Salzman et al., Conceptual Change in Precollege Engineering
Conceptual Change in Precollege Engineering Noah Salzman Purdue University, West Lafayette, IN, USA
[email protected] Johannes Strobel Purdue University, West Lafayette, IN, USA
[email protected] Abstract: Conceptual change is the process where an individual’s understanding of a particular process or phenomenon changes to a more sophisticated, accurate, or expertlike understanding of the same phenomenon. Although well utilized in the learning sciences, conceptual change has not been used as a theoretical framework for engineering education research. This paper provides an overview of conceptual change theories for the engineering education research community. These theories can be broadly divided into either revolutionary or evolutionary approaches to conceptual change. An overview of these two categories is provided, along with the differences in how they address various aspects of conceptual change theory. The paper concludes with a theoretical exploration of the relevance of these theories to precollege engineering education.
Introduction Conceptual change is the process where an individual’s understanding of a particular process or phenomenon changes to a more sophisticated, accurate, or expert understanding of the same phenomenon. Conceptual change is one of the most used theories in the learning sciences. Although well researched in the natural sciences, especially physics, conceptual change has not been commonly utilized as a theoretical framework for engineering education research, particularly for understanding engineering learning at the K-12 level. While searching for published material on conceptual change and engineering, one is most likely to find articles that either (a) assess the effect of engineering on science concepts (for example, see Schnittka & Bell’s, 2011 article Engineering Design and Conceptual Change in Science: Addressing thermal energy and heat transfer in eighth grade) or (b) articles, which describe exclusively the scientific aspects of engineering (for example, see: Krause, Kelly, Tasooji, Corkins, Baker, & Purzer’s, (2010) Effect of pedagogy on conceptual change in an introductory materials science course”). It is puzzling, why there seem to be no clearly developed unique concepts in the domain of engineering. It might be partially explained by that engineering is commonly defined as the practical application of science and math and so other conceptual domains might not be seen necessary. Still the fact that the conceptual basis of engineering seems to be entirely borrowed from other domains, requires further investigation. The purpose of this paper is twofold: (1) to provide an introduction into the different models of conceptual change and (2) assess the usefulness and adequacy of different models in order to explain learning and understanding of engineering in K12. The broader goal is to contribute to a discussion on the conceptual basis of engineering.
Models of Conceptual Change Models of conceptual change can be differentiated by the answers they provide to several key questions: (1) How is change happening? Models of conceptual change tend to take either a revolutionary approach to conceptual change where change is seen as happening extremely quickly in response to anomalous data or cognitive dissonance, or an evolutionary approach where change is seen as something that happens gradually over time(Vosniadou, 2008). (2) What is changing? The literature distinguishes between granular and individual concepts to entire systems of concepts, so called mental models(diSessa & Sherrin, 1998). (3) How stable are conceptual understandings? Different models
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either argue that students carry a stable understanding, which expands or will be revised over time or argue that conceptual understanding is the ad-hoc assembly of fragmented concepts dependent on the given context (Özdemir & Clark, 2007). (4) How do people react to data which contradict or challenge their conceptual understanding? The literature distinguishes between ignoring anomalous data, rejection, domain exclusion, abeyance, reinterpretation, peripheral change, assimilation and theory change (Chinn & Brewer, 1993). Affective variables like motivations and engagement can also influence how students will react to attempts to modify their conceptual understandings. To assess how applicable existing frameworks of conceptual change are for the context of K-12, this paper maps the conceptual space of engineering within the larger science, math and technology concepts. To exist in a physical world requires an intuitive understanding of physics with accompanying naïve theories; engineering does not typically make the same demands as one can navigate the engineered world without generating theories about how it came to be. Although numerous stereotypes exist related to the nature and practice of engineering, the general lack of strongly grounded alternative conceptions or misconceptions about engineering and its processes limit the applicability of revolutionary models of conceptual change to K-12 engineering education. Evolutionary models, in particular diSessa’s Knowledge-in-Pieces model (diSessa, 1993) and both Script theory (Rumelhart, 1980) and Schema theory (Schank & Abelson, 1975), show much greater potential for explaining engineering thinking at the K-12 level. Rather than conceptualizing engineering as a specific knowledge domain, these theories can help to understand engineering as a meta-organizer, helping students to build and reorder connections among existing scientific and mathematic concepts, and strengthening conceptions in this domain. Implications for the introducing of engineering in K-12 will be described in the paper.
