Clark A. Chinn

The Role of Anomalous Data in Knowledge Acquisition: A Theoretical Framework and Implications for Science Instruction

Understanding how science students respond to anomalous data is essential to understanding knowledge acquisition in science classrooms. This article presents a detailed analysis of the ways in which scientists and science students respond to such data. We postulate that there are seven distinct forms of response to anomalous data, only one of which is to accept the data and change theories. The other six responses involve discounting the data in various ways in order to protect the preinstructional theory. We analyze the factors that influence which of these seven forms of response a scientist or student will choose, giving special attention to the factors that make theory change more likely. Finally, we discuss the implications of our framework for science instruction.

Scaffolding and Achievement in Problem-Based and Inquiry Learning: A Response to Kirschner, Sweller, and Clark (2006)

Many innovative approaches to education such as problem-based learning (PBL) and inquiry learning (IL) situate learning in problem-solving or investigations of complex phenomena. Kirschner, Sweller, and Clark (2006) grouped these approaches together with unguided discovery learning. However, the problem with their line of argument is that IL and PBL approaches are highly scaffolded. In this article, we first demonstrate that Kirschner et al. have mistakenly conflated PBL and IL with discovery learning. We then present evidence demonstrating that PBL and IL are powerful and effective models of learning. Far from being contrary to many of the principles of guided learning that Kirschner et al. discussed, both PBL and IL employ scaffolding extensively thereby reducing the cognitive load and allowing students to learn in complex domains. Moreover, these approaches to learning address important goals of education that include content knowledge, epistemic practices, and soft skills such as collaboration and self-directed learning.

An Empirical Test of a Taxonomy of Responses to Anomalous Data in Science

The purpose of this study was to test a taxonomy of seven proposed responses to anomalous data. Our results generally supported the taxonomy but indicated that one additional type of response should be added to the taxonomy. We conclude that there are eight possible responses to anomalous data: (a) ignoring the data, (b) rejecting the data, (c) professing uncertainty about the validity of the data, (d) excluding the data from the domain of the current theory, (e) holding the data in abeyance, (f) reinter- preting the data, (g) accepting the data and making peripheral changes to the current theory, and (h) ac- cepting the data and changing theories. We suggest that this taxonomy could help science teachers in two ways. First, science teachers could use the taxonomy to try to anticipate how students might react to anom- alous data so as to make theory change more likely. Second, science teachers could use the taxonomy as a framework to guide classroom discussion about the nature of scientific rationality. In addition, the tax- onomy suggests directions for future research.

Expanding the Dimensions of Epistemic Cognition: Arguments from Philosophy and Psychology.

Psychological and educational researchers have developed a flourishing research program on epistemological dimensions of cognition (epistemic cognition). Contemporary philosophers investigate many epistemological topics that are highly relevant to this program but that have not featured in research on epistemic cognition. We argue that integrating these topics into psychological models of epistemic cognition is likely to improve the explanatory and predictive power of these models. We thus propose and explicate a philosophically grounded framework for epistemic cognition that includes five components: (a) epistemic aims and epistemic value; (b) the structure of knowledge and other epistemic achievements; (c) the sources and justification of knowledge and other epistemic achievements, and the related epistemic stances; (d) epistemic virtues and vices; and (e) reliable and unreliable processes for achieving epistemic aims. We further argue for a fine-grained, context-specific analysis of cognitions within the five components.

Explanation in Scientists and Children

In this paper we provide a psychological account of the nature and development of explanation. We propose that an explanation is an account that provides a conceptual framework for a phenomenon that leads to a feeling of understanding in the reader/hearer. The explanatory conceptual framework goes beyond the original phenomenon, integrates diverse aspects of the world, and shows how the original phenomenon follows from the framework. We propose that explanations in everyday life are judged on the criteria of empirical accuracy, scope, consistency, simplicity, and plausibility. We conclude that explanations in science are evaluated by the same criteria, plus those of precision, formalisms, and fruitfulness. We discuss several types of explanation that are used in everyday life – causal/mechanical, functional, and intentional. We present evidence to show that young children produce explanations (often with different content from those of adults) that have the same essential form as those used by adults. We also provide evidence that children use the same evaluation criteria as adults, but may not apply those additional criteria for the evaluation of explanations that are used by scientists.

