teach others;
because peers act as teachers, the distance between the language of teaching
and the language of listening is considerably smaller than if the same
content is taught by the regular teacher. Furthermore, we observed that
during discussions with other research teams in their classes, students
learned tremendously; those discursive and tool-related practices which
students found relevant rapidly spread throughout, and changed the classroom
communities.[2]
Investigative Practices of Future Science Teachers
with Science Degrees
To find out what research practices future science teachers with previous
science degrees enact, we asked a group of such students to design their
own research questions and answer them by collecting data on the university
grounds. When the pre-service secondary teachers first entered the research
area (located in "undeveloped" mixed forest at the edge of the university
property) there was considerable discussion in the student pairs about
how they were going to ask "do-able" questions and what those questions
would be. As students continued to work on identifying the area in which
they were going to conduct their research work, staking out boundaries,
and drawing a map of the zone, they started formulating specific questions
to address as they noticed more and more specific details of the zone and
reflected about the equipment which had been made available to them.
The final questions addressed by the preservice teachers had many similarities
to those framed by the Grade 8 students when they first started their outdoor
research many being so conceptually "broad" that it would be difficult
to address them in a single outdoor session. However, in the final analysis,
these students cannot be faulted for not being more competent in the practices,
for they had not been familiar with these processes or with the site in
which they conducted their research. Nevertheless, let us take a look at
the reports which these "experienced" students submitted after their investigations.
Because of the wholistic nature of scientific research (e.g., Roth &
Roychoudhury, 1993), each of the aspects of research discussed in the following
interacts with all of the others. Any problems are likely to ripple through
the entire project.
Research Questions and Research Design
The 25 preservice teachers had considerable difficulty in structuring focus
questions that were appropriate for the type of activity they were doing
in their class. For the particular project these students engaged in, with
few exceptions, causal questions would be quite difficult if not impossible.
Despite this limitation, of the twenty-four questions addressed by the
students fourteen were causal in structure. Causal questions included,
for example, "How does the moisture level affect the distribution and height
of horsetails in our investigative site?" and "Do the exhaust gases from
the cars parking in Lot C directly effect concentration of field flowers
in front of the lot?" This type of question is more suitably addressed
in an experimental regimen whereby moisture level is manipulated and distribution
or growth measured. Similarlily, the second question requires an experiment
in which hypotheses about the effects of exhaust gases are explicitly tested.
This question is further confounded by the "busy road" which paralleled
the parking lot on the other side of the plot on which the students were
working. Even a correlational study maybe problematic because of the free
movement of "exhaust gases" from the two sources back and forth across
the plot of land where the plants were measured. What we can see in these
examples is a common practice in everyday reasoning to use correlations,
even spurious or contingent (case), as proof for a cause and effect relationship.
Here, lacking experience in framing cause-effect type research questions,
these students drew on their everyday practice to ask cause and effect
questions when the context allowed them only to collect correlational data.
Students also addressed issues that had could be addressed only if
the meaning relations differed from those in science. For example, students
used signifiers such as "competition", "biodiversity", "growth", and "productivity"
all of which are embedded in specific networks of meaning in biology that
do not equate to "distribution", counts of limited numbers of organisms,
or "height" as they were used by the students. Much like the middle school
students in the initial phases of previous studies (Roth & Bowen, 1993),
and despite their previous science degrees, these preservice teachers experienced
difficulties constructing productive questions to direct their inquiries.
Our ongoing research suggests that university science courses do not seem
to assisted students in developing the kind of embodied knowledge which
allows scientists to make sense of their world (e.g., Bowen & Roth
1998).
