Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 129
Taking Science to School: Learning and Teaching Science in Grades K-8 5 Generating and Evaluating Scientific Evidence and Explanations Major Findings in the Chapter: Children are far more competent in their scientific reasoning than first suspected and adults are less so. Furthermore, there is great variation in the sophistication of reasoning strategies across individuals of the same age. In general, children are less sophisticated than adults in their scientific reasoning. However, experience plays a critical role in facilitating the development of many aspects of reasoning, often trumping age. Scientific reasoning is intimately intertwined with conceptual knowledge of the natural phenomena under investigation. This conceptual knowledge sometimes acts as an obstacle to reasoning, but often facilitates it. Many aspects of scientific reasoning require experience and instruction to develop. For example, distinguishing between theory and evidence and many aspects of modeling do not emerge without explicit instruction and opportunities for practice. In this chapter, we discuss the various lines of research related to Strand 2—generate and evaluate evidence and explanations.1 The ways in which 1 Portions of this chapter are based on the commissioned paper by Corinne Zimmerman titled, “The Development of Scientific Reasoning Skills: What Psychologists Contribute to an Understanding of Elementary Science Learning.”
OCR for page 130
Taking Science to School: Learning and Teaching Science in Grades K-8 scientists generate and evaluate scientific evidence and explanations have long been the focus of study in philosophy, history, anthropology, and sociology. More recently, psychologists and learning scientists have begun to study the cognitive and social processes involved in building scientific knowledge. For our discussion, we draw primarily from the past 20 years of research in developmental and cognitive psychology that investigates how children’s scientific thinking develops across the K-8 years. We begin by developing a broad sketch of how key aspects of scientific thinking develop across the K-8 years, contrasting children’s abilities with those of adults. This contrast allows us to illustrate both how children’s knowledge and skill can develop over time and situations in which adults’ and children’s scientific thinking are similar. Where age differences exist, we comment on what underlying mechanisms might be responsible for them. In this research literature, two broad themes emerge, which we take up in detail in subsequent sections of the chapter. The first is the role of prior knowledge in scientific thinking at all ages. The second is the importance of experience and instruction. Scientific investigation, broadly defined, includes numerous procedural and conceptual activities, such as asking questions, hypothesizing, designing experiments, making predictions, using apparatus, observing, measuring, being concerned with accuracy, precision, and error, recording and interpreting data, consulting data records, evaluating evidence, verification, reacting to contradictions or anomalous data, presenting and assessing arguments, constructing explanations (to oneself and others), constructing various representations of the data (graphs, maps, three-dimensional models), coordinating theory and evidence, performing statistical calculations, making inferences, and formulating and revising theories or models (e.g., Carey et al., 1989; Chi et al., 1994; Chinn and Malhotra, 2001; Keys, 1994; McNay and Melville, 1993; Schauble et al., 1995; Slowiaczek et al., 1992; Zachos et al., 2000). As noted in Chapter 2, over the past 20 to 30 years, the image of “doing science” emerging from across multiple lines of research has shifted from depictions of lone scientists conducting experiments in isolated laboratories to the image of science as both an individual and a deeply social enterprise that involves problem solving and the building and testing of models and theories. Across this same period, the psychological study of science has evolved from a focus on scientific reasoning as a highly developed form of logical thinking that cuts across scientific domains to the study of scientific thinking as the interplay of general reasoning strategies, knowledge of the natural phenomena being studied, and a sense of how scientific evidence and explanations are generated. Much early research on scientific thinking and inquiry tended to focus primarily either on conceptual development or on the development of reasoning strategies and processes, often using very
OCR for page 131
Taking Science to School: Learning and Teaching Science in Grades K-8 simplified reasoning tasks. In contrast, many recent studies have attempted to describe a larger number of the complex processes that are deployed in the context of scientific inquiry and to describe their coordination. These studies often engage children in firsthand investigations in which they actively explore multivariable systems. In such tasks, participants initiate all phases of scientific discovery with varying amounts of guidance provided by the researcher. These studies have revealed that, in the context of inquiry, reasoning processes and conceptual knowledge are interdependent and in fact facilitate each other (Schauble, 1996; Lehrer et al. 2001). It is important to note that, across the studies reviewed in this chapter, researchers have made different assumptions about what scientific reasoning entails and which aspects of scientific practice are most important to study. For example, some emphasize the design of well-controlled experiments, while others emphasize building and critiquing models of natural phenomena. In addition, some researchers study scientific reasoning in stripped down, laboratory-based tasks, while others examine how children approach complex inquiry tasks in the context of the classroom. As a result, the research base is difficult to integrate and does not offer a complete picture of students’ skills and knowledge related to generating and evaluating evidence and explanations. Nor does the underlying view of scientific practice guiding much of the research fully reflect the image of science and scientific understanding we developed in Chapter 2. TRENDS ACROSS THE K-8 YEARS Generating Evidence The evidence-gathering phase of inquiry includes designing the investigation as well as carrying out the steps required to collect the data. Generating evidence entails asking questions, deciding what to measure, developing measures, collecting data from the measures, structuring the data, systematically documenting outcomes of the investigations, interpreting and evaluating the data, and using the empirical results to develop and refine arguments, models, and theories. Asking Questions and Formulating Hypotheses Asking questions and formulating hypotheses is often seen as the first step in the scientific method; however, it can better be viewed as one of several phases in an iterative cycle of investigation. In an exploratory study, for example, work might start with structured observation of the natural world, which would lead to formulation of specific questions and hypotheses. Further data might then be collected, which lead to new questions,
OCR for page 132
