Laboratory learning, classroom learning:
Looking for convergence/divergence in biomedical engineering
Wendy C. Newstetter
Department of Biomedical Engineering
Nancy J. Nersessian, Elke Kurz-Milcke
College of Computing
Georgia Institute of Technology
Kareen Ror Malone
Department of Psychology
University of West Georgia
Tel: 404-385-2531
Email: wnewstet@bme.gatech.edu
Abstract: We investigated a biomedical engineering (BME) research lab and a BME
undergraduate research course. In this paper we discuss how these sites diverge (e.g. the lab is
action centered, the classroom conceptual) and converge (e.g. knowledge is distributed among
people in both). We use the concept of transferability to develop rich accounts of learning in these
two contrasting sites.
Keywords: Cognitive Science, distributed learning environments, science education, learning
communities
1. Introduction
In the many attempts to improve science learning and instruction, researchers have looked to real world science
settings for models of what students should learn and what they should do in classrooms. Some have identified the
reasoning and problem-solving strategies employed by disciplinary experts and then developed forms of cognitive
apprenticeship that would foster such cognitive strategies in learners (Collins, Brown, & Duguid, 1989; Roth &
Bowen, 1995). Others have looked at the activities that drive research in science such as inquiry, experimentation,
argumentation and designing, and asked learners to engage in similar activities (Cognition et al., 1992; Herrenkohl,
Palinscar, DeWater, & Kawasaki, 1999). Still others have derived implications for educational environments from
observing and describing how tools and representations are used in laboratory settings (Kozma, Chin, Russell, &
Marx, 2000). In all such endeavors, salient aspects of practice are abstracted from the real world laboratory, where
the work of the scientist occurs, and then re-enacted in the instructional setting –in hopes of improving learning. The
laboratory serves as the sending context and the classroom as the receiving context. Best practices from the first are
abstracted and then re-enacted with various types of support in the second. Even though the goal is a kind of
convergence between sites, the re-enactment never fully replicates the original. The laboratory is understood to be
the ideal, while the classroom is at best only an imperfect copy.
Such experiments c an be construed in part as seeking a relationship of generalizability between sites
characterized by a unidirectional transfer of variables. The underlying generalizability argument seems to go that if
certain science practices in labs (variables) lead to success then replacing traditional classroom practices with those
from the lab will likewise ensure learning success. So the relationship between sites resides in the variables that hold
across the two contexts. In contrast to this conception of the relationship between classrooms and real world science
settings, we subscribe to a different one, one which holds that both environments though different offer a certain
parity. This parity derives from differing but locally legitimate goals emerging within the context and the situated
responses to those goals. Our position offers the possibility of a different kind of trans-site traffic, transfer which is
bi-directional rather than unidirectional. Either site could serve as the sending site or the receiving site. Either site
could benefit from developing knowledge in the other.
Such a framework subscribes to the notion of transferability (Lincoln & Guba, 1985) rather than
generalizability. Lincoln and Guba define transferability as “a direct f unction of the similarity between the two
contexts, what we shall call “fittingness”. They go on the describe fittingness as the “degree of congruence between
sending and receiving contexts”(p. 124). This notion differs from generalizability in its explicit incorporation of the
effects of context. Thus, both sites in the transfer situation need to be fully described and understood in order to
determine phenomena that transfer across the two sites. Generalizability, as a matter of abstraction does not depend
on the particular features and specific nature of the sending and receiving sites, only the variables. The sites need
only be part of a known population of contexts that constitute a representative sample.
In our study of thinking and learning in the relatively new discipline of biomedical engineering we are
developing “thick description” (Geertz, 1973) in two contexts for learning—a tissue engineering research laboratory
and an introductory undergraduate BME course that uses a Problem-based Learning (PBL) approach. We want to
investigate the degree of fittingness revealed through instances of congruence and disjuncture in practices across
these two contexts. In so doing, we believe there is an opportunity to better understand and improve learning in both
places.
