Participation in Scientific Practices and Discourse
Main Findings in the Chapter:
The norms of scientific argument, explanation, and the evaluation of evidence differ from those in everyday life. Students need support to learn appropriate norms and language for productive participation in the discourses of science.
Children’s experiences vary with their cultural, linguistic, and economic background. Such differences mean that students arrive in the classroom with varying levels of experience with science and varying degrees of comfort with the norms of scientific practice.
It may be hard for teachers to recognize the strengths that diverse learners bring to the science classroom. However, all students can be successful in science and bring resources that can be built on to develop scientific proficiency.
Motivation and attitudes toward science play a critical role in science learning, fostering students’ use of effective learning strategies that result in deeper understanding of science. Classroom instruction and the classroom context can be designed in ways that enhance motivation and support productive participation in science.
In this chapter, we examine research related to Strand 4: participate productively in scientific practices and discourse. We begin with a discussion of talk and argument. Argumentation is a central activity of scientists, and students need to learn both the language and norms for argumentation
in science. This discussion of talk and argument leads naturally into consideration of the role of cultural habits and values in learning science. Science brings its own norms for both social and cognitive participation, which often differ quite markedly from children’s experiences in everyday settings. Finally, we consider how motivation and identity play a role in science learning and successful participation in science.
TALK AND ARGUMENT
According to Bazerman (1988), the central activity of scientists is argumentation in communities of practice for the purpose of persuading colleagues of the validity of one’s own ideas and the ideas of others (see Latour, 1987). The focus of these arguments is on establishing agreement about the truth of symbolic objects. For example, Bazerman (1988) analyzed Newton’s journal articles over a period of years and concluded that experiments are communicated in a way meant to ensure that his interpretations would seem both logical and inevitable to readers. Newton employed a host of rhetorical devices, from the order of experiments reported (which often did not match their actual sequence) to the wording of sentences, all orchestrated to maximize the likelihood that readers would literally come to see things his way.
In spite of its centrality in science, genuine scientific argumentation is rarely observed in classrooms. Instead, most of the talk comes from teachers, and it seems oriented primarily toward persuading students of the validity of the scientific worldview (Ogborn et al., 1996). As Mehan (1979) described in his now-classic analysis of the structure of talk in school, the tacit turn-taking rules that guide the interaction of teachers and students seem guaranteed to preclude argument of any kind, and perhaps even genuine conversation. Mehan reported that the discourse structure he most frequently observed was one he called the initiate-response-evaluate triad. This familiar form of exchange begins with a teacher asking a question, usually one to which the answer is already known. A student is called on and responds, and the teacher then follows up with a comment that communicates an evaluation of the response. These sequences are intended to find out if the student can provide the answer expected by the teacher, not to communicate anything previously unknown, put forth a claim, justify or debate a point, or offer a novel interpretation.
Many teachers are uncomfortable with argument, perhaps understandably, given that many teach in contexts in which much of their time is spent mediating conflict and persuading students of the value of civil exchange. Skill and persistence are required to help students grasp the difference between scientific argument, which rests on plausibility and evidence and has
the goal of shared understanding, and everyday argument, which relies on power and persuasiveness and assumes that the goal is winning. It is not straightforward to get a middle schooler to see the distinction between disagreeing with an idea and disagreeing with a person.
Moreover, orchestrating effective scientific argument requires having sufficient knowledge of both children and content to perceive on the fly what is scientifically fruitful in students’ talk. Young students tend to use language that is ambiguous, fragmentary, or even contradictory, especially in heated conversation, so the content and structure of their arguments can be difficult to follow. Yet if the educational goal is to help students understand not just the conclusions of science, but also how one knows and why one believes, then talk needs to focus on how evidence is used in science for the construction of explanations. “A prominent, if not central, feature of the language of scientific enquiry is debate and argumentation around competing theories, methodologies, and aims. Such language activities are central to doing and learning science. Thus, developing an understanding of science and appropriating the syntactic, semantic, and pragmatic components of its language require students to engage in practicing and using its discourse” (Duschl and Osborne, 2002, p. 40).
As described in Chapter 6, Gertrude Hennessey, who taught science to students in all grades at her small, rural elementary school, made the development of argument an explicit goal of instruction, starting with first graders. Her goals for these young children were modest, focused primarily on helping students become adept at stating their own beliefs and providing reasons for them. By the fourth grade, however, students were expected to understand and appeal to the scientific criteria of intelligibility, plausibility, fruitfulness, and conceptual coherence for evaluating their evolving beliefs. By sixth grade, students were expected to be actively monitoring the beliefs of their peers, considering the fit of competing explanations to the patterns of evidence that they were observing in their investigations (Hennessey, 2002).
Even understanding that more than one explanation is possible and that alternative explanations should be examined and entertained may take some getting used to (Kuhn, 1991). To keep this awareness at the center of students’ attention, it may be helpful to feature alternative beliefs and explanations, socioscientific issues, and problem-based learning situations as occasional topics of classroom discussion. Eichinger et al. (1991) found that productive argumentation in classrooms is more likely to occur when students are permitted and encouraged to talk and work directly with each other, rather than always having their talk mediated through the teacher. These researchers favored collaborative problem-solving groups as a structure for encouraging peer-to-peer discourse, but they also noted that these groups are not successful unless the teacher works actively to support class-
room norms that emphasize responsibility, tolerance, and the construction of arguments based on theory and evidence. Without teacher intervention, students do not spontaneously adopt either the general social norms or the specific scientific epistemic grounds for conducting productive dialogic discourse (Osborne et al., 2001). Herrenkhol and Guerra (1998) examined the effectiveness of directly teaching specific audience roles for encouraging productive scientific argumentation in the elementary grades. As small teams of fourth graders reported the results of their investigations to the whole class, the audience members were asked to assume responsibility for checking on the presenters by taking one of the following roles: (1) asking clarification questions about predictions and theories, (2) challenging claims about their results, or (3) raising questions about the relationships among their predictions, theories, and results. When students were assigned these roles, the discourse patterns in the classroom showed increased negotiation of shared understanding, monitoring of comprehension, challenges to others’ perspectives, and coordination of theories and evidence.
In addition to its emphasis on discourse patterns, such as forms of scientific argument, science is also associated with a style of language use that does not match everyday kinds of talk. Specialized kinds of activities, like science, are often associated with specialized forms of language. Science, mathematics—and, for that matter, ham radio and drug selling—are activities conducted by organized groups of people who tend to communicate in particular linguistic styles, sometimes called “registers” (Halliday, 1988). Scientific language tends to emphasize the passive voice, especially in its textual forms, and as a result, people are rarely present in science talk or text, as either agents or participants (Lemke, 1990). Scientific language often features abstract nouns that are derived from verbs (e.g., “the revolution of the earth around the sun”) and technical terms that have different meanings than in everyday use (e.g., “force,” “energy”). Narrative and dramatic accounts are avoided, as are colloquial expressions and ambiguous words, such as pronouns with unclear referents. The impression lent by these features of scientific style is that the text is communicating a simple and veridical description of the way the world actually is—a straightforward reading of the book of nature (Olson, 1994), rather than a complex social activity conducted by humans. For these reasons, Lemke (1990) suggests that teachers work deliberately to provide opportunities for students to practice at “talking science.” This goal may be accomplished through a variety of means, such as teaching students how to combine scientific terms in complex sentences, discussing their commonsense theories on science topics, teaching students the genres of science writing, and bridging colloquial and scientific language, for example, by asking students to translate back and forth between scientific and colloquial statements or questions.
