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CHAPTER 3 5

Interdisciplinarity in EngineeringResearch and Learning

Nancy J. Nersessian and Wendy C. Newstetter

Disciplines are distinguished partly for his-torical reasons and reasons of administra-tive convenience (such as the organizationof teaching and appointments) and partlybecause the theories which we constructto solve our problems have a tendency togrow into unified systems. But all this clas-sification and distinction is a comparativelyunimportant and superficial affair. We arenot students of the same subject matter butstudents of problems. And problems maycut right across the border of any subjectmatter or discipline.

Sir Karl Popper Conjectures andRefutations (Popper, 1962, p. 67)

Introduction

Moves beyond disciplinary thought andpractice abound today. National and inter-national funding agencies are creating, facil-itating, fostering boundary crossing andcross-disciplinary synergy and integration asa focal point of their agendas across thesciences, medicine, engineering, humanities,and arts. Research on interdisciplinarity (ID)

as it is practiced in humanities and thesciences is also abundant, ranging from richcase studies of specific instances to biblio-metric analyses that aim to map such thingsas patterns of interaction in scientific fields.To date, however, the research on ID aspracticed in engineering fields is scant, bothwith respect to practice and to education.1

Yet, as noted in the National Academy ofEngineering (NAE) report on the engineerof 2020, the demands of twenty-first centuryengineering are such that education needsto be redefined starting at the undergradu-ate level:

The dissolution of boundaries between disci-plines such that ‘imagination, diversity andcapacity to adapt quickly have become essen-tial qualities for both institutions and indi-viduals, not only to facilitate research, butalso to ensure immediate and broad-basedapplication of research results related to theenvironment. To meet these complex chal-lenges as well as urgent human needs, weneed to . . . frame integrated interdisciplinaryresearch questions and activities to mergedata, approaches, and ideas across spatial,

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temporal and societal scales. (NAE, 2005,p. 36, quoting AC-ERE, 2003)

In this chapter we focus primarily on ID as itis enacted in engineering research laborato-ries. Our focus on practice stems from whatwe call a translational approach to trans-forming engineering education, by whicheducation researchers first investigate thecognitive strategies and learning ecologiesas they occur in the practices of a spe-cific field and then translate findings fromthese investigations into instructional envi-ronments using design-based research. Thisapproach infuses the actuality of the cog-nitive and learning practices found in theengineering workplace into the classroomsetting. The goal is to achieve greater par-ity between the synthetic environment ofthe classroom (in vitro) and the authen-tic environment outside the classroom (invivo) (Newstetter, Behravesh, Nersessian, &Fasse, 2010). As with Popper’s claim earlier,our research supports the position that IDin engineering is problem-driven, and, fur-ther, that the nature of the problems and thevariety of approaches to them require thatwe differentiate among different kinds ofID practices. A recent National Academiesreport defines ID as “a mode of research byteams or individuals that integrates informa-tion, data, techniques, tools, perspectives,concepts and/or theories from two or moredisciplines or bodies of specialized knowl-edge to advance fundamental understand-ing or to solve problems whose solutionsare beyond the scope of a single disciplineor field of research practice” (NAS, NAE, &IM, 2005, p. 26). What is powerful about thisstatement is that it goes beyond the usualfocus on language and communication tohighlight the numerous dimensions acrosswhich ID integration needs to take place.However, although there is recognition inthe literature on ID that there are severalforms, policy statements such as this tend totreat it as though it were all of one kind. Asdeveloped in later sections, our research sup-ports the need for a more nuanced under-standing of the varieties of ID fields, whichcan be characterized as multidiscipline,

interdiscipline, and transdiscipline. In thischapter, each is exemplified with specificcases from engineering.

The structure of the chapter is as follows.Before beginning the analysis that derivesfrom our in situ investigations of ID, webegin with a brief survey of the landscape ofthe main conceptual understandings of ID toprovide readers with entre to the literatureon ID broadly construed. We then draw onanalyses of ID processes in the science stud-ies literature and in our own research to pro-vide some analytic tools for thinking aboutID in practice. We then focus on what IDlooks like in action by considering the varietyof ID in engineering practice, and concludewith implications for learning in engineeringeducation.

Tools for Analyzing Interdisciplinarity

The characterization of ID by the lead-ing contemporary scholar in the field,Julie Klein, resonates with the sentimentexpressed nearly thirty years earlier by Pop-per (see earlier):

Interdisciplinarity has been variously definedin this century: as a methodology, a con-cept, a process, a way of thinking, a philos-ophy, and a reflexive ideology. It has beenlinked with attempts to expose the dangers offragmentation, to reestablish old connections,to explore emerging relations, and to createnew subjects adequate to handle our prac-tical and conceptual needs. Cutting acrossall these theories is a recurring idea, inter-disciplinarity is a means of solving problemsand answering questions that cannot be sat-isfactorily addressed using single methods orapproaches. (Klein, 1990, p. 196)

Thus, ID is best understood as a process(Klein & Newell, 1996) of problem solv-ing, and there is widespread agreement thatthe hallmark of ID processes is integration(Klein, 1990, 1996; Lattuca, 2001; NAS, NAE,& IM, 2005). Much of the focus of researchon ID has been on collaboration in ID teamscomprising members coming from different


interdisciplinarity in engineering research and learning 715

disciplines (see Derry, Schunn, & Gerns-bacher, 2005 for a number of case stud-ies), a practice for which, according to Klein(Klein, 2005) World War II was a “water-shed” (see also Galison, 1997). Given thefocus on integration in most research onID, multidisciplinarity is most often con-trasted with interdisciplinarity because it isargued to fail to achieve lasting integra-tion of disciplinary components. Individ-uals come together from different disci-plines, work together on a problem, andthen return to their disciplinary habits andabodes largely unchanged. The other lesswidely recognized form of ID is transdis-ciplinarity, which is variously construed inthe literature as transcending disciplinaryboundaries through a kind of overarchingsynthesis toward the pursuit of applications(Klein, 2010). Some have also suggested thattransdisciplinarity invites a broader range ofstakeholders from the public or practition-ers recruited to solve an authentic problem(Borrego & Cutler, 2010). In the discussionthat follows, we address all three kinds ofID while trying to distinguish to the extentpossible among them.