Operationalizing Variables
An important step in scientific research is the construction of defensible
claims on the basis of the measurements completed. Here, the operationalization
of variables mediates the relationships between original questions and
claims. Our analyses show that several studies had operationalized their
variables such that the results of the investigation could not be used
to provide answers to the research questions. In part, this was the result
of addressing questions involving biological factors such as "competion"
and "biodiversity" and in asking causal questions in contexts where it
was not possible to address them. However, problematic operationalization
of variables also occurred in situations where these conditions were not
present in such a way that they would interfere with that procedure. Two
types of problems with operationalization became aparent:
i. measured variable ineffectively reflects conceptual intent of initial
question, and
ii. insufficient replication or an inappropriate sampling regime.
The following example illustrates both of these breakdowns. In this
example, students asked the research questions, " How does the side of
a fallen log affect it's biodiversity?" and "How do the burned portions
and the recent ad older exposure of new wood affect the snags biodiversity?"
To address both questions one measure of "biodiversity" was the "frequency/quantity"
of different types of organisms--lichen, moss; small plant growth (non-lichen,
moss); spiders; beetles, larvae; and insects. This provides a constraint
on the perspective on biodiversity and gives rise to a conceptual breakdown
relating to the combining of dissimilar species within the same cells.
There are also difficulties with how the "frequency/quantity" was determined.
For instance, a count of "one" of moss/lichen represents a "patch" of indeterminate
size, not a measure of individual bodies or surface area of coverage. In
another example, a count of "2" insects in a section represents large (macroscopic)
insects visible at the surface, not those beneath the surface of the soil
or under plants. In these cases, insufficient operationalization and sampling
(apart from other problems with that study) meant that even correlational
claims would be inappropriate.
Constructing Tables
Field ecologists frequently structure their data analyses by constructing
tables which assists them in representing data and defining variables (Roth
& Bowen, 1998). Our ongoing ethnography of ecologists suggests that
by using tables scientists ensure that they are collecting all relevant
information--the tables act as memory aid in the data collection phase
of the research. Few of the reports submitted by the preservice teachers
made use of this representation device. Of twelve reports, eight used tables
for representing their data. Of these eight, four were were structured
such that patterns did not emerge. For the four reports that did not use
a table, it was our determination that use of a table would have been appropriate
for the data collected and would have aided interpretation.
Transforming Data
Supported by the heuristic used to scaffold students' inquiries, 10 (of
12) projects contained transformed data. However, graphs were frequently
used in non-standard ways to depict the collected data. For example, there
was frequent use of bar graphs rather than the scatter plots scientists
would have used. Scatter plots (5) rarely included best-fit lines (2) which
we found consistently among Grade 8 students. In other cases, further insights
might have been gained if different representations (such as X-Y-Z plots
or 3-D bar graphs) had been used instead of the bar, scatter, or point-to-point
graphs students actually employed. Many graphs were labeled or structured
in ways that later led to confounding interpretations of the graphs. Four
graphs did not relate to questions that were being addressed, and in some
instances there appeared no reason to construct one of these graphs (such
as plotting a bar graph of averages of measures across a slope). In total,
six of the ten reports which used graphs had some problems with how they
used graphical representations to depict the collected data and this subsequently
affected the claims that could be drawn from those representations.
Claims
In their claims, scientists draw implications from the data collected and
discusses the data in the context of the original question(s). Several
of the reports had conclusions that consistently extended from the data
collected and its representations and transformations. However, many other
of the reports made claims which either did not relate to the original
question(s) or which did not logically extend from the data collected/depicted.
Of these two types of problems, the latter is the more problematic and
was found in ten of the reports (in some reports with regards to one claim,
in others with regard to all of the claims made). In one example, students
concluded that "intraspecific and interspecific competition affects the
growth, density, and distribution of plants." However, they drew this causal
conclusion from a dataset without measures of either "competition" and
"growth." In a scientific context, the claim was therefore unwarranted.