CULTURAL VALUES AND NORMS1
Scientific thinking is both like and unlike the forms of thinking that individuals employ in their everyday lives. For example, in both science and everyday problem solving, people make inferences about the relationships between causes and effects by detecting and evaluating patterns of covariation between potential causes and outcomes (Kelley, 1973; Shultz, 1982). However, it is much less common for people to deliberately apply strategies and heuristics (such as the control-of-variables strategy) to definitively and systematically rule out factors that are not causal, although one does observe this form of thinking in certain specialized contexts, for example, those that call for accurate diagnosis (e.g., car repair, medicine). Moreover, causal relationships that go beyond simple, one cause/one effect can be very difficult for people to detect (Perkins and Grotzer, 2000). Similarly, people do not simply hold a catalog of unrelated beliefs; instead, they use their knowledge to make inferences that are internally consistent and that support explanations and predictions about novel cases (Gopnik and Meltzoff, 1997). Even third graders prefer explanations that are logically consistent (Samarapungavan, 1992). However, this does not necessarily mean that novices are explicitly aware of their beliefs and maintain or discard them by systematically appraising their adherence to the criteria that scientists deliberately apply when they evaluate alternative theories.
Because scientific and everyday thinking do not overlap perfectly, students sometimes find it confusing to grasp the rules of the game as they move between contexts. For example, it has been observed that when they are presumed to be designing and conducting experiments to learn about the causal structure of a complex system, children frequently are doing something else altogether—in some cases, trying to replicate outcomes that they consider interesting or favorable (Kuhn and Phelps, 1982; Tschirgi, 1980; Schauble, Klopfer, and Raghavan, 1991). Negotiating the sometimes subtle transitions between everyday thinking and the thinking valued in domains like science is a challenge for all students. Moreover, it may be particularly difficult for students who have had less experience with the forms of reasoning and talk that are privileged in American middle-class schools. Mainstream students (those who are white, middle- or upper-class, and native speakers of standard English) are more likely than culturally or linguistically diverse students to encounter ways of talking, thinking, and interacting in schools that are continuous with the practices (including knowledge, language, skills, and reasoning) and the expectations that they bring from home.
Any discussion of culture needs to include a caution about the inappropriateness of portraying individuals as possessing trait-like thinking styles typical of cultural, racial, or gender categories. In place of this oversimplified and stereotypical view, Gutierrez and Rogoff (2003) propose that one conceives of individuals as developing a wide range of repertoires of practice—ways of behaving, thinking, and interpreting—for engaging with the varieties of communities and institutions that they encounter in their everyday lives. From this perspective, each person is continually developing an evergrowing multicultural repertoire, fashioned by participating in their everyday rounds of practical activity, which involve historically evolving cultural practices and tools (Cole, 1996; Erickson, 2004). Children are not passive recipients who simply receive or are molded by culture; instead, as they encounter cultural practices and routines, they are affected by them but also transform them, so that the relationships between culture and personal meaning are always fluid and complex.
That said, we note that people’s histories vary, and one’s fluidity in negotiating the transition across cultures and settings may be simultaneously supported and constrained by one’s history. This is important, because classrooms are not neutral settings; they are “saturated with specific cultural and communicative norms” (Foster, Lewis, and Onafowora, 2003, p. 263). An implication is that the educational success of immigrant or U.S.-born racial/ ethnic minority students may depend on their access to cultural and communicative norms that come for free or at much less cost to other students. The structure of classroom norms is often left tacit, making it difficult for students to figure out the rules on their own, especially if these ways of thinking, talking, and behaving are not as frequently encountered in their home communities (Ladson-Billings, 1995; Delpit, 1995).
For example, it is common for Yup’ik children in Alaska to learn by observing experienced adults and participating actively as helpers in adult work and other activities. Verbal interaction is not central to the learning process; observation and participation are considered more important (Lipka, 1998). Similarly, Rogoff and colleagues (2003) have identified a form of learning that they call “intent participation,” in which children learn from keen observation, with little direct instruction. Rogoff et al. report that this pattern of learning tends to be prevalent in communities that are less stratified by age than those in the United States, communities in which children enter contexts of adult activity, including work, with relatively few barriers to their presence or full participation. The strong emphasis in school on explicit verbal instruction may be disconcerting to children from backgrounds that favor intent participation or other forms of learning. Moreover, the specific patterns and uses of discourse, such as the practice of asking questions whose answer is already known to the questioner, may also seem unfamiliar and possibly even bizarre to some.
Lee and Luyks (2006) point out that, as yet, there is little research focused directly on how cultural norms and values may either affect or be capitalized on for the learning of disciplinary knowledge, such as science. For the most part, research on student diversity and research on science learning have been separate literatures that do not frequently contact each other. Similarly, instruction for English language learners typically focuses primarily on English language and literacy development and does not give as much attention to instruction in content domains, such as science (National Research Council, 1997).
However, the research that does exist suggests that two principles can provide some guidance. First is the need to make visible and inspectable the norms and patterns of thinking that constitute the rules of the game in the science classroom, rather than leaving them implicit. If what is valued is left for students to figure out, then those who have had greater home experience with those patterns of thinking will have a clear advantage. Varying solutions to the problem of making the rules of the game understandable have been advocated, although as yet research does not support whether some may be more effective than others.
Lee and Fradd (1996, 1998) suggest that when introducing disciplinary forms of activity, such as scientific inquiry, that may be unfamiliar to students, teachers should begin with explicit, structured, direct instruction. Over time, as students’ grasp of the objectives and procedures develops, teachers can cede increasing control and initiative to them. The goal is to establish and maintain instructional congruence, which Lee (2002) describes as mediating the nature of academic disciplines with students’ language and cultural experiences to make science accessible, meaningful, and relevant. Students need opportunities to explicitly consider and master new ways of thinking, while teachers balance challenge and comfort by ensuring that students understand that their own home norms and practices are valued even as they encounter some that are less familiar.
Lucas et al. (2005) pursued a somewhat different approach to achieving the same goal. Specifically, they made the development, critique, and revision of norms for scientific thinking an ongoing instructional enterprise in a sixth grade classroom over the course of an academic year. Early in the fall, students proposed, debated, and came to agreement on classroom criteria for evaluating “what counts as a good scientific question” and “what we think is persuasive evidence.” For example, everyone agreed that a good question was one to which one did not already know the answer, and that more interesting questions were ones for which one couldn’t just ask someone or look up the answer. The resulting list of criteria was used as a focal class reference as students worked in small teams to design and then pursue investigations in pond ecology.
However, by midyear, students began to argue that the criteria needed to be revised, because new ideas about good questions and good evidence were coming to light as they evaluated their own work and the work of other teams. For example, students argued that they should amend the list by adding the criterion that a good question encourages “piggybacking”—their term for the idea that good questions are inspired by (or piggyback on) the findings of others and in turn, inspire related additional questions. This change reflected the students’ growing understanding that a frequent source of new and interesting questions was the methods pursued by or the findings of other teams. Such shifts in their criteria for questions and evidence accompanied the shift from conceiving of investigation as an activity conducted independently to advance one’s own knowledge toward understanding that a larger community can share responsibility for building and evaluating a publicly shared base of related knowledge.
