Chapter 1: Thinking Differently

“The world we have made as a result of the level of thinking we have done thus far creates problems that we cannot solve at the same level…”

—Albert Einstein1

If Einstein’s dictum is true, that problems cannot be solved by the mindset that created

them, then how can teachers get students to think innovatively about problems that have been

created by past and current mindsets? If teachers are not themselves taught different ways of

thinking and teaching, then how can they be expected to foster thinking differently among

their students? The question therefore becomes, how can teacher-educators foster generative

new ways of thinking” and teaching among student students that transfer to their K-12

practice? Given the need to teach for efficiency2 because of the No Child Left Behind Act and

its high-stakes assessment,3 is it even possible to teach for efficiency and innovation

(Bransford et al., 1999)?

I believe the answer is yes, and argue that systems thinking supported by computer-

based dynamic modeling and simulation provides a new mindset in the spirit of Einstein’s

Dictum, and a timely innovation for teaching traditional disciplinary content. When

contemporary controversies and interdisciplinary topics are analyzed from a systems

perspective, the stage is set for both efficient and innovative collaborative, problem-based

learning and teaching. With respect to systems, the claim is supported by over a decade of

teacher-driven classroom practice of systems thinking and modeling (Scheetz & Benson,

1994; Zaraza, 1995), and the urgent need for computer-assisted, systems understanding to

grasp the counterintuitive and interrelated dynamics of social, economic and environmental

problems (Banathy, 1996; Capra, 2002; Dorner, 1996; Forrester, 1971; McNeill, 2000;

Radzicki & Taylor, 1997; Senge, 1990). The claim with respect to controversies is supported

1 Quoted in Des MacHale, Wisdom (London, 2002). Referred to as “Einstein’s Dictum” in Natural Capitalism (1999).2 In studies related to learning skills and developing expertise, efficiency is associated with technical skills and the ability to perform a task efficiently; in contrast, “adaptive expertise” is associated with an openness to improvement and the intentional use of metacognitive skills to creatively look for ways to do something better and to adapt current skills to new situations (Bransford, Brown, & Cocking, 1999; Hatano & Inagaki, 1986).3 ("No Child Left Behind Act of 2001," 2002).

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by a decade of researcher-driven classroom use of controversies (Linn, Davis, & Bell, 2004),

and the need for a principled integration of educational technology and web-based inquiry into

instructional design (Bopry, 1999; Bransford et al., 1999; Dede, 1998; Jonassen, 2004;

Jonassen & Land, 2000; Linn & Hsi, 2000; Pea et al., 1999; Salomon & Almog, 1998; Shear,

Bell, & Linn, 2004).

Structuring inquiry around contemporary controversies benefits teachers because it

addresses the limited effectiveness of pedagogy that relies on straight transmission of

knowledge by using a meaningful context (CTGV, 1992) to “invite” students to use or

develop their cognitive and self-regulatory skills” (Niemi, 2002). It forces teachers to think

differently about pedagogy because their relationship with the learner changes from content-

centered knowledge purveyor to student-centered catalyst, metacognitive role model, and co-

learner (Minstrell, 2001; Mintzes, Wandersee, & Novak, 1998). Scientific controversies with

social and economic ramifications enable teachers to situate science learning within its social

context, and demonstrate not only that science effects everyone’s lives, but also that it is “one

of the most precious possessions of humankind” (Bell, 2004, p. 236).

For students, the use of contemporary scientific controversies addresses the ennui that

they exhibit towards rote learning of unproblematic material by anchoring inquiry to an

“unsettled” scientific problem (Latour, 1987) with personal meaning (Brown, Collins, &

Duguid, 1989). Students are forced to think differently about learning, not only because their

relationship with their teachers changes, but also because their relationship with the subject

matter changes: from assimilation of “correct” content, to connecting or changing their prior

knowledge with new information in order to evaluate competing claims (Bell, 2004; Mintzes

et al., 1998). It also has the virtue of prompting students to think about how knowledge is

created, by revealing the inferential, contingent and provisional nature of scientific knowledge

(Driver, Asoko, Leach, Mortimer, & Scott, 1994). Learning content through controversies

enables student to experience “how scientific understanding actually unfolds over time…and

intersects with the interests of society” (Bell, 2002, p.237).

Scientific and science-based controversies direct attention to the argumentation

processes by which scientists make claims supported by evidence and the warrant for it, in

order to achieve consensus concerning a particular question. However, where a controversy

involves a dynamic system, attention also needs to be directed to the dynamics of the

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processes involved, i.e., the relationships and the moment-to-moment interactions among the

components. Without learning the significant interactions and how the knowledge is

functionally connected, the most probable outcome is that students’ understandings will

remain superficial and non-operational (diSessa, 1999).

This was demonstrated in the film, A Private Universe (Schneps & Woll, 1989).

Harvard graduates were asked what causes the earth’s seasons, and their answers revealed the

common misconception that seasons are caused by the changing orbital distance between the

sun and the earth (Gardner, 1991; Wandersee, Minztes, & Novak, 1994). Although the

students’ knowledge was correct that the distance between the earth and sun changes during

the year, they also knew that winter and summer seasons occur simultaneously in the northern

and southern hemispheres, and yet they still answered that seasons were caused by changes in

the earth-sun distance.

