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Model building for conceptual changeDavid Jonassen a , Johannes Strobel a & Joshua Gottdenker aa University of Missouri , USAPublished online: 16 Feb 2007.

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Model Building for Conceptual Change

David Jonassen*, Johannes Strobel and Joshua GottdenkerUniversity of Missouri, USA

Conceptual change is a popular, contemporary conception of meaningful learning. Conceptual

change describes changes in conceptual frameworks (mental models or personal theories) that

learners construct to comprehend phenomena. Different theories of conceptual change describe the

reorganization of conceptual frameworks that results from different forms of activity. We argue that

learners’ conceptual frameworks (mental models or personal theories) resulting from conceptual

change are most acutely affected by model-based reasoning. Model-based reasoning is engaged and

fostered by learner construction of qualitative and quantitative models of the content or phenomena

they are studying using technology-based modelling tools. Model building is a powerful strategy for

engaging, supporting, and assessing conceptual change in learners because these models scaffold

and externalize internal, mental models by providing multiple formalisms for representing

conceptual understanding and change. We demonstrate the processes and products of building

models of domain content, problems, systems, experiences, and thinking processes using different

technology-based modelling tools. Each tool provides alternative representational formalisms that

enable learners to qualitatively and quantitatively model their conceptual frameworks.

1. Introduction

The cognitive-constructivist and situated learning movements of the previous decade

have focused the attention of educators on sense making and other conceptions of

meaningful learning. Among the myriad conceptions of meaningful learning,

different research communities (psychology, learning sciences, science and mathe-

matics education) have recently focused much of their attention on conceptual

change (Limon & Mason, 2002; Schnotz, Vosniadou, & Carretero, 1999; Sinatra &

Pintrich, 2003). Conceptual change has become a common conception of meaningful

learning, because it treats learning as an intentional, dynamic, and constructive

process that encompasses developmental differences among learners. ‘‘Conceptual

change is the mechanism underlying meaningful learning’’ (Mayer, 2002, p. 101).

Conceptual change is a process of constructing and reorganizing personal

conceptual models. From an early age, humans naturally build simplified and

intuitive personal theories to explain their world. Through experience and reflection,

*Corresponding author. University of Missouri, 303 Townsend Hall, Columbia, MO 65211, USA.

Email: jonassen@missouri.edu

Interactive Learning Environments

Vol. 13, No. 1–2, April–August 2005, pp. 15 – 37

ISSN 1049-4820 (print)/ISSN 1744-5191 (online)/05/01/2015-23 � 2005 Taylor & Francis

DOI: 10.1080/10494820500173292

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they reorganize and add complexity to their theories or conceptual models. Children

and adults interact with new information to the degree that the information is

comprehensible, coherent, plausible, and rhetorically compelling according to their

existing conceptual models. The cognitive process of adapting and restructuring these

models is conceptual change (Vosniadou, 1999).

Although descriptive, theoretical accounts of conceptual change are replete,

relatively little research addresses how to effectively foster and assess conceptual

change. In this paper, we argue that using computers to build external representations

(computational models) of what they are learning is among the most potentially

powerful and engaging methods for fostering and assessing conceptual change.

Building models reifies our conceptual models, so the models that students build

reflect their conceptual change as it occurs.

The models that students construct also provide evidence of the growth and

reorganization of learners’ conceptual models. That is, the models that students build

can be used to assess their conceptual change. The most commonly used methods for

assessing conceptual change include student protocols collected while students are

engaged in problem solving activities (Hogan & Fisherkeller, 2000), structured inter-

views (Southerland, Smith, & Cummins, 2000), and concept maps (Edmundson,

2000). The analysis of interview and conversation protocols is very difficult and time-

consuming and is plagued with reliability problems. The major premise of this paper

is that the technology-supported models that students construct while representing

domain knowledge, systems they are studying, problems they are solving, experiences

they have had, or the thought processes they use can be used to assess their

conceptual change. How do these models reflect conceptual change?

Jonassen (2000) described several technology tools (databases, concept maps

(semantic networks), spreadsheets, expert system shells, systems modelling tools,

hypermedia construction environments, visualization tools, synchronous and

asynchronous conferencing systems) that when used by learners to construct models

of what they are learning naturally engage them in a variety of critical thinking skills.