If clarifying the norms and thinking patterns characteristic of science is the first important principle for supporting learning for all students, the second is the value of capitalizing on the continuities between students’ everyday thinking, knowledge, and resources and those of practicing scientists. Ann Rosebery and Beth Warren have worked for a decade and a half at identifying key points of contact between students’ ways of knowing and scientific ways of knowing. Their phrase “points of contact” is intended to capture the recognition that there are both continuities and discontinuities between students’ thinking, tool use, and talk and that of science.
In their Cheche Konnen project, conducted with Haitian Creole students and their teachers (the name is translated as “search for knowledge”), Warren and Rosebery observed that the children regularly and spontaneously evoked analogies, arguments, and narratives as a means of making sense of the phenomena they were exploring. For example, one young student who was investigating animal behavior—in this case, the preference of ants for different kinds of habitats—imaginatively projected himself into the habitat, assuming an “ant’s eye view,” a perspective that resulted in his raising doubts about one of the key attributes of comparison in the design of the investigation. His original intention had been to set up an experiment to establish whether ants prefer an environment that is dark to one that is brightly lit, but as this student imagined himself as an ant crawling through the soil, he began to wonder how either side of the chamber—lit or unlit—could possibly appear “light” to an ant wandering around underneath the soil. As he pointed out, “When we put dirt in there, they—they were a little bit walking around but almost all of them were under the dirt, in the darkness” (Warren et al., 2001, p. 541).
Researchers who observe scientists and mathematicians at work have pointed out that, for professionals, too, imagination, narrative, and projec-
tion of oneself as an actor into the context of the investigation appear to serve as important resources for meaning making and discovery (Ochs, Gonzales, and Jacoby, 1996). Over the 15 years of its existence, the Cheche Konnen program has been demonstrating that urban, language-minority students can engage in high-level scientific reasoning and problem solving if they are taught in ways that respect their interests and sense-making (Hudicourt-Barnes, 2003).
Engagement with science begins with willingness to participate in the science classroom, but it must go beyond simply participating to participating in ways that advance science learning. Engle and Conant’s (2002) definition of “productive disciplinary engagement” is a useful frame for thinking about active engagement in the classroom that is grounded in disciplinary norms for both social and cognitive activity. In their view, productive disciplinary engagement refers to classrooms in which “there is contact between what students are doing and the issues and practices of a discipline’s discourse” (Engle and Conant, 2002, p. 402). Furthermore, “students’ engagement is productive to the extent that they make intellectual progress. What constitutes productivity depends on the discipline, the specific task and topic, and where students are when they begin addressing a problem” (p. 403). They further distinguish how engagement and disciplinary engagement might be distinct from productive disciplinary engagement.
Engle and Conant define engagement in terms of students actively speaking, listening, responding, and working and high levels of on-task behavior. Greater engagement can be inferred when more students in the group make substantive contributions to the topic under discussion and their contributions are made in coordination with each other. Engagement also means that students attend to each other, express emotional involvement, and spontaneously reengage with the topic and continue with it over a sustained period of time. Finally, it means that few students are involved in unrelated or off-task activities.
These hallmarks of engagement do not, however, ensure that students are engaged in meaningful ways with the discipline of science. Disciplinary engagement expands to include scientific content and experimental activities (including argumentation based on logic and data patterns). For disciplinary engagement to occur, there must be “some contact between what students are doing and the issues and practices of a discipline’s discourse” (Engle and Conant, 2002, p. 402). Herrenkohl and Guerra (1998) suggest that some identifying features of disciplinary engagement in science include (1) monitoring comprehension, that is, students asking questions to be sure that they fully understand perspectives posed by other students; (2) chal-
lenging others’ perspectives and claims; and (3) coordinating bits of knowledge that can be construed as coordinating theories with evidence.
Finally, to be productively engaged in the discipline, students must make intellectual progress. Whether progress can be considered productive depends on the discipline, the specific task and topic, and where students begin. Productive disciplinary engagement encompasses the additional criteria of demonstrated change over time in student investigations, complexity of argumentation, and use of previous investigations to generate new questions, new concepts, and new investigations.
In this section, we discuss the characteristics of individuals and classrooms that play a role in shaping students’ engagement with science. These include motivation, attitudes, identity, interactions between students’ values and norms and those of the science classroom, and, finally, characteristics of instruction that foster productive participation. We note that students’ motivation, attitudes, and identity toward science develop partly as a consequence of their experience of educational, social, and cultural environments. The educational environment in particular has an important influence on how students view science, their beliefs about their own ability to do science, and whether they feel supported to participate fully in the scientific community of the classroom. Consequently, we see productive participation as partly situation or context specific rather than as a stable personality trait that does not vary across settings.
Motivation, Attitudes, and Identity
Students’ motivation, their beliefs about science, and their identities as learners affect their participation in the science classroom and have consequences for the quality of their learning. More specifically, results of both experimental and classroom-based studies suggest that students’ own goals for science learning, their beliefs about their own ability in science, and the value they assign to science learning are likely to influence their cognitive engagement in science tasks (Lee and Anderson, 1993; Pintrich, Marx, and Boyle, 1993). Motivation, attitudes, and identity encompass cognitive components, such as beliefs about oneself and about science; emotional or affective components, such as values, interests, and attitudes; and behavioral components, such as persistence, effort, and attention.
Researchers studying motivation have developed a dizzying array of theoretical frameworks, making it challenging to develop a coherent picture of motivation, attitudes, and identity and the factors that shape them. The wide array of constructs that researchers have developed in their attempts to understand the components of motivation and attitudes have been organized by reviewers of the literature into a few broad categories (for examples, see Pintrich, Marx, and Boyle, 1993; Wigfield et al., 2006; National Research Coun-
cil and Institute of Medicine, 2004). We chose to use the three dimensions developed in the recent National Research Council report Engaging Schools (2004): components that relate to (1) the students’ feeling “I can do this”; (2) those that relate to the feeling “I want to do this”; and (3) those that relate to the feeling “I belong and this is an important part of who I am.”
Beliefs About Oneself and About Science (“I Can Do Science”)
In general, when children answer the question, Can I do this task? in the affirmative, they try harder, persist longer, perform better, and are motivated to select more challenging tasks (Wigfield et al., 2006). There is some evidence that a sense of being competent and efficacious as a science learner does influence learning. In a study of sixth and seventh grade science classrooms, students who reported feeling highly efficacious in science and who had a strong sense of competence in science tended to use deep learning strategies and were more focused on learning (Anderman and Young, 1994). Some researchers have suggested that students’ perceptions of their ability to learn science might interact with the process of conceptual change, so that if they have confidence in their own learning and thinking strategies, they may be more likely to change their own conceptions (Pintrich, Marx, and Boyle, 1993).
Perceptions of ability usually vary from subject to subject and may vary from one context to another (National Research Council, 2004). Students will not exert effort in academic work if they are convinced they lack the capacity to succeed or have no control over outcomes (Atkinson, 1964; Eccles et al., 1983; Skinner, Wellborn, and Connell, 1990; Skinner, Zimmer-Gembeck, and Connell, 1998, cited in National Research Council, 2004). Students who have negative views of their competence and low expectations for success are more anxious in learning contexts and fearful of revealing their ignorance (Abu-Hilal, 2000; Bandalos, Yates, and Thorndike-Christ, 1995; Harter, 1992; Hembree, 1988, cited in National Research Council, 2004). Belief about the degree to which intellectual ability in a domain is fixed or malleable have also emerged as an import component of motivation. Americans tend to have a concept of intelligence or ability as inherited rather than as developed through effort (Chen and Stevenson, 1995; Dweck, 1999; Stevenson and Lee, 1990). A student who believes, for example, that ability in science is fixed and that she has low ability in it has little hope for success and therefore little reason to try.