How is change happening? Research on conceptual change originated in the domain of science guided by Thomas Kuhn’s (1996) seminal work, The Structure of Scientific Revolutions, originally published in 1962. Kuhn theorized that most science consisted of normal science which contributed to the gradual expansion of scientific knowledge. As scientists perform normal science, they generate data that cannot be adequately explained with current scientific theory. Scientists then begin to develop alternative theories, and when enough data support a particular alternative theory, a paradigm shift or scientific revolution occurs where the scientific community rejects the current theory in favour of the alternative theory and its better explanation and generative potential. Posner, Strike, Hewson, & Gertzog (1982) took Kuhn’s model of scientific revolutions and applied it to understanding how students’ conceptions of scientific phenomena change in the classroom They posited four criteria that would lead students to reject their naïve conception of a phenomenon in favor of a more scientific conception. The students need to be dissatisfied with their current conception due to its inability to adequately explain the phenomenon. They need a new conception that is intelligible given their background knowledge to replace the naïve conception. The new conception needs to be plausible. Finally, the new conception needs to be fruitful and lead to increased understanding of related phenomena or guide their inquiry into these phenomena. Educators facilitate these revolutionary shifts through instruction that creates cognitive dissonance. Chi, Slotta, & De Leeuw (1994) also suggest that conceptual change occurs in a revolutionary manner. They suggest that students’ categorize their scientific concepts, and that groups of concepts are arranged in ontological categories. They postulate three primary ontological categories of scientific concepts: matter, processes, and mental states. Conceptual change that involves rearranging concepts within a particular ontological category is not difficult for the learner. For example, it is not especially difficult to understand that people and dogs are parts of the larger concept of mammals or animals. However, conceptual change that involves moving concepts from one ontological category to another can be difficult. For example, many students believe that electrons in a circuit are matter that behaves similarly to the matter that they observe around them, when to understand a circuit it typically makes more sense to understand electrons as a process of flow. Like Posner et al. (1982), Chi et al. (1994) suggests that these changes in categorization are revolutionary and occur as a result of experiences and instruction.
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In contrast to revolutionary models of conceptual change, evolutionary models suggest that change occurs gradually as a result of numerous experiences. Current research suggests that this is a more realistic time scale for conceptual change regardless of how one understands conceptual change (Özdemir & Clark, 2007).
What is changing? How one understands the term “concept” is a critical component of conceptual change research (diSessa & Sherin, 1998). Jonassen (2006) presents three similarity views of concepts, concepts that are constructed through recognizing similarities in essential characteristics, properties, or attributes. In the classical-attribute isolation view of concepts, a person has learned a concept when he or she can correctly categorize objects based on a set of attributes. The prototype or probabilistic view of concepts also involves categorization, but based on a looser, probabilistic understanding of defining characteristics as opposed to strict rules. In the exemplar view of concepts, people learn concepts by inducing concept descriptions from examples. Similarity views of concepts are limiting because they take concepts out of context and do not account for concepts in use (Jonassen, 2006). Other views of concepts presented by Jonassen (2006) include the actional view of concepts, where concepts are ways of dynamically organizing personal experiences. The theory-based view of concepts suggests that people organize concepts based on their epistemological beliefs (Jonassen, 2006).
Models Vosniadou (2003) suggests that individuals create models of phenomenon based on their experiences with the phenomenon. Individuals build theories (what she describes as naïve physics) which provide a “narrow but nevertheless coherent explanatory framework for conceptualizing the physical world.” Naïve physics can get in the way of students accepting scientific theories due to its coherence, limited but still existent explanatory abilities, and grounding and reinforcement in personal experience. Conceptual change is the process of moving from naïve physics to accepted understandings, and consists of the slow and gradual replacement of the students’ beliefs associated with a particular phenomenon.
