How Do Students Reason About Chemical Substances and Reactions?

Transcription

1 How Do Students Reason About Chemical Substances and Reactions? Vicente Talanquer Introduction One of the central learning goals in chemistry teaching is to help students understand the relationship between the macroscopic properties of substances and their chemical composition and structure at the submicroscopic level (AAAS 1993 ; NRC 1996 ). In particular, we would like students to meaningfully understand how to use atomic molecular models of matter to explain and predict the properties and behavior of relevant materials in their surroundings. Unfortunately, educational research in the last 40 years has shown that developing such an understanding is not an easy task (Gilbert et al ; Kind 2004 ; Nakhleh 1992 ; Taber 2002 ). Many students struggle to make sense of the various particulate models of matter discussed in their chemistry classes, as well as to properly use them to explain and predict phenomena. Many of the difficulties that students face in understanding structure property relationships are described in the now-extensive research literature on alternative conceptions (Duit 2007 ). This body of work reveals how students intuitive ideas influence their reasoning in a variety of chemistry topics, from atomic structure to chemical equilibrium. Results from this research are often presented as a list of naïve ideas that students express about different chemistry concepts. Although this approach allows us to identify explicit conceptions that we may want to diagnose and target in our teaching, this taxonomic description has been criticized on various grounds. Several authors have suggested, for example, that many of these alternative ideas are not necessarily stable conceptions in students minds but rather dynamic cognitive constructs created on the spot as pupils are asked to explain or predict phenomena (Brown and Hammer 2008 ). From this perspective, paying V. Talanquer (*) Department of Chemistry and Biochemistry, University of Arizona, Tucson, AZ 85721, USA vicente@ .arizona.edu G. Tsaparlis and H. Sevian (eds.), Concepts of Matter in Science Education, Innovations in Science Education and Technology 19, DOI / _16, Springer Science+Business Media Dordrecht

2 332 V. Talanquer attention to the underlying cognitive elements that constrain student reasoning may be a more productive approach to understanding and predicting student thinking in chemistry (Taber and García-Franco 2010 ; Talanquer 2006, 2009 ). Based on these ideas, in recent years, we have proposed that many of the alternative conceptions expressed by chemistry students seem to be guided and constrained by common underlying presuppositions (implicit assumptions) about the nature of entities and phenomena in our world, as well as by the application of shortcut reasoning procedures (heuristics) that facilitate decision-making under conditions of limited time and knowledge (Talanquer 2006 ). This way of conceptualizing student reasoning has several pedagogical advantages. First, it helps us make sense of and bring coherence to a variety of reported alternative conceptions and common student errors in different chemistry topics. Moreover, it facilitates making predictions about students ideas and difficulties in many areas. Finally, it provides a framework for analyzing progression of understanding with training in the discipline (Talanquer 2009 ). The central goal of this chapter is to illustrate how the proposed approach can be used to analyze student reasoning about the properties of chemical substances and reactions based on atomic molecular models of matter. Student Reasoning Research on student reasoning in the sciences has been closely related to investigations on conceptual change. Work in this area is frequently framed within one of three major theoretical perspectives frequently referred to as the framework theories approach (Vosniadou et al ), the knowledge-in-pieces standpoint (disessa 1993 ), and the ontological categories stance (Chi 2008 ). Thus, to better understand our approach to the analysis of student reasoning in chemistry, it is important to discuss the core ideas behind each of these theoretical viewpoints. Within the framework theories perspective, student reasoning is assumed to be guided by a network of interrelated knowledge and beliefs about the natural world, such as the idea that physical objects move in continuous paths, which constrain the types of mental models and explanations that people might construct. At the heart of this theoretical approach is the proposition that initial explanations of the natural world are not fragmented ideas but rather form a coherent system of observations, beliefs, and presuppositions, a so-called framework theory (Vosniadou et al ). From the knowledge-in-pieces viewpoint, intuitive knowledge about the world is seen as more fragmented, including a large and diverse collection of phenomenological ideas commonly referred as p-prims (phenomenological primitives); examples of p-prims include notions such as the closer the source, the stronger its effect. These cognitive elements work by being activated by specific circumstances, which may explain the contextuality observed in students answers to questions asked in slightly different ways (disessa and Sherin 1998 ). In the ontological categories approach, human reasoning is assumed to be strongly influenced by the implicit or explicit categories in which people mentally place the different components of the