Each tool imposes its own syntax and affords different kinds of representations of the

understanding. For example, semantic organization tools, such as databases and

concept maps, best support comparison-contrast reasoning. Dynamic modelling

tools, such expert systems and systems dynamics tools, enable learners to represent

and test their causal reasoning. When learners used concept maps to represent

domain knowledge over an entire semester, their knowledge structures differed

significantly from when learners used expert systems to represent their domain

knowledge (Jonassen, 1993). More research is needed to systematically compare the

differences in conceptual models that result from extensive use of different tools for

modelling domain knowledge.

Model building can also be used to represent changes in learners’ conceptual

frameworks. Comparing the models that learners build over time provides fairly

obvious evidence of conceptual change, however, more research is needed to validate

and clarify the use of student constructed models for assessment. In order to assess

conceptual frameworks, rubrics are needed. Jonassen (2000) provides some rubrics

16 D. Jonassen et al.

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for assessing models produced by different tools. For example, Figure 1, lists some

possible rubrics for assessing systems dynamics models, like the ones illustrated in

Figures 4, 7, and 8. Thagard (1992) provides potentially useful rubrics for assessing

conceptual change in models in the form of explanatory coherence. Different kinds of

explanatory coherence can be used to analyse models, including deductive coherence

(logical consistency and entailment among members of a set of propositions),

probabilistic coherence (probability assignments), and semantic (similar meanings

among propositions). Within models, assessors would look for symmetry, explanatory

value, appropriate analogies, contradictions, competition among propositions, and

acceptability of propositions. Extensive work is needed to operationalize these and

other rubrics and criteria for assessing conceptual change in models.

Why do we believe that building models is so productive of learning? In the next

section, we describe the theoretical rationale for model building, model-based

reasoning.

Figure 1. Possible rubrics for assessing systems dynamics models.

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2. Modelling and Model-Based Reasoning

2.1 Model-Based Reasoning

Science and mathematics educators (Confrey & Doerr, 1994; Frederiksen & White,

1998; Hestenes, 1986; Lehrer & Schauble, 2000, 2003) have long recognized the

importance of modelling in understanding scientific and mathematical phenomena.

Using and building models of phenomena is referred to as model-based reasoning.

Modelling is fundamental to human cognition and scientific inquiry (Schwarz &

White, in press). Modelling helps learners to express and externalize their thinking;

visualize and test components of their theories; and make materials more

interesting. Models function as epistemic resources (Morrison & Morgan, 1999).

Johnson-Laird (1983) believes that ‘‘human beings understand the world by

constructing models of it in their minds’’ that are structural analogs of a real-world

or imaginary situations, events, or processes. They embody representations of the

spatial and temporal relations and causal structures connecting the events and

entities depicted.

What are models? Lesh and Doerr (2003) claim that models are conceptual

systems consisting of elements, relations, operations, and rules governing

interactions that are expressed using external notation systems. There are

numerous kinds of models that can be used to represent phenomena in the

world or the mental models that learners construct to represent them. Harris

(1999) describes three kinds of models: theoretical models, experimental models,

and data models. Giere (1999) describes several kinds of models, including

representational models (the central function of models used in science), abstract

models (mathematical models), hypotheses, and theoretical models (abstract

models constructed with theoretical principles, such as Newton’s Laws). Lehrer

and Schauble (2003) describe a continuum of model types including physical

models, representational systems (grounded in resemblance between the model

and the world), syntactic models (summarizing essential functioning of system),

and hypothetical-deductive models (formal abstractions). Whatever they are,

models are qualitatively, functionally, or formally similar to the real objects under

study (Yu, 2002).

Models can be used in a variety of ways. Hestenes (1992) proposed a modelling

process for physics learning that includes four stages: describing the basic and derived

variables in some diagrammatic form; formulating the relationships based on the laws

of physics (writing equations); drawing ramifications of the model; and empirically

validating the ramified model. For Hestenes, ‘‘the model is the message’’ (p. 446). In

physics, the primary purpose of modelling is the prediction of the performance of the

physical systems being modelled.

Historically, most of the modelling research has focused on mathematization as

the primary modelling formalism. Representing phenomena in formulas is perhaps

the most succinct and exact form of modelling. However, most contemporary

researchers argue that qualitative models are just as important as quantitative.