Disciplinary research is often character-ized as taking place in “silos” and interdis-ciplinarity, as moves out of these. How arethese moves made? What facilitates interac-tion and integration? What are the charac-teristics of the interactions and integrations?Here we introduce some metaphors in thescience studies literature on interdisciplinar-ity that we have found to be useful for artic-ulating and analyzing these dimensions.

Trading Zones

In characterizing the development of micro-physics by experimentalists, theorists, engi-neers, and mathematicians, spurred byvarious problems that fueled research anddevelopment during World War II, histo-rian Peter Galison sought a metaphor thatwould capture the movement and interac-tions across boundaries that occurred withinthese cultures. He called the process he wastrying to capture, “intercalation”: coordina-tion without homogenization and used the

metaphor of “trading zone” to colorfully cap-ture this concept. He found the trading zonemetaphor in the thinking of anthropologistsand linguists about how communication andexchange of goods of value can take placeamong radically different communities withno cultural point of reference or language:

Two groups can agree on the rules of exchangeeven if they ascribe utterly different signif-icance to the objects being exchanged; theymay even disagree on the meaning of theexchange process itself. Nonetheless, the trad-ing practices can hammer out a local coor-dination, despite vast global differences. Inan even more sophisticated way, cultures inaction frequently establish contact languages,systems of discourse that can vary from themost function-specific jargons, through semi-specific pidgins, to full-fledged creoles.(Galison, 1997, pp. 782–783)

The notion of a trading zone designates abounded, delimited space in between dis-ciplines where trading processes can occurbecause each participant group needs some-thing from the other to address problemsthat lead to shared projects and goals. Inhis analysis of the trading zones, “language”is expanded to mean any structured sym-bolic system, which can include graphicaland mathematical representations. The cen-tral metaphor is exchange: researchers cometogether for a period and exchanges takeplace, and then everyone goes back to wherethey came from. It is possible that fun-damentally new concepts and techniquesemerge in the zones that then impact theoriginal disciplines as in the case of JulianSchwinger discussed later. Schwinger tookback some of the “pidgin” of the MIT Radi-ation Lab and used it to great effect. How-ever, emergent disciplines are not the focusof Galison’s interpretations, and as we dis-cuss later, trading is not the best metaphorfor these. The main point is that transac-tions take place within the trading zoneand everyone goes back to their disciplinarysilos, with the disciplines largely unchangedalthough occasionally individuals have sig-nificant impact on their disciplines from thiscross-cultural encounter.

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Boundary Objects

In their study of how workers at theMuseum of Vertebrate Zoology (curators,amateur collectors, professional biologists,occasional field hands, science club mem-bers) managed both diversity and coop-eration, sociologist Susan Leigh Star andphilosopher James Griesemer introduced anotion that has had wide impact on studiesof interdisciplinarity: boundary object. Theexample they use is specimens of dead birdswhich had differing meanings for the inter-secting worlds of amateur bird watchers andprofessional biologists in the context of var-ious problems involved in museum work.They designated as boundary objects:

. . . those scientific objects which both inhabitseveral intersecting social worlds . . . and sat-isfy the informational needs of each of them.Boundary objects are both plastic enough toadapt to local needs and the constraints ofthe several parties employing them, yet robustenough to maintain a common identity acrosssites. They are weakly structured in commonuse, and become strongly structured in indi-vidual use. These objects may be abstract orconcrete. They have different meanings in dif-ferent social worlds but their structure is com-mon enough to more than one world to makethem recognizable as a means of translation.(Star & Griesemer, 1989, p. 391)

A boundary object is an entity (concrete orabstract) that has a complex structure suchthat it is compatible with more than oneinterpretation. Parts of that structure mean-ingfully intersect across the communitiesconcerned with it. The notion of a boundaryobject has been extended to a wide range ofinstances in the subsequent literature, suchas the everglades (conservation science: sci-entists of various kinds, environmentalists,government agents), soil (soil science: geol-ogists and botanists), hand-drawn sketches;engineers and architects and cloud cham-ber traces (particle physics: experimental-ists, theorists, instrument makers).

An object or entity is a static notion butone thing boundary objects can do is to leadto the construction of spaces of dynamicinteraction between disciplines, which is

how Galison characterizes the trading zone.Thinking about how new spaces can leadto the emergence of ID engineering fieldshas led our research group to introducethe notion of adaptive problem spaces wheredisciplines intersect and hybridization andother forms of emergence occur. Further, weintroduce the notion of boundary agents tocapture agency of participants in construct-ing these ID spaces.

Adaptive Spaces and Boundary Agents

Whereas zones and objects are bounded inthat they are delimited or constrained spa-tially and temporally we think of adaptiveproblem spaces as finite but unboundedspaces (on analogy with the Einsteinianconception of the physical space of theuniverse) where problem-driven adaptationtakes place in a complex system. In ourwork, we have come to formulate the notionof adaptive spaces as follows:

Adaptation of complex systems is a processof continually revising and reconfiguring thecomponents from which these are built, asthese gain experience. Research in adaptivespaces is driven by complex interdisciplinaryproblems, and these require that the individ-uals themselves achieve a measure of inter-disciplinary integration in methods, concepts,models, materials – in how they think andhow they act. Adaptive spaces are distributedin space and time. They are dynamic anddiachronic and span mental and materialworlds. (Nersessian, 2006)

Unlike the inhabitants of trading zones whoreturn to their disciplines after working on aproblem, researchers and artifacts within theadaptive space become to varying extentshybrid systems and inhabit regions that canthemselves give rise to new hybrid disci-plines (interdisciplines such a biomedicalengineering) or can be more varied (transdis-ciplines such as integrative systems biology).Although the central metaphor of a trad-ing zone is exchange, the central metaphorof an adaptive space is emergence. The peo-ple who are forging the adaptive space toadvance the processes of interdisciplinary

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emergence through their activities we des-ignate as boundary agents.

The literature on ID has tended to focusmore on integration of language, methods,theories, and so forth, with less attentiondirected toward the individuals who do theintegration and the ways ID impacts themas researchers. Understanding the kinds ofadaptations and transformations researchersneed to undergo to become boundaryagents raises issues of cognitive developmentthrough learning, identity, and the develop-ment of interactional skills suited to the vari-ety of ID practice. The latter skills have beencalled interactional expertise by the sociolo-gist Harry Collins (Collins & Evans, 2002).Although his use of the term focuses on theidea that participants in ID work need, tosome extent, to learn the languages of theother discipline(s), we expand the notion tocomprise other facets of ID interaction.