Gender differences in competence beliefs are reported as early as kindergarten or first grade, especially in such gender role–stereotyped domains as science. For example, boys hold higher competence beliefs than girls for mathematics and sports, even after all relevant differences in skill level are controlled. In contrast, girls have higher competence beliefs than boys for
reading and English, music and arts, and social studies (Jacobs et al., 2002). The extent to which children endorse the cultural stereotypes regarding which sex is more likely to be talented in a given domain predicts the extent to which girls and boys distort their ability self-concepts and expectations (Eccles and Harold, 1992). However, these gender differences are relatively small when they are found (Marsh, 1989).
Another perspective on the role of beliefs about competence is offered by research on stereotype threat. Steele (1997) proposed stereotype vulnerability and disidentification to help explain the underachievement of stereotyped groups. He and his colleagues describe a process by which some students in a group may disidentify with a particular domain, like school or science, due to widely held stereotypes about their lack of ability in it. To protect their own sense of self, some students disidentify with the domain and stop trying to achieve in it. Those students in the group who remain identified with the domain—that is, it is important to them and they want to succeed in it—may then suffer the effects of stereotype threat. This threat produces lowered performance in the domain, particularly in situations in which the stereotypes about their groups are made salient. Research based on this theory offers evidence that the process operates for black students in school in general and for women in stereotypically male domains, such as mathematics. These results have clear implications for performance in science, for which people tend to hold stereotypes about who has natural aptitude and who does not.
A key mediator of experiencing stereotype threat appears to be beliefs about the nature of intelligence. In a recent experimental intervention with college students, researchers found that by encouraging black students to adopt a mind set in which they viewed their own intelligence as malleable, they were able to increase their enjoyment and engagement in academics as well as their grades compared with controls (Aronson, Fried, and Good, 2002).
Goals, Values, and Interest (“I Want To Do Science”)
Even if students believe they can succeed in science, they will not exert effort unless they see some reason to do so. They may have very different reasons for engaging in academic work, and typically there is a very complex set of reasons for engaging in any one task. Researchers have used a number of theoretical frameworks to explain the psychological processes involved in students’ decisions to engage in a particular task.
Goals. Goals represent the different purposes that students may adopt in different achievement situations. Researchers working within this theoretical framework distinguished two broad orientations that students can have toward
their learning. A learning orientation (also called a task-involved or mastery orientation) means that the student is focused on improving skills, mastering material, and learning new things. A performance orientation (also called an ego orientation) means that the student focuses on maximizing favorable evaluations of his competence and minimizing negative evaluations. Both goals have been observed among students in elementary and middle school science classrooms (Anderman and Young, 1994; Lee and Brophy, 1996; Meece, Blumenfeld, and Hoyle, 1988).
A number of studies have shown that these two different goal orientations can lead to different patterns of cognitive engagement (Pintrich, Marx, and Boyle, 1993). Research on adoption of mastery goals shows consistent positive consequences for learning. When children are mastery oriented they are more highly engaged in learning, use deeper cognitive strategies, and are intrinsically motivated to learn. This relation has been shown in science classrooms (Anderman and Young, 1994; Lee and Brophy, 1996; Meece, Blumenfeld, and Hoyle, 1988).
Which goals children adopt in a classroom are influenced by their own beliefs, especially beliefs about ability, as well as their experiences in classroom settings. Children holding an incremental view of intelligence tend to have mastery or learning goals, whereas children holding an entity view tend to have performance goals (Dweck and Legget, 1988). The classroom context appears to have a strong influence on which goals students adopt. When tasks are more challenging and meaningful, students tend to adopt mastery goals. In addition, classrooms that provide students with choice or control over their activities and emphasize gaining understanding rather than outperforming other students foster a mastery orientation.
Values. An individual’s valuing of a task, in conjunction with her competence beliefs, influences performance as well as choices about continued investment in the activity. For example, people will be most likely to enroll in courses and choose careers in which they think they will do well and that have high task value for them. In fact, the value placed on a task predicts course plans and enrollment decisions more strongly than self-concept or expectancy of success on the task.
There are age-related declines in children’s valuing of certain academic tasks that vary by domain. In one of the few longitudinal studies conducted over 12 years, children’s valuing of language arts declined most during elementary years and then leveled off. Their valuing of mathematics declined the most during the high school years (Jacobs et al., 2002; Fredericks and Eccles, 2002).
There are also gender differences in the value children place on such activities as sports, social activities, and academic subjects (Eccles et al., 1989, 1993; Wigfield et al., 1991). Early work indicated that boys begin to
value mathematics more than girls in early adolescence. However, more recent studies show that boys and girls value mathematics equally during adolescence (Jacobs et al., 2002). However, girls are less interested in science, with the exception of biology and engineering, than are boys (Wigfield et al., 2002).
Culture and ethnicity can influence parents’ behaviors and children’s motivation through values, goals, and general belief systems (for example, see Garcia Coll and Pachter, 2002; Gutman and Midgley, 2000; Luster, Rhoades, and Haas, 1989). Cultural differences can affect motivation through variations in valued activities (e.g., athletic versus musical competence), valued goals (e.g., communal goals versus individualistic goals), and approved means of achieving one’s goals (e.g., competitive versus cooperative means).
Intrinsic motivation and interest. Individuals who are intrinsically motivated do activities for their own sake and out of interest in the activities. This is usually contrasted with being extrinsically motivated—that is, doing activities for instrumental or other reasons, such as receiving a reward. Some research shows that students who are intrinsically interested in an activity are more likely than students who are not intrinsically interested to see challenging tasks as worthwhile (Pittman, Emery, and Boggiano, 1982), think more creatively (Amabile and Hennessey, 1992), exert effort (Downey and Ainsworth-Darnell, 2002; Miserandino, 1996), and learn at a conceptual level (Ryan, Connell, and Plant, 1990).
Classroom practices appear to have an impact on students’ intrinsic interest. Guthrie and colleagues have demonstrated positive effects on motivation of a program called concept-oriented reading instruction (CORI). CORI integrates reading with science inquiry and includes four instructional strategies to enhance motivation: support for student autonomy, support for competence, learning goals, and real-world interaction (Guthrie et al., 1996, 2000). One study of CORI involved third and fifth grade classrooms in three schools. Teachers were assigned to teach either CORI or to continue with traditional instruction. At the end of the school year, students in CORI reported greater curiosity for reading science than students in traditional instruction (Guthrie et al., 2000). They also reported using more cognitive strategies for reading comprehension. In additional research on the program, students in CORI classrooms also showed improved comprehension of science texts and higher scores on standardized tests of science content (Guthrie et al., 2004).
Interest is closely related to the notion of intrinsic motivation, although interest is generally seen as being more specific in focus. Researchers distinguish between individual and situational interest. Individual interest is a relatively stable stance toward certain domains, like science; situational in-
terest is determined by specific features of an activity or task. In sixth grade science classrooms, Lee and Brophy (1996) found evidence that students’ interest and motivation to learn were both domain general and situational. For example, students’ interest varied from task to task even within a single unit on matter and molecules.