Qualitative representation is a missing link in novice problem solving (Chi,

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Feltovich, & Glaser, 1981; Larkin, 1983). When students try to understand a

problem in only one way, especially when that way conveys no conceptual

information about the problem, students do not understand the underlying

systems they are working in. So, it is necessary to help learners to construct a

qualitative representation of the problem as well as a quantitative. Qualitative

problem representations both constrain and facilitate the construction of

quantitative representations (Ploetzner & Spada, 1998). A well-known model-

based curriculum known as MARS focuses specifically on qualitative reasoning

with models because as non-scientists, students can continue to reason with

qualitative models, and those models play important roes in guiding and

interpreting quantitative representations (Raghavan & Glaser, 1995).

2.2 Mental Models and Conceptual Change

Modelling is an important method for engaging conceptual model formation, that is,

conceptual change (Nersessian, 1999). The ability to form mental models is a basic

characteristic of human cognitive system, and these conceptual models are essential

for conceptual development and conceptual change (Vosniadou, 2002). When

solving problems, learners construct models in memory and apply those models to

solving the problem rather than by applying logical rules (Vandierendonck &

deVooght, 1996). As soon as problems are presented, learners construct an initial

model and integrate new information into the model in order to make the model look

and function like the problem. The conceptual models that learners construct are

generally believed to be analogous conceptions of the world. However, the models

that learners construct often result in analogical, incomplete, or even fragmentary

representations of how those objects and the system they are in work (Farooq &

Dominick, 1988).

Conceptual change does not necessarily result in meaningful conceptual models.

Constructing conceptual models by being told about the world engages weak

restructuring of conceptual systems (Carey, 1985; Vosniadou, 1994). Because

students lack intentionality, they fail to construct robust mental models of the

phenomena they are studying. Reproductive learning in schools too often requires the

mere articulation of an existing conceptual framework that entails only changes in

relationships between concepts (Carey, 1988), not restructuring of conceptual

models by learners. Stronger or more radical conceptual change requires significant

restructuring of conceptual models.

Several researchers have demonstrated the relationship between modelling and

conceptual models (Frederiksen & White, 1998; Mellar, Bliss, Boohan, Ogborn, &

Tompsett, 1994; White, 1993). Conceptual change is task-dependent (Schnotz &

Preuß, 1999). We argue that the task that most naturally engages and supports the

construction and reorganization of mental models is the use of a variety of tools for

constructing physical, visual, logical, or computational models of phenomena.

Building representational and interpretive models using technologies provides learners

the opportunities to externalize, restructure, and test their conceptual models.

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2.3 Model Construction versus Use

We learn from models by building them and using them (Morgan, 1999). Learning

from building models involves finding out what elements fit together in order to

represent a phenomenon or a theory of it. Modelling requires making certain choices,

and it is in those choices that the learning process lies. Morgan believes that we can

also learn from using models, however, that depends on the extent to which we can

transfer the things we learn from manipulating the model to our theory or the real

world. ‘‘We do not learn much from looking at a model – we learn a lot more from

building the model and from manipulating it’’ (Morrison & Morgan, 1999, pp. 11 –

12).

Despite the cognitive benefits of building models, technology-based learning

environments more often exemplify model-using. Models are commonly used as the

intellectual engine in software, such as intelligent tutoring systems and microworlds.

In these systems, the model is implicit in the exploratory options provided by the

software, but the model is not explicitly demonstrated. More importantly, the model

is immutable. Not only do learners have no access to the model, but also they cannot

change it, except to manipulate a set of pre-selected variables within the model.

Learners will interact with these black-box systems and infer the propositions

embedded in the model in order to test hypotheses. Research shows that interacting

with model-based environments does result in development and change of mental

models (Frederiksen & White, 1998; Mellar, et al., 1994; White, 1993).

Fewer projects have focused on model building. Hartley (1998) describes a

computer-based language (VARILAB) through which users qualitatively describe

physical systems. A few microworlds have been created for model building. For

example, NetLogo and Agent Sheets are programmable environments for building

models of populations and simulating them. They are especially useful for modelling

complex systems that evolve over time. This systems dynamic view provides a new

perspective to students, who write instructions to hundreds of independent agents

operating at the same time.

Why is model building so much more productive for learning and conceptual

change than model using? When solving a problem or answering a complex

conceptual question, learners must construct a mental model of the phenomena and

use that model as the basis for prediction, inference, speculation, or experimentation.