Alan Kay (1995) attributed the persistence of this misconception among some of the

brightest and best-educated students in America to a lack of the necessary “operational”

knowledge that would have enabled them to make the connections between the significant

facts that they did know. I argue that the Harvard graduates who remembered the correct

“answer” were just as unlikely to have operational understanding because the conventional

answer (the tilt of the earth’s axis) represents a necessary condition, but is incomplete.

Conceptual change research does not support the inference that students have constructed

operational understanding on the basis of their knowing one condition (Mintzes, Wandersee,

& Novak, 2000; Wandersee et al., 1994; White & Gunstone, 1989). Furthermore, teaching and

assessing based on such a simplistic answer may not only deflect attention away from the key

dynamic processes involved, but also seriously diminish students’ incentive to learn them.

The Premise Behind the Research

When the subject of an inquiry involves a dynamic system in which behavior changes

over time, a systems paradigm4 provides the most useful framework with which to approach it.

A systems paradigm functions as an advanced organizer (Ausubel, 1968) because it includes

the relevant parts and their properties, the relationships among the parts (how they are

connected), how the parts interact (the processes involved), the conditions upon which the

4 A paradigm is a set of organizing principles for thinking about particular types of problems.

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interactions are contingent, and the interdependencies among the parts (the feedback or

circular causal loops), which are inherent in the system, and are all needed to ensure a

comprehensive framework for inquiry about the system. A systems paradigm develops both

analyzing and synthesizing metacognitive skills because reasoning about dynamic systems not

only requires analysis of the parts and how they stand in relationship to each other, but also

synthesis of how they function together to produce systemic behaviors (Costanza, 2003). Most

importantly, a systems paradigm explicitly directs attention to the deeper structures and

significant processes because it focuses on how the system actually works moment-to-

moment, rather than on the more readily observable surface structures and events (diSessa,

1999).

This can be illustrated by framing the question, “what explains the earth’s seasons?”

as a systems inquiry.5 The unit of analysis is the earth-sun system in which the two parts, the

sun and the earth, are related to each other, respectively, as “star” and “orbiting planet.” The

sun’s star status signifies the solar processes that produce energy in the form of

electromagnetic radiation, which continuously flows outward in all directions. At the distance

of the earth’s orbit (93 million miles from the sun), the sun’s energy flows at the rate of about

1370 watts/square meter/ second.6 The earth’s status as a planet signifies that the amount of

energy the earth accumulates is contingent on its own planetary properties. For example, if the

solar energy flow interacted only with solid surfaces, then most of the energy would be re-

emitted back into space, and the earth’s mean temperature would be a frosty -18 C (0 F).

However, one of the properties of the earth is an atmosphere that the solar energy

must flow through before reaching the surface. As it does, it interacts with different parts of

the atmosphere (e.g., particles of minerals and molecules of nitrogen, oxygen, and carbon

dioxide), creating a cascade of reactions before reaching the earth’s surface. The net result of

all of the atmospheric interactions is retention of sufficient energy (accumulation) to raise the

5 A system inquiry involves dynamic interactions among parts that generate changes in behavior over time (c.f., Forrester, 1971). System inquiries have led to the discovery of system archetypes - categories of systems and behaviors that occur in numerous natural and social environments. C. West ‘Churchman identified three images of inquiring systems: deterministic, goal-seeking, and probabilistic (Churchman, 1971). 6 This “flow rate” is properly referred to as a “flux” because it is a rate per unit area.

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earth’s mean global temperature to a toasty +15 C, the well-known “greenhouse effect” from

which “nearly all of the complexity of the natural world emerges.7

Framing the inquiry around energy flow and accumulation in relationship to key

atmospheric processes, enables the contingent conditions that lead to the seasonal changes to

be explored within the relevant structural relationships. It also introduces processes related to

other phenomena, such as ocean warming and global climate change, which the traditional

static, geometric, and phenomenologically superficial explanation ignores. A logical argument

can, therefore, be made for explicitly teaching both static and dynamic concepts and

principles. It can be further argued that dynamic concepts can best be taught by combining

systems principles with dynamic modeling methods, because building or manipulated models

and running simulations provide students hands-on opportunities to interactively design and/or

test multiple hypothesis, including their own alternative conceptions. In the process, they are

also building both analytical and integrative metacognitive skills.

A New Teaching Paradigm

Educational researchers have argued for decades that science teaching should be based

on the structure of knowledge in each discipline, meaning the important concepts and

processes by which scientific understanding is gained (Bruner, 1960; Schwab, 1978). These

recommendations have yet to shift most teaching towards deeper, structural knowledge – how

the important concepts are related and work together. Yet, it is this level of operational

understanding that is believed to distinguish expert from novice with respect to knowledge,

problem-solving capabilities, and performance (Bransford et al., 1999; Chi, Glaser, & Farr,

1988; Clement, 2000).

The traditional reductivist approach to education, with its emphasis on events rather

than behavior over time, parts rather than relationships, and isolated processes instead of

systems, carries the implicit expectation on the part of educators that students will figure out

for themselves how everything actually works together (Hannon & Ruth, 2000). As the

7 Jerome Bruner asserted that “to learn structure…is to learn how things are related” (Bruner, 1960, p.7), and used a dynamic systems as an example, namely, that to understand tides requires knowing how gravity acts upon a free-moving elastic body. By analogy, to understand the seasons requires knowing how solar insolation acts upon a rotationally tilted planetary body.