Taking Science to School: Learning and Teaching Science in Grades K-8 revised hypotheses, and yet another round of data collection. The phase of asking questions also includes formulating the goals of the activity and generating hypotheses and predictions (Kuhn, 2002). Children differ from adults in their strategies for formulating hypotheses and in the appropriateness of the hypotheses they generate. Children often propose different hypotheses from adults (Klahr, 2000), and younger children (age 10) often conduct experiments without explicit hypotheses, unlike 12- to 14-year-olds (Penner and Klahr, 1996a). In self-directed experimental tasks, children tend to focus on plausible hypotheses and often get stuck focusing on a single hypothesis (e.g., Klahr, Fay, and Dunbar, 1993). Adults are more likely to consider multiple hypotheses (e.g., Dunbar and Klahr, 1989; Klahr, Fay, and Dunbar, 1993). For both children and adults, the ability to consider many alternative hypotheses is a factor contributing to success. At all ages, prior knowledge of the domain under investigation plays an important role in the formulation of questions and hypotheses (Echevarria, 2003; Klahr, Fay, and Dunbar, 1993; Penner and Klahr, 1996b; Schauble, 1990, 1996; Zimmerman, Raghavan, and Sartoris, 2003). For example, both children and adults are more likely to focus initially on variables they believe to be causal (Kanari and Millar, 2004; Schauble, 1990, 1996). Hypotheses that predict expected results are proposed more frequently than hypotheses that predict unexpected results (Echevarria, 2003). The role of prior knowledge in hypothesis formulation is discussed in greater detail later in the chapter. Designing Experiments The design of experiments has received extensive attention in the research literature, with an emphasis on developmental changes in children’s ability to build experiments that allow them to identify causal variables. Experimentation can serve to generate observations in order to induce a hypothesis to account for the pattern of data produced (discovery context) or to test the tenability of an existing hypothesis under consideration (confirmation/ verification context) (Klahr and Dunbar, 1988). At a minimum, one must recognize that the process of experimentation involves generating observations that will serve as evidence that will be related to hypotheses. Ideally, experimentation should produce evidence or observations that are interpretable in order to make the process of evidence evaluation uncomplicated. One aspect of experimentation skill is to isolate variables in such a way as to rule out competing hypotheses. The control of variables is a basic strategy that allows valid inferences and narrows the number of possible experiments to consider (Klahr, 2000). Confounded experiments, those in which variables have not been isolated correctly, yield indetermi-
OCR for page 133
Taking Science to School: Learning and Teaching Science in Grades K-8 nate evidence, thereby making valid inferences and subsequent knowledge gain difficult, if not impossible. Early approaches to examining experimentation skills involved minimizing the role of prior knowledge in order to focus on the strategies that participants used. That is, the goal was to examine the domain-general strategies that apply regardless of the content to which they are applied. For example, building on the research tradition of Piaget (e.g., Inhelder and Piaget, 1958), Siegler and Liebert (1975) examined the acquisition of experimental design skills by fifth and eighth graders. The problem involved determining how to make an electric train run. The train was connected to a set of four switches, and the children needed to determine the particular on/off configuration required. The train was in reality controlled by a secret switch, so that the discovery of the correct solution was postponed until all 16 combinations were generated. In this task, there was no principled reason why any one of the combinations would be more or less likely, and success was achieved by systematically testing all combinations of a set of four switches. Thus the task involved no domain-specific knowledge that would constrain the hypotheses about which configuration was most likely. A similarly knowledge-lean task was used by Kuhn and Phelps (1982), similar to a task originally used by Inhelder and Piaget (1958), involving identifying reaction properties of a set of colorless fluids. Success on the task was dependent on the ability to isolate and control variables in the set of all possible fluid combinations in order to determine which was causally related to the outcome. The study extended over several weeks with variations in the fluids used and the difficulty of the problem. In both studies, the importance of practice and instructional support was apparent. Siegler and Liebert’s study included two experimental groups of children who received different kinds of instructional support. Both groups were taught about factors, levels, and tree diagrams. One group received additional, more elaborate support that included practice and help representing all possible solutions with a tree diagram. For fifth graders, the more elaborate instructional support improved their performance compared with a control group that did not receive any support. For eighth graders, both kinds of instructional support led to improved performance. In the Kuhn and Phelps task, some students improved over the course of the study, although an abrupt change from invalid to valid strategies was not common. Instead, the more typical pattern was one in which valid and invalid strategies coexisted both within and across sessions, with a pattern of gradual attainment of stable valid strategies by some students (the stabilization point varied but was typically around weeks 5-7). Since this early work, researchers have tended to investigate children’s and adults’ performance on experimental design tasks that are more knowledge rich and less constrained. Results from these studies indicate that, in
OCR for page 134
Taking Science to School: Learning and Teaching Science in Grades K-8 general, adults are more proficient than children at designing informative experiments. In a study comparing adults with third and sixth graders, adults were more likely to focus on experiments that would be informative (Klahr, Fay, and Dunbar, 1993). Similarly, Schauble (1996) found that during the initial 3 weeks of exploring a domain, children and adults considered about the same number of possible experiments. However, when they began experimentation of another domain in the second 3 weeks of the study, adults considered a greater range of possible experiments. Over the full 6 weeks, children and adults conducted approximately the same number of experiments. Thus, children were more likely to conduct unintended duplicate or triplicate experiments, making their experimentation efforts less informative relative to the adults, who were selecting a broader range of experiments. Similarly, children are more likely to devote multiple experimental trials to variables that were already well understood, whereas adults move on to exploring variables they did not understand as well (Klahr, Fay, and Dunbar, 1993; Schauble, 1996). Evidence also indicates, however, that dimensions of the task often have a greater influence on performance than age (Linn, 1978, 1980; Linn, Chen, and Their, 1977; Linn and Levine, 1978). With respect to attending to one feature at a time, children are less likely to control one variable at a time than adults. For example, Schauble (1996) found that across two task domains, children used controlled comparisons about a third of the time. In contrast, adults improved from 50 percent usage on the first task to 63 percent on the second task. Children usually begin by designing confounded experiments (often as a means to produce a desired outcome), but with repeated practice begin to use a strategy of changing one variable at time (e.g., Kuhn, Schauble, and Garcia-Mila, 1992; Kuhn et al. 1995; Schauble, 1990). Reminiscent of the results of the earlier study by Kuhn and Phelps, both children and adults display intraindividual variability in strategy usage. That is, multiple strategy usage is not unique to childhood or periods of developmental transition (Kuhn et al., 1995). A robust finding is the coexistence of valid and invalid strategies (e.g., Kuhn, Schuable, and Garcia-Mila, 1992; Garcia-Mila and Andersen, 2005; Gleason and Schauble, 2000; Schauble, 1990; Siegler and Crowley, 1991; Siegler and Shipley, 1995). That is, participants may progress to the use of a valid strategy, but then return to an inefficient or invalid strategy. Similar use of multiple strategies has been found in research on the development of other academic skills, such as mathematics (e.g., Bisanz and LeFevre, 1990; Siegler and Crowley, 1991), reading (e.g., Perfetti, 1992), and spelling (e.g., Varnhagen, 1995). With respect to experimentation strategies, an individual may begin with an invalid strategy, but once the usefulness of changing one variable at a time is discovered, it is not immediately used exclusively. The newly discovered, effective strategy is only slowly incorporated into an individual’s set of strategies.