2. Learning in an interdiscipline
Biomedical engineering may have begun as a multidisciplinary activity, where researchers from different
communities collaborated towards a common goals and cross-trained as they worked on a problem, but it is moving
rapidly towards a synthesis that we refer to as an interdiscipline. In the Postscript to the 2 nd Edition of The Structure
of Scientific Revolutions, Thomas Kuhn (Kuhn, 1962) introduced the notion of a “disciplinary matrix” as a way of
capturing salient aspects of scientific practice that we find useful. A disciplinary matrix comprises: 1) symbolic
generalizations, which specify the laws and definitions of a theory; 2) shared commitment to beliefs, which include
those about the ontologies and models; 3) shared exemplars, which include the paradigmatic examples and methods
provided to students in textbooks and laboratory exercises; and 4) shared values - such as “quantitative products are
better than qualitative” or “theories should be simple” or “theories should have social utility or - not,” which
function to provide a sense of community. The notion of an interdiscipline is meant to capture the phenomenon
where these aspects of two disciplinary matrices become melded to an extent that that new ways of thinking and
working have evolved. The resulting scientific practice can be understood as a hybrid of discourses, activity,
representations and ways of working. One of the objectives of our research is to investigate whether true members
of an interdiscipline continue to struggle with the possible contradictions or tensions that exist between two
communities of practice.
3. Transferability as a methodological construct
Studies of learning, in both experimental laboratories and more natural settings, normally seek to identify
findings that are generalizable from one site to other sites exhibiting similar populations and constraints. In the last
two decades, however, researchers from various disciplines have begun to compare learning in and out of school
settings, sites that are very different both in terms of population and constraints. Lave (Lave, 1998) in her study of
everyday problem-solving illustrated how mathematical reasoning strategies stressed in school settings did not carry
over into everyday settings like supermarkets and kitchens. Resnick (Resnick, 1987) likewise proposed four ways in
which in-school learning differs from that found out-of-school and suggested reasons why it was important to pay
attention to outdoor models of learning. In our study of biomedical engineering (BME) thinking and learning, we are
building on that work by simultaneously conducting observational studies in two different contexts for BME
learning: one clas sroom-based, the other laboratory-based. Rather than working from the notion of generalizability
we are using empirical research to determine the degree of transferability (Lincoln & Guba, 1985) between a tissue
engineering laboratory and a problem-based leaning (PBL) classroom.
4. Context I: The tissue engineering laboratory
We began our investigation of reasoning and problem solving in the context of a Tissue Engineering laboratory
in October 2001. Of particular interest to us in undertaking this study was the anticipated interplay/integration of
engineering and biology reasoning and problem solving at a cognitive level. While engineering as a constellation of
sub-disciplines is often characterized as engaging in abstraction and quantitative modeling, biology in contrast is
frequently characterized as undertaking qualitative systems description. We wondered whether and how two such
different approaches to work and problem solving are productively integrated or even reconciled in biomedical
engineering laboratories. To find out, over the last six months we have been using a mixed methods approach
combining ethnographic study of the current work in the laboratory with cognitive -historical analysis of the
historical records to produce biographical trajectories for lab members, research problems, and lab technology. Both
approaches aim to recover the salient representational, methodological and reasoning practices that have been
developed there and in the field.
The way they work in the laboratory
The laboratory under study is one of many that comprise the NSF-funded Tissue Engineering ERC at
Georgia Tech. All of these laboratories apply engineering principles and methods to the study of living cells and
tissues for the eventual development of engineered biological substitutes. Our laboratory focuses mainly on vascular
biological substitutes. Other laboratories in the Center target orthopedic substitutes. In each case, however, the
challenges are the same. Biological substitutes as systems for the human body must replicate the functions of the
tissues being replaced. This means that the materials used to “grow” these substitutes must coalesce in a way that
mimics the properties of native tissues. It also means that the cells embedded in whatever material is chosen must
also replicate the capabilities and behaviors of native cells so that the higher level tissue functions can be achieved.
Moreover, the type of cells identified for embedding in the substitutes must be readily available and compatible with
adjacent tissues. This requires a method for ensuring cell growth, proliferation, and production. Because the test bed
for these activities cannot be the human body, biomedical engineers have to design facsimiles of that environment
where the necessary experiments can occur at each of the levels identified. Finally there is the problem of scale-up.