Interest is tied to the quality of learning (Alexander, Kulikowich, and Jetton, 1994; Hidi, 2001; Renninger, Ewen, and Lasher, 2002: Schiefele, 1996, 1999). For example, personal interest influences students’ selective attention, effort and willingness to persist at a task, and acquisition of new knowledge (Hidi, 1990). Situational interest is more influenced than personal interest by characteristics of the classroom and the nature of the task. For example, challenge, choice, novelty, fantasy, and surprise can increase students’ situational interest (Malone and Lepper, 1987). Recent studies suggest that interest is particularly predictive of achievement when there is a context that allows for choice. For example, interest in mathematics predicts achievement only at higher grade levels when students have a choice between more or less advanced courses (Koller, Baumert, and Schnabel, 2001).
Research on the development of interest indicates that children tend to have general or universal interests at first, which become more specific relatively quickly (Eccles, Wigfield, and Schiefele, 1998; Todt, 1990). Between ages 3 and 8, gender-specific interests emerge (Eccles, 1987; Ruble and Martin, 1998). For example, Johnson et al. (2004) found that among 4-year-olds, boys were more likely to have strong individual interests in conceptual domains such as dinosaurs or dogs than were girls. Girls’ interests were generally more aligned with the arts (drawing, painting) or with activities related to forming and elaborating on social relationships (pretend play, dolls). Between ages 9 and 13, emerging self-concept is assumed to be linked more directly to social group affiliation and cognitive ability, leading to occupational interests consistent with one’s social class and ability self-concepts (Cook et al., 1996). After age 13 or 14, students develop more differentiated and individualized vocational interests based on a notion of their internal, unique self. The development of vocational interests is thus a process of continuous elimination of interests that do not fit the individual’s emerging sense of self, which includes gender, social group affiliation, ability, and then personal identity (Todt, 1990).
Identity (“I Belong”)
Identity involves how people view themselves, how they present themselves, and how others see them (Holland et al., 1998; Wenger, 1998). A child’s identity as a learner is contested and influenced by different practices in everyday interactions, as well as in the cultural institutions he uses (Bruner,
1996; Ogbu, 1995). Research on identity and learning in specific domains builds on the premise that how one learns and what one learns are fundamentally related to the kind of person one wants to become.
Developing an identity that includes excelling in science may be more challenging for some students than others. The culture of science is foreign to many students, both mainstream and nonmainstream, and the challenges of science learning may be greater for students whose cultural traditions are discontinuous with the ways of knowing characteristic of science and school science (Cobern, 1996; Jegede and Okebukola, 1991; Lee, 1999). It is also true, however, that nonmainstream students frequently bring values and practices to the classroom that can be seen as continuous with scientific practices. However, such experiences that could serve as intellectual resources for new learning in science classrooms may not be easily recognized.
The challenge for these students in learning science is “to study a Western scientific way of knowing and at the same time respect and access the ideas, beliefs, and values of non-Western cultures” (Snively and Corsiglia, 2001, p. 24). The ability to make cultural transitions is critically important to nonmainstream students’ academic success. Giroux (1992), among others, has used the notion of border crossing to describe this process. To succeed academically, nonmainstream students must learn to negotiate the boundaries that separate their own cultural environments from the culture of science and school science (Aikenhead, 2001; Jegede and Aikenhead, 1999).
Even within the cultural mainstream, relatively few children’s primary socialization is so science-oriented as to be perfectly continuous with the demands of school science. Thus, border crossing between the culture of science and the culture of the everyday world is demanding for all students in science classes (Driver et al., 1994). At times, students may find themselves caught in conflicts between what is expected of them in science classes and what they experience at home and in their community. If they appear too eager or willing to engage in science inquiry, they may find themselves estranged from their family or peers. If they appear reluctant to participate, they risk marginalization from school and subsequent loss of access to learning opportunities. Although some students may successfully bridge the cultural divide between home and school, others may become alienated and even actively resist learning science.
In order to manage these differences, a child from a marginalized culture may temporarily adopt an identity for science learning experiences (Heath, 1982). Better understanding how children come to integrate science into their existing culture, rather than temporarily adopting an identity, may make it possible to create formal and informal science learning environments that are more accessible and meaningful.
Classrooms That Promote Productive Participation
Findings from research on motivation, attitudes, and identity converge with findings from research on engagement with science to highlight the importance of the classroom in fostering productive participation. Engle and Conant argue that the preconditions for productive disciplinary engagement involve providing appropriately challenging activities, allowing students to take authority over their learning but making sure that their work can be scrutinized by others (teachers and students), and using criteria acceptable to scientific disciplines (e.g., logical consistency, explanatory power). In addition, students need to have access to the resources they need (texts, laboratory equipment, recording devices) to evaluate their claims and communicate them to others.
One study (Cornelius and Herrenkohl, 2004) explicitly employed the notion of productive disciplinary engagement and connected it to analyses of participant structures and discourse. In their study of a pair of sixth grade girls investigating sinking and floating, the researchers found evidence that the students took an active role in generating ideas, engaging in scientific argumentation with their peers, and learning how to use persuasive discourse to convince others of the validity of those ideas.
Other studies have demonstrated that K-8 students in both urban and suburban public schools can engage in such scientific activities as investigating floating and sinking (Herrenkohl et al., 1999; Lee and Fradd, 1996; Palincsar et al., 2001; Varelas, Luster, and Wenzel, 1999); ecology (Hogan and Corey, 2001; Rosebery, Warren, and Conant, 1992); the classification and growth of plants and animals (Brown, Reveles, and Kelly, 2005; Lehrer and Schauble, 2004; Lehrer et al., 2000; Warren and Rosebery, 1996); motion down inclined planes (Lehrer et al., 2000); and density functions of material kind (Lehrer et al., 2001).
Most of the above studies employed ethnographic case analyses of a small number of classrooms or groups of students. A few studies employed a mixture of quantitative and qualitative analyses (Herrenkohl and Guerra, 1998; Lee and Fradd, 1996; Palincsar et al., 2001). The smallest number of studies focused on students in grades K-2 (e.g., Lehrer, Schauble, and Petrosino, 2001); the largest number of studies examined students in grades 5 or 6.
These studies tend to define disciplinary engagement differently and tend to employ different tasks or focus on different participant populations, making it difficult to easily summarize results across studies (other than to show that young children, poor students, and students with mild disabilities are capable—under the right conditions—of high-level disciplinary engagement with scientific concepts and procedures in formal educational settings). Most of the studies reviewed demonstrate that disciplinary engagement can
be achieved, but few appear to demonstrate productive disciplinary engagement (notable exceptions include Herrenkohl et al., 1999; Lehrer et al., 2001; Palincsar et al., 2001; Rosebery, Warren, and Conant, 1992).
As this chapter illustrates, science learning involves much more than individual cognitive activity. It is an inherently social and cultural process that requires mastery of specialized forms of discourse and comfort with norms of participation in the scientific community of the classroom. However, the rules for engaging in arguments and evaluating evidence that students learn in their everyday lives are sometimes dissimilar and even contradictory to those employed in science. Students often need support or explicit guidance to learn scientific norms for interacting with peers as they argue about evidence and clarify their own emerging understanding of science and scientific ideas. Genuine scientific argumentation with peer-to-peer interaction is rarely observed in science classrooms. Instead, teachers tend to dominate in a pattern of the teacher posing a question, the students responding, and the teacher following with an evaluative comment. Supporting argument in the science classroom requires a departure from this typical pattern.
Variations in students’ cultural and linguistic backgrounds translate into quite different learning histories and stances toward science. Making the norms and patterns of thinking in science visible in the classroom is one approach to supporting science learning in diverse student populations. Another is to capitalize on the continuities between students’ everyday thinking, knowledge, and resources and those of practicing scientists.