3 How Do Students Reason About Chemical Substances and Reactions? 333 systems of interest. For example, we can safely assume that solid objects will persist in time and space, that is, they will move in continuous paths and they will not spontaneously change shape or decrease in size. If an object does not behave in such a way, then we would not think of it as a solid object (Chi 2008 ). Although the research literature on conceptual change sometimes portrays the above theoretical perspectives as competing research paradigms, the analysis of recent work within each of these research camps reveals points of agreement on several key issues. However, it also highlights the challenges that we face in characterizing students knowledge as coherent versus fragmented, as stable versus dynamic, or as consistent across tasks versus highly contextualized. Rather than taking a particular stance in the conceptual change debate, the goal of our work has been to generate a framework in which elements from different perspectives in the field are used to build an explanatory and predictive approach to the analysis of students ideas about fundamental chemistry concepts. The central goal is to create interpretative tools that can help teachers make sense of a wide range of alternative ideas that students may express as they learn chemistry. To illustrate our core ideas, let us describe what may happen in our students minds as they confront a task that requires the analysis of some entity or phenomenon. Research in cognitive and developmental psychology suggests that when people interact with an object or event, prior knowledge, perceptual information, and language cues are used by the mind to build a mental representation for recognition and categorization purposes (Baillargeon et al ; Gelman 2009 ). Once a mental representation is created, associative thinking, analogical reasoning, and metaphorical linking help us classify the entity or phenomenon as belonging to a certain category within or across knowledge domains (Bowdle and Gentner 2005 ; Vosniadou and Ortony 1989 ). For example, we may recognize a rock as a solid object because it feels rigid and heavy. Our categorizations of entities and phenomena have crucial repercussions on how we reason with and about them (Chi 2008 ). This is mainly because people implicitly assume that the properties of entities and phenomena are determined by the underlying properties that define the category to which they belong. Categories capture causal patterns and guide and constrain our reasoning about what is possible. Let us imagine that we ask a young child to justify or explain the presence of tiny droplets of water on the external surface of a glass full with water just taken out of the refrigerator. Based on prior experiences, it is likely that he or she will think of the phenomenon as a causal process, that is, he or she will assume the existence of an active agent responsible for the event (Andersson 1986 ). Now, depending on the context, his or her prior knowledge, as well as perceptual and language cues, the child may decide that this is a transfer event and propose, for example, that someone with wet hands touched the glass. However, in the process of building the explanation, the child may remember seeing water filtering through paper or ceramic vases. Thus, he or she may choose to suddenly look at the phenomenon as a passing- through event in which water from the inside passed through the glass. The assumptions that people make about the properties and behaviors of the members of a given category act as cognitive constraints that guide and support, but

4 334 V. Talanquer also constrict, their reasoning. These cognitive constraints help us make decisions about what behaviors are possible or not and about what variables are most relevant in determining behavior. They also support the development or application of decision rules and heuristics to make predictions about how the object will behave when involved in different processes or events. These cognitive elements give rise to dynamic but constrained knowledge systems whose goal is not necessarily to achieve global conceptual coherence, but rather local explanatory coherence and efficient inference and decision-making as we work through a specific task in a determined context (Brown and Hammer 2008 ; Sloman 1996 ). Cognitive constraints do not provide fully mechanistic models of entities and phenomena, but help us recognize relevant properties and sense relational patterns. They allow us to make reasonable, adaptive inferences about the world given limited time and knowledge. They often generate acceptable answers with little effort, but sometimes lead to severe and systematic biases and errors (Hatano and Inagaki 2000 ; Keil 1990 ). A variety of researchers in cognitive science, developmental psychology, and science education have identified diverse implicit cognitive elements that seem to guide and support, but also constrain, students reasoning in different domains. They have referred to them in different ways, such as core knowledge (Spelke and Kinzler 2007 ), implicit presuppositions (Vosniadou 1994 ), ontological beliefs (Chi 2008 ), phenomenological primitives (disessa 1993 ), intuitive rules (Stavy and Tirosh 2000 ), fast and frugal heuristics (Todd and Gigerenzer 2000 ), and conceptual resources (Redish 2004 ). As can be seen in this list, major proponents of the three dominant theoretical perspectives in conceptual change discussed at the beginning of this section (Chi 2008 ; disessa 1993 ; Vosniadou 1994 ) highlight the existence of cognitive elements that, once activated, act as constraints on further reasoning. However, there is considerable debate on the extent to which these types of implicit cognitive elements form coherent integrated knowledge systems or more fragmented collections of cognitive biases (Brown and Hammer 2008 ; Vosniadou et al ). It is likely that their level of integration may vary depending on the nature of the knowledge domain and the prior knowledge and experiences of each individual. Our approach to the analysis of student reasoning in chemistry has been that, beyond issues of coherence, stability, and contextuality of students ideas about the world, we need to better understand the nature of the cognitive elements that guide and constrain student thinking. Our analysis of the research literature on students alternative conceptions in chemistry, together with the results of our own research studies, suggest that these cognitive constraints seem to fall into two major groups (Talanquer 2006 ): Tacit presuppositions about the properties and behavior of the entities and phenomena in the domain ( implicit assumptions ) Reasoning strategies to make judgments and decisions under conditions of uncertainty ( heuristics ) The extent to which these cognitive elements form an integrated and comprehensive knowledge system may vary from student to student. However, our claim is that tacit categorization decisions about the nature of chemical entities and phenomena