Constructing a physical, analogical, or computational model of the world reifies the

learner’s mental model. One reason that constructed models are so powerful is

because of their intellectual autonomy. Models are autonomous because they are

independent of theories and the world, which allows them to function as tools or

instruments of investigation (Morrison & Morgan, 1999).

Rather than providing black box models that learners manipulate in an attempt to

induce the underlying model, we argue that the most effective way of engaging,

supporting, and assessing radical or synthetic conceptual change is by building and

comparing models that represent incommensurate conceptual systems. When

students discover conceptual anomalies or inconsistencies in their own conceptual

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structures by modelling them, they are more likely to revise and restructure them. In

order to recognize and resolve perturbations or anomalies, learners must use

experimentation or some other high engagement process such as modelling to

compare rival conceptions (Dole & Sinatra, 1998).

Constructing technology-mediated models of phenomena is a conceptually

engaging task because:

. Model building is a natural cognitive phenomenon. When encountering unknown

phenomena, humans naturally begin to construct personal theories about those

phenomena that are represented as models.

. Modelling is essentially constructivist – constructing personal representations of

experienced phenomena.

. Modelling supports hypothesis testing, conjecturing, inferring, and a host of other

important cognitive skills.

. Modelling requires learners to articulate causal reasoning, the cognitive basis for

most scientific reasoning.

. Modelling is important because it is among the most conceptually engaging

cognitive processes that can be performed, which is a strong predictor of

conceptual change.

. Modelling results in the construction of cognitive artefacts (externalized mental

models).

. When students construct models, they own the knowledge. Student ownership is

important to meaning making and knowledge construction.

. Modelling supports the development of epistemic beliefs. Epistemologically, what

motivates our efforts to make sense of the world? According to Wittgenstein

(1953), what we know is predicated on the possibility of doubt. We know many

things, but we can never be certain that we know it. As already described,

comparing and evaluating models requires understanding that alternative models

are possible and that the activity of modelling can be used for testing rival models

(Lehrer & Schauble, 2003).

In the next section, we describe how a variety of technology-based modelling tools

can be used by learners to construct conceptual models that reflect their conceptual

change. Student construction of models of phenomena that they are learning using

technology-based modelling tools scaffolds conceptual change processes in learners.

There exists a dynamic and reciprocal relationship between internal mental cons-

tructions and the external models that students construct.

3. Modelling Different Phenomena

Because the mental models that we construct are dynamic and multi-modal, consisting

of structural knowledge, procedural knowledge, executive or strategic knowledge,

spatial representations, personal reflection, and even metaphorical knowledge

(Jonassen & Henning, 1999), no single kind of model can reflect the complexity of

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mental models. In order to represent the underlying complexity of mental models,

learners should learn to use a variety of tools to represent the complexity of their

conceptual models. The different knowledge representations provided by each tool

results in different kinds of knowledge (Jonassen, 1993). That is, each tool represents

a different formalism for representing different aspects of what we know (Jonassen,

2000). In this section, we describe the range of phenomena that can be modelled

using different modelling tools. Most of these models are what Lehrer and Schauble

(2000) refer to as syntactic models. That is, they are formal models, each of which

imposes a different syntax on the learner that conveys a different relational

correspondence between the model and the phenomena it is representing. The

purpose of syntactic models is to summarize the essential function of the system

being represented.

3.1 Modelling Domain Knowledge

Students can use a variety of computer-based modelling tools, such as semantic

networking (concept mapping) tools or systems modelling tools, to construct their

models of domain knowledge. For example, Figure 2 illustrates a single frame of an

extensive semantic network on the Shakespearean play, Macbeth. Produced with

Semantica, this representation of the learner’s cognitive structure related to the play

enables the learner to create multiple, interrelated maps. Clicking on one of the

Figure 2. One frame of a semantic network on Macbeth.

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concepts on the periphery brings another map into view with that concept at the

centre. Learners must identify important concepts related to the play, but more

importantly, they must link those concepts together with clear, descriptive links. As

students study domain content in a course, they add concepts and links to represent

their growing knowledge base. Comparing your semantic network with the teacher’s

or with other learners’ facilitates conceptual change, as students see how other models

represent and structure the same ideas.