Interdisciplinary Engineeringin Action

Our research has led us to classify varietiesof ID practice in terms of the kinds of engi-neering these have been producing. In thissection we provide examples of ID engi-neering in research laboratories for each ofthe varieties: multidiscipline, interdiscipline,and transdiscipline. In a multidiscipline, par-ticipants from disciplines come together inresponse to a problem, create a local inte-gration to solve that problem, and go backto their respective disciplines, with theselargely unchanged by the transient interac-tion. In contrast to the current ID litera-ture surveyed in the preceding text, we castsuch multidisciplinary interactions as fallingwithin the category of ID research becauseproblem solutions do require and achieveintegration, even if the disciplines them-selves are largely not impacted. We havenot investigated this form of ID ourselves,but use an interesting historical case asillustration: microwave engineering researchwithin the MIT Rad Lab in which engi-neers and physicists collaborated on the

problem of radar development in WorldWar II.

In our investigations of engineeringresearch laboratories, we have found thatan interdiscipline might be thought of asa hybrid discipline – one that emergeswhen the integrative activities of partici-pants move beyond collaboration to create anew hybrid field in which there is stable andsustainable integration in concepts, methods,technologies, and materials in the service ofaddressing an ongoing range of problems.We use biomedical engineering as an exem-plar.

The notion of a transdiscipline is harderto articulate, but the basic idea is thatresearchers draw largely on the knowledge,methods, etc. of a discipline, but addressproblems that require penetration by oneor more other disciplines. That is, interac-tions are likely to mutually effect changesin understanding, methods, and other prac-tices in regions of the participating disci-plines that seep into the adaptive space.The case we look at here is integrative sys-tems biology, which involves interactionsamong researchers in engineering, comput-ing, and biosciences. In this context, the pre-fix “trans” signifies that this enterprise seepsinto, penetrates, specific prior practices ofthe mother fields and a further emergentproblem space opens with multiple possi-bilities for interaction and integration.

Multidiscipline: Microwave Engineeringin the MIT Rad Lab in World War II

This exemplar provides an instance of IDcollaboration that was driven by a problemexternal to the communities and the collab-oration was pragmatic and expedient. Theproblem of creating radar systems for thewar effort brought electrical engineers andtheoretical physicists together in what cameto be known as the MIT Rad Lab. We pro-vide only a brief account because our dis-cussion relies on secondary sources. As Gal-ison analyzes the interactions in the tradingzones between physics and engineering, themain interdisciplinary problem was one oftranslation of the complex mathematics of

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electromagnetic field theory into a formelectrical engineers, accustomed to alge-bra and circuit language, could understandand use to analyze wave guides, which arelong, hollow metal boxes with disconti-nuities (Galison, 1997). Electrical engineerswere by-and-large not familiar with themathematics of field theory and even forthe physicists, the usual method of solv-ing the electromagnetic field equations forall points in the field proved an intractableproblem. The physicist Julian Schwingerdeveloped a mathematical notion of “equiv-alent circuits.” Reducing complex electro-magnetic field representations to circuit rep-resentations with which electrical engineerswere familiar greatly simplified the calcula-tions required and enabled the engineers topredict various aspects significant to radardesign in advance of constructing the arti-fact. Thus, exchange was affected by meansof simplified diagrammatic representationsand equations, which performed as bound-ary objects, equally meaningful to the physi-cists and the engineers.

The exchange led to the developmentof radar, and with success in the tradingzone everyone then went back to their disci-plinary silos. However, the exchange processalso had a significant impact on the thinkingof the person we would call the boundaryagent, Julian Schwinger. As Galison details,not only did Schwinger’s methodologicaland conceptual innovation figure centrallyin the development of radar, but when heleft the zone and went back to particlephysics he had a new way of thinking thatultimately led to his notion of renormaliza-tion in quantum electrodynamics. That is, indeveloping interactional expertise in electri-cal engineering, Schwinger also developed amode of thinking about complex phenom-ena in terms of minimal structural aspects.Schwinger himself linked the seeminglyunrelated domains of radar and QEED in amemorial lecture for the Japanese physicistTomonaga: “The waveguide investigationsshowed the utility of organizing a theory toisolate those inner structural aspects that arenot probed under the given experimentalcircumstances. . . . And it is this viewpoint

that [led me] to the quantum electrodynam-ics concept of self-consistent subtraction orrenormalization” (Galison, 1997, p. 826).

Interdiscipline: Biomedical EngineeringResearch Labs

This exemplar is drawn from our eight-year study of cognitive and learning prac-tices in research laboratories in biomedicalengineering (BME). In this case the interdis-ciplinarity is explicit, reflective, and inten-tional, with the ultimate aim of stabilizinginto an “interdisciplinary discipline” or inter-discipline. Pioneering engineers in the fieldwanted to move beyond multidisciplinarycollaborations to creating the integrativeindividual biomedical engineer. Researchersbelieve the challenge of biomedical engi-neering now and in the future to be thatthe research problems are inherently inter-disciplinary, calling for the integration ofconcepts, methods, materials, models, andso forth into emergent hybrid systems withinthe adaptive problem space of BME. Thecases we have examined in some depth comefrom tissue engineering and neural engineer-ing. Here we discuss salient interdisciplinefeatures these labs have in common andthen provide some brief details of a hybridresearcher in tissue engineering.

The BME labs are hybrid engineering andbiological science environments. The hybridnature of these laboratories is reflected in thebioengineered physical simulation model-systems designed, built, and experimentedwith by the labs. This hybridity is also foundin the characteristics of the researcher-students who are part of an educational pro-gram designed explicitly to produce indi-viduals who are interdisciplinary, integrativebiomedical engineers who can also act inindustry and in academia as boundary agentsin interaction with collaborators from any ofthe three disciplines. Research in biomed-ical engineering often confronts the prob-lem that it is both impractical and uneth-ical to carry out experiments directly onanimals or human subjects. In our stud-ies of two pioneering biomedical engineer-ing research laboratories we have found a

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common investigative practice is to design,build, and experiment by means of in vitrosystems, which parallel certain features ofin vivo systems. When biological and engi-neering components are brought togetherin an investigation, researchers refer to thisas a “model-system.” As one respondentstated: “when everything comes togetherI would call it a ‘model-system’ [ . . . ] Ithink you would be very safe to use that[notion] as the integrated nature, the bio-logical aspect coming together with an engi-neering aspect . . . ” These physical modelsare hybrid artifacts engineered to capturewhat researchers deem to be salient prop-erties and behaviors of biological systems(Nersessian & Patton, 2009). They are struc-tural, behavioral, or functional analogs of invivo biological phenomena of interest withengineering constraints that impose sim-plifications and idealizations unrelated tothe biological systems they model. Theseemergent hybrid objects are not boundaryobjects, but are integrated artifacts under-stood in the same way by the community ofresearchers.