5 How Do Students Reason About Chemical Substances and Reactions? 335 involved in a given problem trigger implicit knowledge and reasoning strategies that act as constraints on further reasoning. Thus, a major goal of our research work has been to characterize the most common and overarching constraints that seem to guide naïve learners reasoning about chemical entities and phenomena. In the following sections, we present examples of our approach and discuss what our results reveal about changes in student reasoning with training in the discipline. Implicit Assumptions The categorization on an entity or phenomenon as belonging to a certain class triggers implicit assumptions about its properties and behavior. For example, if we think of an atom as a rigid solid ball, we will expect it to be impenetrable and to move in continuous trajectories; we will assume that many of its properties, such as mass, volume, or color, will persist over time and space. Thus, paying close attention to the implicit or explicit categorization decisions made by students about the nature of chemical substance and processes can provide invaluable information about the underlying assumptions that guide their thinking. To illustrate this idea, in this section, we discuss two examples of overarching assumptions about the nature of chemical substances and reactions that seem to constrain the reasoning of a large fraction of chemistry students. Research on secondary school and college students ideas about the properties of atoms and molecules indicates that many students tend to assign similar properties to the submicroscopic components of a substance as to a macroscopic sample of the material. Thus, if the substance is red, its particles are assumed to have the same color; if the material expands when heated, its atoms or molecules should do the same (Kind 2004 ; Nakhleh 1992 ; Taber and García-Franco 2010 ). In our research, we have described this way of thinking as relying on an inheritance assumption, in which a person implicitly presupposes that substances inherit their properties from those of the individual submicroscopic components (Talanquer 2006, 2009 ). One may hypothesize that this assumption results from conceiving chemical substances as simple, in the case of chemical elements, or composite, when thinking about chemical compounds, aggregates, or clusters of atoms with relatively fixed properties. If this is the case, the properties of a macroscopic sample of a given substance are likely to be conceived as resulting from the weighted average of the properties attributed to the distinct types of particles present in the system (additive thinking). To test this hypothesis, we have conducted several studies in which we have asked students to make predictions about the properties of chemical compounds based on information about the macroscopic properties of the chemical elements that react to form such compounds (Talanquer 2008 ). Figure 1 illustrates prototypical results for students entering their first general chemistry course at the college level (GC1), students that finished such a course (GC2), and students entering a graduate program in chemistry (GS). The figure includes results for students predictions about the (a) color and (b) state of matter of the chemical product of the reaction between generic

6 336 V. Talanquer Fig. 1 Predictions of different groups of chemistry students for ( a ) the color and ( b ) the state of matter of the product of the chemical reaction of chemical substances with known properties. GC1 first semester of general chemistry, GC2 second semester of general chemistry, GS graduate students

7 How Do Students Reason About Chemical Substances and Reactions? 337 chemical elements depicted using either particulate or symbolic representations. In each case, students were asked to select the most likely properties of the product or to indicate whether more information was needed to make the prediction. As shown in Fig. 1, a significant proportion of students entering a general chemistry course at the college level (GC1) seem to think of chemical compounds as composite aggregates with properties determined by the weighted average of the intrinsic properties of its constituent particles. Thus, for example, they predict the color of the product of a one-to-one reaction between a yellow substance and a blue substance to be green (Fig. 1a ), and they consider that the product of a one-totwo reaction between a gaseous and a solid substance, respectively, is more likely to be solid than gas (Fig. 1b ). We have observed the same type of reasoning in students predictions of a variety of physical and chemical properties, such as smell, taste, malleability, electrical conductivity, and chemical reactivity of the product. In general, over two thirds of this population of students commonly selects answers that indicate that they apply additive thinking to make their predictions. Figure 1 also illustrates the little impact that a single general chemistry course has on students implicit assumptions about the nature of chemical elements and compounds. Although in general there is a smaller proportion of GC2 students who seem to rely on additive thinking to make their predictions, the differences in GC1and GC2 students responses to these types of questions are consistently nonsignificant. Major differences are more commonly detected when comparing student populations with markedly different years of training in the discipline, such as the entering college students (GC1) and entering graduate students (GS) in Fig. 1. These results suggest that conceptualizing physical and chemical properties as emerging from the interactions of the many components in a given system rather than as a simple combination of the intrinsic properties of such components is a rather difficult task for most students. The analysis of students alternative conceptions about physical and chemical processes suggests that chemistry students also seem to hold strong implicit assumptions about the nature of these types of events (Andersson 1986 ; Kind 2004 ; Taber 2002 ; Taber and García-Franco 2010 ; Talanquer 2006, 2010 ). Some of these presuppositions seem to stem from a conceptualization of chemical reactions as processes that are driven by (a) leading agents acting upon or within a system or by (b) intentional agents with well-defined purposes. In general, we may expect students to think of chemical reactions as driven by active agents when they recognize the presence of a potential initiator (e.g., spark, match) or they identify some atoms or molecules as more reactive within a system (e.g., higher electronegativity, more polar). On the other hand, claims of purposeful or intentional behavior are more likely to be made when the presence of a leading or enabling agent is not obvious and some states of a system are assumed to be more desirable than others. In these cases, students may consider that atoms or molecules react in order to attain a more stable final state (e.g., full valence shell, lower energy) or reinstate equilibrium.