Learners can also produce models to represent causal relationships among

domain concepts. For example, Figure 3 illustrates a spreadsheet simulation of the

generation and growth of a pathogen, bacillus. In this case, the model was

constructed by students who were studying the food product development process

and wanted to better understand the effects of temperature on the growth of

pathogens. Fresh food products are always susceptible to pathogen growth, so it was

necessary to understand the generation process and any impediments in order to

determine the feasibility of introducing new fresh food products. If the spreadsheet

had been constructed by the teacher to support student queries, it would be an

example of model using. Spreadsheets enable the visual representation of most

kinds of mathematical models, and they are able to create very sophisticated

simulation models.

Figure 3. Spreadsheet simulation of pathogen generation.

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Domain knowledge is often presented and understood in a very rote fashion. By

modelling domain knowledge, students must understand conceptual relationships

among the entities within the domain in order to construct the model.

3.2 Modelling Problems

In order to transfer problem-solving skills, problem solvers need to construct some

sort of mental representation of the problems they are solving, that is, they need to

represent the problem space (Simon, 1978). Various computer tools may be used to

build representations of personal problem spaces. These personal problem

representations serve a number of functions (Savelsbergh, de Jong, & Ferguson-

Hessler, 1998):

. To guide further interpretation of information about the problem,

. To simulate the behaviour of the system based on knowledge about the properties

of the system, and

. To associate with and trigger particular solution schemas (procedure).

The qualitative models provided by the various tools described in this paper

assume many different forms and organizations. They may be spatial or verbal,

and they may be organized in many different ways. For example, Table 1 lists the

verbal decisions and the decision factors that are combined with If-Then rules (too

many to display) in a student-built expert system that represents forecasting

problems solved in a meteorology class. Predicting weather requires qualitative

models of weather conditions and effects that are described by combinations of

factors and probabilities that lead to particular predictions. The expert system

qualitatively represents the causal reasoning that forecasters use to predict the

weather.

Qualitative representations of complex problem spaces can also support the

solution of ill-structured problems. For example, Figure 4 illustrates a systems

dynamics model of an ill-structured policy analysis problem, how to facilitate the

cessation of armed conflict between the Palestinians and the Israelis. The model,

constructed with Stella (a systems dynamics tool) attempts to articulate some of the

causes of the problem along with some solutions. The relationships between the

various entities included in Figure 4 are described quantitatively so that the model

may be tested. As new variables are included in the model, it must be retested to

assess the effects of the new variables.

Identifying the nature of the problem and the important factors that are required to

solve problems are two key skills separating expert problem solvers from novices.

Building models of problems helps learners to determine the nature of the problem

along with possible solutions for the problem. For ill-structured problems, problem

solving starts with the acceptance that not only are there multiple solutions to the

problem, but that there is not necessarily only one problem. In fact, most ill-

structured problems consist of several interrelated problems that need to be

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integrated before one should begin considering solutions. When modelling the

problem space, the assumptions necessary for evaluating problem solutions are made

explicit. In this process the problem space will go through a series of changes, so the

model of the space will also be changed.

3.3 Modelling Systems

When learners study phenomena as systems, they develop a more integrated view of

the world. There are several, related systemic conceptions of the world, including

open systems thinking, human or social systems thinking, process systems, feedback

systems thinking, or systems dynamics. In order to predict the effects of changes to

system conditions on system outputs over time is the very root of scientific reasoning.

Building and testing models of systems support systems thinking.

Table 1. Decisions and factors in an expert system on weather forecasting

SEVERE WEATHER PREDICTOR

This module is designed to assist the severe local storms forecaster in assessing the potential for

severe weather using soundings. The program will ask for measures of instability and wind shear, as

well as other variables important in the formation of severe weather. Instability and wind shear

parameters are easily calculated using programs such as SHARP, RAOB, and GEMPAK. The other

variables can be found on surface and upper-air charts.

ADVICE

The following output indicates the potential for severe weather in the environment represented by

the sounding you used. A number between 1 and 10 indicates the confidence of the guidance. A

higher number indicates a greater confidence

Severe Weather (Tornadoes, Hail, and/or Straightline Winds)

Severe Weather Possible

Severe Weather Not Likely

Severe Weather Likely

Severe Weather Potential

QUESTIONS (Decision Factors)

What is the value of CAPE (J/kg)? 576, 72 to 76, 0 to 72, 4 0

What is the Lifted Index (LI) value (C)? 0, 0–25, 25–75, 4 75

What is the Convective Inhibition (CIN) (J/kg)? 0, 1–3, 4 3

What is the Lid Strength Index (LSI) (C)? 4 450, 250–449, 150–249, 0–150, 5 150, 5 0

What is the value of storm-relative helicity? 4 6, 4–6, 2–4, 5 2

What is the value of 0–6 km Positive Shear (s-1)?