In five of the reports, claims were made which were not related to the
original question, although in only two cases was this done and the original
question not addressed (in another two cases, no claims were made related
to a question posed in the study at all). Overall, problems with the claims'
sections arose more frequently from claims made which did not extend from
the collected data, a quite frequent problem, rather than from claims which
did not address the original question.[3]
An Ecologist at Work
Over an 18-month period, we recorded and documented the construction of
facts in field ecology. In this discipline, many members understand themselves
as being engaged in an observational science operating without the grand
theories one might find in physics. Although much of what members know
is derived from naturalistic observation, the facts which they report in
academic settings (posters, presentations, articles) are purely based on
measurements. However, these observational data are diverse in their origin
and error size and are collected over a considerable geographical area
(here a 20 km stretch) and over long periods of time (3 years). Considerable
work is required to coordinate these diverse data in time and space before
individual data can be converted into population statistics. Along the
trajectory from data to reported fact, ecologists are not always certain
whether they have observed something. The following quotes from different
stages in the construction of one lizard species[4]
illustrate such uncertainties.
(Sam, herpetologist, seeking to catch lizards in one of her field
sites:) `I usually find about five a day. I sort of am getting this
feeling that they are more active later in the day. They can't tolerate,
I think preferred temperature is about 20, mid 20's or maybe high 20's.
Probably mid. So in the real heat of the day I don't look for the animals
`cause they're buried down too deep and then I go out again in the 4 to
6 kind of range and lately I've noticed I've had better luck'.
(Sam in the field laboratory, timing lizards as she chases them
along a race track:) `I don't know if I will be able to use these speed
measures, but I do it anyway. Maybe there is something, maybe not'.
(Sam presenting the results of her work in a colloquium:) `And
it turns out the longer the lizards are kept in the lab, the slower they
run. Which is kind of interesting, but I can statistically control for
this effect and go on to look to see if there are other things that are
important. And it turns out there are. One of the things that's important
is what sex you are. Adult males are typically shorter than adult females,
their body lengths are shorter. And adult males also have relatively longer
back legs than adult females. And it turns out that this body length and
back leg length is important for predicting how fast it runs'.
On the one hand, we see self-doubts in the field and field laboratory
where Sam talked about the uncertainties of finding lizards and whether
the sprint trials with the animals she captured and returned to the field
laboratory were any good. On the other hand, far away from the field in
a more formal academic setting, we observe the matter-of-factness of propositional
knowledge about lizards such as the statistical significance of correlations
between sprint speed (dependent variable) and body length and back leg
length (independent variables). On the one hand we see factual statements
and hard inscriptions, on the other hand there are uncertainties related
to objects, instruments, and measurement processes in the field. From our
participant observer perspective, there existed a sharp and seemingly irreconcilable
contrast. We must wonder how such firm statements and claims are possible
when they have emerged from myriads of decisions and uncertainties that
are evident during the ecologist's field work.
A (lay) sociologist of science and student of Science in Action
(Latour, 1987) working backwards and tracing (authoritative) statements
and inscriptions, would first find printouts from statistics software that
had operated on a large database, which itself was imported from another,
spreadsheet software package into which numbers had been entered during
the ecologist's past field seasons. From here on, our `science lover' would
find a dizzying heap of proliferating inscriptions. There would be field
notebooks; tables partially filled with records of widely varying origins;
forms; printouts containing codes and coding schemes; numbered metal tags
for field use; labelled and code-bearing vials, socks, plastic holding
boxes and wooden enclosures all identifiable by a one- or two-digit painted
number. Linked to these `first' inscriptions she would notice an array
of diverse instruments and associated measurement practices. Surprisingly,
she would find little in terms of concepts, laws, and theories but, as
her participants would tell her repeatedly, `a lot of conceptual mayhem
that lies beneath all of that'. Because the ecologists understand their
practice as an observational science and because this particular project
is concerned with correlations of phenotypic aspects of the lizards, much
of the ecological fieldwork appears to be driven by what is do-able in
terms of measurement. However, there are also considerable variations in
measurement practices, scales of measurement, and measurement error. That
is, our scientists' activities and the resulting data on which their claims
are built arise from a variegated observational topology associated with
considerable co-ordination work which allow the statistical correlation
of quite unlike aspects of the study object. Thus, the distillation of
the field work, the lizard as it becomes visible to the scientists' audiences,
is not just a natural object, not just an individual construction, and
not just a social construction but arises out of the interaction of nature,
individual, and culture.