Once these tissue substitutes have been successfully engineered, the means for manufacturing large enough
quantities to ensure a supply that can respond to hospital needs is a problem in itself. Whatever the focus of Tissue
Engineering activity in the ERC, these are the questions that researchers must address.
The interface between the constraints of doing biological work and applying an approach that is based on
engineering marks every step of the learning process and the projects of the lab under study. To create biological
substitutes (also referred to as models and grafts) cells are embedded on a scaffolding material. This process of
seeding results in a “construct”, a first step in moving towards engineered tissues. But this first step involves
numerous problems to be solved. The cells must come from somewhere—animals or humans. They must be grown
in sufficient numbers to ensure that enough get embedded to create a construct. The scaffolding material needs to
have properties that promote adhesion, appropriate distribution and proliferation. Once on the scaffold they need to
develop properties that are human-like. And those cells need to behave in ways that support identified local and
higher-level functions.
To eventually be able to engineer these biological substitutes, BME experts have to answer many kinds of
questions. One set of questions seem to revolve around the in vivo/in vitro distinction, a difference that is definitive
of the interface between engineering and biological concerns. Which scaffolding material, fibrin or collagen will be
better for embedding the cells? What contaminated the cells embedded on the construct? How can I improve the
mechanical integrity of the collagen scaffolding? How can I enhance cell proliferation and distribution on the
construct? How can I get the cells to seed on the scaffolding? How can I get the cells off the construct to investigate
changed properties? We have developed a provisional typology of question types and an account of the problem
solving strategies used to tackle these types of problems. Of particular interest to us are the tools or representations
that mediate the process of problem seeing and solving. We have been trying to describe and articulate how each
problem type is articulated, how it is then transformed, and the form of its re-presentation and the data that the new
form feeds back. The reason that this transformative process is necessary is because the laboratory researchers must
mediate the biological ends of their work through devices that are derived from the application of engineering. They
cannot find immediate answers to their questions in the real site of question generation—the human body. One
cannot try out various kinds of scaffolding in in vivo environments. To compensate for this, BME researchers use in
vitro models or locally built devices, like bioreactors and flow loops, to screen and control specific elements they
want to examine. So a goal for all the tissue -engineering laboratories is to optimize in vitro models so as to move
closer and closer to in vivo situations. In doing so, they gain a better understanding of the in vivo context. At the
same time, in vivo studies identify locales where engineers can do their work, problems where they can apply
engineering analysis and quantitative and device modeling. That is why the in vivo/in vitro division/distinction is so
much a part of the cognitive framework guiding practice in the laboratory.
The way they learn in the laboratory
Obviously the interdisciline of biomedical engineering depends upon disciplinary frameworks for both
biology and engineering. Participation in the laboratories demands knowledge, skill and expertise in both the
engineering and life sciences; however, the vast majority of students currently conducting research in the
laboratories have been educated exclusively as engineers. The students’ encounters with the biological aspects of
biomedical engineering occur in a very specific context through a particular modality. What most report that they
initially experience as apprentices is what they refer to as the “biological side”. By this they mean the painstaking,
methodical work that goes into growing and feeding the cells, transferring the cells to the constructs, using pipettes
and sterile equipment and then removing the constructs from glass mandrels so they can conduct experiments on
them. Mastering the time intensive steps is a major learning challenge. It requires physical dexterity, anticipation of
movement, and planned action to avoid various kinds of contamination. Some of this is skill—eye hand
coordination--and some is knowledge—knowing where to do each step—the sterile hood or the incubator, the order
of the steps, the possible missteps that will ruin what has already been done.
Thus, the first learning activity must refer to the biological nature of the material and the embodied nature
of the practices to be learned and is observational. The undergraduate watches as the senior student-researcher grows
the cells, seeds them and removes them. The gross steps are written down and kept in a book for anyone to use. It is
however, the subtle aspects of the protocol—angling the hand in a certain way to avoid touching the side of the
glass—that are not written down and which cause the most problems. The next step is for the learner to try the steps
on her own. Often a laboratory member looks on and does moment-to-moment correction. There is bound to be
failure and the rhythm of the learning is dictated by the complexity of the devices they are attempting to master,
Novices characterize these protocols as “complicated” because “you have to focus on the moment” and you have to
“think about things you can’t see.” Engineers have learned to grapple with things they cannot see, but this sort of
invisibility and the recalcitrance of the biological side of things are different than the challenges faced by engineers.
