Studies exploring how students learn and understand science processes such as diffusion and natural selection typically find that students provide misconceived explanations of how the patterns of such processes arise (such as why giraffes’ necks get longer over generations, or how ink dropped into water appears to “flow”). Instead of explaining the patterns of these processes as emerging from the collective interactions of all the agents (e.g., both the water and the ink molecules), students often explain the pattern as being (...) caused by controlling agents with intentional goals, as well as express a variety of many other misconceived notions. In this article, we provide a hypothesis for what constitutes a misconceived explanation; why misconceived explanations are so prevalent, robust, and resistant to instruction; and offer one approach of how they may be overcome. In particular, we hypothesize that students misunderstand many science processes because they rely on a generalized version of narrative schemas and scripts (referred to here as a Direct-causal Schema) to interpret them. For science processes that are sequential and stage-like, such as cycles of moon, circulation of blood, stages of mitosis, and photosynthesis, a Direct-causal Schema is adequate for correct understanding. However, for science processes that are non-sequential (or emergent), such as diffusion, natural selection, osmosis, and heat flow, using a Direct Schema to understand these processes will lead to robust misconceptions. Instead, a different type of general schema may be required to interpret non-sequential processes, which we refer to as an Emergent-causal Schema. We propose that students lack this Emergent Schema and teaching it to them may help them learn and understand emergent kinds of science processes such as diffusion. Our study found that directly teaching students this Emergent Schema led to increased learning of the process of diffusion. This article presents a fine-grained characterization of each type of Schema, our instructional intervention, the successes we have achieved, and the lessons we have learned. (shrink)

We use our knowledge of causal relationships to imagine possible events. We also use these relationships to look deep into the past and infer events that were not witnessed or to infer what can not be directly seen in the present. Knowledge of causal relationships allows us to go beyond the here and now. This chapter introduces a new theoretical framework for how this very basic concept might be mentally represented. It proposes an epistemological theory of causation — that is, (...) a theory that specifies the nature of people's knowledge of causation, the notion of causation used in everyday language and reasoning. (shrink)

It is argued that causal understanding originates in experiences of acting on objects. Such experiences have consistent features that can be used as clues to causal identification and judgment. These are singular clues, meaning that they can be detected in single instances. A catalog of 14 singular clues is proposed. The clues function as heuristics for generating causal judgments under uncertainty and are a pervasive source of bias in causal judgment. More sophisticated clues such as mechanism clues and repeated interventions (...) are derived from the 14. Research on the use of empirical information and conditional probabilities to identify causes has used scenarios in which several of the clues are present, and the use of empirical association information for causal judgment depends on the presence of singular clues. It is the singular clues and their origin that are basic to causal understanding, not multiple instance clues such as empirical association, contingency, and conditional probabilities. (shrink)

When people make judgments about the effects of a perturbation on populations of species in a food web, their judgments exhibit the dissipation effect: a tendency to judge that effects of the perturbation weaken or dissipate as they spread out through the food web from the locus of the perturbation. In the present research evidence for two more phenomena is reported. Terminal locations are points in the food web with just a single connection to the rest of the web. Judged (...) changes tended to be higher for species at terminal locations than for species the same distance from the perturbation but at nonterminal locations. Branches are points in the web where a route splits into two or more routes. Judged changes tended to be lower for species following branching points than for species the same distance from the perturbation but not following branching points. It is proposed that the findings can be explained as effects of a mental model employing concepts of influence and resistance. Under this model a perturbation is a change in energy level at a point in the system that acts as an influence affecting the rest of the system. The basic concepts in this model are domain‐general and on that basis it is predicted that the dissipation effect should be found in judgments of any physical system to which notions of influence and resistance can be applied. (shrink)

This article presents the results of an experiment which investigated elementary school children's explanations of the day/night cycle. First, third, and fifth grade children were asked to explain certain phenomena, such as the disappearance of the sun during the night, the disappearance of stars during the day, the apparent movement of the moon, and the alteration of day and night. The results showed that the majority of the children in our sample used in a consistent fashion a small number of (...) relatively well‐defined mental models of the earth, the sun, and the moon to explain the day/night cycle. These mental models of the day/night cycle were empirically accurate, logically consistent and revealed some sensitivity on the part of the children to issues of simplicity of explanation. The younger children formed Initial mental models which provided explanations of the day/night cycle based on everyday experience (e.g., the sun goes down behind mountains, clouds cover up the sun). The older children constructed synthetic mental models (e.g., the sun and the moon revolve around the stationary earth every 24 hours; the earth rotates in an up/down direction and the sun and moon are fixed on opposite sides) which represented attempts to synthesize the culturally accepted view with aspects of their Initial models. A few of the older children appeared to have constructed a mental model of the day/night cycle similar to the scientific one. A theoretical framework is outlined which explains the formation of initial, synthetic, and scientific models of the day/night cycle in terms of the reinterpretation of a hierarchy of constraints, some of which are present early in the child's life, and others which emerge later out of the structure of the acquired knowledge. (shrink)