Professional Vision
Lizards are literally visible to those in the field and field laboratory
who catch and `process' the animals. In this sense, Sam, our ecologist,
has a relationship with the lizards as living beings that move around,
are difficult or easy to catch, and show specific behaviours allowing her
to identify each individual--some of whom she gives names. She describes
changes in observations on female lizards by taking the perspective of
the animals. Her depiction of lizards shares a lot in common with naturalist
portrayals of animal life.[5]
But, from her perspective, all she can collect by means of literal seeing
is `anecdotal' information; in the parlance of her field, even the colour
descriptions one might find in field guides are anecdotal (Law & Lynch,
1990). In the context of her domain, behavioural ecology, Sam has to make
lizards visible to others by means of a different perceptual machinery
that allows her to arrive at `scientific' descriptions of the animals of
interest. This process requires animals to go through the process of digitisation,
a conversion that involves `hard' numbers and electronic bits, before other
ecologists can `believe their eyes'. But as part of this process of converting
nature to electronic bits, Sam's field activities are open to an indeterminable
horizon of contingencies which arise in the course of the scientific work
that results in `knowledge about the lizard'. Through Sam's work, the lizard
is constructed and thereby made visible, involving a proliferation of inscriptions,
conversions of nature into numbers, and electronic digits. Lizards, as
they are seen by our `science lover', are reflexive of endlessly awkward
processes and objects left behind in the field laboratory and terrain.
That is, literal seeing while important to the individual ecologist is
not as important as the `observations' possible once the animals and their
environment have undergone multiple transformations. Instruments, field
laboratory, inscriptions, and associated practices therefore constitute
pieces of an observational machinery that does not have the even, spherical
surface of the human retina but has its own multi-dimensional, heterogeneous,
and heteromaterial topology. This article is about the topology of this
observational machinery which, not unlike biological retinas, turns nature
into (afferent) digital signals that are the data for subsequent processing
in centres of computing. These signals are not pure in any sense but are
always and inevitably formed by the enacted disciplinary practices. That
is, any relevant aspect about the lizard's life history or natural history
emerges from the interplay between the domain of inquiry and existing discursive
practices and material configurations (Coulter & Parsons, 1991; Goodwin,
1994). In the present study, these discursive practices were more related
to instruments and measurement and less to unifying concepts and theories
regarding ecology and evolution.
My central claim therefore is that observing lizards is more than a
matter of watching a few animals in the field--though naturalists, amateurs,
and our participating scientist may have done this in the past and still
do--but lizards have to be observed using the whole observational machinery
available to a field ecologist, including analogue and digital scales,
analogue and digital length-measuring devices (e.g., ruler, tape, map measurer,
paces), Munsell charts, and digital stop watches. As a result of physically
bringing together natural phenomena and the instrumental topology, numbers
composed of digits begin to fill rows and columns of a spreadsheet. The
entire laboratory machinery is used to produce digits which are then summarised,
compared, correlated and otherwise processed by means of statistical software
producing tables and graphs (i.e., series of non-trivial and arbitrary
transformations of subsequent stages). These tables and graphs, in the
context of some form of text, originate, support, and legitimize scientific
propositions about the lizard such as `sprint speed is inversely related
to lizard length' or `maternal tail length predicts number of offspring'.
In the process of the scientific work, lizards as biting, writhing, running,
feeding, and defecating creatures are turned into series of numbers composed
of one or more digits--first on paper and subsequently into the electronic
digits of spreadsheet and statistical software.
Mathematization of Nature
Ethnomethodologically specified, measurement is a hopelessly vulgar competence.