8 338 V. Talanquer Fig. 2 Predictions of different groups of chemistry students for ( a ) the leading agent in a chemical reaction between a more reactive (A) and a less reactive (B) species and ( b ) the underlying reason why acids and bases react via proton transfer. GC1 fi rst semester of general chemistry, GC2 second semester of general chemistry, GS graduate students We have explored students assumptions about centralized causality (active or enabling agents) and teleology (intentional agents) in chemical reactions using questionnaires and individual interviews. Figure 2 includes prototypical distributions of answers for two representative questions posed to students with different levels of training in chemistry. In the question associated with Fig. 2a, students were

9 How Do Students Reason About Chemical Substances and Reactions? 339 told that compound A (more reactive) reacted with compound B (less reactive) to form compound C and then asked to decide which species could be identified as the most likely starter of the process (A, B, any of them, neither of them). As shown in this figure, close to 60 % of the students in each of the groups, regardless of level of training, indicated that the molecules of the more reactive compound would initiate the reaction by acting on molecules of the less reactive substance. On the other hand, results depicted in Fig. 2b correspond to a question that asked students to judge which of the following phenomena was most likely responsible for proton transfer between an acid and a base: (a) the molecules of the base attack the molecules of the acid and take the protons away; (b) the molecules of the acid spontaneously donate protons to molecules of the base; (c) hydrogen ions are transferred between molecules so that the two types of species become more stable; and (d) hydrogen ions randomly move between molecules of the acid and the base but the energy cost of this transfer is not the same in both directions. In this case, close to 60 % of the students in each of the groups selected the answer that implied intentional behavior to attain stability. Quite surprisingly, our results suggest that assumptions of centralized causality or teleology in the behavior of reacting atoms and molecules do not subside with training in the discipline. In fact, in some cases they seem to become more prevalent. One may speculate that this particular result may be linked to frequent student exposure to conventional mechanistic representations in chemistry that depict electron- rich species as acting on electron-deficient species. Based on our results, it is difficult to ascertain the extent to which chemistry students at the different educational levels actually attribute intentional behaviors to atoms or molecules. Our analysis of general chemistry textbooks reveals that our own educational resources and ways of teaching may foster this type of thinking (Talanquer 2007 ). However, results from our studies clearly indicate that a large proportion of students tend to judge teleological statements as truthful explanations of chemical reactivity. Similarly, many students exhibit a clear preference for teleological justifications of chemical change versus explanations that describe chemical reactivity as the result of energetically or entropically biased random processes (emergent processes). Heuristics Implicit assumptions about the nature of chemical entities and phenomena help students make predictions about their properties. These presuppositions help students identify variables or cues that may be relevant in any given context. However, chemical substances and processes tend to be multivariate complex systems, and making proper judgments and decisions about their behavior frequently requires careful identification of and discrimination among many variables. For example, in deciding whether sodium chloride (NaCl) can be expected to have a higher melting point than sodium bromide (NaBr), we need first to recognize that these two substances can be modeled as ionic compounds. Then, we should acknowledge that