OCR for page 135
Taking Science to School: Learning and Teaching Science in Grades K-8 An individual’s perception of the goals of an investigation also has an important effect on the hypotheses they generate and their approach to experimentation. Individuals tend to differ in whether they see the overarching goal of an inquiry task as seeking to identify which factors make a difference (scientific) or seeking to produce a desired effect (engineering). It is a question for further research if these different approaches characterize an individual, or if they are invoked by task demand or implicit assumptions. In a direct exploration of the effect of adopting scientific versus engineering goals, Schauble, Klopfer, and Raghavan (1991) provided fifth and sixth graders with an “engineering context” and a “science context.” When the children were working as scientists, their goal was to determine which factors made a difference and which ones did not. When the children were working as engineers, their goal was optimization, that is, to produce a desired effect (i.e., the fastest boat in the canal task). When working in the science context, the children worked more systematically, by establishing the effect of each variable, alone and in combination. There was an effort to make inclusion inferences (i.e., an inference that a factor is causal) and exclusion inferences (i.e., an inference that a factor is not causal). In the engineering context, children selected highly contrastive combinations and focused on factors believed to be causal while overlooking factors believed or demonstrated to be noncausal. Typically, children took a “try-and-see” approach to experimentation while acting as engineers, but they took a theory-driven approach to experimentation when acting as scientists. Schauble et al. (1991) found that children who received the engineering instructions first, followed by the scientist instructions, made the greatest improvements. Similarly, Sneider et al. (1984) found that students’ ability to plan and critique experiments improved when they first engaged in an engineering task of designing rockets. Another pair of contrasting approaches to scientific investigation is the theorist versus the experimentalist (Klahr and Dunbar, 1998; Schauble, 1990). Similar variation in strategies for problem solving have been observed for chess, puzzles, physics problems, science reasoning, and even elementary arithmetic (Chase and Simon, 1973; Klahr and Robinson, 1981; Klayman and Ha, 1989; Kuhn et al., 1995; Larkin et al., 1980; Lovett and Anderson, 1995, 1996; Simon, 1975; Siegler, 1987; Siegler and Jenkins, 1989). Individuals who take a theory-driven approach tend to generate hypotheses and then test the predictions of the hypotheses. Experimenters tend to make data-driven discoveries, by generating data and finding the hypothesis that best summarizes or explains that data. For example, Penner and Klahr (1996a) asked 10-to 14-year-olds to conduct experiments to determine how the shape, size, material, and weight of an object influence sinking times. Students’ approaches to the task could be classified as either “prediction oriented” (i.e., a theorist: “I believe that weight makes a difference) or “hypothesis oriented” (i.e., an
OCR for page 136
Taking Science to School: Learning and Teaching Science in Grades K-8 experimenter: “I wonder if …”). The 10-year-olds were more likely to take a prediction (or demonstration) approach, whereas the 14-year-olds were more likely to explicitly test a hypothesis about an attribute without a strong belief or need to demonstrate that belief. Although these patterns may characterize approaches to any given task, it has yet to be determined if such styles are idiosyncratic to the individual and likely to remain stable across varying tasks, or if different styles might emerge for the same person depending on task demands or the domain under investigation. Observing and Recording Record keeping is an important component of scientific investigation in general, and of self-directed experimental tasks especially, because access to and consulting of cumulative records are often important in interpreting evidence. Early studies of experimentation demonstrated that children are often not aware of their own memory limitations, and this plays a role in whether they document their work during an investigation (e.g., Siegler and Liebert, 1975). Recent studies corroborate the importance of an awareness of one’s own memory limitations while engaged in scientific inquiry tasks, regardless of age. Spontaneous note-taking or other documentation of experimental designs and results may be a factor contributing to the observed developmental differences in performance on both experimental design tasks and in evaluation of evidence. Carey et al. (1989) reported that, prior to instruction, seventh graders did not spontaneously keep records when trying to determine and keep track of which substance was responsible for producing a bubbling reaction in a mixture of yeast, flour, sugar, salt, and warm water. Nevertheless, even though preschoolers are likely to produce inadequate and uninformative notations, they can distinguish between the two when asked to choose between them (Triona and Klahr, in press). Dunbar and Klahr (1988) also noted that children (grades 3-6) were unlikely to check if a current hypothesis was or was not consistent with previous experimental results. In a study by Trafton and Trickett (2001), undergraduates solving scientific reasoning problems in a computer environment were more likely to achieve correct performance when using the notebook function (78 percent) than were nonusers (49 percent), showing that this issue is not unique to childhood. In a study of fourth graders’ and adults’ spontaneous use of notebooks during a 10-week investigation of multivariable systems, all but one of the adults took notes, whereas only half of the children took notes. Moreover, despite variability in the amount of notebook usage in both groups, on average adults made three times more notebook entries than children did. Adults’ note-taking remained stable across the 10 weeks, but children’s frequency of use decreased over time, dropping to about half of their initial
OCR for page 137
Taking Science to School: Learning and Teaching Science in Grades K-8 usage. Children rarely reviewed their notes, which typically consisted of conclusions, but not the variables used or the outcomes of the experimental tests (i.e., the evidence for the conclusion was not recorded) (Garcia-Mila and Andersen, 2005). Children may differentially record the results of experiments, depending on familiarity or strength of prior theories. For example, 10- to 14-year-olds recorded more data points when experimenting with factors affecting force produced by the weight and surface area of boxes than when they were experimenting with pendulums (Kanari and Millar, 2004). Overall, it is a fairly robust finding that children are less likely than adults to record experimental designs and outcomes or to review what notes they do keep, despite task demands that clearly necessitate a reliance on external memory aids. Given the increasing attention to the importance of metacognition for proficient performance on such tasks (e.g., Kuhn and Pearsall, 1998, 2000), it is important to determine at what point children and early adolescents recognize their own memory limitations as they navigate through a complex task. Some studies show that children’s understanding of how their own memories work continues to develop across the elementary and middle school grades (Siegler and Alibali, 2005). The implication is that there is no particular age or grade level when memory and limited understanding of one’s own memory are no longer a consideration. As such, knowledge of how one’s own memory works may represent an important moderating variable in understanding the development of scientific reasoning (Kuhn, 2001). For example, if a student is aware that it will be difficult for her to remember the results of multiple trials, she may be more likely to carefully record each outcome. However, it may also be the case that children, like adult scientists, need to be inducted into the practice of record keeping and the use of records. They are likely to need support to understand the important role of records in generating scientific evidence and supporting scientific arguments. Evaluating Evidence The important role of evidence evaluation in the process of scientific activity has long been recognized. Kuhn (1989), for example, has argued that the defining feature of scientific thinking is the set of skills involved in differentiating and coordinating theory and evidence. Various strands of research provide insight on how children learn to engage in this phase of scientific inquiry. There is an extensive literature on the evaluation of evidence, beginning with early research on identifying patterns of covariation and cause that used highly structured experimental tasks. More recently researchers have studied how children evaluate evidence in the context of self-directed experimental tasks. In real-world contexts (in contrast to highly controlled laboratory tasks) the process of evidence evaluation is very messy