Our interest lays, therefore, in the local, in situ practices by means
of which ecologists conduct their measurement-related activities which
includes producing local judgements related to the practical adequacy,
accuracy, and appropriate correspondence between measuring device and measured
phenomena. Despite the potential dangers of `going native' arising from
our own training as natural scientists, and following an ethnomethodological
maxim (Lynch, 1991), I did not treat scientific measurement as a homogeneous
set of methods and standards but as an ecology of heterogeneous techniques
that allow scientists to attain locally recognisable and locally adequate
measures. In my studies, I provided documentary evidence that measurement,
though a familiar term of the ecology trade, is neither a coherent interdisciplinary
practice nor coherent for the same scientist across situations, even along
physically similar dimensions (e.g., `length' and `distance'). Although
the measurements generated by our ecologists may have had referents in
the practices of the discipline, what we observed was how each of the scientists
locally elaborated and enacted for him/herself the meaning of the cultural
referents in this setting. Each participant also elaborated a sociology
of science in the sense that they enacted their practices such as to be
accountable to the field at large.
Mathematisation has been described as one aspect of the process of
scientific seeing (Lynch, 1990). I provide here some indication about just
how mathematisation is achieved in the context of ecological field
work and describe some of the discovering practices in ecological fieldwork
to disclose the order of the local contingencies of the day's work in an
ecology field research camp. What I show is how the scientists in the field
made their work accountable in, of, and as instances of measurement practices.
However, what mathematisation achieves is more than just seeing, for it
is no longer individuals that are seen but a conglomerate of indistinguishable
replaceable individuals. It is the move from `this lizard is doing X' to
`lizards do X', from the psychology of the individual to the sociology
of the masses. Because sweeping and `fuzzy' generalisations describes no
one individual in specific, statements such as `leg length determines speed'
can stand as a factual statement about this animal. This epistemological
rupture between the description of individual animals to statements about
all the animals as a class is associated with scientists' shift from the
field laboratory practices to data processing in computers, and a similar
shift from filling rows of a spreadsheet associated with individual animals
to processing columns of numbers that summarise individuals into classes.
Tables and graphs, which abound in scientific publications, are visual
documents that integrate the substantive, mathematical, and literary resources
of scientific investigation, and create the impression that the objects
or relations they represent are inherently mathematical. At the
same time, the real mystery is the adequation of mathematics with the empirical
world not the superimposition of one mathematical form with another. This
isomorphic relationship between mathematics and the empirical world, which
is an a priori given for many scientists, was topicalised in the form of
the couplet {Fundamental structure <--> Mathematical form} (Lynch, 1991).
However, rather than being a simple relation or adequation, the double
arrow has to be understood as a possibly infinite chain of inscriptions
linked together by the embodied practices of their users. Reference is
therefore a quality of a chain of representations that mediates between
the purer elements of empirical world and mathematical form toward the
extremities of the chain; there is no longer an adequatio rei et intellectus,
just a chain that can be extended infinitely at both ends (Latour, 1993).
Discussion & Conclusion
Enculturation has been theorized in terms of the habitus that forms when
people--physically and socially situated in the world--participate with
others in activities and, as a matter of course, adopt an unthematized
practical sense for doing things in particular ways (Bourdieu, 1997). My
work goes beyond the work of earlier research on research practices (e.g.,
Latour, Woolgar, Knorr-Cetina, and Lynch) both in its scope--from grade
8 to professional practice--and in terms of the work observed--in work
settings (outside) and on specially designed interpretation tasks (inside).
In these studies our work is beginning to disclose interesting discontinuities
and contrasts. First, there were considerable discontinuities between the
investigative activities of practicing scientists and those of university
students and recent graduates with bachelors and masters degrees in science.
At the same time, surprising competencies were exhibited by pairs of Grade
8 students that, at a minimum, matched those of the university graduates
in science despite the vast differences in the educational backgrounds.