Schemata Rumelhart(1980) describes a schema as a “data structure for representing generic concepts stored in memory.” Schemata represent a prototype theory of meaning; to illustrate this he uses several analogies, comparing a schema to a play, a theory, and a procedure. Schemata are like plays in that they provide a structured environment that allows room for interpretation, and do not contain every detail of a situation. A schema contains both variables which operate with a set of constraints and constants. Schemata are used to interpret a particular event, object or situation. Like a theory, a schema can be used to predict outcomes given a particular set of variables. A schema is comprised of subschemata. Schema can be activated either top-down, when a schema activates subschemata, or bottom-up, when a subschema activate a schema which it belongs to. Schemata support both perception and memory. A schema based system suggests three modes of learning: accretion, tuning, and restructuring. Accretion is the gradual building of knowledge through partial comprehension of new material. Tuning involves modification or evolution of existing schemata through the modification of variable constraints or default values, replacing constant portions of a schema with a variable one, or turning a variable into a constant. Restructuring involves creating new schemata through patterned generation done by creating a new schema by modifying an old one or schema induction where the learner recognizes a schema through repeated exposure in multiple contexts over time. Anderson(1977) makes two major statements in support of a schema theory of understanding. First, he emphasizes that people do not construct understanding by “selecting a template from a great mental warehouse of templates abstracted from prior experience”, essentially discounting Behaviorist theories of understanding. Constructing understanding must be a more dynamic process that involves constructing interpretations. Second, he suggests that “abstract schemata program individuals to
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generate concrete scenarios”. They provide a framework of understanding which the person fills in as a given situation dictates. Anderson supports a dialectical theory of schema (conceptual) change, and suggests that Socratic dialogue is an effective strategy to encourage schema change.
Scripts Schank & Abelson’s (Schank & Abelson, 1975; Schank & Abelson, 1977) represents a very specific understanding of schemata and builds on the analogy of schemata as being akin to the script of a play. Script theory grew out of early research in artificial intelligence on language processing. The authors believed that knowledge can be described as a sequence of events in a particular context, which they refer to as a script. The following are several examples of scripts presented by Schank & Abelson(1975, pp. 151-152): “I. John went into the restaurant. He ordered a hamburger and a coke. He asked the waitress for the check and left. II. John went to a restaurant. He ordered a hamburger. It was cold when the waitress brought it. He left her a very small tip. III. Harriet went to a birthday party. She put on a green paper hat. Just when they sat down to eat the cake, a piece of plaster fell from the ceiling onto the table. She was lucky, because the dust didn’t get all over her hair. IV. Harriet went to Jack's birthday party. The cake tasted awful. Harriet left Jack's mother a very small tip.” Example I represents a typical simple script, in this case a script recognizable as eating at a restaurant. Example II is a restaurant script with a slight, predictable variation. Example III starts out as a typical birthday script, but then transitions to a different script with the falling of the plaster. Example IV starts out as a reasonable birthday script, but the last line does not make sense because prior experience with birthday parties do not suggest that leaving tips is an appropriate activity at a birthday party. This example illustrates how scripts bound conceptions of an experience, and how cues inform the choice of an appropriate script. Goals and context motivate the choice of scripts that an individual will choose to enact in a given situation. Going back to the simple example of the restaurant script, an individual would choose to enact this script if he or she is hungry, near a restaurant, and has the money and time to eat at the restaurant.
Networks of p-prims diSessa & Sherin (1998) propose a very different model of conceptual change. They propose the phenomenological primitive (p-prim) as the fundamental building block of knowledge. Students construct p-prims from their basic experiences and observations of the world. These p-prims are organized by conceptual networks where students establish loose connections between the p-prims. Through experiences that cultivate appropriate p-prims and build connections, students can move towards understanding phenomena in a more scientific manner. Each of these models represent slightly different understandings of concepts, with different degrees of relevancy to K-12 engineering that will be explored later in this paper.