OCR for page 138
Taking Science to School: Learning and Teaching Science in Grades K-8 and requires an understanding of error and variation. As was the case for hypothesis generation and the design of experiments, the role of prior knowledge and beliefs has emerged as an important influence on how individuals evaluate evidence. Covariation Evidence A number of early studies on the development of evidence evaluation skills used knowledge-lean tasks that asked participants to evaluate existing data. These data were typically in the form of covariation evidence—that is, the frequency with which two events do or do not occur together. Evaluation of covariation evidence is potentially important in regard to scientific thinking because covariation is one potential cue that two events are causally related. Deanna Kuhn and her colleagues carried out pioneering work on children’s and adults’ evaluation of covariation evidence, with a focus on how participants coordinate their prior beliefs about the phenomenon with the data presented to them (see Box 5-1). Results across a series of studies revealed continuous improvement of the skills involved in differentiating and coordinating theory and evidence, as well as bracketing prior belief while evaluating evidence, from middle childhood (grades 3 and 6) to adolescence (grade 9) to adulthood (Kuhn, Amsel, and O’Loughlin, 1988). These skills, however, did not appear to develop to an optimal level even among adults. Even adults had a tendency to meld theory and evidence into a single mental representation of “the way things are.” Participants had a variety of strategies for keeping theory and evidence in alignment with one another when they were in fact discrepant. One tendency was to ignore, distort, or selectively attend to evidence that was inconsistent with a favored theory. For example, the protocol from one ninth grader demonstrated that upon repeated instances of covariation between type of breakfast roll and catching colds, he would not acknowledge this relationship: “They just taste different … the breakfast roll to me don’t cause so much colds because they have pretty much the same thing inside” (Kuhn, Amsel, and O’Loughlin, 1998, p. 73). Another tendency was to adjust a theory to fit the evidence, a process that was most often outside an individual’s conscious awareness and control. For example, when asked to recall their original beliefs, participants would often report a theory consistent with the evidence that was presented, and not the theory as originally stated. Take the case of one ninth grader who did not believe that type of condiment (mustard versus ketchup) was causally related to catching colds. With each presentation of an instance of covariation evidence, he acknowledged the evidence and elaborated a theory based on the amount of ingredients or vitamins and the temperature of the
OCR for page 139
Taking Science to School: Learning and Teaching Science in Grades K-8 BOX 5-1 Evaluation of Covariation Evidence Kuhn and her colleagues used simple, everyday contexts, rather than phenomena from specific scientific disciplines. In an initial theory interview, participants’ beliefs about the causal status of various variables were ascertained. For example, sixth and ninth graders were questioned about their beliefs concerning the types of foods that make a difference in whether a person caught a cold (35 foods in total). Four variables were selected on the basis of ratings from the initial theory interview: two factors that the participant believed make a difference in catching colds (e.g., type of fruit, type of cereal) and two factors the participant believed do not make a difference (e.g., type of potato, type of condiment). This procedure allowed the evidence to be manipulated so that covariation evidence could be presented that confirmed one existing causal theory and one noncausal theory. Likewise, noncovariation evidence was presented that disconfirmed one previously held causal theory and one noncausal theory. The specific manipulations were therefore tailored for each person in the study. Participants then evaluated patterns of covariation data and answered a series of questions about what the evidence showed for each of the four variables. Responses were coded as evidence based when they referred to the patterns of covariation or instances of data presented (e.g., if shown a pattern in which type of cake covaried with getting colds, a participant who noted that the sick children ate chocolate cake and the healthy ones ate carrot cake would be coded as having made an evidence-based response). Responses were coded as theory based when they referred to the participant’s prior beliefs or theories (e.g., a response that chocolate cake has “sugar and a lot of bad stuff in it” or that “less sugar means your blood pressure doesn’t go up”). food the condiment was served with to make sense of the data (Kuhn, Amsel, and O’Loughlin, 1988, p. 83). Kuhn argued that this tendency suggests that the student’s theory does not exist as an object of cognition. That is, a theory and the evidence for that theory are undifferentiated—they do not exist as separate cognitive entities. If they do not exist as separate entities, it is not possible to flexibly and consciously reflect on the relation of one to the other. A number of researchers have criticized Kuhn’s findings on both methodological and theoretical grounds. Sodian, Zaitchik, and Carey (1991), for example, questioned the finding that third and sixth grade children cannot distinguish between their beliefs and the evidence, pointing to the complex-
OCR for page 157
Taking Science to School: Learning and Teaching Science in Grades K-8 purposes of science, students should not only understand the procedures for generating and reading displays, but they should also be able to critique them and to grasp the communicative advantages and disadvantages of alternative forms for a given purpose (diSessa, 2004; Greeno and Hall, 1997). The structure of the data will affect the interpretation. Data interpretation often entails seeking and confirming relationships in the data, which may be at varying levels of complexity. For example, simple linear relationships are easier to spot than inverse relationships or interactions (Schauble, 1990), and students often fail to entertain the possibility that more than one relationship may be operating. The desire to interpret data may further inspire the creation of statistics, such as measures of center and spread. These measures are a further step of abstraction beyond the objects and events originally observed. Even primary grade students can learn to consider the overall shape of data displays to make interpretations based on the “clumps” and “holes” in the data. Students often employ multiple criteria when trying to identify a “typical value” for a set of data. Many young students tend to favor the mode and justify their choice on the basis of repetition—if more than one student obtained this value, perhaps it is to be trusted. However, students tend to be less satisfied with modes if they do not appear near the center of the data, and they also shy away from measures of center that do not have several other values clustered near them (“part of a clump”). Understanding the mean requires an understanding of ratio, and if students are merely taught to “average” data in a procedural way without having a well-developed sense of ratio, their performance notoriously tends to degrade into “average stew”—eccentric procedures for adding and dividing things that make no sense (Strauss and Bichler, 1988). With good instruction, middle and upper elementary students can simultaneously consider the center and the spread of the data. Students can also generate various forms of mathematical descriptions of error, especially in contexts of measurement, where they can readily grasp the relationships between their own participation in the act of measuring and the resulting variation in measures (Petrosino, Lehrer, and Schauble, 2003). Scale Models, Diagrams, and Maps Although data representations are central to science, they are not, of course, the only representations students need to use and understand. Perhaps the most easily interpretable form of representation widely used in science is scale models. Physical models of this kind are used in science education to make it possible for students to visualize objects or processes that are at a scale that makes their direct perception impossible or, alternatively, that permits them to directly manipulate something that otherwise