Lab A, in tissue engineering, seeks todesign off-the-shelf vascular tissue replace-ments for the human cardiovascular system.Some intermediate problems that drive theresearch are: producing “constructs” (bloodvessel wall models composed of living tis-sue that mimic properties of natural vessels);examining and enhancing their mechani-cal properties; and creating endothelial cellsources through mechanical manipulationof stem cells. Lab D, in neural engineer-ing, seeks to understand the ways neu-rons learn in the brain and, potentially, tocreate aids for neurological disabilities. Itsintermediate investigations center on find-ing evidence of plasticity in a “dish” ofmulti-electrode neuron arrays, and produc-ing controlled “muscle” activity in robotsor in simulated agents, all of which consti-tute their model-systems. Given space con-straints we can consider only a brief exam-ple from one lab, tissue engineering, whichillustrates its nature as an adaptive space inwhich hybrid BME researchers and artifactsemerge.

Research in Lab A stems from the insightits director had in the early 1970s: “charac-teristics of blood flow [mechanical forces]actually were influencing the biology of thewall of a blood vessel. And even more thanthat. . . . it made sense to me that, if therewas this influence of flow on the underly-ing biology of the vessel wall, that some-how that cell type [endothelial] had to beinvolved.” The central problem became thatof understanding the nature of these influ-ences of mechanical force on the vascularbiology, codified in the hybrid concept arte-rial shear: frictional force of blood flow par-allel to the plane of flow through the lumen.Lab research is directed toward both funda-mental problems, such as of endothelial cellbiology, and potential application problems,such as engineering a viable artery substi-tute. Tackling these problems had led to thedevelopment of a number of hybrid model-systems. The processes of creating and usingmodels drive researchers to be integrativeinterdisciplinary individuals. The design ofmodel-systems incorporates engineering andbiological constraints, making them hybridobjects used to simulate the in vivo phenom-ena of interest and provide sites of experi-mentation.

The construct model (see earlier) is nowthe central focus of research in Lab A. Thenature of the model changes along variousdimensions depending on the constraints ofthe experiment in which it will be used.For instance, it can be seeded with smoothmuscle cells and endothelial cells, or sim-ply the latter, and the components of thecollagen scaffolding can vary. At any giventime, its design is based on what is currentlyunderstood of the biological environment ofendothelial cells in cell and vascular biol-ogy, the kinds of materials available, and bio-engineering techniques thus far developed.Building the construct has led to new hybridmethods for the engineering of living tissue.Once built, a specific construct model canbe manipulated by various means as part ofan engineered model-system. One form ofmanipulation is by the flow channel device(“flow loop”), an engineered model of thein vivo force of blood flow over the lumen.



The flow loop design is based on the fluidmechanics of a long channel with a rectan-gular cross-section. Exposing the endothe-lial cells lining the construct to shear stresses“conditions” the cells, and can be the locus ofexperiment itself (e.g., relating to cell mor-phology or gene expression), or just one stepin a multi-model process.

A diagram, drawn by the Lab A direc-tor in response to our request that he “drawa picture” of the research in his lab, pro-vides a glimpse into the dimensions of theadaptive space of Lab A (Figure 35.1). Hemapped not only the problems (“major bar-riers”), but also the technologies (at the bot-tom), the relations of researchers to bothof these and to one another in that space.This map begins to articulate an adaptivespace distributed across problems, methods,technologies, and members; as well as con-necting the lab to resources and commu-nities external to it. For example, for LabA “gene profiling” requires using technol-ogy at a nearby medical school. The inves-tigative practice of in vitro simulation isdeeply implicated in these mappings. Toaddress the “barrier” of “mechanical proper-ties” of endothelial cells in vivo, for instance,requires designing and using flow chambersand collagen gel constructs.

Given the hybrid nature of the in vitromodels, a major learning challenge for theseresearchers is to develop selective, inte-grated understandings of biological con-cepts, methods, and materials and engi-neering concepts, methods, and materials.By “selective,” we mean that a researcher-learner needs to integrate, in thinking andexperimenting, only those dimensions ofbiology and engineering relevant to his orher research goals and problems. For exam-ple, in Lab A, researchers need to developan integrated understanding of the endothe-lial cell in terms of the stresses of fluiddynamics of blood flow in an artery. Further,in designing and conducting experimentswith devices, researchers need to understandwhat engineering constraints they possessderiving from their design and construction,and what limitations these impose on thesimulation and subsequent interpretation

and inferences. That is, the device needsto be understood both as device qua modelof in vivo phenomena and device qua engi-neered model.

Building activities centered on the in vitromodels are pervasive in the lab and serveseveral functions. The artifact models con-nect the cognitive practice of in vitro sim-ulation with social practices; for instance,much initial mentoring and learning of lab-oratory ethos takes place in the contextof cell culturing – something all newcom-ers must master. The processes of buildinghybrid physical models also provide oppor-tunities for the researcher to build integratedmental representations (Nersessian, 2009).For instance, building a physical constructto condition with the flow loop facilitatesbuilding a mental representation that selec-tively integrates concepts from cell biologyand fluid dynamics – one that represents, forexample, biological aspects of the endothe-lial cells with respect to mechanical forcesin terms of the integrated concept of arte-rial shear rate (force of blood as it flows overthese cells, causing elongation, proliferation,and so forth).