Errors can ruin an experiment and cost several weeks of work. When the learning involves very expensive
equipment like the confocal microscope, the learner feels intimidated, less willing to take chances. So the learning
steps and decisions are constrained by the types of equipment that come on board as the learner moves forward in
the research activities. The rhythm of learning is dictated by the complexity of the “devices” they are attempting to
master.
Another problem laboratory novices experience is time. The researcher has to go with the rhythm of these
experiments (“cells need to be fed on Saturday and Sunday”) something very new for undergraduates used to
controlling activities to their own advantage. So learning to do this “biological work” is achieved and individually
evaluated through growing participation in and understanding of the complicated protocols that prescribe the use of
materials and the timing of various activities. Scaffolding for this kind of learning comes mainly from other
laboratory members who are available to demonstrate procedures, answer questions, oversee procedures, or direct
the learner towards resources. The learner-in-the-laboratory evaluates her own learning such that she either
determines that she will watch someone else do the work, undertake this work with the help of a laboratory member,
or do it alone.
The challenges in this type of action-centered learning is that the steps being taken and learned are not well
grounded in the higher level goals of the research in the laboratory. The undergraduate learners are very adept after
3 month in explaining “how” they do their work. They are much less able to explain “why” this work is important,
where it fits in other types of activity in the laboratory and how it helps to drive forward a larger research agenda.
They are most definitely learning at the procedural level but it is not clear that they are developing conceptual
understanding to compliment and anchor the “how-to” knowledge.
New Ph.D. students, although entering the laboratories with a greater amount of disciplinary knowledge,
encounter similar difficulties. Again we see “engineering-heavy, biology-light” learners who also encounter the
same problems of growing and seeding cells onto constructs. Like the undergraduates, they struggle with the
biological side o f things. However, this encounter is further complicated when the student is charged with picking
up where others left off. In the laboratories we have investigated so far, we find the PI gives students projects rather
than their identifying problems for themselves. As a result, they are often expected to pick up and carry on with a
path of work started by a former or soon-to-graduate student. It is common for the “tools” of that line of research to
be passed on to these new Ph.D. students. In one case, a bioreactor designed by a recently graduated Ph.D. student
had been passed on to a student who had been in the laboratory only four months. In asking her to explain how this
bioreactor worked and how it fit into the research the laboratory was undertaking, we found she had experienced
several learning challenges. First there was the problem of the device itself. How did it work? How did the parts fit
together? What could it do? This she could address by taking it apart and rebuilding it from the parts up.
Then there was the next step of understanding how this device would fit into her research. This was something she
was struggling with. Of particular interest to us was her observation that she “had trouble relating to the way he used
to work with it”. This reflection indicated to us the situatedness of each of the devices in the laboratory. BME
researchers, assistants, and students, have biographies that become part of the laboratory history; similarly, the
multiple and diverse objects that are manipulated and transfigured in the BME laboratory. The biographies of these
objects have to be told on multiple levels, including their physical shaping and re-shaping, their changing
contribution to the models that are developed in the BME laboratory at any particular time, and the concepts that
dominate the research activity. Learning the device is learning its biography—how it came about, how it has evolved
and how it currently works as a knowledge-making device in the laboratory. This takes time and requires growing
membership in the practices of the laboratory.
Overall learning challenges in the laboratory include the following
1. The construct production protocols that are the bedrock of TE are hard to learn from a procedural perspective.
2. Engineering students find this work complicated and different from what they are used to.
3. Learning happens incrementally, on-demand and just-in-time. Laboratory members apprentice newcomers to
the practices but not to the larger goals. Knowledge is distributed in diverse and complex ways across the
people, devices, texts and other kinds of representations in the laboratory.
4. Laboratory members design and iteratively rebuild devices to conduct in vitro experiments. Devices, their
design, and use are highly contextualized. Learning them involves learning their construction, their functions
and their utility in a larger research program and their evolution within it.
5. Learning in the laboratories is compartmentalized into biology learning and engineering work. Knowledge is
not hybridized but rhythmically paced such that some phases are biology-driven and other engineering-driven.