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Harvard graduates illustrate, very bright and well-educated students do not accomplish such a

synthesis on their own, even within a single discipline.

Ironically, one of the most important knowledge structures to emerge from Newtonian

science was the concept of dynamic systems (Kline, 1995; Prigogine & Stengers, 1984).8 The

traditional discipline of celestial mechanics successfully represents the solar system as a

deterministic dynamic system in order to calculate and predict past and future positions of its

parts. However, the deterministic paradigm has been unsuccessful in understanding and

predicting complex dynamic systems’ behaviors that are either stochastic or chaotic in nature,

as many are in chemistry, biology, and even celestial mechanics are (e.g., the famous three-

body problem). Ludwig von Bertalanffy was confronted by its limitation during his biological

research early in the 20th century, which led him to the discovery that similar patterns in

natural phenomena emerge from dynamic system structures in many different disciplines,

including physics, biology, and social sciences (Bertalanffy, 1968).9

Increasing numbers of scientists find it necessary to study complex systems as wholes,

in addition to their constituent parts. Should curriculum and instructional design include

dynamic systems concepts, structures, and models? Various modeling approaches have

already attracted interest among educational researchers (Colella, Klopfer, & Resnick, 2001;

Frederiksen & White, 1998; Perkins & Grotzer, 2000) and teachers alike (Zaraza & Fisher,

1999). One reason is the development of enabling computer technologies with the capacity to

compute at lightening speeds and run programs of models that create simulations of complex

dynamic systems. These technologies are now available to both researchers and educators.

Statement of the Problem

Model-building and problem-based learning already support specific disciplinary

learning goals. However, they have yet to realize their potential to connect the academic silos

that have “fragmented the world into bits and pieces called disciplines and subdisciplines,

8 Newton conceptualized the “law-abiding” system of celestial mechanics. Thermodynamic (stochastic) systems, quantum mechanics, relativistic physics, and chaotic nonlinear systems, have been conceptualized to explain “non-law-abiding” behavior patterns in physical, chemical, biological and social systems (Forrester, 1994; Harte, 2002; Prigogine & Stengers, 1984).9 Bertalanffy recognized that the relationship among the parts, not the nature of the parts, determine system behaviors. Interactions among bacteria in a petrie dish, an economy, and an ecosystem may exhibit similar system behaviors, such as exponential growth and collapse.

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hermetically sealed from other such disciplines [so that] after 12 or 16 or 20 years of

education, most students graduate without any broad, integrated sense of the unity of things”

(Orr, 1994). Interdisciplinary teaching methods are crucial because physical, biological, and

social processes do not operate independently of each other, nor do they exhibit simple linear

relationships in which cause and effects are always, or even usually, close in time and space

(Bertalanffy, 1968; Forrester, 1971). Therefore, the primary problem that this research seeks

to address is the emphasis on traditional reductivist teaching methods at the expense of

synthesis. Several other problems are related, and also addressed by the pedagogic and

research methodologies.

Educational research has consistently demonstrated that pedagogies based on non-

problematic, static, and unanchored knowledge presentations are ultimately inefficient

because these methods do not engage students in meaningful learning, successfully foster

conceptual change, or prepare students for applying what they have learned in non-ideal, real

world situations (Bransford et al., 1999; Brown et al., 1989; Bruer, 1993; Miller, 1956;

Perkins & Salomon, 1989; Spiro, Coulson, Feltovich, & Anderson, 1988). The problem can be

characterized as knowledge stripped of emotional impact and personal saliency.

Clearly, educators need to prepare students to be knowledgeable and skillful in

particular fields, but educators also need to prepare students to solve problems and develop

what has been referred to as adaptive expertise (Bransford et al., 1999; Hatano & Inagaki,

1986). This involves learning how to anticipate and avoid or minimize problems before they

emerge (Perkins, 1986). Real world problems are typically embedded in complex systems,

suggesting that the ability to think differently, i.e., counterintuitively and systemically, will be

needed (Ackoff, 1999; Banathy, 1996; Kay, 1995; Senge, 1990; Dorner, 1996; Forrester,

1971; Senge, 1990). Most real world problems are also interdisciplinary, suggesting the ability

to synthesize and integrate knowledge across domains will also be required.

Collaboration across disciplines has not been traditionally encouraged in schools for a

number of practical and logical reasons, not the least of which is that different subject matter

curriculum is implemented by teachers with substantively different knowledge domains and

pedagogic approaches. Even when efforts have been limited to two subjects, structural,

intellectual, and emotional barriers required concerted effort for collaborative teaching efforts

to succeed (Wineburg & Grossman, 2000).