How stable are the conceptual understandings? A critical difference between theories of conceptual change is their assumptions about the coherence and stability of students’ understanding. As mentioned earlier, Vosniadou (2003) believes that Proceedings of the Research in Engineering Education Symposium 2011 - Madrid
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students’ conceptions are theory-like, relatively coherent, and change fairly slowly over time. Although not explicitly addressed, schema and script theory suggest relatively stable understanding built from experiences as well. diSessa (2008) makes a strong argument against both coherence and stability in understanding. He primarily argues against parsimony, in other words trying to oversimplify or ignore pieces of data in the name of creating an elegant theory. Assuming stability results in losing complexity and depth and nuance of understanding. When assuming a stable model, especially that multiple students share the same incorrect stable model, one loses awareness of how individuals construct understanding of a given phenomenon, and what concepts the individual learner may be struggling with. Assuming a stable model may also suggest that student models have the same level of validity as normative scientific models, that they are well reasoned in their development and generative when applied to new situations. This needs to be counterbalanced with a respect for students’ models and ideas. diSessa also argues that there is no way to measure stability or coherence of a student’s model, so when researchers describe this they are in fact imposing their own views or biases towards their own theories of conceptual change on the student. Assuming that students possess a stable, incorrect understanding that needs to be replaced with a normative, scientific understanding may make it more difficult to discover and work with the student’s fundamental pieces of knowledge or p-prims. Rather than trying to restructure a stable theory through revolutionary or evolutionary conceptual change, instruction should focus on developing the p-prims necessary to build the scientific model and the meta-cognitive awareness to appreciate and verify the validity of the scientific model. Assuming a stable, individual model ignores the social aspect of knowledge and understanding, and presumes an individual independent of both social and experiential context. Finally, assuming than an individual’s theories are stable and “science-like” in their nature may also incorrectly lead to the assumption that these theories change in manner consistent with Kuhn’s (1996) description of scientific revolutions, and cognitive conflict will successfully lead students to the scientific theory or understanding of a given phenomena.
How do people react to anomalous data? Anomalous data is data that does not conform to a student’s present understanding of a particular phenomenon (Chinn & Brewer, 1993). Educators often assume that presenting a student with anomalous data will result in cognitive conflict that will lead him or her to reject or modify their naïve understanding of a particular phenomenon in favour of an understanding that is more closely aligned with accepted scientific explanations (Posner, Brewer, Strike & Herzog, 1982). However, this is often not the case. An excellent example is students attempting to transition from understanding the Earth as flat to understanding that it is a sphere (Vosniadou, 2003): Students find it difficult to reconcile their classroom instruction which states that the Earth is a sphere with their own experiences which suggest that the Earth is flat. Instead of rejecting the flat earth model, they instead wind up with a dual Earth model where there is one Earth that is a sphere and travels through the solar system, and a different flat Earth that they actually live on. Understandings built on personal experience do not change readily when students are exposed to anomalous data. Chinn & Brewer (1993) developed a theoretical framework to explain the wide variety of ways that students respond to anomalous data. They identified seven different psychological responses that students may have regarding their naïve theory (call it Theory A) in response to anomalous data that supports a scientific theory (Theory B). Students may simply ignore the anomalous data, and forget about or ignore the data. They may also reject the data, which is similar to ignoring the data except that they can provide a reason for rejecting the data. These reasons could include assuming that there was a methodological error in generating the data, believing that the anomalous data are a fluke due to random variation in the data, or believing that the data are fraudulent. Students may also exclude the data from the domain of Theory A, believing that their theory is not supposed to explain the anomalous data. They may also hold the data in abeyance, acknowledging its validity but believing that their theory will be able to explain the anomalous data in the future. Students can also reinterpret the data so that it fits better with Theory A. Finally, the students actually engage in conceptual change
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and make peripheral changes to Theory A (weak conceptual change), or accept the data and change Theory A, possibly so that it more closely resembles Theory B (strong conceptual change). Students’ responses to anomalous data vary due to a variety of factors (Chinn & Brewer, 1993). Several characteristics of students’ prior knowledge have a strong effect on their responses to anomalous data. This includes the entrenchment of their current understanding based on the amount of experience they have with the phenomenon in question. The students’ ontological beliefs about the fundamental categories and properties of the world can be particularly difficult to change because they can support understanding across domains. Their epistemological beliefs about what constitutes knowledge and sound theories can also affect their responses to anomalous data. The students’ background knowledge and familiarity with mathematical, scientific, or other concepts related to the theory in question can also affect how they react to anomalous data. Characteristics of the alternative theory can also influence how students will respond to anomalous data that supports this alternative theory. An alternative theory must be available, it must be accurate, the scope of the theory must match the data, the theory must be consistent, and should be simple. Finally, the anomalous data itself must be credible, unambiguous so it cannot easily be reinterpreted, and be confirmed by multiple data sources. Students must also be willing to commit to the deep processing necessary to evaluate the anomalous data and potentially modify their understanding. Each of these factors can be addressed through appropriate instructional techniques.