Models of Data: A Theory of How People Evaluate Data

This article reports the results of a study investigating how undergraduates evaluate realistic scientific data in the domains of geology and paleontology. The results are used to test several predictions of a theory of data evaluation, which we call models-of-data theory. Models-of-data theory assumes that when evaluating data, the individual constructs a particular kind of cognitive model that integrates many features of the data with a theoretical interpretation of the data. The individual evaluates the model by attempting to generate alternative causal explanations for the events in the model. We contrast models-of-data theory with other proposals for how data are cognitively represented and show that models-of-data theory gives a good account of the pattern of written evaluations of data produced by the undergraduates in the study. We discuss theoretical and instructional implications of the theory.

Distinguishing Between Understanding and Belief

Clark A. Chinn is an assistant professor of education at Rutgers University; Ala Samarapungavan is an associate professor of education at Purdue University. S A FIFTH-GRADE TEACHER, has just completed a science unit on molecules, and her class has done well on the unit test that she just handed back. After going over the test, the class heads to recess. Sandra overhears one student who received a high test score asking another, “Do you really believe that stuff about molecules?” The other replies, “No way!” The teacher has never heard such an exchange in 10 years of teaching. She wonders if it is rare for students to disbelieve ideas they have encountered in class or if this occurs regularly and she has just never noticed. In this article, we will show that students frequently do not believe what they are learning in school, in science, and in other classes. Because of this, teachers must seriously consider the role of persuasive teaching in their classes. A key distinction that underpins this article is the distinction between understanding an idea and believing that idea. We think that educational theory and practice have been hampered by a neglect of this distinction. Most theoretical and practical work has conceptualized learning as knowledge change. However, the conceptualization of learning as changes in knowledge confuses changes in understanding with changes in belief. This confusion can lead to mistaken conclusions about how to plan instruction. This article is divided into three parts. First, we discuss the distinction between understanding and belief. We provide an illustration showing that teachers’ interpretations of what students have learned can be seriously in error when they do not consider both understanding and belief. Second, we present some examples to show that students’ understandings and beliefs often diverge, which makes it necessary to take both into account when thinking about what students are learning. Many of our examples are drawn from science, because most of our own research is in this area. But we think divergences between understanding and belief occur in other school subjects, as well. Third, we discuss implications for teachers. These implications center on a metaphor of teaching as persuasion.

Conceptual Change—Multiple Routes, Multiple Mechanisms: A Commentary on Ohlsson (2009)

This article presents a commentary on Stellan Ohlssons (2009) theory of conceptual change by resubsumption and competitive evaluation of cognitive utility. We note two features of Ohlssons theory that we think are particularly strong. We then argue that Ohlssons theory explains one route to conceptual change but that there are many other routes to conceptual change that require mechanisms other than Ohlssons. We discuss a number of these multiple routes to conceptual change. Theories of conceptual change should attempt to explain the broad diversity of routes along which conceptual change occurs.

Scientists' Responses to Anomalous Data: Evidence from Psychology, History, and Philosophy of Science

This paper presents an analysis of the forms of response that scientists make when confronted with anomalous data. We postulate that there are seven ways in which an individual who currently holds a theory can respond to anomalous data: (1) ignore the data; (2) reject the data; (3) exclude the data from the domain of the current theory; (4) hold the data in abeyance; (5) reinterpret the data; (6) make peripheral changes to the current theory; or (7) change the theory. We analyze psychological experiments and cases from the history of science to support this proposal. Implications for the philosophy of science are discussed.

Mental Models in Data Interpretation

This paper presents a cognitive account of the process of evaluating scientific data. Our account assumes that when individuals evaluate data, they construct a mental model of a data-interpretation package, in which the data and theoretical interpretations of the data are integrated. We propose that individuals attempt to discount data by seeking alternative explanations for events within the mental model; data-interpretation packages are accepted when the individual cannot find alternative accounts for these events. Our analysis indicates that there are many levels at which data-interpretation packages can be accepted or denied.