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Similarly, researchers who work with preservice teachers have encountered different

attitudes among different subject matter teachers (Sullenger et al., 2000). Some preservice

teacher attitudes appear to mirror the cultural divide between the sciences and humanities

observed by C. P. Snow (1959), as well as the hierarchical positioning that privileges the

“hard” over the “soft” sciences lamented by J. Stephen Gould (1989). Stephen J. Kline points

out that the divide between the humanities and sciences impedes progress on coming to

judgment on issues that are value-laden, but are greatly benefited by scientific understanding

(Kline, 1995). Hierarchical attitudes based on subject or discipline impedes collaboration and

teamwork within education because of the resentment that such ranking engenders among

teachers of different disciplines (Sullenger et al., 2000). Research suggests that there is a

genuine need for preservice teachers to be introduced to different disciplinary world views, as

well as problem-solving around collaboration, teamwork, and curriculum integration

(Spalding, 2002; Sullenger et al., 2000; Wineburg & Grossman, 2000).

Lastly, there is an urgent need to understand natural systems and how human systems

impact them. For the first time in the history of the planet, one species (ours) is quantitatively

matching or surpassing planetary flows of materials such as lead and carbon dioxide beyond

the earth’s ability to absorb them (Ayres, 1992).10 Numerous examples of the rise and fall of

past societies have been documented, and provide cause for concern based on the similarities

between the actions of past societies and current behavior, with respect to environmental,

population, and economic stresses and external as well as internal conflicts (Diamond, 2005;

Kennedy, 1987; Tainter, 1988).

In a report published by the National Research Council, the consensus was that if

human activities are not modified within two generations, irreparable damage may occur to

some of the natural systems upon which we depend (NRC, 1999). The Millennium Ecosystem

Assessment (MA) Synthesis Report, provides a more recent consensus among 1,300 experts

from 95 countries that the ongoing degradation of 15 of the 24 ecosystem services examined is

increasing the “likelihood of potentially abrupt changes that will seriously affect human well-

being” (such as the emergence of new diseases, sudden changes in water quality, creation of

“dead zones” along the coasts, the collapse of fisheries, and shifts in regional climate).11

10 Cited in Hawken, Lovins, & Lovins (1999). 11 The full report is online at URL: http://www.millenniumassessment.org/en/Products.Synthesis.aspx.

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The convergence of such diverse scientific studies, and the unusual efforts by

scientists to communicate the significance of their research findings beyond their own

communities, indicate that we are at a turning point in our own society with respect to (now)

global-encompassing problems related to population, energy, pollution, and natural resources.

These problems will certainly require systems design solutions (Banathy, 1996). The project

of education can make important contributions by adapting systems approaches in our own

disciplinary and interdisciplinary learning and teaching communities.

Background

In the following section, the terms system and controversy are defined for the

purposes of this research, and system thinking, system dynamics modeling, and controversy are

located within the academic and education landscapes.

Systems

A system is a collection of related parts which interact with each other to function as

a whole (Kauffman, 1980). This definition implies that a system is dynamic because the

elements interact over time. Dynamic systems exhibit behaviors that change over time related

to accumulations and rates of change.12 Rates of change may also change over time because of

internal connections (relationships among the parts) referred to as “feedback loops.” Positive

feedback loops that reinforce (exacerbate) behavior and negative feedback that maintain

(stabilize) behavior are key systems concepts. A fundamental principle is that systems

typically cause their own behavior because of the effects of internal positive and negative

feedback loop structures. This does not preclude systems that are open to their environment,

from being affected by exogenous events and processes.

Systems thinking has been proposed as a discipline in its own right (Senge, 1990),

because it provides an alternative to traditional scientific thinking by assuming that it is not

possible to analyze the parts of a system in isolation from each other in order to understand the

system’s behavior as a whole. A system’s approach to problem-solving assumes that it is the

system’s structure, (i.e., the relationships among its parts) that causes the system’s behavior

(Richmond, 1994). When the relationships among the parts are not linear and include multiple 12 In a collection, the elements neither interact nor exhibit behavior that changes over time, whether they are together or not.

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feedback loops, the system will behave in ways that are difficult, if not impossible to

anticipate (Forrester, 1971; Meadows, 1982; Senge, 1990).

The complexity inherent in nonlinear dynamic systems precludes both predictive

certainty and one-to-one correspondence between causes and their effects. This means that

reductivist methods of analyzing parts in isolation from the whole may be necessary, but will

also be insufficient for predicting the behaviors of such systems (Prigogine & Stengers, 1984;

Sole & Goodwin, 2000). Although reductionism is undeniably powerful in many scientific

endeavors, it is arguably insufficient with respect to projects dealing with complex dynamic

systems, whether physical, biological or social (Capra, 1982; Churchman, 1971; Dorner, 1996;

Goodwin, 1994; Kline, 1995; Prigogine & Stengers, 1984). Complexity introduces a higher

level of uncertainty because complex systems are not intelligible in terms of Cartesian

rationality, where the “gold standard” for knowledge is based on measurement and

demonstrable one-to-one correspondence between cause and effect (Frodeman, 1996).

This has led to the use of synthesizing methodologies in addition to traditional

analytical methods to the extent that there now exist (at least) two fundamentally different

paradigms by which scientific research is approached: the Newtonian world view, and the

Darwinian world view (Harte, 2002). The former comprises classical reductionist

methodologies and is based on a machine model in which it is logical to analyze parts

independent of each other and the whole (Capra, 1982). Within this mechanistic and atomistic

tradition, scientists have studied either two-variable problems (linear causal trains) with the

goal of determining “perfect solutions” and “exact predictions,” or “unorganized complexity”

using statistical methods (Bertalanffy, 1968; Weaver, 1948).