A comparative reading of the transcripts of group interpretations made
by Grade 8 students (who engaged in innovative science curricula for their
second year) and university students in a fifth year teacher education
course (who had obtained science degrees but who had experienced the traditional
fare of science courses) shows very little difference in the resources
and practices enacted during the tasks and very similar breakdowns.
One major breakdown for most university students occurred when they
had to deal with actual, scattered data that did not fall onto a line graph
(linear, quadratic); the same situation was dealt with much more gracefully
by the Grade 8 students experienced at preparing convincing representations
of real data. A second breakdown was experienced by the university students
because they took graphical models as representing real data so that they
sought for ways how "impossible" data could have ever been collected. That
is, my research indicates that present schooling at the elementary, secondary,
and undergraduate levels does little to introduce students to the authentic
scientific representation practices. My ongoing work among doctoral students
and postdoctoral researchers suggests that it is during these years that
individuals begin to participate in those experimental practices
which we documented for practicing scientists. On the other hand, my experience
with a special curriculum in a Grade 8 ecology class suggests that even
at this early age, students can effectively engage and participate in scientific
representation practices.
Traditional pedagogy--associated with the literature on general problem
solving skills that can be transferred across settings (e.g., Anderson
1985)--also assumes that learning proceeds from the general to the particulars.
Yet my work shows that without knowledge of particulars that can serve
as referents, students have difficulties in making sense of the representations
(as signs). On the other hand, those students with great familiarity of
particulars and how to transform particulars into graphical representations
also showed high levels of inscription-related competencies. The discontinuity
between the graphing-related practices of most students (other than those
in our Grade 8) and those enacted by practicing professionals may therefore
arise from these differences in knowledge of particulars and familiarity
in and with transformation practices. My ethnographic work among graduate
students and postdocs in the field of ecology shows that the transitions
from school to research practices do not come easy.
Ultimately, my work on scientific practices (mostly relating to representations)
from Grade 8 to professional practice constitutes only a small slice of
the kind of work that needs to be done to understand trajectories of scientific
competence from elementary school to everyday life (professional science,
activism, lay involvement). I am convinced that there is even a definite
place for members of the science (and technology) studies community to
cooperate with science educators both in terms of the research to be conducted
as well as in policy making and curriculum design. It is to be hoped that
this kind of work constitutes a beginning in dealing with our own conceptual
blind spots so that we can investigate the discontinuities in the practices
at the interface of schooling and everyday activity outside thereafter.
Acknowledgments
This symposium paper draws on a number of studies conducted over the past
decade. It was made possible, in part, by a grant of the Social Science
and Humanities Research Council of Canada (410-93-1127). My thanks go to
Michael Bowen and Sylvie Boutonné who assisted in the collection
and transcription of the databases on which I have drawn.
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[1]
In our experience, fixing the data is a rather common phenomenon in traditionally-conducted
high school and university laboratories. We observed or discovered situations
in which students with scholarships to the top Canadian and American universities
"fudged" their data by one order of magnitude to make their measurements
fit existing theory. Little did they know that in most high school and
undergraduate laboratories, the closest one can come with this experiment
is about one order of magnitude.
[2]
Here, I draw on an extensive report of science practices presented in Bowen
and Roth (1999).
[3]
Here, I rely on a presentation given to sociologists of science (Roth &
Bowen, 1998).
[4]
There are different lizard species. To protect the identity of our informants
as much as possible, we refer to the animals as `lizards' rather than the
specific species.
[5]
Eileen Crist (1996) provides an interesting analysis of the discourse used
by naturalists in describing animal life. She argues that naturalists'
(thick) descriptions of animal activity are in stark contrast to scientific
writing of generic individuals and typical cases which makes animal behaviour
appear automated. In Lynch's (1988) descriptions of laboratory rats, the
transition from naturalistic rats to analytic animals was accomplished
by means of the `sacrifice'.