Figure 1: The Cognitive Reconstruction of Knowledge Model (From Dole & Sinatra, 1998) Another way of understanding students’ responses to anomalous data is the Cognitive Reconstruction of Knowledge Model (CRKM), shown in Figure 1 (Dole & Sinatra, 1998). The model begins with an existing conception. A learner’s willingness to change is based on the strength of this existing conception, its coherence, and the learner’s commitment. The authors identified several motivations that can lead to conceptual change, including dissatisfaction with the concept, personal relevance, social context, and the need for cognition. Conceptual change requires a message with a new, alternative concept that must be comprehensible, coherent, plausible, and rhetorically compelling. If all of these elements line up and the learner is highly engaged, strong conceptual change can occur. If the learner is not engaged, weak conceptual change can occur, and in either case it is also possible that no conceptual change will occur. An important aspect of Dole & Sinatra’s (1998) model is that it identifies the role of learners’ affective states in determining if conceptual change will occur. They recognize that motivation and engagement play a significant role in how students will respond to attempts to encourage conceptual change. Proceedings of the Research in Engineering Education Symposium 2011 - Madrid
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Pintrich, Marx, & Boyle (1993) also reject the notion of “cold conceptual change” that suggests that conceptual change depends nearly exclusively on students’ prior knowledge and experiences. They suggest, that students’ goals, values, self-efficacy and control beliefs all significantly influence conceptual change.
Conceptual Change in K-12 Engineering Engineering is not a strongly conceptual domain, which limits the applicability of theories of conceptual change that presume stable, theory-like alternative conceptions. Science concepts are different from engineering concepts in terms of necessity: To walk, drive a car, or throw a piece of garbage in a trash can requires an intuitive understanding of mechanics and the physics of motion. To understand medical treatments, care for a child or pet, or procreation requires at least a basic model of biology. Cooking or cleaning requires a basic model of chemistry. Existence forces us to develop basic science conceptions, which become either the naïve theories or p-prims that provide the building blocks or necessitate conceptual change, depending on if one subscribes to a “knowledge in pieces” or “naïve theories” view of conceptual change. If engineering is defined as the process of developing a technology to solve a particular problem, existence does not foster the development of engineer in the same way that it fosters the development of personal scientific theories, thus leaving the K-12 engineering student with fewer alternative conceptions that need to be addressed. One can be a fairly adept user and consumer of technology without an underlying understanding of how that technology was created or why it works. Research on conceptual change in K-12 engineering therefore tends to take the form of studies that focus on understanding students’ misconceptions about the nature and practice of engineering as explored through assessment like the Draw an Engineer test (Cunningham & Lachapelle, 2007). Script theory shows much greater potential for explaining conceptual change in K-12 engineering. Engineering design has been identified as a critical aspect of K-12 engineering (NAE Committee on K-12 Engineering Education, 2009), and teaching students an engineering design process provides an illustrative example of how scripts can be used to explain engineering. As part of everyday life, most students develop an engineering script to solve problems that involves creating a solution and testing it. This script is repeated until a satisfactory solution is found. This script can be modified to develop a more nuance model of engineering design that brings in factors like customers, constraints due to available resources, and using testing of the design and other sources of data to inform revisions of the design. Engineering can provide a context for building connection between mathematics and science concepts which can help students to develop a deeper understanding of mathematics and science. Applying mathematics and science concepts to the solution of real world problems can lead to measurable gains in understanding (Schnittka, Bell, & Richards, 2009). Drawing from diSessa’s (1998) model, engineering can serve as the conceptual network that ties together concepts from mathematics and science and encourage the development of broadly applicable engineering habits of mind like optimization and working with constraints.
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