The second major paradigm comprises emerging integrative and holistic

methodologies, based on an ecological model in which parts are analyzed in relationship to

each other and their environment in order to explain observed phenomena. Within this

growing body of research, scientists seek to understand organized complexity in which there

are interactions among a large number of variables, and the observable behavior is an

emergent property of the system rather than a simple sum of the system’s parts.

My position is that both paradigms and methodologies are useful, and both are

incomplete. The question is not which paradigm is correct, but which paradigm best matches

the nature of the problem and the purpose of the investigation. I argue that teacher education

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programs and K-12 curriculum need to explicitly teach both paradigms, not just in scientific

subjects, but in any subject where dynamic linear and circular causal processes are involved.

The dual approach is particularly relevant when trying to educate students about complex,

interdisciplinary topics that contain elements of each paradigm. These two epistemologies are

not optional or enriching aspects of learning, they represent the two major paradigms currently

providing the basis for designing research and evaluating evidence related to issues of great

importance to citizens and society as a whole. Critically evaluating competing knowledge

claims and evidence necessarily must include thinking critically about the appropriateness of

the assumptions and paradigm with respect to the problem being debated (Toulmin, 1958).

Systems Research in Academia

Broadly speaking, there are five areas of systems science13 that have emerged on the

academic landscape to deal with complex scientific and social problems: (1) general systems

theory (GST), (2) system dynamics (SD), (3) systems design, (4) earth systems science (ESS),

and (5) nonlinear dynamical systems (DS), which is also known as the science of emergence,

or complexity. GST was developed by Ludwig von Bertalanffy in response to thermodynamic

problems in biology (Bertalanffy, 1950). SD was created when Jay Forrester applied

cybernetic principles and feedback control theory developed by Norbert Wiener to industrial,

business, and social systems (Forrester, 1961; Wiener, 1961). Systems design emerged out of

many design professions, including architecture, industrial design, operations research, and

organizational research (Ackoff & Emer, 1972; Argyris & Schon, 1978; Banathy, 1996;

Checkland & Scholes, 1990; Churchman, 1971).

ESS evolved as scientists applied interdisciplinary methods and models to investigate

complex natural phenomena such as global climate (Earth Systems Education: Origins and

Opportunities. Science Education for Global Understanding. Second Edition, 1992).14

Complexity science emerged when mathematical modeling methods (Mandelbrot, 1982) were

13 This informal typology includes the system design studies located in engineering and business contexts (e.g., control theory, operations research, cybernetics), but the focus of this research is on educational paradigms and methods that are related to the physical, life, and social sciences.14 Earth Systems Science is being institutionalized in academic programs such as the Department of Earth System Science at the UC Irvine, the Earth System Science Center at Pennsylvania State University, the Institute for Computational Earth System Science at UC Santa Barbara), as well as Earth System Science course offerings in a number of universities.

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applied to non-linear systems in the physical, biological, and social sciences, such as

meteorology (Lorenz, 1963), ecology (May, 1973), chemistry (Nicolis & Prigogine, 1977),

evolutionary biology (Goodwin, 1994), economics (Krugman, 1996), archeology (Kohler &

Gumerman, 2000) and systems biology.15

The shared paradigm across these diverse domains is that the whole is more than the

sum of its parts and the behavior of the system under investigation cannot be deduced or

inferred by knowing the nature and behavior of the system’s individual parts (Bertalanffy,

1950; Forrester, 1968; Goodwin, 1994). Two simple chemistry examples illustrate this

concept: the properties of oxygen and hydrogen cannot be “added together” to predict the

properties of water, any more than the properties of salt can be predicted from knowing that

chlorine is a poisonous gas and sodium is an explosive metal. It is the interactions of their

atoms in relationship to each other that creates the dramatically different behaviors and

properties of water and salt molecules.

The significance of this perspective is that scientists are able to deterministically

predict behavior for only the simplest of systems (Kline, 1995). Attempts to do so for

nonlinear dynamic systems (i.e., complex systems) typically produces “unintended”

consequences or “side affects.” The unexpected results are systemic outcomes just like the

desired results. Their occurrence surprises only because the model of the system, on which the

predictions were based, is incomplete (Lovins & Lovins, 1996).

Modeling Systems

One way to gain predictive power in complex systems, albeit with an inescapable

level of uncertainty, is to combine analyses of individual components and their relationships

with dynamic methods of aggregation to create a numeric model of the phenomenon. This

concept was first envisioned by a meteorologist in the 1920’s, but it was not until after the

Second World War that the computational capacity of computers was harnassed by

programming to do the calculations required by complex weather models (Levenson, 1989).

Since then, computer models and simulations have become cultural tools in the Vygotskian

sense (Vygotsky, 1986). As useful as these methods have been, even the most complex

models remain incomplete. Therefore, computer simulations may provide useful heuristics,

15 The Institute for Systems Biology at the University of Washington: http://www.systemsbiology.org/.

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but they do not deterministically predict a system’s behavior or outcomes in the positivist

tradition (Oreskes, Shrader-Frechette, & Belitz, 1994).