What is the value of storm-relative helicity (m2 s-1)? Left Entrance, Right Entrance, Left Exit, Right

Exit, None

Which quadrant of the jet streak is the area of interest in? Cold Front, Dryline, Convergence Zone,

Outflow Boundary, Nothing Significant

Is there likely to be a significant trigger mechanism? Yes, No

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There are a variety of computer-based tools for modelling systems. Based on

systems dynamics, tools like Stella, PowerSim, and VenSim enable learners to

construct testable models of phenomena using a graphic interface. The relation-

ships among the entities represented in the model in Figure 4, for instance, are

quantitatively described using elementary algebra. The iterative testing and

revising of the model to insure that it predicts theoretically viable outcomes is

one of the most conceptually engaging processes possible. When expected values

do not result from the model, learners are faced with a cognitive conflict that they

must resolve. Resolving that conflict is a rich example of a conceptual change

process.

Another class of modelling tool that enables learners to inductively construct

models of systems is a class of microworlds such as NetLogo, AgentSheets, and

Eco-Beaker. These population dynamics tools require learners to construct rules

about the nature of the behaviour in systems that they can immediately test in the

resulting simulation. Figure 5 models the growth of miniature organisms in an

environment. After inputting initial values and growth parameters, learners can then

environmentally perturb the system and retest the growth patterns. In this case, the

model shows the effects of a hurricane on the growth of Bryzoa. These tools

represent a complex, systemic view of the world. That is, they explore the self-

organizing nature of phenomena in the world, a perspective rarely experienced by

students.

Figure 4. Systems dynamics model of Arab-Israeli conflict.

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3.4 Modelling Experiences (Stories)

Stories are the oldest and most natural form of sense making. Stories are the ‘‘means

[by] which human beings give meaning to their experience of temporality and

personal actions’’ (Polkinghorne, 1988, p. 11). We owe our history and culture to

stories. Humans appear to have an innate ability and predisposition to organize and

Figure 5. Modelling the effect of a hurricane on Bryzoan using EcoBeaker.

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represent their experiences in the form of stories. Telling stories is a primary means

for negotiating meanings (Bruner, 1990; Lodge, 1990; Witherell, 1995), and they

assist us in understanding human action, intentionality, and temporality (Bruner,

1990; Huberman, 1995).

Stories can function as a substitute for direct experience. Some people believe that

hearing stories is tantamount to experiencing the phenomenon oneself (Ferguson,

Bareiss, Birnbaum, & Osgood, 1991). In other words, the memory structures used for

understanding the story are the same as those used to understand the experience.

Given the lack of previous experiences by novices, experiences available in a case

library of stories augments their repertoire of experiences.

Collecting and indexing stories of practitioners’ experiences provides a meaningful

learning experience for learners. The cognitive theory describing how stories are

recalled and reused is case-based reasoning (CBR). An encountered problem (the new

case) prompts the reasoner to retrieve cases from memory, to reuse the old case (i.e.

interpret the new in terms of the old), which suggests a solution (Aamodt & Plaza,

1996). If the suggested solution will not work, then the old and or new cases are revised.

When their effectiveness is confirmed, then the learned case is retained for later use.

Students can engage in conceptual change by modelling people’s experiences, that

is, collecting and indexing stories about their experiences. The easiest tool for cap-

turing stories in order to model experiences is the database. The database in Figure 6

Figure 6. Entry in database of Northern Ireland stories.

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recounts one of many stories that were collected by students studying the conflict in

Northern Ireland. The database contains many stories that have been indexed by

topic, theme, context, goal, reasoning, religion, and so forth. When students analyse

stories in order to understand the issues, they better understand the underlying

complexity of an phenomenon in terms of the diverse social, cultural, political, and

personal perspectives reflected in the stories. Encountering this diversity of beliefs

provides anomalous data that entails the need to change one’s conceptual models of

the world.

3.5 Modelling Thinking (Cognitive Simulations)

Rather than modelling content or systems, learners can also model the kind of

thinking that is required to solve a problem, make a decision, or complete some other

task. That is, learners can use computer-based modelling tools to construct cognitive

simulations. ‘‘Cognitive simulations are runable computer programs that represent

models of human cognitive activities’’ (Roth, Woods, & People, 1992, p. 1163). ‘‘The

computer program contains explicit representations of proposed mental processes

and knowledge structures’’ (Kieras, 1990, pp. 51 – 52). Building cognitive simula-

tions attempts to reify mental constructs for analysis and theory building and testing,

as we illustrate in Figures 7 and 8.

The purpose of building cognitive simulations was traditionally for representing

cognitive processes for the purpose of building intelligent tutors. We argue that

having learners build models of cognitive processes intentionally engages them in

metacognition, which results in conceptual change, which is the basic thesis of this

paper.

Conceptual change occurs when learners change their understanding of

concepts they use and the conceptual frameworks that encompass them. However,

those change processes vary with content and context, and so there are multiple

theoretical perspectives on conceptual change. For some researchers (Siegler,

1996; Smith, diSessa, & Roschelle, 1993), conceptual change is an evolutionary

process of conceptual aggrandizement and gradual transformation of knowledge

states. Other theories conceive of conceptual change as revolutionary (Thagard,

1992) resulting from cognitive conflict (Strike & Posner, 1985). That is, when

information to be acquired is inconsistent with personal beliefs and presupposi-

tions (Vosniadou, 1994), revision of conceptual frameworks is essential. That is

the kind of conceptual change that is most likely supported by building models,

because inconsistencies in conceptual models are addressed more directly. In

order to illustrate the process of building models of cognitive processes, we

illustrate our use of systems dynamics models to foster our conceptual

understanding of three different theories of conceptual change. By constructing

models of the different theories of conceptual change, we were able to manifest,

assess, and correct our own understanding of the theories. That is, while building

models of each theory, we reconciled out naıve personal theories with the different

theoretical accounts.

Modelling 29

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3.5.1 Cognitive conflict. Most researchers believe that conceptual change arises from

the interaction between experience and current conceptions during problem solving

or some higher-order cognitive activity. When there is a discrepancy between

experienced events and the learner’s intellectual expectations, cognitive conflict

occurs (Strike & Posner, 1985). If an external agent convinces the learner that his/her

current conceptions are inconsistent with domain standards, then the learner may be

convinced of the need for conceptual change.

The first step in conceptual change is the awareness of a contradiction (Luque,

2003), followed by the awareness of a need for change. This may be the most difficult

part of the conceptual change process. In our model of the cognitive conflict theory

of conceptual change in Figure 7, experiences flow into the model at the top. Those

experiences may be misinterpreted or correctly interpreted. Restructuring mis-

conceptions is affected by a number of ‘‘strategies for understanding’’ (hidden in

the diamond at left). Those strategies include analogizing, exemplifying, and

Figure 7. Cognitive conflict model of conceptual change.

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imaging. Together, these strategies form a coefficient that determines the level of

restructuring.

Cognitive conflict is not always sufficient for causing conceptual change. As

illustrated in the bottom structure in Figure 7, the disposition to change conceptions

(accept new conceptualizations) depends on acceptance factors (ability to interpret

the experience, problem-solving ability, and necessity embedded in the acceptance

factors diamond; Chan, Burtis, & Bereiter, 1997); generic factors (experience in the

domain, prior knowledge, and knowledge in related fields); rejection factors

(tendencies to exclude, ignore, or reinterpret the conflict; Chinn & Brewer, 1993);

and epistemic beliefs).

3.5.2 Ontology shifting. Another prominent theory of conceptual change is ontology

shifting. Meaning for a concept is determined by the category to which the concept is

assigned. Chi, Slotta, and deLeeuw (1994) define three primary ontological

categories based on shared attributes: matter, processes, and mental states. As long

as a category does not share attributes with any other category, it is ontologically

distinct. Conceptions of science are often incorrect because they are assigned to the

wrong ontological category. These misconceptions must be corrected in order for

students to achieve deeper understanding (Chi & Roscoe, 2002). In order to

understand the phenomenon, students must shift ontological categories. Shifting

ontological categories is a radical form of conceptual change. This radical form of

conceptual change requires the shifting of concepts or propositions from one

ontological category to another.

Figure 8. Stella model of ontology shifting theory of conceptual change.