OCR for page 158
Taking Science to School: Learning and Teaching Science in Grades K-8 they could not handle. The ease or difficulty with which students understand these models depends on the complexity of the relationships being communicated. Even preschoolers can understand scale models used to depict location in a room (DeLoache, 2004). Primary grade students can pretty readily overcome the influence of the appearance of the model to focus on and investigate the way it functions (Penner et al., 1997), but middle school students (and some adults) struggle to work out the positional relationships of the earth, the sun, and the moon, which involves not only reconciling different perspectives with respect to perspective and frame (what one sees standing on the earth, what one would see from a hypothetical point in space), but also visualizing how these perspectives would change over days and months (see, for example, the detailed curricular suggestions at the web site http://www.wcer.wisc.edu/ncisla/muse/). Frequently, students are expected to read or produce diagrams, often integrating the information from the diagram with information from accompanying text (Hegarty and Just, 1993; Mayer, 1993). The comprehensibility of diagrams seems to be governed less by domain-general principles than by the specifics of the diagram and its viewer. Comprehensibility seems to vary with the complexity of what is portrayed, the particular diagrammatic details and features, and the prior knowledge of the user. Diagrams can be difficult to understand for a host of reasons. Sometimes the desired information is missing in the first place; sometimes, features of the diagram unwittingly play into an incorrect preconception. For example, it has been suggested that the common student misconception that the earth is closer to the sun in the summer than in the winter may be due in part to the fact that two-dimensional representations of the three-dimensional orbit make it appear as if the foreshortened orbit is indeed closer to the sun at some points than at others. Mayer (1993) proposes three common reasons why diagrams mis-communicate: some do not include explanatory information (they are illustrative or decorative rather than explanatory), some lack a causal chain, and some fail to map the explanation to a familiar or recognizable context. It is not clear that school students misperceive diagrams in ways that are fundamentally different from the perceptions of adults. There may be some diagrammatic conventions that are less familiar to children, and children may well have less knowledge about the phenomena being portrayed, but there is no reason to expect that adult novices would respond in fundamentally different ways. Although they have been studied for a much briefer period of time, the same is probably true of complex computer displays. Finally, there is a growing developmental literature on students’ understanding of maps. Maps can be particularly confusing because they preserve some analog qualities of the space being represented (e.g., relative position and distance) but also omit or alter features of the landscape in ways that
OCR for page 159
Taking Science to School: Learning and Teaching Science in Grades K-8 require understanding of mapping conventions. Young children often initially confuse maps of the landscape with pictures of objects in the landscape. It is much easier for youngsters to represent objects than to represent large-scale space (which is the absence of or frame for objects). Students also may struggle with orientation, perspective (the traditional bird’s eye view), and mathematical descriptions of space, such as polar coordinate representations (Lehrer and Pritchard, 2002; Liben and Downs, 1993). CONCLUSIONS There is a common thread throughout the observations of this chapter that has deep implications for what one expects from children in grades K-8 and how their science learning should be structured. In almost all cases, the studies converge to the position that the skills under study develop with age, but also that this development is significantly enhanced by prior knowledge, experience, and instruction. One of the continuing themes evident from studies on the development of scientific thinking is that children are far more competent than first suspected, and likewise that adults are less so. Young children experiment, but their experimentation is generally not systematic, and their observations as well as their inferences may be flawed. The progression of ability is seen with age, but it is not uniform, either across individuals or for a given individual. There is variation across individuals at the same age, as well as variation within single individuals in the strategies they use. Any given individual uses a collection of strategies, some more valid than others. Discovering a valid strategy does not mean that an individual, whether a child or an adult, will use the strategy consistently across all contexts. As Schauble (1996, p. 118) noted: The complex and multifaceted nature of the skills involved in solving these problems, and the variability in performance, even among the adults, suggest that the developmental trajectory of the strategies and processes associated with scientific reasoning is likely to be a very long one, perhaps even lifelong. Previous research has established the existence of both early precursors and competencies … and errors and biases that persist regardless of maturation, training, and expertise. One aspect of cognition that appears to be particularly important for supporting scientific thinking is awareness of one’s own thinking. Children may be less aware of their own memory limitations and therefore may be unsystematic in recording plans, designs, and outcomes, and they may fail to consult such records. Self-awareness of the cognitive strategies available is also important in order to determine when and why to employ various strategies. Finally, awareness of the status of one’s own knowledge, such as
OCR for page 160
Taking Science to School: Learning and Teaching Science in Grades K-8 recognizing the distinctions between theory and evidence, is important for reasoning in the context of scientific investigations. This last aspect of cognition is discussed in detail in the next chapter. Prior knowledge, particularly beliefs about causality and plausibility, shape the approach to investigations in multiple ways. These beliefs influence which hypotheses are tested, how experiments are designed, and how evidence is evaluated. Characteristics of prior knowledge, such as its type, strength, and relevance, are potential determinants of how new evidence is evaluated and whether anomalies are noticed. Knowledge change occurs as a result of the encounter. Finally, we conclude that experience and instruction are crucial mediators of the development of a broad range of scientific skills and of the degree of sophistication that children exhibit in applying these skills in new contexts. This means that time spent doing science in appropriately structured instructional frames is a crucial part of science education. It affects not only the level of skills that children develop, but also their ability to think about the quality of evidence and to interpret evidence presented to them. Students need instructional support and practice in order to become better at coordinating their prior theories and the evidence generated in investigations. Instructional support is also critical for developing skills for experimental design, record keeping during investigations, dealing with anomalous data, and modeling. REFERENCES Ahn, W., Kalish, C.W., Medin, D.L., and Gelman, S.A. (1995). The role of covariation versus mechanism information in causal attribution. Cognition, 54, 299-352. Amsel, E., and Brock, S. (1996). The development of evidence evaluation skills. Cognitive Development, 11, 523-550. Bisanz, J., and LeFevre, J. (1990). Strategic and nonstrategic processing in the development of mathematical cognition. In. D. Bjorklund (Ed.), Children’s strategies: Contemporary views of cognitive development (pp. 213-243). Hillsdale, NJ: Lawrence Erlbaum Associates. Carey, S., Evans, R., Honda, M., Jay, E., and Unger, C. (1989). An experiment is when you try it and see if it works: A study of grade 7 students’ understanding of the construction of scientific knowledge. International Journal of Science Education, 11, 514-529. Chase, W.G., and Simon, H.A. (1973). The mind’s eye in chess. In W.G. Chase (Ed.), Visual information processing. New York: Academic. Chen, Z., and Klahr, D. (1999). All other things being equal: Children’s acquisition of the control of variables strategy. Child Development, 70, 1098-1120. Chi, M.T.H. (1996). Constructing self-explanations and scaffolded explanations in tutoring. Applied Cognitive Psychology, 10, 33-49.