Given the disciplinary breadth and importance of systems and model-based research

today, it is not surprising that systems perspectives and modeling methods are being

incorporated in both, K-12 classrooms (Gould-Kreutzer, 1993; Zaraza, 1995), and research on

learning and cognition (Mandinach & Cline, 1994; Resnick, 1999; Roberts, 1978).

Two general modeling strategies have emerged in education: one is based on

individual interactions, the other on aggregate interactions. Agent-based models, a category of

which includes cellular automata models, simulate the emergent behavior of individual entities

interacting with each other and their environment over time (Resnick, 1990, 1994, 1996).

System dynamics (SD) models represent a category of aggregated modeling methods, and

simulate the emergent behavior of aggregated quantities, for example, water flow and

accumulation over time (Zaraza & Fisher, 1999). Both types of models are designed to

flexibly incorporate multiple interactions and feedback loop structures that are characteristic

of complex systems and responsible for many of the counterintuitive and unpredictable

emergent behaviors associated with them.

Controversies

A controversy may simply be defined as “the clash of opposing opinion” about an

issue for which there is dissent (Brante, 1993). A useful synonym is argument, and although

the two words may be used interchangeably, argument draws attention to the process of the

debate for the purposes of this study. Another distinction of importance with respect to

educaitonal research, as well as society at large, is the difference between a scientific

controversy, which primarily involves contending knowledge claims among scientists; and a

science-based controversy, which not only involves contending scientific arguments, but also

has social, political, or economic elements or consequences (Brante, 1993).

Controversy in Academia

The sociologist, Bruno Latour, who has observed scientists in their natural settings of

laboratory and fieldwork, distinguishes between science that is “settled” and science “in-the-

making” (Latour, 1987). In order for a knowledge claim to become settled, consensus must

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first be achieved within a particular community of scholars by a social process that marshals

support through evidence-based logical argumentation. The form of the evidence and

argument varies depending upon the discipline and the nature of the evidence.

Although this sounds relatively straightforward, three trends are making this much

more complex and interesting. One is towards interdisciplinary, multidisciplinary and

transdisciplinary research (Klein, J. T., Grossenbacher-Mansuy, W., Haberli, R., Bill, A.,

Scholz, R. W., & Welti, M., 2001; Kline, 1995; Orr, 1994).16 Another is the increasing

importance of computer models and simulations in the sciences and social sciences (Krugman,

1996; Meadows, Meadows, Rander, & Behrens III, 1972; Stott et al., 2000) (Stott, P. A., Tett,

S. F. B., Jones, G. S., Allen, M. R., Mitchell, J. F. B., & Jenkins, G. J. 2000). The third is a

profound shift in scientific research from positivistic certainty based on a Newtonian world

view to indeterminate complexity based on a Darwinian world view (Harte, 2002).17

Today, many of the most interesting and important scientific controversies are

problems that require crossing disciplinary boundaries to reconcile evidence generated by

different disciplinary researchers (Klein, 1990; Kline, 1995; Weingart & Stehr, 2000).

Reaching scientific consensus concerning global climate change, for example, required

geologists, biologists, chemists and physicists, to name only a few of the disciplinary

perspectives, to adopt a synoptic disciplinary viewpoint (Stott et al., 2000). Regional and local

problems reflect similar needs for a synoptic disciplinary view point in order to settle

controversies, such as the causes of “dead zones”18 in the Great Lakes, Gulf of Mexico,

Chesapeake Bay and Hood Canal (Warner, Kawase, & Newton, 2002). Although the

16 For the purposes of this research, interdisciplinary refers to research that appropriates methods from more than one discipline, for example, biophysics research. Multidisciplinary, transdisciplinary and cross-disciplinary refers to research in different disciplinary fields that study the same problem. As the nature of academic research evolves, the forms of research methods, evidence and argument is also becoming more interdisciplinary and multidisciplinary (Klein, 1996; Kline, 1995; Salter & Hearn, 1996).17 Another way of describing this difference is between the classic celestial mechanics physics world view and the ecological, environmental and evolutionary world view, also known as the science of emergence (Capra, 1982; Sole & Goodwin, 2000).18 Dead zones are low-oxygen areas in lakes and coastal waters caused by high nutrient levels from pollution that have resulted in massive algal blooms followed by equally massive die-offs. Although living algae generates oxygen in the surface waters, the dead algae sinks and is consumed in deeper waters by bacteria that remove dissolved oxygen from the water in the process of feeding on the organic matter. With lowered dissolved oxygen levels, desirable species of fish die or leave the area, and the entire ecosystem becomes impoverished.

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proximate cause (depleted oxygen content) can be measured, the cause or causes of the

oxygen depletion, and therefore, its mitigation or prevention, is often controversial and

requires a multidisciplinary perspective.

Modeling Controversies

Although multidisciplinary studies have been necessary, they have not been sufficient

to achieve consensus involving complex systems without numeric models and computer

technologies. Computer-generated simulations of numeric models have become the basis for

settling controversies ranging from global climate change (Stott et al., 2000) to the role of

phosphorus in Lake Erie’s eutrophication (Chapra & Reckhow, 1979; Harte, 2002). For the

science-based controversy related to Lake Erie, simulations provided the heuristic that was

used as the basis for policy changes related to pollution, which ultimately resulted in improved

water quality in most U.S. lakes (Harte, 2002).