5. Context II: BMED 1300
For the two introductory courses in the undergraduate biomedical engineering curriculum, BMED 1300/2300,
the BME Department at Georgia Tech has adopted a model of learning and a set of educational practices that have
been used in medical education for more than a decade. Referred to as Problem-based Learning or PBL, this
approach draws on constructivist pedagogy, which assumes that learning is t he product of both cognitive and social
interaction arrived at through authentic problem solving. Faculty members in this new department have the explicit
goal of creating practitioners of an interdiscipline from the outset, to obviate the need for the multidisciplinary cross
training. They recognize not only the complexity of learning in this interdiscipline, but also the inadequacy of
traditional engineering classroom configurations of lectures and limited discussions for developing the kinds of
biological and engineering knowledge that complement each other. For that reason, the department has decided to
bring students into the field with a first course that forces them to engage immediately with big problems in BME.
As an environment for learning, the PBL classroom has five goals: the construction of useful BME knowledge, the
development of BME-specific reasoning strategies, the development of effective self-directed learning strategies,
increased motivation for learning and improved collaboration skills. The classic PBL version used in medical
education utilizes rich medical problems, which support free inquiry. This freedom encourages student-directed
learning and increased learning motivation (Barrows, 1985; Hmelo, 1998)
Likewise students in BMED 1300 learn to apply engineering principles and reasoning strategies in the context
of complex, open-ended problems in biomedical engineering. Problem designers on the BME faculty develop
authentic problems that resemble those in the field. Student teams of eight tackle these problems over a three-week
period with the facilitation of a tutor. They identify what they know that is useful in solving the problem. They
identify areas for research and inquiry. They bring the fruits of their inquiry into the group to be used in tackling the
problem. They practice using the reasoning strategies of engineers in moving forward towards a problem solution.
At the end, the student teams create concept maps that articulate the content area of the problem and present their
solutions to the other student teams for comparison and critique. The final product is of less importance than the
problem-solving process and the knowledge building that have occurred. A sample problem is provided below.
The U.S. Government has requested preliminary proposals for the design of a device that can detect the
Ebola virus. The device should be designed so that it is able to respond to the presence of the Ebola
virus as rapidly and with as much sensitivity as is possible and is capable of remaining functional for two
weeks or more in the field. The design that best meets these criteria will be fully funded to complete the
design of the device. The large biomedical engineering Company that you work for has decided to
compete for funding for this project. The company’s strategy is to construct a mammalian-cell-based
device that is capable of detecting the Ebola virus. Your team is responsible for developing the detection
assay. Other teams will be responsible for implementing this assay in the field. You know that dying cells
often lose their membrane integrity and release enzymes into the extracellular environment. Your team
believes it should be possible to correlate the level of enzyme activity released by the cells with the
concentration of Ebola virus that the cells are exposed to.
6. Divergence between sites
As two very different contexts for learning, the research laboratory and the PBL classroom diverge in important
ways. While learning in the laboratory is initially action-centered, PBL learning is concept-driven. Laboratory
learners use their bodies to mediate the kinds of knowledge they are building---their hands to transfer cells, their
eyes and noses to detect contamination. The PBL learner, in contrast, is using her mind to find avenues for problem
structuring, for making sense of research articles and for figuring out what steps to take next. While the problem in
PBL frames activity and sets the stage for student work from the top down, design and building on the laboratory
bench top offer learning opportunities at many levels. Initially the overwhelming need is to master cell culturing
steps from the bottom up. Soon they must master use of certain devices like the flow loop or the bioreactor.
Learning device use happens at several levels. First there is assembly of a device like a flow loop piece by piece.
How the device will be used to advance knowledge is learning at a totally different level. At these stages of
learning, the laboratory mentoring system is critical. In the course of many dyadic interactions, the mentor
apprentices her mentee to the various kinds of tacit and explicit knowledge that support her in moving her research
program forward. While novice researchers are given laboratory research papers to read, these do not serve initially
as learning scaffolds. Rather it is the intimate physical-interactional relationship with the mentor that develops as
cell-culturing techniques develop that characterizes first learning steps in the lab.