For all students, motivation and attitudes toward science play an important role in science learning. Becoming proficient in science requires students to actively engage in scientific tasks and participate in scientifically meaningful ways. Willingness to participate is shaped by students’ own beliefs, their previous experience with science, and aspects of the science classroom. For example, students’ belief in their ability in science, the value they place on science, their desire to master science, and their interest in science all have consequences for the quality of their engagement in the classroom and subsequent learning.
In turn, instruction can be designed in ways that foster a positive orientation toward science and promote productive participation in science classrooms. Such approaches include offering choice, providing meaningful tasks and an appropriate level of challenge, giving students authority over their learning while making sure their work can be examined by others, and making sure they have access to the resources they need to evaluate their claims and communicate them to others.
Abu-Hilal, M.M. (2000). A structural model of attitudes towards school subjects, academic aspiration and achievement. Educational Psychology, 20, 75-84.
Aikenhead, G.S. (2001). Cross-cultural science teaching: Praxis. A paper presented at the annual meeting of the National Association for Research in Science Teaching, St. Louis, MO, March 26-28.
Alexander, P.A., Kulikowich, J.M., and Jetton, T.L. (1994). The role of subject-matter knowledge and interest in the processing of linear and nonlinear texts. Review of Educational Research, 64, 201-252.
Amabile, T.M., and Hennessey, B.A. (1992). The motivation for creativity in children. In A.K. Boggiano and T.S. Pittman (Eds.), Achievement and motivation: A social-developmental perspective (pp. 54-72). New York: Cambridge University Press.
Anderman, E.M., and Young, A. (1994). Motivation and strategy use in science: Individual differences and classroom effects. Journal of Research in Science Teaching, 31, 811-831.
Aronson, J., Fried, C.B., and Good, C. (2002). Reducing the effects of stereotype threat on African American college students by shaping theories of intelligence. Journal of Experimental Social Psychology, 38(2), 113-125.
Atkinson, J.W. (1964). An introduction to motivation. New York: D. Van Nostrand.
Bandalos, D.L., Yates, K., and Thorndike-Christ, T. (1995). Effects of math self-concept, perceived self-efficacy, and attributions for failure and success on test anxiety. Journal of Educational Psychology, 87, 611-623.
Bazerman, C. (1988). Shaping written knowledge: The genre and activity of the experimental article in science. Madison: University of Wisconsin Press.
Brown, B.B., Reveles, J.M., and Kelly, G.J. (2005). Scientific literacy and discursive identity: A theoretical framework for understanding science learning. Science Education, 89, 779-802.
Bruner, J. (1996). The culture of education. Cambridge, MA: Harvard University Press.
Chen, C., and Stevenson, H.W. (1995). Motivation and mathematics achievement: A comparative study of Asian-American, Caucasian-American, and East Asian high school students. Child Development, 66, 1215-1234.
Cobern, W.W. (1996). Constructivism and non-Western science education research. International Journal of Science Education, 18(3), 295-310.
Cole, M. (1996). Cultural psychology: A once and future discipline. Cambridge, MA: Belknap Press of Harvard University Press.
Cook, T.D., Church, M.B., Ajanaku, S., Shadish,W.R., Jr., Kim, J., and Cohen, R. (1996). The development of occupational aspirations and expectations of inner-city boys. Child Development, 67(6), 3368-3385.
Cornelius, L.L., and Herrenkohl, L.R. (2004). Power in the classroom: How the classroom environment shapes students’ relationships with each other and with concepts. Cognition and Instruction, 22(4), 389-392.
Delpit L. (1995). Other people’s children: Cultural conflict in the classroom. New York: W.W. Norton.
Downey, D.B., and Ainsworth-Darnell, J.W. (2002). The search for oppositional culture among black students. American Sociological Review, 67, 156-164.
Driver, R., Asoko, H., Leach, J., Mortimer, E., and Scott, P. (1994). Constructing scientific knowledge in the classroom. Educational Researcher, 23(7), 5-12.
Duschl, R.A., and Osborne, J. (2002). Supporting and promoting argumentation discourse in science education. Studies in Science Education, 18, 39-72.
Dweck, C. (1999). Self-theories: Their role in motivation, personality, and development. Philadelphia, PA: Taylor and Francis.
Dweck, C., and Legget, E. (1988). A social-cognitive approach to motivation and personality. Psychological Review, 95, 256-273.
Eccles, J.S. (1987). Adolescence: Gateway to gender-role transcendence. In D.B. Carter (Ed.), Current conceptions of sex roles and sex typing: Theory and research. New York: Praeger.
Eccles, J., Adler, T., Futterman, R., Goff, S., Kaczala, C., Meece, J., and Midgley, C. (1983). Expectancies, values, and academic behaviors. In J.T. Spence (Ed.), Achievement and achievement motivation (pp. 75-146). San Francisco, CA: W.H. Freeman.
Eccles, J., and Harold, R. (1992). Gender differences in educational and occupational patterns among the gifted. In N. Colangelo, S.G. Assouline, and D.L. Ambroson (Eds.), Talent development: Proceedings from the 1991 Henry B. and Jocelyn Wallace national research symposium on talent development (pp. 2-30). New York: Trillium Press.
Eccles, J.S., Wigfield, A., Flanagan, C.A., Miller, C., Reuman, D.A., and Yee, D. (1989). Self-concepts, domain values, and self-esteem: Relations and changes at early adolescence. Journal of Personality, 57(2), 283-310.
Eccles, J., Wigfield, A., Harold, R.D., and Blumenfeld P. (1993). Age and gender differences in children’s self- and task perceptions during elementary school. Child Development, 64(3), 830-847.
Eccles, J.S., Wigfield, A., and Schiefele, U. (1998). Motivation to succeed. In N. Eisenberg (Ed.), Handbook of child psychology: Social, emotional, and personality development, fifth edition (pp. 1017-1095). New York: Wiley.
Eichinger, D., Anderson, C.W., Palinscar, A.S., and David, Y.M. (1991). An illustration of the roles of content knowledge, scientific argument, and social norms in collaborative problem solving. Paper presented at the annual meeting of the American Educational Research Association, April, Chicago, IL.
Engle, R.A., and Conant, F.R. (2002). Guiding principles for fostering productive disciplinary engagement: Explaining an emergent argument in a community of learners classroom. Cognition and Instruction, 20, 399-483.
Erickson, F. (2004). Culture in society and in educational practices. In J. Banks and C.M. Banks (Eds.), Multicultural education: Issues and perspectives (5th ed.). New York: Wiley.
Foster, M., Lewis, J., and Onafowora, L. (2003). Anthropology, culture, and research on teaching and learning: Applying what we have learned to improve practice. Teachers College Record, 105(2), 261-277.
Fredericks, J.A., and Eccles, J.S. (2002). Children’s competence and value beliefs from childhood through adolescence: Growth trajectories in two male-sex-typed domains. Developmental Psychology, 38(4), 519-533.
Garcia Coll, C.T., and Pachter, L.M. (2002). Ethnic and minority parenting. In M.H. Bornstein (Ed.), Handbook of parenting(vol. 4, pp. 1-20). Mahwah, NJ: Lawrence Erlbaum Associates.
Giroux, H.A. (1992). Border crossings: Cultural workers and the politics of education. New York: Routledge.
Gopnik, A., and Meltzoff, A.N. (1997). Words, thoughts, and theories. Cambridge, MA: MIT Press.
