. We present a vision for learning environments, called Science Learning Spaces, that are rich in engaging content and activities, provide constructive experiences in scientific process skills, and are as instructionally effective as a personal tutor. A Science Learning Space combines three independent software systems: 1) lab/field simulations in which experiments are run and data is collected, 2) modeling/construction tools in which data representations are created, analyzed and presented, and 3) tutor agents that provide just-in-time assistance in higher order skills like experimental strategy, representational tool choice, conjecturing, and argument. We believe that achieving this ambitious vision will require collaborative efforts facilitated by a component-based software architecture. We have created a feasibility demonstration that serves as an example and a call for further work toward achieving this vision. In our demonstration, we combined 1) the Active Illustratio...

This article explores how students ’ epistemological ideas about the nature of science interact with their conceptual understanding of a particular domain, as reflected in written explanations for an event of natural selection constructed by groups of high school students through a technology-supported curriculum about evolution. Analyses intended to disentangle conceptual and epistemic aspects of explanation reveal that groups sought plausible causal accounts of observed data, and were sensitive to the need for causal coherence, while articulating explanations consistent with the theory of natural selection. Groups often failed to explicitly cite data to support key claims, however, both because of difficulty in interpreting data and because they did not seem to see explicit evidence as crucial to an explanation. These findings reveal that students have productive epistemic resources to bring to bear during inquiry, but highlight the need for an epistemic discourse around student-generated artifacts to deepen both the conceptual and epistemological understanding students may develop through inquiry. Inquiry-based approaches to science education emphasize processes of inquiry, such as asking questions, generating and interpreting data, and forming conclusions

ABSTRACT: Science education reforms consistently maintain the goal that students develop an understanding of the nature of science, including both the nature of scientific knowledge and methods for making it. This paper articulates a framework for scaffolding epistemic aspects of inquiry that can help students understand inquiry processes in relation to the kinds of knowledge such processes can produce. This framework underlies the design of a technology-supported inquiry curriculum for evolution and natural selection that focuses students on constructing and evaluating scientific explanations for natural phenomena. The design has been refined through cycles of implementation, analysis, and revision that have documented the epistemic practices students engage in during inquiry, indicate ways in which designed tools support students ’ work, and suggest necessary additional social scaffolds. These findings suggest that epistemic tools can play a unique role in supporting students ’ inquiry, and a fruitful means for studying students ’ scientific epistemologies.

Kerr, MacCoun, & Kramer, 1996). Teamwork is not guaranteed to provide either of these advantages. With respect to (1) combining complementary inputs, increasing the size of an organization tends to reduce its overall efficiency unless there is also an increase in departmentalization and standardization of tasks (Blau, 1970). The latter features, however, reduce flexibility of response in a changing or novel environment (Donaldson, 1995). A related problem is goal displacement, in which specialized units lose sight of the larger organizational purpose, and pursue their own goals as if they were fixed ends rather than means, which should be reevaluated when conditions change (Scott, 1998). With respect to (2) better decisions, groups may be affected by socialization biases, such as groupthink, which induce conformity rather than diversity of thought (Janus, 1972; March, 1996.). For this reason, group decisions tend to be better when individuals think about the problem independently befo

The theoretical stance explicated in this chapter assumes that scientific discoveries often require that the problem solver (either the scientist or the inventor) re-conceptualizes the problem in a way that crosses ontological categories. Examples of the highest level of ontological categories are entities, processes, and mental states. Discoveries might be explained as the outcome of the process of switching the problem representation to a different ontological category. Examples from contemporary and the history of science will be presented to support this radical ontological change hypothesis.

Disciplines that study science are relatively well established in philosophy, history, and sociology. Psychology of science, by comparison, is a late bloomer but has recently shown signs of codification. The authors further this codification by integrating and reviewing the growing literature in the developmental, cognitive, personality, and social psychology of science. Only by integrating the findings from each of these perspectives can the basic questions in the study of scientific behavior be answered: Who becomes a scientist and what role do biology, family, school, and gender play? Are productivity, scientific reasoning, and theory acceptance influenced by age? What thought processes and heuristics lead to successful discovery? What personality characteristics distinguish scientists from nonscientists and eminent from less eminent scientists? Finally, how do intergroup relations and social forces influence scientific behavior? A model that integrates the consensual empirical findings from the psychology of science is pro-posed. Without the addition of a psychological dimension, I believe, it is impossible to appreciate fully the essence

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To understand deep cognitive change, we have to understand how learners can go beyond their own prior knowledge. We propose a displacement scenario in which a learner acquires a target idea in a different context and then transfers that idea into a target context. We used virtual reality technology to implement a displacement scenario for teaching 2nd grade children that the Earth is round. The rather large pre- to posttest improvement was stable over four months.

A central goal of scientific explanation is to account for patterns of data. An important way to assess students' abilities to construct scientific explanations is to examine how they use data as evidence. A good deal of cognitive research has explored how students respond to specific pieces of data and evaluate them in terms of a current theory or belief. This work finds that, from a normative view, students often ignore data that they ought to consider when evaluating claims, or assimilate such data in ways that do not damage their current theories. Researchers differ on whether or not such reasoning reflects an epistemological stance that is fundamentally non-scientific, or is essentially reasonable and consistent with scientific practice. This study examines high school students' efforts to use complex, multi-faceted data sets to construct causal explanations of natural selection phenomena, consistent with the theory of natural selection. The study seeks answers to two questions. What data do students select to use as evidence for their explanations? How do they refer to specific data to justify particular causal claims? Content analyses of explanations examined how students referred to data as evidence: the features of inscriptions (e.g., graphs, field notes) they referred to, and the justifications they gave for the importance of specific data. Students largely cited relevant, but insufficient, data for claims, and preferred numerical data over textual field notes. These and other uses of evidence indicate not just students' understanding of what specific data mean, but their ideas about what counts as persuasive evidence. These findings highlight the need for instruction focused on epistemic criteria for good explanations, in addition to the conceptual relations ...

Conducting observational investigations of behaviors and processes is an important method for gen-erating scientific knowledge. This paper describes a methodology for assisting students in the processes of observational inquiry and theory articulation and its instantiation in a set of digital video tools. We describe a high school biology curriculum where students use these tools to investigate video clips of animal behavior and develop theories about how and why these behaviors evolved. We focus our discus-sion on an investigation model that scaffolds students through the processes of observing and explaining video as data and the computational and curricular supports that were designed to make these processes explicit. We conclude with a presentation of preliminary results to illustrate the types of explanations that emerged from working with the software and curriculum and a discussion of issues that emerged during the course of the research.