In experimental situations models tendto be put into interlocking configurations,that is, models stand in particular relationsto other models. A brief look at the designand execution of a significant Lab A exper-iment will provide a means of articulatingthis dimension of bioengineering integra-tion. Soon after one graduate student (des-ignated A7 in Figure 35.1) arrived (with abackground in chemical engineering) shewas designated the “person who would takethe construct in vivo,” meaning that herresearch was directed toward conductingexperiments with an animal that serves asa model for the human body in the contextof the experiment. This objective immedi-ately required that she would (1) need todesign and build a construct that would bothmore closely mimic the functional charac-teristics of an in vivo artery than was used inmost other experiments and would have suf-ficient strength to withstand the force of invivo blood flow; (2) modify the flow loopso that it would work with constructs in



Figure 35.1. The adaptive problem space of the tissue engineering lab at a specific period in time asdrawn by the lab director.

tubular form, and (3) arrange for an animal(baboon) to be surgically altered so as toexperiment with the construct outside of itsbody and in a minimally invasive way. It alsorequired that she bring together the strandsof research being conducted by nearly all theother lab members, as represented by the labdirector in Figure 35.1. As she expressed it,“to go to an in vivo model we have to haveall, well most of the aspects that people havestudied.”

When we started, she had been in the lab-oratory about a year, but was still in the pro-cess of defining the specific goals and prob-lems of her research. Her final overarchingformulation of the problem was to deter-mine whether it would be possible to use cir-culating endothelial cells (“progenitor cells”)derived from a patient’s peripheral bloodto line the vascular graft. The endothelialcells that line the artery are among the mostimmune sensitive cells in the body. If the



patient’s own endothelial progenitor cellscould be harvested and used that wouldgreatly enhance the potential of a vasculargraft. However, the progenitor cells do notmodulate thrombosis, which is a functionof the mature cells. She hypothesized thatshear stress conditioning (by means of theflow loop) the construct before implantationwould solve the problem of platelet forma-tion and the resulting thrombosis.

It is instructive to examine her own suc-cinct summary statement as an example ofdimensions of hybridization.

We used the shunt to evaluate plateletdeposition and that would be – in otherwords – were the cells, as a function of thetreatment that they were given before theywere seeded onto the engineered tissue, ableto prevent blood clotting? And so we specifi-cally measured the number of platelets thatwould sit down on the surface. More plateletsequals a clot. So, it ended up being that wewere able to look at the effects of shear stresspreconditioning on the cells ability to pre-vent platelets and found that it was actuallynecessary to shear precondition these bloodderived cells at an arterial shear rate, whichI used 15 dynes per square centimeter com-pared to a low shear rate, which in my case Iused like 1 dyne per square centimeter, so, apretty big difference. But I found that the arte-rial shear was necessary to enhance theirexpression of anti-coagulant proteins andtherefore prevent clotting. So in other words,the shear that they were exposed to beforegoing into the shunt was critical in terms ofmagnitude, for sure.

The bold terms mark reference both tohybrid interdisciplinary models as they func-tion in her understanding and reasoningand to the various hybrid physical mod-els. To unpack a few of her expressions“the cells” are the endothelial progenitorcells she extracted from baboon blood andseeded onto the “engineered tissue” (vascu-lar construct model). The “treatment” theyreceived was “shear stress preconditioning”conducted by using the modified flow loopmodel. The objective of her research wasto determine if, and at what level, thepreconditioning (“arterial shear” simulation)

of constructs would “enhance their [cells]expression of anti-coagulant proteins” (“pre-vent platelets”). She found, through sev-eral iterations of the entire model-system(“used the shunt [animal model] to evalu-ate platelet deposition”), that the in vivohuman arterial shear rate (“15 dynes/cm2”)was required for sufficient protein expres-sion (“was critical in terms of magnitude”).Likewise, her designing and experiment-ing by means of the hybrid baboon model-system led to a revision of her conceptualunderstanding of the vascular construct soas to reflect the necessity of using arte-rial shear in order to prevent thrombosis.From this articulate summary of researchproject and findings, we can detect that thisresearcher has developed into both an inte-grative biomedical engineer and a potentialboundary agent with interactional expertisefor the relevant disciplinary communities,such as medicine.

Transdiscipline: Integrative SystemsBiology Research Labs

This exemplar draws from our ongoinginvestigation of an emerging transdiscipline,integrative systems biology (ISB), whichfocuses on two labs that self-identify asconducting research in ISB; one that doesonly computational modeling and the otherthat does both modeling and experimenta-tion. They are both largely populated bystudent researchers with engineering back-grounds. The modeling lab has various bio-science external collaborators. The overar-ching problem of the labs – and the fieldof ISB in general – is: How to developa non-reductionist understanding of howmultilevel biological systems function? Asthe modeling lab director stated, “[systemsmodeling] allows us to merge diverse dataand contextual pieces of information intoquantitative conceptual structures; analyzethese structures with the rigor of mathemat-ics; yield novel insight into biological sys-tems; suggest new means of manipulationand optimization.” Our findings are prelim-inary in this exemplar since we have onlybeen conducting this research for two years.



What is striking is that the various possibleconfigurations for research in this adaptivespace are numerous and continue to emerge,and our cases provide only a subset. Still,we have gained some important insightsinto transdisciplinary engineering researchin ISB.

Although there are many different kindsof researchers in this space at present, theaspiration of this field, still in its infancy,might be characterized as addressing theresearch problems that lie at the intersec-tions of computing/applied mathematics,biosciences, and engineering to create anemergent transdisciplinary space that allowsfor multiple kinds of adaptations and bound-ary agents. The goal is integration of novelhigh throughput technologies, modeling,and experimentation to address biologi-cal problems, but the way the field looksat present, researchers in ISB will largelyremain in disciplinary fields while workingin collaboration with other disciplinary part-ners. But unlike the multidiscipline, in thiscase each field in the adaptive ISB problemspace will likely penetrate and change sig-nificant practices in regions of the collab-orating fields. For instance, for the aspira-tions of the field to succeed, modeling needswill lead to changes in biological practices,for example, with respect to the kinds ofdata collected; high-throughput technolo-gies that generate reams of data have andwill continue to change the practices of bothbioscientists and modelers.

Initially, we, along with much that iswritten about this emerging field, cast theparticipants as computer scientists/appliedmathematicians, biologists, and engineers.So, it was quite interesting to note earlyon that they tend to identify themselvesand other members of the community func-tionally as “modelers” (those who applymathematics and develop computationalmodels/simulations) and “experimentalists”(those who conduct bench top experimen-tation) which we see as already a moveinto a transdisciplinary adaptive space. Inour study nearly all the researchers haveengineering backgrounds, though most iden-tify themselves as having had a “generalist”

education. As one participant from Europestated of her electrical engineering degree:“they learned us to learn, not to learn some-thing.” As discussed in the Conclusion sec-tion such a generalist background mightbe important for developing the cognitiveflexibility required to become integrativeresearchers in this field.