The use of model-based simulations to reach consensus with respect to scientific

controversies is a relataively recent application of the tools. With respect to nonlinear systems,

such unexpected behaviors19 have emerged that entirely new fields of research have been

generated, and with them, a profoundly different world view of science from the traditional

mechanistic and predictable one (Harte, 2002). Although scientists are traditionally trained to

“think in terms of linear causality…we need new ‘tools of thought’: one of the greatest

benefits of models is precisely to help us discover these tools and learn how to use them”

(Prigogine & Stengers, 1984). This includes gaining new insights about the “complex

interplay between individual and collective aspects of behavior...[seeing] a distinction

between states of the systems in which all individual initiative is doomed to insignificance,

and …bifurcation regions in which an individual, idea or new behavior can upset the global

state” (Prigogine & Stengers, 1984).

Complexity reveals that even in physics, the validity of knowledge claims in scientific

arguments is not actually determined by the same deductive reasoning processes that are

possible in mathematics and geometry. To prove a scientific claim would require knowing

everything that would need to be known with respect to the natural phenomenon, which is not

19 “Counterintuitive” was introduced by Jay Forrester at MIT to express the impossibility of predicting how nonlinear models would behave (Forrester, 1971)

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possible. Therefore, what is stated as “known” in science, is an approximation and subject to

change, even in physics, unlike geometric theorems (Feynman, 1995).

This is not a new idea, and scientific argumentation has traditionally used the process

of inductive reasoning based on making logical inferences about evidence.20 For example,

when a scientific prediction is verified by empirical or experimental evidence, it typically

settles the argument, i.e., the “real” world confirms scientists’ ideas about it (Driver et al.,

1994). But “settled” knowledge remains incomplete and vulnerable to revision as new

measurements and understandings are learned through the continuous interplay between

theory and experiment, even in classical physics. Newton’s laws related to matter and the

speed at which it moves are useful approximations over a wide range of scales; however, “the

higher the speed, the more wrong they are, and philosophically we are completely wrong with

the approximate law (Feynman, 1995).21

Scholars have developed different approaches to argumentation because most

scientific controversies cannot be settled using the same methods as math and classical

physics (Kahane, 1971; Toulmin, 1958).22 Stephen Toulmin developed an argument model

containing three parts: 1) the new knowledge claim, 2) the data that supports it, and 3) the

warrant, or assumptions the claim is founded on, either implicit or explicitly stated (Toulmin,

1958). The power of Toulmin’s model is in the explicit inclusion of the warrant, or

assumptions that support the claim that is being made about the evidence.

The Systems-Controversy Connection

Warrants for a knowledge claim are based on the scientific paradigm, or organizing

principles that guided the research. The paradigm is effectively the theoretical basis for the

inquiry. Scientific inquiry (be it empirical, experimental, or theoretical) involves the collection

of data by specific methods, analysis of the data by specific methods, and interpretation based

on theory or paradigm that guided the research. This entire process is what transforms data

into evidence that can be used to make knowledge claims (Latour, 2000).

20 Logic is called the “science of inference.”21 Italics in the original.22 Howard Kahane’s “Logic and Contemporary Rhetoric: The Use of Reason in Everyday Life” (Kahane, 1971) is now in its ninth edition, and evaluates knowledge claims on the basis of whether or not the arguments are fallacious based on the rules of logic, or inference.

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One way to evaluate the warrant for a knowledge claim is to determine if the research

methods were appropriate for the research question, and the paradigm (theory) it was based

on. Another way is to evaluate the appropriateness of the paradigm itself. For example, if the

research methods assume that there is a linear causal chain, and the system is known to be

linear, then the warrant for the evidence is strengthened. A systems paradigm (parts,

properties, relationships, processes, feedback loops, and contingencies) can provide a

principled basis for evaluating a knowledge claim, regardless of the type of system for which

the data was originally collected (e.g., linear, nonlinear, simple, complex), provided, naturally,

that the inquiry involve a dynamic system.

Although good pedagogic practices have always included controversies that engage

students in learning, it has only been in the last two decades that researchers have begun to

investigate controversy as a component of principled instructional design (Bell, 2002; Johnson,

1985; Newton, Driver, & Osborne, 1999; Zeidler, 1997). Problematizing subject matter has

supported inquiry and critical thinking as well as more meaningful learning (Evensen & Hmelo,

2000; McNeil, 1999). Teaching methods based on controversies have resulted in higher levels of

engagement and improvements in students’ retention of content, critical thinking, problem

solving, decision making and creative insights (Johnson, 1985; Johnson & Johnson, 1989;

Johnson & Johnson, 1995).23

I argue that explicitly using a systems paradigm to structure inquiry about controversies

that contain a systems component, represents a way to increase the congruence between theory,

evidence, and the warrant for the evidence, and improve student learning even more. This

argument has a parallel in geology,24 where standards in used for mineral analyses—the closer in

composition the standard is to the mineral being analyzed, the more accurate the analysis of the

mineral will be. I believe that the closer our analytical paradigms are to the nature of the subject

of the inquiry, the more critically and accurately we will be able to think and learn about them.

Purpose of Study

23 Academic controversy has evolved as a distinct process from debate. The goal in an academic controversy is to arrive at a consensus (Johnson & Johnson, 1995). In contrast, debate is a competition between two sides of an argument that work against each other. The goal in traditional debate is for one side to put forth the better argument and therefore win the debate.24 Essentially all analytical methods, regardless of discipline, use standards that are as close as possible in composition to the target material.

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Given the sea changes occurring in academic research, the purpose of this study was

to learn what potential benefits, interactions and responses would emerge from introducing a

new way of thinking, in terms of systems, to preservice teachers who were tasked to

collaboratively develop curriculum for teaching a contemporary controversy. The intervention

was a complex design study created by the researcher to introduce preservice teachers to the

systems paradigm and tools as a way to collaboratively connect and teach subject matter

related to a contemporary controversy. Important systems tools were introduced as part of the

intervention. The controversy was related to genetically modified food, and involved complex

systems at multiple levels (e.g., genetic, ecological, social).

Educational researchers and other cognitive scientists have made impressive gains in

understanding how students learn (Bransford et al., 1999), yet American students are still

being outperformed by students in Chinese Taipei, Singapore, Hungary, Japan, Korea,

Netherlands, Australia, the Czech Republic and England according to the most recent Trends

in Mathematics and Science Survey results (Martin et al., 2000; Mattheis et al., 1992). At the

same time, teachers report feeling ill-prepared in the critical areas of science, technology and

meeting the needs of an increasingly diverse student population (Contexts of Elementary and

Secondary Education: Teachers' Readiness to Use Computers and the Internet, 2001;

Indicator of the month: Teachers' feelings of preparedness, 1999).

Considered together, these two trends suggest that rather than continuing to “leapfrog”

over preservice teachers to study student learning in authentic environments, it may also be

essential to study preservice teacher learning in their authentic environments, e.g., in

education courses. Of particular importance is their responses and receptivity to new methods

that are known to support student learning and that address the critical areas of science,

technology and an increasingly diverse student population.

The Research Question

The question behind this research was, “How will preservice teachers benefit from

using a systems perspective and science-based controversy to collaboratively develop

interdisciplinary curriculum?” The focus of this study was appropriately, the whole system of

interacting parts including the ecologically valid classroom setting, the complexity of the

design-based intervention, the different subject matter preservice teachers, and the instructor-

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researcher. Although there were external influences (the experiment was not accomplished in

a closed system), most of the important interactions were among the preservice teachers and

the instructor-researcher in the study.

The outcome of the study demonstrates the challenge in getting preservice teachers to

learn to see controversies using a systems thinking paradigm. There are very few teacher

preparation programs where preservice teachers are introduced to systems concepts,25 and little

research has been conducted to provide more than anecdotal evidence about system dynamics’

pedagogical power, potential to foster transfer, or affordances for integrating curriculum and

promoting team teaching and teacher collaboration. Introducing preservice teachers to new

pedagogic methods that are being used in more K-12 schools by increasing numbers of

experienced teachers answers calls for teacher education reform that aligns with K-12 reform

have been made (Darling-Hammond & Sykes, 1999; Goodlad, 1994; Lampert & Ball, 1999).

Current state and national standards also call for teaching K-12 students about systems and

systems concepts. Teacher education programs are logical sites for introducing innovative

methods such as systems thinking and science-based controversies.

In the next chapter, key literature related to the study is reviewed. Specifically,

Chapter 2 examines the background related to systems thinking and computer modeling in K-

12 education, processes that promote conceptual change, and how controversies support

learning.

Chapter 3 describes the research methods, including an overview of the study design

and how the research questions were addressed by it, the instructional intervention, including

the rationale for the major components, the setting and participants, and strategies for data

collection and analysis.

Chapter 4 presents the results of the interviews and the implementation of the design

study, including the final demonstration project.

Chapter 5 presents the inventory results from the questionnaires the preservice

teachers completed before and during the design study. This is followed by an analysis of how

their answers to these questions help to make sense of their responses to systems thinking and 25 Lesley College in Boston, MA offers an elective course called Educational Uses of Systems Thinking, Models and Simulations, which describes system thinking as “the art and science of examining real world complexity and understanding patterns in relationships. Through computer-based exploration and discussion, students will develop understanding about this framework and the use of models and simulations as thinking tools in educational settings.”

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contemporary controversies as approaches to teaching, learning, integrating curriculum, and

working collaboratively with teachers in other subject matter areas. A comparative analysis is

presented between the language arts and social studies preservice teachers who participated in

the interviews.

Chapter 6 presents a synthesis of the study’s results, and the study’s significance

within teacher education research. The limitations of the study, along with the implications for

future research conclude this work.

This study shows that preservice teachers display a range in receptivity to, and

perceptions of benefits from innovative pedagogies, based in part, on their own K-12 and

teacher education experiences. Prior experiences, either in coursework or field observations,

increase both, receptivity, because they reinforce a particular method’s importance, and

fluency, because they provide more experience with the method.

This study was not designed simply for the purpose of introducing new teaching

methods to preservice teachers. It was also designed to stimulate preservice teachers’

reflection about, and openness to different approaches to their own learning and teaching.

Success in getting preservice teachers to think and teach differently in teacher education

programs may foreshadow their own innovative teaching and their students’ successful

learning outcomes in the K-12 educational system.

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