OCR for page 161
Taking Science to School: Learning and Teaching Science in Grades K-8 Chi, M.T.H., de Leeuw, N., Chiu, M., and Lavancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18, 439-477. Chinn, C.A., and Brewer, W.F. (1998). An empirical test of a taxonomy of responses to anomalous data in science. Journal of Research in Science Teaching, 35, 623-654. Chinn, C.A., and Brewer, W. (2001). Model of data: A theory of how people evaluate data. Cognition and Instruction, 19(3), 323-343. Chinn, C.A., and Malhotra, B.A. (2001). Epistemologically authentic scientific reasoning. In K. Crowley, C.D. Schunn, and T. Okada (Eds.), Designing for science: Implications from everyday, classroom, and professional settings (pp. 351-392). Mahwah, NJ: Lawrence Erlbaum Associates. Chinn, C.A., and Malhotra, B.A. (2002). Children’s responses to anomalous scientific data: How is conceptual change impeded? Journal of Educational Psychology, 94, 327-343. DeLoache, J.S. (2004). Becoming symbol-minded. Trends in Cognitive Sciences, 8, 66-70. DiPerna, E. (2002). Data models of ourselves: Body self-portrait project. In R. Lehrer and L. Schauble (Eds.), Investigating real data in the classroom: Expanding children’s understanding of math and science. Ways of knowing in science and mathematics series. Willington, VT: Teachers College Press. diSessa, A.A. (2004). Metarepresentation: Native competence and targets for instruction. Cognition and Instruction, 22(3), 293-331. Dunbar, K., and Klahr, D. (1989). Developmental differences in scientific discovery strategies. In D. Klahr and K. Kotovsky (Eds.), Complex information processing: The impact of Herbert A. Simon (pp. 109-143). Hillsdale, NJ: Lawrence Erlbaum Associates. Echevarria, M. (2003). Anomalies as a catalyst for middle school students’ knowledge construction and scientific reasoning during science inquiry. Journal of Educational Psychology, 95, 357-374. Garcia-Mila, M., and Andersen, C. (2005). Developmental change in notetaking during scientific inquiry. Manuscript submitted for publication. Gleason, M.E., and Schauble, L. (2000). Parents’ assistance of their children’s scientific reasoning. Cognition and Instruction, 17(4), 343-378. Goodwin, C. (2000). Introduction: Vision and inscription in practice. Mind, Culture, and Activity, 7, 1-3. Greeno, J., and Hall, R. (1997). Practicing representation: Learning with and about representational forms. Phi Delta Kappan, January, 361-367. Hausmann, R., and Chi, M. (2002) Can a computer interface support self-explaining? The International Journal of Cognitive Technology, 7(1). Hegarty, M., and Just, A. (1993). Constructing mental models of machines from text and diagrams. Journal of Memory and Language, 32, 717-742. Inhelder, B., and Piaget, J. (1958). The growth of logical thinking from childhood to adolescence. New York: Basic Books. Kahneman, D., Slovic, P, and Tversky, A. (1982). Judgment under uncertainty: Heuristics and biases. New York: Cambridge University Press.
OCR for page 162
Taking Science to School: Learning and Teaching Science in Grades K-8 Kanari, Z., and Millar, R. (2004). Reasoning from data: How students collect and interpret data in science investigations. Journal of Research in Science Teaching, 41, 17. Keselman, A. (2003). Supporting inquiry learning by promoting normative understanding of multivariable causality. Journal of Research in Science Teaching, 40, 898-921. Keys, C.W. (1994). The development of scientific reasoning skills in conjunction with collaborative writing assignments: An interpretive study of six ninth-grade students. Journal of Research in Science Teaching, 31, 1003-1022. Klaczynski, P.A. (2000). Motivated scientific reasoning biases, epistemological beliefs, and theory polarization: A two-process approach to adolescent cognition. Child Development, 71(5), 1347-1366. Klaczynski, P.A., and Narasimham, G. (1998). Development of scientific reasoning biases: Cognitive versus ego-protective explanations. Developmental Psychology, 34(1), 175-187. Klahr, D. (2000). Exploring science: The cognition and development of discovery processes. Cambridge, MA: MIT Press. Klahr, D., and Carver, S.M. (1995). Scientific thinking about scientific thinking. Monographs of the Society for Research in Child Development, 60, 137-151. Klahr, D., Chen, Z., and Toth, E.E. (2001). From cognition to instruction to cognition: A case study in elementary school science instruction. In K. Crowley, C.D. Schunn, and T. Okada (Eds.), Designing for science: Implications from everyday, classroom, and professional settings (pp. 209-250). Mahwah, NJ: Lawrence Erlbaum Associates. Klahr, D., and Dunbar, K. (1988). Dual search space during scientific reasoning. Cognitive Science, 12, 1-48. Klahr, D., Fay, A., and Dunbar, K. (1993). Heuristics for scientific experimentation: A developmental study. Cognitive Psychology, 25, 111-146. Klahr, D., and Nigam, M. (2004). The equivalence of learning paths in early science instruction: Effects of direct instruction and discovery learning. Psychological Science, 15(10), 661-667. Klahr, D., and Robinson, M. (1981). Formal assessment of problem solving and planning processes in preschool children. Cognitive Psychology, 13, 113-148. Klayman, J., and Ha, Y. (1989). Hypothesis testing in rule discovery: Strategy, structure, and content. Journal of Experimental Psychology: Learning, Memory, and Cognition, 15(4), 596-604. Kline, M. (1980). Mathematics: The loss of certainty. New York: Oxford University Press. Konold, C. (1989). Informal conceptions of probability. Cognition and Instruction, 6, 59-98. Koslowski, B. (1996). Theory and evidence: The development of scientific reasoning. Cambridge, MA: MIT Press. Koslowski, B., and Okagaki, L. (1986). Non-human indices of causation in problem-solving situations: Causal mechanisms, analogous effects, and the status of rival alternative accounts. Child Development, 57, 1100-1108.
