The purpose of this paper is to present and contrast two approaches to representing the structure of complex, dialectical arguments. Previous research has focused mainly on representing single arguments presented by a single arguer; this analysis examines the naturalistic give and take of dialectical argumentation among fourth graders. One approach to representing dialectical arguments is the argument network approach, which views the arguments as webs of interlocking premises and conclusions. The second approach is the causal network approach, which treats many of the ideas presented in the discussions as events linked in causal, narrative sequences. The two approaches capture different but complementary aspects of the structure of the arguments.

Philosophers have disagreed about whether theories that make successful predictions are more believable than theories that merely explain data that have aheady been discovered. Predictivists believe that theories that make successful predictions have an edge over theories that offer only retrospective explanations of the same data. Nonpredictivists maintain that whether a theory predicts data or explains data retrospectively is irrelevant to the believability of the theory. The purpose of this paper is to report on three psychological experiments designed to determine whether undergraduates behave as predictivists or nonpredictivists when they evaluate theories. Results indicate that subjects behaved as nonpredictivists when one theory predicted a body of data and a second theory was devised later to explain the same data retrospectively. However, subjects behaved as predictivists in the situation in which a theory retreated in the face of anomalous data by adding an auxiliary hypothesis; for instance, theories that predicted data by adding the necessary auxiliary hypotheses before the data came in were more believable than theories that added the auxiliary hypothesis in reaction to the data. These results suggest that cognitive models of theory choice that assume that people are nonpredictivists may require modification.

A crucial aspect of understanding knowledge acquisition and theory change is understanding how people respond to anomsilous information. We propose that there are seven fundamental responses that people make to anomalous information. We provide evidence from the history of science and from psychology for each of these responses, and we present the results of a study that explores some of the factors that determine these responses.

In order to understand conceptual change, it is crucial to understand how people respond to anomalous information. The purpose of this paper is to present a framework for understanding how people respond to anomalous data and why they respond as they do. First, w e present a taxonomy of seven responses to anomalous data. Second, we present an analysis of eight factors that are hypothesized to influence which of these seven responses an individual will choose. Finally, w e present the results of an experiment that investigates severjj of these eight factors. A key to understanding conceptual change is understanding how people respond to anomalous information. Information that contradicts an individual's current beliefs is important because without it, an individual has no need to alter current conceptions. Without the goad of anomalous information, current conceptions are perfectly adequate for understanding the world. A particularly important form of anomalous information is anomalous data. Anomalous data have played a central role in conceptual change in the history of science (Kuhn, 1962) and in science education (Chinn & Brewer, in press). Moreover, most artificial intelligence systems that model scientific discovery and theory change use jmomalous data to trigger the theory change process (e.g., Kulkarni & Simon, 1988). Chinn and Brewer (1992, in press) have proposed a detailed taxonomy of possible responses to emomalous data. W h e n an individual w h o holds theory A encounters anomalous data, which may be accompjmied by an alternative theory B, the individual can choose one of seven responses to the anomalous data: 1. Igitore the data. 2. Reject the data because of methodological flaws, random error, or alleged fraud. 3. Exclude the data from the domain of theory A b asserting that theory A is not intended to explain th data. 4. Hold the data in abeyance, i.e., concede that theory A cannot explain the data at present but asset that theory A will be elaborated in the future so tha it can explain the data. 5. Accept but reinterpret the data so as to make thi data consistent with theory A. 6. Accept the data and make minor, peripheral changes to theory A. 7. Accept the data and change theories, possibly ti theory B. Of these seven responses, only the last two involv any change m theory A , and only the last produces change that can be called conceptual change. Th first six responses are theory-preserving response because the individual discounts the anomalous data in order to protect theory A. In this paper, we address a crucial issue in conceptual change: What causes people to respond t( anomalous data as they do? For example, why doe an individual reject data in one instance, reinterpret data in another instance, and change theories in ye another instance? W e propose a set of eight factor that influence h ow people respond to anomalous data then we report the results of an experiment designei to investigate several of these factors.

Most philosophers of science now believe that scientific data are theory laden, i.e., the evaluation of data is influenced by prior theoretical beliefs. Although there is historical and psychological evidence that is consistent with the theory-laden position, experimental evidence is needed to directly test whether prior beliefs influence the evaluation of scientific data. In a fully counterbalanced design, one group of subjects received evidence that dinosaurs were cold-blooded, and another group of subjects received evidence that dinosaurs were warm-blooded. The subjects reported a strong belief in whichever theory they had read about. Then subjects were presented with a piece of data that supported one theory and contradicted the other theory. The identical piece of data was rated as more believable when it was consistent with the subject's theory than when it was inconsistent. These results provide clear support for the position that scientific data are theory laden.

Many studies investigating inquiry learning in science domains have appeared over the years. Throughout this period, inquiry learning has been regularly criticized by scholars who favor direct instruction over inquiry learning. In this vein, Zhang, Kirschner, Cobern, and Sweller (2022) recently asserted that direct instruction is overall superior to inquiry-based instruction and reproached policy makers for ignoring this fact. In the current article we reply to this assertion and the premises on which it is based. We review the evidence and argue that a more complete and correct interpretation of the literature demonstrates that inquiry-based instruction produces better overall results for acquiring conceptual knowledge than does direct instruction. We show that this conclusion holds for controlled, correlational, and program-based studies. We subsequently argue that inquiry-based and direct instruction each have their specific virtues and disadvantages and that the effectiveness of each approach depends on moderating factors such as the learning goal, the domain involved, and students' prior knowledge and other student characteristics. Furthermore, inquiry-based instruction is most effective when supplemented with guidance that can be personalized based on these moderating factors and can even involve providing direct instruction. Therefore, we posit that a combination of inquiry and direct instruction may often be the best approach to support student learning. We conclude that policy makers rightfully advocate inquiry-based instruction, particularly when students’ investigations are supplemented with direct instruction at appropriate junctures.