10 340 V. Talanquer their physical properties will be largely determined by the charge and size of the ions present in the system. Next, we should remember or find a way to infer the actual ion charge and size values, and, finally, we should be able to integrate all of this information to make a decision. Research on student reasoning when facing these types of problems indicates that many chemistry students do not apply this analytical way of reasoning, but rather rely on shortcut reasoning strategies (heuristics) to make their decisions (Maeyer and Talanquer 2010 ; McClary and Talanquer 2011 ). Heuristic reasoning in judgment and decision-making has been analyzed from a variety of research perspectives (Sloman 1996 ; Todd and Gigerenzer 2000 ; Evans 2006 ; Stavy and Tirosh 2000 ). Despite differences in conceptualization and approach (Evans 2008 ), existing frameworks highlight the capacity of the human mind to make decisions with very little time and information, using implicit and preconscious reasoning mechanisms. These types of reasoning strategies have been characterized as fast and frugal because they employ a minimum amount of time and information to generate a choice or decision and adaptive or ecologically rational because they fit to the structure of the environment in which they are used (Todd and Gigerenzer 2000 ). Heuristic processing can be expected to dominate over more analytical ways of thinking when a person has less knowledge, capacity, or motivation to do well in a task. Although heuristics usually provide satisfactory answers, they do not always lead to the correct solution and seem to be responsible for many systematic biases and errors in human reasoning. Most of the research on heuristic reasoning has been completed in nonacademic contexts. However, there is clear evidence that this mode of thinking is also commonly used by students in science and mathematics classrooms (Stavy and Tirosh 2000 ; Leron and Hazzan 2006 ). In the particular case of chemistry, the application of heuristic reasoning has been reported by different authors. For example, Taber and Bricheno ( 2009 ) have described the different types of heuristics used by secondary school students when completing chemical word equations. Our own research has revealed the prevalent use of heuristic reasoning by college chemistry students when asked to compare diverse physical and chemical properties of chemical compounds based on information about their composition and structure (Maeyer and Talanquer 2010 ; McClary and Talanquer 2011 ). In the following paragraphs, we summarize major results emerging from these latter types of studies that provide insights into how chemistry students make decisions about the properties of chemical substances. Our investigations of student heuristic reasoning in chemistry have relied on tasks that ask college students enrolled in general or organic chemistry classes to rank sets of three or four chemical substances based on the relative value of physical (e.g., boiling point, melting point) or chemical (e.g., acidity, basicity) properties. Research data have been collected in the form of both short questionnaire responses and individual interviews. The results of our studies suggest that a large proportion of chemistry students rely on heuristic strategies, rather than analytical thinking based on atomic molecular models of matter, to make ranking decisions. Heuristic reasoning allowed participants in our studies to reduce cognitive effort by minimizing the number of cues that they needed to evaluate to make a decision, facilitating the

11 How Do Students Reason About Chemical Substances and Reactions? 341 recall of cue values, or simplifying the evaluation of cue effects. Unfortunately, this way of reasoning often led students astray. Among the major shortcut reasoning strategies used by chemistry students to make ranking decisions, we may highlight the following: Recognition : When using this heuristic, a decision is made by selecting an object in a set based on the extent to which the object is recognized and is known to exhibit the property under comparison (Goldstein and Gigerenzer 2002 ). For example, many students may select NaCl as the most water-soluble substance in a set that also included NaBr and NaI simply because they recognize sodium chloride as common soluble substance. Representativeness : In this case, the decision is made assuming commonalities in properties and behaviors between objects with similar appearance (Gilovich et al ). For example, in making decisions about relative acid strength, students may rely on the presence of certain functional groups, such as the carboxylic ( COOH) and the hydroxyl ( OH) groups, to judge the representativeness of a substance as a strong acid or a strong base. One-reason decision-making : When using this type of heuristic, the decision is based on the search for a single differentiating cue that can be used to choose between given options (Todd and Gigerenzer 2000 ). In general, the final decision is based on selecting the option with the higher cue value on the choice criterion. In this sense, once a differentiating cue is identified, the decision is typically made using a more A more B type of intuitive rule (Stavy and Tirosh 2000 ). For example, in choosing which compound, MgO or BaO, has a higher melting point, a student may stop the search for relevant cues once he or she realizes that Ba is heavier than Mg, thus using weight to make the decision. The choice of weight as a differentiating characteristic could be informed by implicit assumptions about the factors that determine how difficult it is to melt a solid. This student is then likely to select BaO as the compound with the highest melting point using a more weight higher melting point type of intuitive rule. Given that heuristics tend to be task-specific reasoning strategies, one can expect that the application of specific heuristics will depend on specific task features. Thus, which type of heuristic is used will be strongly influenced by the particular content and structure of the problem at hand. For example, in our studies, the use of the recognition heuristic was commonly triggered by questions that included common substances, such as NaCl, with known high values in the ranking criterion (e.g., solubility). On the other hand, tasks in which differences in atomic composition were the salient differentiating features between substances tended to favor the application of a one-reason decision-making heuristic based on the identification of differences in implicit atomic properties (e.g., electronegativity, atomic mass, or size). Although this latter approach may be certainly useful in generating the correct answers, our results indicate that many students struggle to identify relevant factors, misapply them, or overgeneralize their range of application. One-reason decisionmaking was also frequently applied when differences between substances were due to implicit structural or electronic factors. However, in this case, students frequently