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Our model of ontology shifting in Figure 8 conveys Chi’s theory that

misconceptions are a result of the misclassification of concepts in incorrect

ontological categories. When concepts are misclassified, they cannot be used to

solve problems because they carry the wrong assumptions about relations among

concepts. The misclassification can be fixed by moving conceptions to another (more

appropriate) ontological category. The model in Figure 8 shows three ontological

categories with inflows of concepts from other categories and outflows into other

categories. The reclassification process is controlled by the error switching entity

which in turn is affected by weak strategies, such as replacing meanings, coexisting

meanings, learning new category properties, abandoning a concept’s meaning, or

learning new concepts. This model does not adequately convey the complexity of the

theory of ontology shifting, but it greatly enhanced our understanding of the

assumptions and the processes.

The models that we have demonstrated illustrate our understanding of two

different theories of conceptual change. These models and our conceptual under-

standing of the theories changed as we added complexity and tested our model. They

are representations of our mental models of conceptual change processes. The

assumptions and syntax of systems dynamics that underlies the models as well as the

medium of visualization enables us to recognize aspects of these theories that are

otherwise inaccessible using language only.

4. Limitations of Model Building

Although we have made a strong case for using technology-mediated model building

for fostering conceptual change, we must acknowledge some limitations of the

process. There are at least three different limitations of the models and the modelling

processes.

4.1 Cognitive Load as Limitation to Modelling

Model building may place significant demands on working memory, resulting

in high cognitive load. Sweller and his colleagues (Mwangi & Sweller, 1998;

Tarmizi & Sweller, 1988; Ward & Sweller, 1990) found that integrating textual

and diagrammatic information describing the same problems placed heavier

demands on working memory, known as the split-attention effect. Requiring

students to integrate multiple sources of information, a fundamental require-

ment of most modelling tools, is more difficult and may make model building

difficult. As conceptual understanding improves with practice, cognitive load will

diminish.

4.2 Contradictions as Limitation to Modelling

Any activity system has potential contradictions that may impede workflow and

learning (Engestrom, 1987). That is, aspects of any activity may contradict each

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other. Because of the difficulty in producing models and integrating them in

classroom activity systems, the outcomes of such activities may be compromised. As

an empirical example of this notion, Barab, Barnett, Yamagata-Lynch, Squire, and

Keating (2002) used activity theory as an analytical lens for understanding the

transactions and pervasive tensions that characterized course activities. They

discovered substantive contradictions between the use of a simulation tool, which

the students enjoyed and were engaged by, and learning of the astronomy content,

which was the goal of the teacher. Because modelling is engaging and demanding, the

modelling processes may contradict with content acquisition. With persistence,

the conceptual understanding of content will be greater than can be achieved without

the tool, we believe.

4.3 Fidelity as Limitation to Modelling

Many misconceptions prevail about models. One is the identity hypothesis.

Although one goal in building models is to reify ideas and phenomena, the models

themselves are not, as many people tacitly believe, identical to the phenomena

themselves. Models are representations of interpretations of phenomena in the

world, not the objects themselves. All models are at best inexact replicas of the real

phenomenon.

Another misconception of models relates to their stability. Models are usually

synchronic representations of dynamic processes. Phenomena change over time,

context, and purpose. Models often do not. Assuming that models are literal and

immutable representations of phenomena will surely lead to misconceptions.

Phenomena in the world are typically far more complex than anything that can be

represented by any model. Modelling always involves certain simplifications and

approximations that have to be decided independently of the theoretical requirements

or data conditions (Morrison & Morgan, 1999).

5. Summary

Student construction of models of phenomena that they are learning using

technology-based modelling tools scaffolds conceptual change in learners by

facilitating multiple representations of knowledge. The models that students

construct also provide strong evidence that can be used to assess conceptual change

in the learners. There exists a dynamic and reciprocal process between internal

mental constructions and the external, models that students construct.

Research is needed to determine which kinds of models (domain knowledge,

problems, systems, experiences, or cognitive simulations) or which kinds of modelling

tools are more likely to result in more complete or meaningful conceptual change.

Which tools can be more readily adopted by learners is a function of individual

differences in cognition, and which kinds of tools afford the best representation of

conceptual understanding is not known. Modelling provides rich research opportu-

nities in the effects of knowledge representation on conceptual change.

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