One PBL problem called for teams to develop a technique for increasing the supply of adult stem cells. In fact
this is a problem about bioreactors and their design. To undertake such a design, students needed to develop a great
deal of both biological and engineering knowledge. They needed to understand the mechanisms of the cell at very
fine levels. They needed to understand how environments affect cell change. In the context of the problem, the
groups shuttled back and forth between biology and engineering. This learning was not procedural or embodied, but
conceptual. What was missing from the PBL environment was the stark specter of failure that haunts the laboratory
learner. Failure in the laboratory tells the novice when her learning in not sufficient and cues her to go seek more
knowledge. PBL in BMED 1300 offered no real accountability in terms of problem solution. There was not stark
experience of faulty learning and as a result, faculty often commented that many of the solutions had serious flaws.
As enacted, PBL did not offer students the action-centered, open-to-failure form of learning witnessed in the
laboratory. And since this is where the streams of biology and engineering meet, this is not inconsequential. These
forms of divergence begin to suggest possible enhancements to learning in both contexts. Laboratory learners could
use help in understanding the problems they are tackling conceptually or from the top down. However, PBL learners
would benefit from the possibility of failure as a instigator of iteratively deeper learning.
7. Convergence between sites
Given the far-reaching differences between a university research laboratory where cutting edge discoveries are
the goal and a freshman level introductory class, it was surprising to find significant instances of congruence.
Laboratory learning depends on recognizing that knowledge is distributed across people, texts, devices, and other
forms of external representation. Different laboratory members are repositories of different kinds of knowledge.
Various people in other parts of the building like the histologist in the basement are other knowledge hubs. Novices
soon learn to negotiate and work with this kind of distribution, asking help of the right people, and learning where
the knowledge resides. This same kind of distribution also occurs in the PBL classroom. Some students have
substantial biology knowledge having taken AP courses in high school. Others are strong in math skills and others in
physics. The team soon learns who might have certain kinds of knowledge and uses that person as a resource. On
certain problems, the PBL groups moved out of their groups and into the BME building, knocking on professor’s
doors in search of an answer. They even talked on a conference call for more than an hour with the CEO of a
company doing research on a device for destruction of cows contaminated with Mad Cow disease. One freshman at
the end of the term commented on how the teaming of people with different skills and knowledge had been critical
in the problem solving process and also worked towards developing an excellent team ethos.
The just-in-time learning that dictates what gets taken up as critical in the laboratory was also seen in the PBL
groups. Further, the shuttling between disciplines that is evident in the laboratory was evident in the class.
Sometimes PBL teams foraged for a week in the life sciences, trying to understand cell metabolism. The next week
they would be trying to understand quantitative glucose models from an engineering book. Classroom discourse
would one day be dominated by nouns and descriptors of processes. The next day the discourse might be mediated
by formulas and Greek symbols. Where they were in the problem dictated what they learned, the forms of discourse
and the mediating languages.
The need for agency in learning that determines success or failure in the laboratory is just as critical in the PBL
classroom. In the first problems, students seemed able to only find resources on the Internet. Soon, they discovered
the library on campus and the many resources there. Next they discovered the on-line journals and even started
taking trips over to another university to the medical school library to really begin to wrestle with newly published
studies. These moves were caused by impasses and beset with difficulties. Articles from medical journals are
complex and jargon-laden. Freshman or new Ph.D. students, for that matter, have to struggle to understand this
highly coded information. Agency in learning and obligation to the team, however, motivates students to push
themselves but to also admit when they cannot understand. Knowing when to ask for help and knowing that help is
available is something they learn in both contexts.
8. Conclusion
The construct of transferability offers the possibility of developing rich accounts of learning in two contrasting
sites. Instead of stripping away the context so as to isolate and then transfer variables, such an approach celebrates
the uniqueness of context so as to identify the many possible supports for learning. Over the next two years we
anticipate continuing to discover ways to describe, translate and enhance learning practices in both contexts of
science learning—research laboratories and undergraduate BME classrooms.
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10. Acknowledgments
We thank Jim Davies, Matthew Labbe, Raul Necochea, and Victoria Smith for their contributions to the research on
this project. We gratefully acknowledge the support of the National Science Foundation ROLE grant REC0106773
in carrying out this research.