12 342 V. Talanquer considered surface features of chemical representations, such as number of atoms or bonds of certain type represented in the different molecular drawings, to make their decisions (McClary and Talanquer 2011 ). Conclusions and Implications The central goal of this work has been to illustrate how the analysis of chemistry student thinking based on the identification of implicit assumptions about the nature of chemical substances and reactions, together with the elicitation of shortcut reasoning strategies used to make decisions, can help us better explain and predict the difficulties that our students face when asked to use atomic molecular models of matter to analyze structure property relationships. This analytical framework is also useful for revealing ways of thinking that are resistant to change with training in the discipline. Ultimately, the proposed approach to the analysis of student reasoning in chemistry highlights the need to go beyond the simple identification of specific alternative conceptions in different topics to look for underlying patterns in student thinking that need to be uncovered and critically analyzed in the chemistry classroom. Although for purposes of description we found convenient to present examples of implicit assumptions and reasoning heuristics separately, it is important to recognize that these cognitive constraints often operate in conjunction with each other. For example, in trying to explain boiling point elevation in aqueous solutions, students often apply a centralized causality assumption (i.e., there is a leading causal agent) and rely on heuristics such as covariance or proximity to look for a probable cause (Talanquer 2006 ). Thus, many of them incorrectly consider that the phenomenon is due to the attractive forces exerted by solute particles on nearby water molecules (Talanquer 2010 ). Implicit assumptions about the nature of objects and events, either at the macroscopic or submicroscopic levels, guide students search and selection of relevant cues in making decisions or building explanations. In fact, in many cases, the use of certain heuristic will be triggered by implicit assumptions about the nature of the system of interest. For example, in predicting the color or state of matter of the products for the chemical reactions depicted in Fig. 1a and b, respectively, an inheritance assumption justifies the use of the weighted average of the properties of the elemental components (additive heuristic) to make the prediction. Given a certain task, different students can be expected to rely on implicit assumptions and heuristic reasoning in distinct ways. Prior knowledge and level of understanding will affect students ideas about what factors are relevant and how their effects should be weighed. The less knowledge of a topic and the less familiarity with a certain task, the higher the likelihood that students will rely on both domain-general heuristics and naïve implicit assumptions to make decisions, build explanations, or provide justifications. The larger and more integrated the knowledge base, and the more experience solving certain types of tasks, the more likely

13 How Do Students Reason About Chemical Substances and Reactions? 343 for people to make the correct assumptions and to apply either analytical reasoning or appropriate domain-specific heuristics in solving these problems. It should be noted that expert chemists actually rely on a variety of heuristic rules based on the association of pairs of variables to make plausible predictions; these associative rules link the structural features of substances to their physical and chemical properties. For example, the more polar or polarizable molecules are, the higher the boiling and melting points of the substance; the larger the ion charge in an ionic compound, the higher its expected melting point. What our studies have revealed is that although students also tend to use associative rules as a basic strategy to make predictions about the properties of chemical substances, they often either build wrong associations or use them incorrectly (Maeyer and Talanquer 2010 ; McClary and Talanquer 2011 ). It is also common for students to discount the effect of multiple variables when building explanations or making predictions in different types of tasks. If they identify the potential effect of more than one variable, they tend to treat them independently and additively, rather than integrating several factors in a problem into a coherent whole, as experts often do. The identification of the tacit cognitive constraints that guide but also constrict students thinking at different educational levels may not be an easy task. These are preconscious cognitive elements that need to be inferred from the careful and critical analysis of students decisions, explanations, and predictions while working on different tasks. Fortunately, we can rely on results from research in areas such as child development (Baillargeon et al ; Spelke and Kinzler 2007 ), human reasoning (Gilovich et al 2002 ; Todd and Gigerenzer 2000 ), and language and thought (Pinker 2007 ) to guide our analysis. There are also insightful studies in science education that describe common reasoning patterns or strategies used by students when analyzing physical or biological entities that can be relevant in understanding student thinking in chemistry. These studies include investigations on students ideas about causality (Andersson 1986 ; Grotzer 2003 ), dynamic systems (disessa 1993 ; Resnick 1994 ; Viennot 2001 ), and physical quantities (Reiner et al ). It is common for educators to state that, contrary to what happens with core concepts and ideas in physics and biology, many of the alternative conceptions expressed by chemistry students originate in the chemistry classroom rather than through their daily interactions with the natural world. The argument is based on the claim that students do not have much prior knowledge or experiences related to many of the abstract entities or processes introduced by chemistry teachers (e.g., atoms, molecules, chemical equilibrium). However, this viewpoint fails to recognize that people strongly rely on analogical and metaphorical reasoning to make sense of abstract concepts and ideas, and, thus, implicit assumptions about the nature of concrete objects and events play a major role on how students interpret anything that the teacher says. For example, it is likely that when a teacher describes a chemical substance as made up of tiny particles, many students infer that a substance may be thought of as a simple aggregate of small pieces of the material. To blame instruction for these types of conceptualizations is an oversimplification of a complex problem, and it is out of step with what we know about how people construct understandings.