Lab G comprises only modelers and isled by a senior pioneer in biological sys-tems modeling. The overarching problemof the lab is to develop rigorous computa-tional models of biological phenomena atthe systems level. Most have never done thiskind of modeling before entering the lab.The biological problems researchers workon are provided by experimentalists externalto the lab. Experimentalists usually contactthe lab director asking him to model somedata they consider have potential to benefitfrom such analysis in areas as varied as biofu-els, Parkinson’s disease, atherosclerosis, andheat shock in yeast. The fact that they largelydepend on bioscience problems that are gen-erated external to the lab has the implicationthat researchers (who are engineers) have todevelop the facility to go deeply into theexperimental literature that changes witheach modeling project with little coursework or bench top experience in biology.The lab does formulate its own researchproblems in methods development, such asnew methods of parameter estimation.

Lab C comprises both researchers whodo only modeling and those who do bothmodeling and experimentation. The direc-tor is a young assistant professor who is fullyconversant in both modeling and experi-mental methods. The overarching problemof the lab is to understand cell signalingdynamics in a reduction–oxidation (redox)environment in immunological contexts;in effect, to integrate redox cell biologyand biochemistry research through systemsmodeling. The specific biological problems,thus far have been chosen by the lab,include immunosenescence and drug resis-tance in acute lymphoblastic anemia. Theirmethodological research has largely been inthe experimental area, such as the designand development of microfluidic devices to

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generate high-throughput data for theirmodels. Experimentation in Lab C isdirected toward getting parameters neededto develop and validate models that theydevelop initially from the same kind of lit-erature searching as lab G. Experimentationis conducted either by the student who isbuilding the model, who is developing intoa hybrid researcher, or, for the pure model-ers, by the lab director or the lab technician,who has an MS in molecular biology.

Unlike BME, which has a relatively uni-fied vision of how research and trainingshould proceed, ISB is experiencing whatthe Lab G director calls a “philosophicaldivide.” First, with respect to lab structure,Lab G is an instance of what we call a uni-modal lab. Such a lab can comprise all mod-elers or all experimentalists, such that thetransdisciplinarity manifests as two separateresearch partners undertaking complimen-tary but different activities. The philosophythat underlies this research modality is that alab does the best research if its members aredeeply engaged in only one kind of activity.A potential disadvantage of this modalityis that each research partner is dependenton the sustained interest and engagementof the other for successful biosystems mod-eling, despite there being little interactionbetween them. From the modeler’s perspec-tive, there is often a significant phase lagbetween model building and generation ofthe needed experimental data, as the Labdirector put it, “you need 10 experimentalistsfor every modeler” and everyone needs “on-going technical problems to work on so thattime is not wasted [waiting for experimen-tal data].” As for advantages of unimodal-ity, our research provides insight into onlythe modeler’s perspective. One advantageis that the development of operating prin-ciples and novel theoretical approaches aredriven by researchers developing perspec-tives across different domains and also bythe poverty of data.

Lab C is what we characterize as abimodal lab were bimodality can manifest aseither a within-lab collaboration between anexperimentalist (or hybrid researcher) and a

modeler or by a hybrid researcher who car-ries out his or her own modeling and exper-imentation. This approach has the advan-tage of the lab being able to focus deeplyon biological problems of its own choos-ing and of being able to design its ownexperiments and collect data as needed ina more timely manner. A potential disad-vantage is that the within lab collaborationscould prove not to be able to meet the needsof someone who does only modeling (unlessthe lab has a large number of people engagedin experimentation); another is that it isan open question as to whether the hybridresearchers will be able to develop the requi-site level of expertise in both modeling andexperimentation. This latter question pointsto another philosophical divide in the field:how best to train those wishing to becomehybrid researchers.

The main divide in training hybridbimodal researchers is over whether suchtraining should be sequential or parallel. Allthree possible configurations of training arerepresented in the labs we have studied.A postdoctoral researcher, who collaborateswith Lab G was first trained as an experi-mentalist and then migrated to learning anddoing only modeling. The Lab C directorfirst did only modeling and then nearly fiveyears into her Ph.D. started on experimentalwork. She believes her student researcherswho want to become hybrids need to betrained simultaneously in both. As she said,“I tell my students never to do this [sequen-tial]. You should always do these things inparallel. I ran into the learning curve earlygraduate students face – only here I was 4.5years in and starting from scratch on someof these things.” The Lab G director believesthat students should only be trained in oneor the other, because otherwise there is “theproblem of diluting both sides” with “model-ing lite and experimenting lite.” If a personwants to become bimodal, then he or sheshould train sequentially through a post-docin the other area.

Although the jury will be out on thisdivide for some time, it is interesting to com-pare the perspectives of the two who have



recently completed their training. The post-doc in Lab G when asked how he would runhis own lab and student training stated, “Ilose a lot of time going from one side to theother . . . its more efficient to have a studentdoing lab work and another dealing with theproblems of modeling . . . They should be inthe same lab, they need to see each otherworking.” On the other hand, the Lab Cstudent who recently graduated with a dis-sertation project that combined both saidshe was concerned about having someoneelse do her experimental work because theymight not “really understand the modelingproject.. can’t accurately come up with agood enough experimental protocol to getwhat it is I need.” Of her own experience,she said “I like the idea that as I’m buildingmy model things are popping up in my headon wow this would be a good experiment.I plan out the experiment and then do it. Ilike the idea I’m being trained to do both soI have enough tools in my toolbox.”

General Discussion

In the Rad Lab case we see multidisciplinaryinteraction within a trading zone whereSchwinger was a central boundary agent.The equivalent circuits are the boundaryobjects. There was integration of conceptsand methods specific to the wave guide cal-culations. We would argue in some casestrading zones can also be adaptive spaces.For instance, in this example one emergentphenomenon was a new way of thinking forSchwinger which he used in a highly pro-ductive manner to resolve problems in hisdiscipline. On the engineering side, a newfield of microwave engineering emerged.The interaction was driven by problemsstemming from a specific situation, WorldWar II, and collaboration was not driven byinternal problems originating in the disci-plinary fields. Multidisciplinary interactionsare often serendipitous, related to a specificproblem, and transient. Once the problemhas a satisfactory solution (or proves insolu-ble) the collaboration ends and the practicesof the participating disciplines are largely

unaltered, even when a new field might havebeen spun off.