OCR for page 163
Taking Science to School: Learning and Teaching Science in Grades K-8 Koslowski, B., Okagaki, L., Lorenz, C., and Umbach, D. (1989). When covariation is not enough: The role of causal mechanism, sampling method, and sample size in causal reasoning. Child Development, 60, 1316-1327. Kuhn, D. (1989). Children and adults as intuitive scientists. Psychological Review, 96, 674-689. Kuhn, D. (2001). How do people know? Psychological Science, 12, 1-8. Kuhn, D. (2002). What is scientific thinking and how does it develop? In U. Goswami (Ed.), Blackwell handbook of childhood cognitive development (pp. 371-393). Oxford, England: Blackwell. Kuhn, D., Amsel, E., and O’Loughlin, M. (1988). The development of scientific thinking skills. Orlando, FL: Academic Press. Kuhn, D., and Dean, D. (2005). Is developing scientific thinking all about learning to control variables? Psychological Science, 16(11), 886-870. Kuhn, D., and Franklin, S. (2006). The second decade: What develops (and how)? In W. Damon, R.M. Lerner, D. Kuhn, and R.S. Siegler (Eds.), Handbook of child psychology, volume 2, cognition, peception, and language, 6th edition (pp. 954-994). Hoboken, NJ: Wiley. Kuhn, D., Garcia-Mila, M., Zohar, A., and Andersen, C. (1995). Strategies of knowledge acquisition. Monographs of the Society for Research in Child Development, Serial No. 245(60), 4. Kuhn, D., and Pearsall, S. (1998). Relations between metastrategic knowledge and strategic performance. Cognitive Development, 13, 227-247. Kuhn, D., and Pearsall, S. (2000). Developmental origins of scientific thinking. Journal of Cognition and Development, 1, 113-129. Kuhn, D., and Phelps, E. (1982). The development of problem-solving strategies. In H. Reese (Ed.), Advances in child development and behavior (vol. 17, pp. 1-44). New York: Academic Press. Kuhn, D., Schauble, L., and Garcia-Mila, M. (1992). Cross-domain development of scientific reasoning. Cognition and Instruction, 9, 285-327. Kunda, Z. (1990). The case for motivated reasoning. Psychological Bulletin, 108, 480-498. Larkin, J.H., McDermott, J., Simon, D.P, and Simon, H.A. (1980). Expert and novice performance in solving physics problems. Science, 208, 1335-1342. Latour, B. (1990). Drawing things together. In M. Lynch and S. Woolgar (Eds.), Representation in scientific practice (pp. 19-68). Cambridge, MA: MIT Press. Lehrer, R. (2003). Developing understanding of measurement. In J. Kilpatrick, W.G. Martin, and D.E. Schifter (Eds.), A research companion to principles and standards for school mathematics (pp. 179-192). Reston, VA: National Council of Teachers of Mathematics. Lehrer, R., Giles, N., and Schauble, L. (2002). Data modeling. In R. Lehrer and L. Schauble (Eds.), Investigating real data in the classroom: Expanding children’s understanding of math and science (pp. 1-26). New York: Teachers College Press. Lehrer, R., and Pritchard, C. (2002). Symbolizing space into being. In K. Gravemeijer, R. Lehrer, B. van Oers, and L. Verschaffel (Eds.), Symbolization, modeling and tool use in mathematics education (pp. 59-86). Dordrecht, The Netherlands: Kluwer Academic.
OCR for page 164
Taking Science to School: Learning and Teaching Science in Grades K-8 Lehrer, R., and Romberg, T. (1996). Exploring children’s data modeling. Cognition and Instruction, 14, 69-108. Lehrer, R., and Schauble, L. (2000). The development of model-based reasoning. Journal of Applied Developmental Psychology, 21(1), 39-48. Lehrer, R., and Schauble, L. (2002). Symbolic communication in mathematics and science: Co-constituting inscription and thought. In E.D. Amsel and J. Byrnes (Eds.), Language, literacy, and cognitive development: The development and consequences of symbolic communication (pp. 167-192). Mahwah, NJ: Lawrence Erlbaum Associates. Lehrer, R., and Schauble, L. (2003). Origins and evolution of model-based reasoning in mathematics and science. In R. Lesh and H.M. Doerr (Eds.), Beyond constructivism: A models and modeling perspective on mathematics problem-solving, learning, and teaching (pp. 59-70). Mahwah, NJ: Lawrence Erlbaum Associates. Lehrer, R., and Schauble, L., (2005). Developing modeling and argument in the elementary grades. In T.A. Rombert, T.P. Carpenter, and F. Dremock (Eds.), Understanding mathematics and science matters (Part II: Learning with understanding). Mahwah, NJ: Lawrence Erlbaum Associates. Lehrer, R., and Schauble, L. (2006). Scientific thinking and science literacy. In W. Damon, R. Lerner, K.A. Renninger, and I.E. Sigel (Eds.), Handbook of child psychology, 6th edition (vol. 4). Hoboken, NJ: Wiley. Lehrer, R., Schauble, L., Strom, D., and Pligge, M. (2001). Similarity of form and substance: Modeling material kind. In D. Klahr and S. Carver (Eds.), Cognition and instruction: 25 years of progress(pp. 39-74). Mahwah, NJ: Lawrence Erlbaum Associates. Liben, L.S., and Downs, R.M. (1993). Understanding per son-space-map relations: Cartographic and developmental perspectives. Developmental Psychology, 29, 739-752. Linn, M.C. (1978). Influence of cognitive style and training on tasks requiring the separation of variables schema. Child Development, 49, 874-877. Linn, M.C. (1980). Teaching students to control variables: Some investigations using free choice experiences. In S. Modgil and C. Modgil (Eds.), Toward a theory of psychological development within the Piagettian framework. Windsor Berkshire, England: National Foundation for Educational Research. Linn, M.C., Chen, B., and Thier, H.S. (1977). Teaching children to control variables: Investigations of a free choice environment. Journal of Research in Science Teaching, 14, 249-255. Linn, M.C., and Levine, D.I. (1978). Adolescent reasoning: Influence of question format and type of variables on ability to control variables. Science Education, 62(3), 377-388. Lovett, M.C., and Anderson, J.R. (1995). Making heads or tails out of selecting problem-solving strategies. In J.D. Moore and J.F. Lehman (Eds.), Proceedings of the seventieth annual conference of the Cognitive Science Society (pp. 265-270). Hillsdale, NJ: Lawrence Erlbaum Associates. Lovett, M.C., and Anderson, J.R. (1996). History of success and current context in problem solving. Cognitive Psychology, 31(2), 168-217.
OCR for page 165
Taking Science to School: Learning and Teaching Science in Grades K-8 Masnick, A.M., and Klahr, D. (2003). Error matters: An initial exploration of elementary school children’s understanding of experimental error. Journal of Cognition and Development, 4, 67-98. Mayer, R. (1993). Illustrations that instruct. In R. Glaser (Ed.), Advances in instructional psychology (vol. 4, pp. 253-284). Hillsdale, NJ: Lawrence Erlbaum Associates. McClain, K., Cobb, P., Gravemeijer, K., and Estes, B. (1999). Developing mathematical reasoning within the context of measurement. In L. Stiff (Ed.), Developing mathematical reasoning, K-12 (pp. 93-106). Reston, VA: National Council of Teachers of Mathematics. McNay, M., and Melville, K.W. (1993). Children’s skill in making predictions and their understanding of what predicting means: A developmental study. Journal of Research in Science Teaching, 30, 561-577. Metz, K.E. (2004). Children’s understanding of scientific inquiry: Their conceptualization of uncertainty in investigations of their own design. Cognition and Instruction, 22(2), 219-290. Mokros, J., and Russell, S. (1995). Children’s concepts of average and representativeness. Journal for Research in Mathematics Education, 26(1), 20-39. National Council of Teachers of Mathematics. (2000). Principles and standards for school mathematics. Reston, VA: Author. Nisbett, R.E., Krantz, D.H., Jepson, C., and Kind, Z. (1983). The use of statistical heuristics in everyday inductive reasoning. Psychological Review, 90, 339-363. Penner, D., Giles, N.D., Lehrer, R., and Schauble, L. (1997). Building functional models: Designing an elbow. Journal of Research in Science Teaching, 34(2), 125-143. Penner, D.E., and Klahr, D. (1996a). The interaction of domain-specific knowledge and domain-general discovery strategies: A study with sinking objects. Child Development, 67, 2709-2727. Penner, D.E., and Klahr, D. (1996b). When to trust the data: Further investigations of system error in a scientific reasoning task. Memory and Cognition, 24, 655-668. Perfetti, CA. (1992). The representation problem in reading acquisition. In P.B. Gough, L.C. Ehri, and R. Treiman (Eds.), Reading acquisition (pp. 145-174). Hillsdale, NJ: Lawrence Erlbaum Associates. Petrosino, A., Lehrer, R., and Schauble, L. (2003). Structuring error and experimental variation as distribution in the fourth grade. Mathematical Thinking and Learning, 5(2-3), 131-156. Pollatsek, A., Lima, S., and Well, A.D. (1981). Concept or computation: Students’ misconceptions of the mean. Educational Studies in Mathematics, 12, 191-204. Ruffman, T., Perner, I., Olson, D.R., and Doherty, M. (1993). Reflecting on scientific thinking: Children’s understanding of the hypothesis-evidence relation. Child Development, 64(6), 1617-1636. Schauble, L. (1990). Belief revision in children: The role of prior knowledge and strategies for generating evidence. Journal of Experimental Child Psychology, 49(1), 31-57. Schauble, L. (1996). The development of scientific reasoning in knowledge-rich contexts. Developmental Psychology, 32(1), 102-119.