Guthrie, J., Van Meter, P., McCann, A., Wigfield, A., Bennett, L., Poundstone, C., Rice, M., Faibisch, F., Hunt, B., and Mitchell, A. (1996). Growth of literacy engagement: Changes in motivation and strategies during concept-oriented reading instruction. Reading Research Quarterly, 31, 306-332.
Guthrie, J., Wigfield, A., and VonSecker, C. (2000). Effects of integrated instruction on motivation and strategy use in reading. Journal of Educational Psychology, 92, 331-341.
Guthrie, J., Wigfield, A., Pedro Barbosa, P., Perencevich, K.C., Taboada, A., Davis, M.H., Scafiddi, N.T., and Tonks, S. (2004). Increasing reading comprehension and engagement through concept-oriented reading instruction. Journal of Educational Psychology, 96, 403-423.
Gutierrez, K.D., and Rogoff, B. (2003). Cultural ways of learning: Individual traits or repertoires of practice. Educational Researcher, 32(5), 19-25.
Gutman, L.M., and Midgley, C. (2000). The role of protective factors in supporting the academic achievement of poor African American students during the middle school transition. Journal of Youth and Adolescence, 29, 223-248.
Halliday, M.A.K. (1988). On the language of physical science. In M. Ghadessy (Ed.), Registers of written English: Situational factors and linguistic features (pp. 162-178). London, England: Pinter.
Harter, S. (1992). The relationship between perceived competence, affect, and motivational orientation within the classroom: Processes and patterns of change. In A.K. Boggiano and T.S. Pittman (Eds.), Achievement and motivation: A social-developmental perspective (pp. 77-114). New York: Cambridge University Press.
Heath, S.B. (1982). What no bedtime story means: Narrative skills at home and school. Language Socialization, 11, 49-76.
Hembree, R. (1988). Correlates, causes, and treatment of test anxiety. Review of Educational Research, 58, 47-77.
Hennessey, M.G. (2002). Metacognitive aspects of students’ reflective discourse: Implications for intentional conceptual change teaching and learning. In G.M. Sinatra and P.R. Pintrich (Eds.), Intentional conceptual change. Mahwah, NJ: Lawrence Erlbaum Associates.
Herrenkohl, L., and Guerra, M. (1998). Participant structures, scientific discourse, and student engagement in fourth grade. Cognition and Instruction, 16(4), 431-473.
Herrenkohl, L.R., Palincsar, A.S., DeWater, L.S., and Kawasaki, K. (1999). Developing scientific communities in classrooms: A sociocognitive approach. Journal of the Learning Sciences, 8(3-4), 451-493.
Hidi, S. (1990). Interest and its contribution as a mental resource for learning. Review of Educational Research, 60(4), 549-571.
Hidi, S. (2001). Interest, reading, and learning: Theoretical and practical considerations. Educational Psychology Review, 13(3), 191-209.
Hogan, K., and Corey, C. (2001). Viewing classrooms as cultural contexts for fostering scientific literacy. Anthropology and Education Quarterly, 32(2), 214-243.
Holland, D., Lachicotte, W., Skinner, D., and Cain, C. (1998). Identity and agency in cultural worlds. Cambridge, MA: Harvard University Press.
Hudicourt-Barnes, J. (2003). The use of argumentation in Haitian Creole science classrooms. Harvard Educational Review, 73(1), 73-93.
Jacobs, J.E., Lanza, S., Osgood, D.W., Eccles, J.S., and Wigfield, A. (2002). Changes in children’s self-competence and values: Gender and domain differences across grades one through twelve. Child Development, 73(2), 509-527.
Jegede, O.J., and Aikenhead, G.S. (1999). Transcending cultural borders: Implications for science teaching. Research in Science and Technology Education, 17, 45-66.
Jegede, O.J., and Okebukola, P.A. (1991). The effect of instruction on socio-cultural belief hindering the learning of science. Journal of Research in Science Teaching, 28, 275-285.
Johnson, K., Alexander, J., Spencer, S., Leibham, M., and Neitzel, C. (2004). Factors associated with the early emergence of intense interests within conceptual domains. Cognitive Development, 19, 325-343.
Kelley, B.B. (1973). The process of causal attribution. American Psychologist, 107-128.
Koller, O., Baumert, J., and Schnabel, K. (2001). Does interest matter? The relationship between academic interest and achievement in mathematics. Journal for Research in Mathematics Education, 32(5), 448-470.
Kuhn, D. (1991). The skills of argument. New York: Cambridge University Press.
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.
Ladson-Billings, G. (1995). Toward a theory of culturally relevant pedagogy. American Educational Research Journal, 32(3), 465-491.
Latour, B. (1987). Science in action: How to follow scientists and engineers through society. Cambridge, MA: Harvard University Press.
Lee, O. (1999). Science knowledge, world views, and information sources in social and cultural contexts: Making sense after a natural disaster. American Educational Research Journal, 36(2), 187-219.
Lee, O. (2002). Science inquiry for elementary students from diverse backgrounds. In W. Secada (Ed.), Review of research in education (vol. 26, pp. 23-69). Washington, DC: American Educational Research Association.
Lee, O., and Anderson, C.W. (1993). Task engagement and conceptual change in middle school science classrooms. American Educational Research Journal, 30, 585-610.
Lee, O., and Brophy, J. (1996). Motivational patterns observed in sixth-grade science classrooms. Journal of Research in Science Teaching, 33(3), 303-318.
Lee, O., and Fradd, S.H. (1996). Interactional patterns of linguistically diverse students and teachers: Insights for promoting science learning. Linguistics and Education: An International Research Journal, 8, 269-297.
Lee, O., and Fradd, S.H. (1998). Science for all, including students from non-English language backgrounds. Educational Researcher, 27(3), 12-21.
Lee, O., and Luykx, A. (2006). Science education and student diversity: Synthesis and research agenda. New York: Cambridge University Press.
Lehrer, R., Carpenter, S., Schauble, L., and Putz, A. (2000). Designing classrooms that support inquiry. In J. Minstrell and E. van Zee (Eds.), Inquiring into inquiry learning and teaching in science (pp. 80-99). Washington, DC: American Association for the Advancement of Science.
Lehrer, R., and Schauble, L. (2004). Modeling natural variation through distribution. American Educational Research Journal, 41(3), 635-679.
Lehrer, R., Schauble, L., and Petrosino, A.J. (2001). Reconsidering the role of experiment in science education. In K. Crowley, C. Schunn, and T. Okada (Eds.), Designing for science: Implications from everyday classrooms and professional settings (pp. 251-277). Mahwah, NJ: Lawrence Erlbaum Associates.
Lehrer, R., Schauble, L., Strom, D., and Pligge, M. (2001). Similarity of form and substance: Modeling material kind. In S.M. Carver and D. Klahr (Eds.), Cognition and instruction: Twenty-five years of progress (pp. 39-74). Mahwah, NJ: Lawrence Erlbaum Associates.
Lemke, J.L. (1990). Talking science: Language, learning and values. Norwood, NJ: Ablex.
Lipka, J. (1998). Transforming the culture of schools: Yup’ik Eskimo examples. Mahwah, NJ: Lawrence Erlbaum Associates.
Lucas, D., Broderick, N., Lehrer, R., and Bohanan, R. (2005, Nov/Dec). Making the grounds of scientific inquiry visible in the classroom. ScienceScope, 29(3), 39-42.
Luster, T., Rhoades, K., and Haas, B. (1989). The relation between parental values and parenting behavior: A test of the Kohn hypothesis. Journal of Marriage of the Family, 51, 139-147.