In the BME lab cases there is emergenthybridization in an adaptive space initiatedby pioneering engineers who participated incollaborations and thus acted as boundaryagents in the early days of the field. To cre-ate the emergent hybrid systems of thought,methods and materials they believed wouldmove the field forward required a differ-ent model of research than that of tworesearchers from different disciplines collab-orating. BME’s answer has been to designa different kind of researcher – individu-als, who might be considered themselves as“hybrid systems.” The integrative biomed-ical engineer is both a self-sufficient IDresearcher and prepared to be a boundaryagent able to collaborate with researchersin other disciplines. Over time this ID fieldis transforming into an interdiscipline thatintegrates elements of all three original disci-plines, and creates individuals who identifyas hybrid biomedical engineers.

In the ISB lab cases there is interpenetra-tion of disciplines that are mutually effectingchanges as well as various kinds of emergentadaptations at their intersections. This is anewly configuring adaptive space and howits research practices and researchers willevolve is quite open at present. Althoughthere are fully hybrid individuals emergingin this space, the current aims of the partic-ipants seem likely to make this exception,rather than the rule. The implication of thisis that developing interactional expertise iscrucial to functioning as boundary agents inthis adaptive space. From our research thusfar, it appears that the requisite interactionalexpertise is not sufficiently developed forwhat one researcher called “synergistic” col-laboration in at least three ways. First, modelbuilding begins with the development ofa pathway for the biological phenomenonunder investigation. The pathway performsas a boundary object in that it is a meaning-ful representation for both experimentalistsand modelers. At present it is the primarymeans of communication of research resultsand ideas between collaborators. However,



our investigation indicates that the modelneeds to become a boundary object becauseit is the vehicle of integration and the enginethat is pushing systems understanding for-ward. Experimentalists need to understandhow the data will be used in order to conductexperiments that will provide sufficientlyinformative data. Second, modelers need todevelop experimental understanding at thebench top level in order to know enoughabout experimental design and execution tohave a realistic sense of such things as whatis experimentally feasible, the reliability ofthe data, and the costly and time consumingnature of experimentation. Finally, all par-ticipants need a basic systems understandingas provided by engineering fields.

Conclusion: Implications for Learning

Given the varieties of interdisciplinarityillustrated in these case studies, how canwe best prepare engineering students to par-ticipate in these interdisciplinary configura-tions? Are there certain pedagogical config-urations that best support the developmentof interactional expertise making it possiblefor teams to find common ground, to iden-tify and leverage boundary objects and tomore smoothly exchange information andintent toward reaching a commonly valuedgoal? And where do we situate these learn-ing experiences in the overall curriculum?

We contend that while each variety ofinterdisciplinarity implies a particularizedpedagogic approach, in all cases, studentsneed to develop what has been called cog-nitive flexibility (Spiro, Feltovich, Jacobson,& Coulson, 1992) or the ability and knowl-edge to engage a problem domain, an objector a representation from more than oneperspective. We see this as the ability toadapt in interdisciplinary problem spaces,becoming boundary agents in problem sit-uations that require them, while also lever-aging boundary objects with their intersect-ing social worlds/meanings. Developing thisability requires students to work in complex,ill-structured knowledge domains not sim-ple, well-structured ones. Problem-driven

learning experiences are particularly appro-priate in developing cognitive flexibilitybecause such experiences situate students inreal-world, complex situations that requireteams to work together to achieve a goaland possibly to pursue and evaluate multipleroutes and solutions. To date there has notbeen much research conducted on designinglearning environments targeted specificallytoward creating interdisciplinary engineers(Richter & Paretti, 2009). Here, we offer anexample from our own institution of threedifferent learning environments where stu-dents can practice a specific form of inter-disciplinarity.

Bio-inspired Design: A MultidisciplinaryExperience

In recent years, engineers have started tolook to biology as a source and inspira-tion for design solutions, on the assumptionthat evolutionary adaptation has producedsimple but elegant solutions to complexproblems in the natural world. However,translating between the descriptive worldof biology and the quantitative systemsworld of the engineer is challenging. Likethe trading zone on an African river,these “tribes’ do not share a common lan-guage even though they may share com-mon concepts. Developing the kind ofinteractional expertise that makes it possi-ble to span the boundaries of these disci-plines is of paramount importance. Workingwith this concept, the bio-inspired designcourse seeks to develop individuals who areable to translate biological solutions intoengineered designs. To do this, studentsfrom biology, engineering and industrialdesign work on two team projects over theterm towards developing the interdepen-dent skills of interactional expertise and ana-logical reasoning (Vattam & Goel, 2011; Vat-tam, Helms, & Goel, 2010) that will enablethem to find commonalities across the dis-ciplines. Analogical reasoning entails look-ing beyond surface features or applicationof a given object in one domain for a deeperstructure than can be mapped or trans-lated into another domain or application.



Such reasoning facilitates exchange acrossdisciplinary borders.

Leveraging the idea of a boundary object,teams first are invited to identify a bio-logical solution in the natural world andthen to translate that into a solution in theengineering world (solution-driven design).In this first case, the design in nature canbe viewed from the biological perspective(evolutionary adaptation) but also the engi-neering perspective (function and form).The second project works in the oppositedirection where an engineering problem isidentified and the team seeks a biologicalsolution that can be applied. The multi-disciplinary teams work together on bothprojects learning to translate from one per-spective/world to the others while practic-ing looking at the same object from anotherperspective. Whereas, the first project bringsthe engineer and design student deeply intothe world of biology, the second brings thebiologist into the world of the engineer anddesigner. Learning to parse a problem solu-tion from biological, engineering, and indus-trial design perspectives promotes the kindof cognitive flexibility indicative of bound-ary agents and those able to barter in trad-ing zones. This educational model for pro-moting a multidisciplinary practice can alsobe found in a number of capstone designexperiences (Adams, Beltz, Mann, & Wil-son, 2010; Adams, Mann, Forin, & Jordan,2009; Adams, Mann, Jordan, & Daly, 2009;McNair,Newswandera, & Borrego, 2011) aswell as community-based service projects.

Problems in Biomedical Engineering:An Interdiscipline Experience

Biomedical engineers need to be true inte-grative, hybrid thinkers and problem-solversif they are to utilize engineering analysisand methods to design healthcare solutions.The model-based reasoning exemplifiedin the development of in vitro devicediscussed in the preceding text dependson the ability to simultaneously view anobject or application or representationfrom multiple dimensions and perspectives(Nersessian, 2002, 2008; Nersessian & Patton,

2009). This integrative ability needs to bepracticed repeatedly over time and in avariety of circumstances. Because learnersneed multiple opportunities to practicethis integration, developing a biomedicalengineer is not about a single course, buta total curriculum systematically designedto foster flexible, responsive model-basedproblem solving. The Problems in BiomedicalEngineering course was developed using atranslational approach whereby the designprinciples for the course were derived fromour ethnographic investigations of learningin bioengineering research laboratories(Newstetter, 2006; Newstetter et al., 2010).The goal was to create an adaptive space byreplicating some of the kinds of (authentic)activities undergraduates and graduate stu-dents undertake in the research labs to theextent possible in a (synthetic) classroom.

Learning in this class is driven by theneed to solve three complex, interdisci-plinary problems over the term. The prob-lem sequence is set but each problem can bechanged sufficiently on the surface so thatevery term is different. The problems allrequire teams of eight students to integrateknowledge and skills from biosciences andengineering in arriving at a problem solutionrelated to a medical context. As an example,the first problem focuses on the challengesof screening for disease. The team needsto evaluate current screening technologiesfor a given cancer and then make recom-mendations for future screening protocolsbased on research using peer-reviewed sci-ence and medical and engineering articles.Overall, the problem requires the integra-tion of cancer biology, probability statistics,and screening technologies across the molec-ular to the whole body scale. Often cost–benefit analyses and social issues becomepart of the problem solution. To supportthis integration and complex problem solv-ing, teams work in specially designed 10 × 10

classrooms with writable walls which theyuse to represent, explain, and speculate indi-vidually and as a group. They also bene-fit from interacting with a faculty or postdoc facilitator who makes his/her reasoningand problem solving strategies more “visible”



through asking probing questions just as alab director would do with lab members.The goal of this course is for students tostart the process of learning to integrateskills, knowledge, methods, and represen-tations from the sciences and engineeringtoward solving real-world problems, prac-tices that define what it means to be inter-disciplinary.

Three Possible Models:A Transdisciplinary Experience

Preparing students for transdisciplinarypractice implies a very different educationalscenario from the two above. First, our stud-ies suggest that graduate school is where thisneeds to happen. We have found that theresearchers who inhabit this world and whoclaim identities as either modelers or experi-menters have sophisticated skills and knowl-edge already, which they bring to bear on thelab problems. This deep disciplinary train-ing gives them what they need to begin theirgraduate work. At the same time, they com-monly have blind spots to the needs, values,or constraints of the other camp. Model-ers need a certain kind of data, which theexperimenters may not value and so theywill not take the time to perform the exper-iments. At the same time, the experimental-ist may see the modeler as just reproducingher study in silico, which is not particularlyinteresting or relevant. These misalignmentscan lead to a certain stereotyping one of theother, which is counterproductive. We offerthree different models for creating adaptivespaces to bring greater alignment and under-standing to the modeler/experimenter con-figuration.

A first remedy followed by Lab G was atemporary summer excursion into the othercamp. Two graduate student modelers fromengineering backgrounds spent two monthslearning experimental procedure, conduct-ing their own experiments and collectingdata. In doing so, they have begun to developan appreciation for the challenge of gather-ing data both from the time and expenseperspective. They also developed the abilityto read papers with enhanced understanding

of techniques, equipment, and proceduresused in lab work. Another benefit was theconfidence the modelers developed in talk-ing directly with the experimenters (inter-actional expertise) from spending someintense time at the bench top. On theflip side, if experimentalists were to spendtime with modelers, they could bettersee the possibilities of modeling for pre-diction, speculation, and experimentation.They could also better understand why themodeler needs certain kinds of data. Further,the experimentalist could become a betterconsumer of modeling papers for his or herown work.

A second model would be the designof a collaborative laboratory-based gradu-ate course that paired modelers with exper-imenters. The task would be to addressa problem that could benefit from bothapproaches working in tandem. The needfor interactional expertise would be obviousas the team traversed and engaged the sametask from two different methodological andepistemological perspectives.

A final model would be an integrativemodeling course where experimenters andmodelers were again paired to address aproblem area, for example, a disease like cys-tic fibrosis. The experimenter could be veryhelpful in finding resources for the modeland translating them for the modeler. Like-wise, the modeler would need to articulateher needs in a way that would allow theexperimenter to be a resource. These educa-tional models do not advocate for develop-ing fully hybrid (bimodal) researchers, butrather for individual adaptation of the kindthat creates symbiosis or mutualism, whereboth come to appreciate and see the value ofthe practices of the other. Such educationalexperiences can promote the kind of cog-nitive flexibility and interactional expertisefor the transdisciplinary space where values,practices, and epistemologies differ.

Acknowledgments

We gratefully acknowledge the support ofthe U.S. National Science Foundation grants



REC0106733, DRL0411825, and DRL0909971

in conducting this research. Our analysisderives from research conducted with ElkeKurz-Milcke, Lisa Osbeck, Ellie Harmon,Christopher Patton, Vrishali Subramanian,and Sanjay Chandrasekharan. We thank themembers of the research labs for allowingus into their work environment, letting usobserve them, and granting numerous inter-views. Finally we appreciate the commentsof the anonymous reviewers of this chapterand the editors for their helpful recommen-dations.

Footnote

1. An exception on practice is the chapter on civilengineering by Culligan and Pena-Mara in therecent Oxford handbook of interdisciplinarity(Frodeman, Klein, & Mitcham, 2010, pp. 161–174), which provides a valuable resource fordeveloping a broad understanding of the cur-rent “terrain” of ID research. Notable excep-tions on education are a survey of the currentlandscape with respect to undergraduate edu-cation (Lattuca, Trautvetter, Codd, Knight, &Cortes, 2011) and research on undergraduatedesign teams (see, e.g., Adams, Beltz, Mann,& Wilson, 2010; Adams, Mann, Forin, & Jor-dan, 2009; Adams, Mann, Jordan, & Daly, 2009;McNair, Newswandera, Chad, & Borrego,2011).

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