OCR for page 166
Taking Science to School: Learning and Teaching Science in Grades K-8 Schauble, L., Glaser, R., Duschl, R., Schulze, S., and John, J. (1995). Students’ understanding of the objectives and procedures of experimentation in the science classroom. Journal of the Learning Sciences, 4(2), 131-166. Schauble, L., Glaser, R., Raghavan, K., and Reiner, M. (1991). Causal models and experimentation strategies in scientific reasoning. Journal of the Learning Sciences, 1(2), 201-238. Schauble, L., Glaser, R., Raghavan, K., and Reiner, M. (1992). The integration of knowledge and experimentation strategies in understanding a physical system. Applied Cognitive Psychology, 6, 321-343. Schauble, L., Klopfer, L.E., and Raghavan, K. (1991). Students’ transition from an engineering model to a science model of experimentation. Journal of Research in Science Teaching, 28(9), 859-882. Siegler, R.S. (1987). The perils of averaging data over strategies: An example from children’s addition. Journal of Experimental Psychology: General, 116, 250-264. Siegler, R.S., and Alibali, M.W. (2005). Children’s thinking (4th ed.). Upper Saddle River, NJ: Prentice Hall. Siegler, R.S., and Crowley, K. (1991). The microgenetic method: A direct means for studying cognitive development. American Psychologist, 46, 606-620. Siegler, R.S., and Jenkins, E. (1989). How children discover new strategies. Hillsdale, NJ: Lawrence Erlbaum Associates. Siegler, R.S., and Liebert, R.M. (1975). Acquisition of formal experiment. Developmental Psychology, 11, 401-412. Siegler, R.S., and Shipley, C. (1995). Variation, selection, and cognitive change. In T. Simon and G. Halford (Eds.), Developing cognitive competence: New approaches to process modeling (pp. 31-76). Hillsdale, NJ: Lawrence Erlbaum Associates. Simon, H.A. (1975). The functional equivalence of problem solving skills. Cognitive Psychology, 7, 268-288. Simon, H.A. (2001). Learning to research about learning. In S.M. Carver and D. Klahr (Eds.), Cognition and instruction: Twenty-five years of progress (pp. 205-226). Mahwah, NJ: Lawrence Erlbaum Associates. Slowiaczek, L.M., Klayman, J., Sherman, S.J., and Skov, R.B. (1992). Information selection and use in hypothesis testing: What is a good question, and what is a good answer. Memory and Cognition, 20(4), 392-405. Sneider, C., Kurlich, K., Pulos, S., and Friedman, A. (1984). Learning to control variables with model rockets: A neo-Piagetian study of learning in field settings. Science Education, 68(4), 463-484. Sodian, B., Zaitchik, D., and Carey, S. (1991). Young children’s differentiation of hypothetical beliefs from evidence. Child Development, 62(4), 753-766. Stevens, R., and Hall, R. (1998). Disciplined perception: Learning to see in technoscience. In M. Lampert and M.L. Blunk (Eds.), Talking mathematics in school: Studies of teaching and learning (pp. 107-149). Cambridge, MA: Cambridge University Press. Strauss, S., and Bichler, E. (1988). The development of children’s concepts of the arithmetic average. Journal for Research in Mathematics Education, 19(1), 64-80.
OCR for page 167
Taking Science to School: Learning and Teaching Science in Grades K-8 Thagard, P. (1998a). Ulcers and bacteria I: Discovery and acceptance. Studies in History and Philosophy of Science. Part C: Studies in History and Philosophy of Biology and Biomedical Sciences, 29, 107-136. Thagard, P. (1998b). Ulcers and bacteria II: Instruments, experiments, and social interactions. Studies in History and Philosophy of Science. Part C: Studies in History and Philosophy of Biology and Biomedical Sciences, 29(2), 317-342. Toth, E.E., Klahr, D., and Chen, Z. (2000). Bridging research and practice: A cognitively-based classroom intervention for teaching experimentation skills to elementary school children. Cognition and Instruction, 18(4), 423-459. Trafton, J.G., and Trickett, S.B. (2001). Note-taking for self-explanation and problem solving. Human-Computer Interaction, 16, 1-38. Triona, L., and Klahr, D. (in press). The development of children’s abilities to produce external representations. In E. Teubal, J. Dockrell, and L. Tolchinsky (Eds.), Notational knowledge: Developmental and historical perspectives. Rotterdam, The Netherlands: Sense. Varnhagen, C. (1995). Children’s spelling strategies. In V. Berninger (Ed.), The varieties of orthographic knowledge: Relationships to phonology, reading and writing (vol. 2, pp. 251-290). Dordrecht, The Netherlands: Kluwer Academic. Warren, B., Rosebery, A., and Conant, F. (1994). Discourse and social practice: Learning science in language minority classrooms. In D. Spencer (Ed.), Adult biliteracy in the United States (pp. 191-210). McHenry, IL: Delta Systems. Wolpert, L. (1993). The unnatural nature of science. London, England: Faber and Faber. Zachos, P., Hick, T.L., Doane, W.E.I., and Sargent, C. (2000). Setting theoretical and empirical foundations for assessing scientific inquiry and discovery in educational programs. Journal of Research in Science Teaching, 37(9), 938-962. Zimmerman, C., Raghavan, K., and Sartoris, M.L. (2003). The impact of the MARS curriculum on students’ ability to coordinate theory and evidence. International Journal of Science Education, 25, 1247-1271.
Representative terms from entire chapter: