Interactive Learning Environments Vol. 13, No. 1–2, April–August 2005, pp. 15 – 37
Model Building for Conceptual Change David Jonassen*, Johannes Strobel and Joshua Gottdenker University 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 mathematics 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:
[email protected] ISSN 1049-4820 (print)/ISSN 1744-5191 (online)/05/01/2015-23 Ó 2005 Taylor & Francis DOI: 10.1080/10494820500173292
16 D. Jonassen et al. 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 interviews (Southerland, Smith, & Cummins, 2000), and concept maps (Edmundson, 2000). The analysis of interview and conversation protocols is very difficult and timeconsuming 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
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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.
18 D. Jonassen et al. 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 modelbased 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.
20 D. Jonassen et al. 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 constructions 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
22 D. Jonassen et al. 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.
24 D. Jonassen et al. 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, & FergusonHessler, 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 illstructured problems consist of several interrelated problems that need to be
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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 Severe Severe Severe Severe
Weather Weather Weather Weather Weather
(Tornadoes, Hail, and/or Straightline Winds) Possible Not Likely Likely Potential
QUESTIONS (Decision Factors) What is the value of CAPE (J/kg)? 5 76, 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
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.
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Figure 4. Systems dynamics model of Arab-Israeli conflict.
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 relationships 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 selforganizing nature of phenomena in the world, a perspective rarely experienced by students.
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Figure 5. Modelling the effect of a hurricane on Bryzoan using EcoBeaker.
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
28 D. Jonassen et al. 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 capturing 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 simulations 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 presuppositions (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 naı¨ve personal theories with the different theoretical accounts.
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Figure 7. Cognitive conflict model of conceptual change.
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 misconceptions is affected by a number of ‘‘strategies for understanding’’ (hidden in the diamond at left). Those strategies include analogizing, exemplifying, and
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Figure 8. Stella model of ontology shifting theory of conceptual change.
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.
32 D. Jonassen et al. 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 understanding 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 requirement 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 (Engestro¨m, 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 opportunities in the effects of knowledge representation on conceptual change.
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