14 344 V. Talanquer One can certainly recognize that some approaches to teaching chemistry may foster the development and entrenchment of certain alternative conceptions. For example, traditional ways of introducing students to concepts about chemical bonding may be responsible for the pervasive use of the octet rule as an explanatory framework for chemical stability and reactivity (Taber 1998, 2009 ). However, I would claim that the pervasiveness and resiliency of this type of thinking is likely associated with students cognitive bias toward teleological explanations when they fail to recognize leading causal agents or another type of causal mechanism. From this perspective, changing students ideas in this area will require more than changing how the octet rule is introduced and discussed in the chemistry classroom. It will demand helping students acknowledge the implicit assumptions that they make when building their explanations as well as helping them evaluate the validity of their arguments. It will also require exposing students to alternative ways of thinking, critically analyzing their scope and limitations. Our studies consistently reveal that a significant fraction of chemistry students at all educational levels do not think of atoms and molecules at the submicroscopic scale or of chemical substances and reactions at the macroscopic level, as dynamic entities whose properties emerge from the dynamic interactions among their components. On the contrary, they treat them as simple or composite static objects with intrinsic powers or intentions. Consequently, many students fail to build or even consider mechanistic explanations based on the analysis of competing random processes involving many subcomponents. To help students in this area, several authors have proposed different types of instructional interventions. For example, Grotzer ( 2003 ) has shown the positive effects of involving students in inquiry-learning experiences that draw their attention to different ways of modeling causal relations in a system. Slotta and Chi ( 2006 ) have demonstrated that explicitly training students to recognize core attributes of emergent processes can help them gain a deeper understanding of fundamental concepts. Jacobson and Wilensky ( 2006 ) have illustrated the power of interactive computer simulations in helping students recognize different emergent levels in the analysis of complex systems. In general, our results suggest that chemistry education would benefit by a more careful analysis of the underlying assumptions and reasoning heuristics that guide students thinking in different contexts. This knowledge would aid instructors in designing learning opportunities that can better help students monitor their own reasoning while engaged in specific tasks. This type of work should involve students in collectively analyzing the nature of the most common misleading assumptions, distracting factors, and appealing reasoning heuristics for the less experienced thinkers. Our studies also highlight the need to engage chemistry students at all educational levels in model-building and model-analyzing activities that force them to make their thinking explicit, guide them in the construction of alternative models and arguments, and make them discuss and reflect on their explanatory and predictive power.

15 How Do Students Reason About Chemical Substances and Reactions? 345 References American Association for the Advancement of Science (AAAS). (1993). Benchmarks for science literacy. Washington, DC: Oxford University Press. Andersson, B. (1986). The experimental gestalt of causation: A common core to pupils preconceptions in science. European Journal of Science Education, 8 (2), Baillargeon, R., Li, J., Ng, W., & Yuan, S. (2009). An account of infants physical reasoning. In A. Woodward & A. Needham (Eds.), Learning and the infant mind (pp ). New York: Oxford University Press. Bowdle, B., & Gentner, D. (2005). The career of metaphor. Psychological Review, 112 (1), Brown, D. E., & Hammer, D. (2008). Conceptual change in physics. In S. Vosniadou (Ed.), International handbook of research on conceptual change (pp ). New York: Routledge. Chi, M. T. H. (2008). Three kinds of conceptual change: Belief revision, mental model transformation, and ontological shift. In S. Vosniadou (Ed.), International handbook of research on conceptual change (pp ). New York: Routledge. disessa, A. A. (1993). Toward an epistemology of physics. Cognition and Instruction, 10, disessa, A. A., & Sherin, B. L. (1998). What changes in conceptual change? International Journal of Science Education, 20 (10), Duit, R. (2007). Bibliography STCSE : Students and teachers conceptions and science education. Kiel: Leibniz Institute for Science Education, IPN. Available at stcse/ Evans, J. S. B. T. (2006). The heuristic-analytic theory of reasoning: Extension and evaluation. Psychonomic Bulletin & Review, 13 (3), Evans, J. S. B. T. (2008). Dual-processing accounts of reasoning, judgment, and social cognition. Annual Review of Psychology, 59, Gelman, S. A. (2009). Learning from others: Children s construction of concepts. Annual Review of Psychology, 60, Gilbert, J. K., De Jong, O., Justi, R., Treagust, D., & van Driel, J. (Eds.). (2002). Chemical education: Towards research-based practice. Dordrecht: Kluwer. Gilovich, T., Griffin, D., & Kahneman, D. (Eds.). (2002). Heuristics and biases: The psychology of intuitive judgment. Cambridge: Cambridge University Press. Goldstein, D. G., & Gigerenzer, G. (2002). Models of ecological rationality: The recognition heuristic. Psychological Review, 109 (1), Grotzer, T. A. (2003). Learning to understand the forms of causality implicit in scientifically accepted explanations. Studies in Science Education, 39, Hatano, G., & Inagaki, K. (2000). Domain-specific constraints on conceptual development. International Journal of Behavioral Development, 24 (3), Jacobson, M. J., & Wilensky, U. (2006). Complex systems in education: Scientific and educational importance and implications for the learning sciences. The Journal of the Learning Sciences, 15 (1), Keil, F. C. (1990). Constraints on constraints: Surveying the epigenetic landscape. Cognitive Science, 14 (1), Kind, V. (2004). Beyond appearances: Students misconceptions about basic chemical ideas (2nd ed.). London: Royal Society of Chemistry. Leron, U., & Hazzan, O. (2006). The rationality debate: Application of cognitive psychology to mathematics education. Educational Studies in Mathematics, 62, Maeyer, J., & Talanquer, V. (2010). The role of intuitive heuristics in students thinking: Ranking chemical substances. Science Education, 94, McClary, L., & Talanquer, V. (2011). Heuristic reasoning in chemistry: Making decisions about acid strength. International Journal of Science Education, 33 (10), Nakhleh, M. B. (1992). Why some students don t learn chemistry. Journal of Chemical Education, 69 (3),