Malone, T.W., and Lepper, M.R. (1987). Making learning fun: A taxonomy of intrinsic motivations for learning. In R.E. Snow and M.J. Farr (Eds.), Aptitude, learning, and instruction: Cognitive and affective process analysis (vol. 3, pp. 223-253). Hillsdale, NJ: Lawrence Erlbaum Associates.
Marsh, H.W. (1989). Age and sex effects in multiple dimensions of self-concept: Preadolescence to early adulthood. Journal of Educational Psychology, 81, 417-430.
Meece, J.L., Blumenfeld, P.C., and Hoyle, R.H. (1988). Students’ goal orientations and cognitive engagement in classroom activities. Journal of Educational Psychology, 80, 514-523.
Mehan, H. (1979). Learning lessons: Social organization in the classroom. Cambridge, MA: Harvard University Press.
Miserandino, M. (1996). Children who do well in school: Individual differences in perceived competence and autonomy in above-average children. Journal of Educational Psychology, 88, 203-214.
National Research Council. (1997). Improving schooling for language-minority children: A research agenda. D. August and K. Hakuta (Eds.). Washington, DC: National Academy Press.
National Research Council and Institute of Medicine. (2004). Engaging schools: Fostering high school students’ motivation to learn. Committee on Increasing High School Students’ Engagement and Motivation to Learn. Board on Children, Youth, and Families, Division of Behaviorial and Social Sciences and Education. Washington, DC: The National Academies Press.
Ochs, E., Gonzales, P., and Jacoby, S. (1996). When I come down I’m in a domain state: Talk, gesture, and graphic representation in the interpretive activity of physicists. In E. Ochs, E. Schegloff, and S. Thompson (Eds.), Interaction and grammar. Cambridge, MA: Cambridge University Press.
Ogborn, J., Kress, G., Martins, I., and McGillicuddy, K. (1996). Explaining science in the classroom. Buckingham, England: Open University Press.
Ogbu, J.U. (1995). Cultural problems in minority education: Their interpretations and consequences-part one. Urban Review, 27, 189-205.
Olson, D.R. (1994). The world on paper. New York: Cambridge University Press.
Osborne, J.E., Erduran, S., Simon, S., and Monk, M. (2001). Enhancing the quality of argument in school science. School Science Review, 82(301), 63-70.
Palincsar, A.S., Magnusson, S.J., Collins, K.M., and Cutter, J. (2001). Making science accessible to all: Results of a design experiment in inclusive classrooms. Learning Disability Quarterly, 24, 15-32.
Perkins, D.N., and Grotzer, T.A. (2000). Models and moves: Focusing on dimensions of causal complexity to achieve deeper scientific understanding. Paper presented at the annual conference of the American Educational Research Association, April, New Orleans, LA.
Pintrich, P.R., Marx, R.W., and Boyle, R.A. (1993). Beyond cold conceptual change: The role of motivational beliefs and classroom contextual factors in the process of conceptual change. Review of Educational Research, 63(2), 167-199.
Pittman, T.S., Emery, J., and Boggiano, A.K. (1982). Intrinsic and extrinsic motivational orientations: Reward-induced changes in preference for complexity. Journal of Personality and Social Psychology, 42, 789-797.
Renninger, K.A., Ewen, L., and Lasher, A.K. (2002). Individual interest as context in expository text and mathematical word problems. Learning and Instruction, 12, 467-491.
Rogoff, B., Paradise, R., Mejia Arauz, R., Correa-Chavez, M., and Angelillo, C. (2003). Firsthand learning through intent participation. Annual Review of Psychology, 54, 175-203.
Rosebery, A.S., Warren, B., and Conant, F.R. (1992). Appropriating scientific discourse: Findings from language minority classrooms. Journal of the Learning Sciences, 2(1), 61-94.
Ruble, D.N., and Martin, C.L. (1998). Gender development. In W. Damon (Ed.), Handbook of child psychology: Vol. 3 (pp. 933-1016). New York: Wiley.
Ryan, R.M., Connell, J.P., and Plant, R.W. (1990). Emotions in non-directed text learning. Learning and Individual Differences, 2, 1-17.
Samarapungavan, A. (1992). Children’s judgments in theory-choice tasks: Scientific rationality in childhood. Cognition, 45(1), 1-32.
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), 859-888.
Schiefele, U. (1996). Topic interest, text representation, and quality of experience. Contemporary Educational Psychology, 21(1), 3-18.
Schiefele, U. (1999). Interest and learning from text. Scientific Studies of Reading, 3, 257-279.
Shultz, T.R. (1982). Rules of causal attribution. Monographs of the Society for Research in Child Development, 47(1).
Skinner, E.A., Wellborn, J.G., and Connell, J.P. (1990). What it takes to do well in school and whether I’ve got it: The role of perceived control in children’s engagement and school achievement. Journal of Educational Psychology, 82, 22-32.
Skinner, E.A., Zimmer-Gembeck, M., and Connell, J. (1998). Individual differences and the development of perceived control. Monographs of the Society in Child Development, 63(2-3), 1-220.
Snively, G., and Corsiglia, J. (2001). Discovering indigenous science: Implications for science education. Science Education, 85, 6-34.
Steele, C. (1997). A threat in the air: How stereotypes shape intellectual identity and performance. American Psychologist, 52, 613-629.
Stevenson, H., and Lee, S.Y. (1990). Contexts of achievement. Monographs of the Society for Research in Child Development, 55(1-2), Serial No. 221.
Todt, E. (1990). Development of interest. In H. Hetzer (Ed.), Applied developmental psychology of children and youth. Wiesbaden, Germany: Quelle & Meyer.
Tschirgi, J.E. (1980). Sensible reasoning: A hypothesis about hypotheses. Child Development, 51, 1-10.
Varelas, M., Luster, B., and Wenzel, S. (1999). Meaning making in a community of learners: Struggles and possibilities in an urban science class. Research in Science Education, 29(2), 227-245.
Warren, B., Ballenger, C., Ogonowski, M., Rosebery, A.S., and Hudicourt-Barnes, J. (2001). Rethinking diversity in learning science: The logic of everyday sense-making. Journal of Research in Science Teaching, 38(5), 529-552.
Warren, B., and Rosebery, A.S. (1996). “This question is just too, too easy!” Students’ perspectives on accountability in science. In L. Schauble and R. Glaser (Eds.), Innovations in learning: New environments for education (pp. 97-126). Mahwah, NJ: Lawrence Erlbaum Associates.
Wenger, E. (1998). Communities of practice: Learning, meaning and identity. Cambridge, MA: Cambridge University Press.
Wigfield, A., Battle, A., Keller, L., and Eccles, J. (2002) Sex differences in motivation, self concept, career aspiration and career choice: Implications for cognitive development. In A. McGillicuddy-De Lisi and R. De Lisi (Eds.), Biology, society and behaviour: The development of sex differences in cognition (pp. 93-124). Westport, CT: Ablex.
Wigfield, A., Eccles, J.S., MacIver, D., Reuman, D.A., and Midgley, C. (1991). Transitions during early adolescence: Changes in children’s domain-specific self-perceptions and general self-esteem across the transition to junior high school. Developmental Psychology, 27, 552-565.
Wigfield, A., Schiefele, U., Eccles, J., Roeser, R.W., and Davis-Kean, P. (2006). Development of achievement motivation. In W. Damon and N. Eisenberg (Eds.), Handbook of child psychology: Social, emotional, and personality development (6th ed., vol. 3). New York: Wiley.