16 346 V. Talanquer National Research Council (NRC). (1996). National Science Education Standards. Washington, DC: National Academy Press. Pinker, S. (2007). The stuff of thought: Language as a window into human nature. New York: Penguin. Redish, E. F. (2004). A theoretical framework for physics education research: Modeling student thinking. In E. F. Redish & M. Vicentini (Eds.), Proceedings of the international school of physics, Enrico Fermi course CLVI. Amsterdam: Ios Press. Reiner, M., Slotta, J. D., Chi, M. T. H., & Resnick, L. B. (2000). Naive physics reasoning: A commitments to substance-based conceptions. Cognition and Instruction, 18 (1), Resnick, M. (1994). Turtles, termites, and traffic jams: Explorations in massively parallel microworlds. Cambridge, MA: MIT Press. Sloman, S. A. (1996). The empirical case for two systems of reasoning. Psychological Bulletin, 119 (1), Slotta, J. D., & Chi, M. T. H. (2006). Helping students understand the challenging topics in science through ontology training. Cognition and Instruction, 24, Spelke, E. S., & Kinzler, K. D. (2007). Core knowledge. Developmental Science, 10, Stavy, R., & Tirosh, D. (2000). How students (mis-)understand science and mathematics: Intuitive rules. New York: Teachers College Press. Taber, K. S. (1998). An alternative conceptual framework from chemistry education. International Journal of Science Education, 20 (5), Taber, K. (2002). Chemical misconceptions Prevention, diagnosis and cure. Vol. I: Theoretical background. London: Royal Society of Chemistry. Taber, K. (2009). College students conceptions of chemical stability: The widespread adoption of a heuristic rule out of context and beyond its range of application. International Journal of Science Education, 31 (10), Taber, K. S., & Bricheno, P. A. (2009). Coordinating procedural and conceptual knowledge to make sense of word equations: Understanding the complexity of a simple completion task at the learner s resolution. International Journal of Science Education, 31, Taber, K. S., & García-Franco, A. (2010). Learning processes in chemistry: Drawing upon cognitive resources to learn about the particulate structure of matter. The Journal of the Learning Sciences, 19 (1), Talanquer, V. (2006). Common sense chemistry: A model for understanding students alternative conceptions. Journal of Chemical Education, 83 (5), Talanquer, V. (2007). Explanations and teleology in chemistry education. International Journal of Science Education, 29 (7), Talanquer, V. (2008). Students predictions about the sensory properties of chemical compounds: Additive versus emergent frameworks. Science Education, 92 (1), Talanquer, V. (2009). On cognitive constraints and learning progressions: The case of structure of matter. International Journal of Science Education, 31 (15), Talanquer, V. (2010). Exploring dominant types of explanations built by general chemistry students. International Journal of Science Education, 32 (18), Todd, P. M., & Gigerenzer, G. (2000). Precis of simple heuristics that make us smart. The Behavioral and Brain Sciences, 23, Viennot, L. (2001). Reasoning in physics: The part of common sense. Dordrecht: Kluwer. Vosniadou, S. (1994). Capturing and modeling the process of conceptual change. Learning and Instruction, 4, Vosniadou, S., & Ortony, A. (Eds.). (1989). Similarity and analogical reasoning. New York: Cambridge University Press. Vosniadou, S., Vamvakoussi, X., & Skopeliti, I. (2008). The framework theory approach to the problem of conceptual change. In S. Vosniadou (Ed.), International handbook of research on conceptual change (pp. 3 34). New York: Routledge.