It is often assumed that engaging in a one‐on‐one dialogue with a tutor is more effective than listening to a lecture or reading a text. Although earlier experiments have not always supported this hypothesis, this may be due in part to allowing the tutors to cover different content than the noninteractive instruction. In 7 experiments, we tested the interaction hypothesis under the constraint that (a) all students covered the same content during instruction, (b) the task domain was qualitative physics, (c) (...) the instruction was in natural language as opposed to mathematical or other formal languages, and (d) the instruction conformed with a widely observed pattern in human tutoring: Graesser, Person, and Magliano's 5‐step frame. In the experiments, we compared 2 kinds of human tutoring (spoken and computer mediated) with 2 kinds of natural‐language‐based computer tutoring (Why2‐Atlas and Why2‐AutoTutor) and 3 control conditions that involved studying texts. The results depended on whether the students' preparation matched the content of the instruction. When novices (students who had not taken college physics) studied content that was written for intermediates (students who had taken college physics), then tutorial dialogue was reliably more beneficial than less interactive instruction, with large effect sizes. When novices studied material written for novices or intermediates studied material written for intermediates, then tutorial dialogue was not reliably more effective than the text‐based control conditions. (shrink)

A productive way to think about imagistic mental models of physical systems is as though they were sources of quasi‐empirical evidence. People depict or imagine events at those points in time when they would experiment with the world if possible. Moreover, just as they would do when observing the world, people induce patterns of behavior from the results depicted in their imaginations. These resulting patterns of behavior can then be cast into symbolic rules to simplify thinking about future problems and (...) to reveal higher order relationships. Using simple gear problems, three experiments explored the occasions of use for, and the inductive transitions between, depictive models and number‐based rules. The first two experiments used the convergent evidence of problem‐solving latencies, hand motions, referential language and error data to document the initial use of a model, the induction of rules from the modeling results, and the fallback to a model when a rule fails. The third experiment explored the intermediate representations that facilitate the induction of rules from depictive models. The strengths and weaknesses of depictive modeling and more analytic systems of reasoning are delineated to motivate the reasons for these transitions. (shrink)

Our research addresses the issue of teaching and learning concepts in science education as an empirical question. We study the process of conceptualization by closely examining the unfolding of classroom lesson sequences. We situate our work within the practice turn line of research on epistemic practices in science education. We also adopt a practice turn approach when it comes to the learning of concepts, as we consider conceptualization as being inherent within epistemic practices. In our work, pedagogical practices are modeled (...) as learning games and epistemic practices in science education are characterized as enacted epistemic games emerging through the unfolding of learning games. Science practices are modeled as source epistemic games since they are the source of the knowledge at stake in pedagogical practices. From this point, we examine closely how playing learning games can enable students to play enacted epistemic games and then in turn the source epistemic games at the core of conceptual understanding. Thus, the main contribution of this paper is to link pedagogical practices to epistemic practices in science education and to science practices in general. Our method is consistent with this epistemological framework as our case study on the concept of earthquakes in a 5th grade classroom sequence illustrates. Following an investigation of two experienced teachers and their classes during a teaching unit, our analysis shows how teaching effectiveness is determined by a dialectic. This entails on the one hand a didactic continuity between learning games and enacted epistemic games and, on the other, an epistemic continuity between enacted epistemic games and source epistemic games. (shrink)

An analysis of students’ difficulties for a curricular topic may help the educator to gain better insight into students’ reasoning about that topic which is a prerequisite for high-quality teaching. The purpose of this study was to demonstrate how distractor analysis may be used for identifying students’ difficulties in a certain topic. In order to be in position to perform invariant measurement and to easily relate students’ difficulties to their achievement levels, we decided to take a Rasch modeling approach. Our (...) study included 14 wave optics items and 286 students from five universities in Slovenia, Croatia, and Bosnia and Herzegovina. Rasch modeling was used to estimate item and student measures, as well as to create option probability curves which allowed us to relate students’ achievement levels to the choice of individual distractors. It has been found that all 14 included items function in line with the Rasch model. In 10 out of 14 items there were distractors chosen by at least 25% of students. For several out of these 10 items, the option probability curves indicated that attractiveness of individual distractors depended on students’ ability levels. We could conclude that the Rasch-based distractor analysis may provide very useful information for differentiation of physics instruction. Analiza studentskih poteškoća vezanih za određenu nastavnu temu može pomoći predavaču u stjecanju boljeg uvida u studentsko razumijevanje određene teme, što je preduvjet za visokokvalitetnu nastavu. Svrha ovog istraživanja sastojala se u demonstriranju načina na koji analize ometača mogu biti iskorištene za identificiranje studentskih poteškoća u određenom području. Kako bismo mogli provoditi invarijantna mjerenja i lako povezati studentske poteškoće s razinama njihovih postignuća, odlučili smo se studiju provesti u okviru Rasch formalizma. Naše istraživanje obuhvatilo je 14 zadataka iz područja valne optike i 286 studenata s pet sveučilišta u Sloveniji, Hrvatskoj te Bosni i Hercegovini. Rasch modeliranje korišteno je za procjenu težine zadataka i sposobnosti studenata, kao i za kreiranje krivulja vjerojatnosti ponuđenih opcija što nam je omogućilo povezivanje razine razumijevanja studenata s izborom pojedinačnih ometača. Utvrđeno je da svih 14 zadataka funkcionira u skladu s Rasch modelom. Za 10 zadataka imali smo ometače koje je odabralo najmanje 25 % studenata. Kod nekoliko od ovih 10 zadataka krivulje vjerojatnosti ponuđenih opcija ukazuju na to da atraktivnost pojedinačnih ometača ovisi o razinama sposobnosti učenika. Možemo zaključiti da analiza ometača primjenom Rasch modeliranja može dati vrlo korisne informacije za diferencijaciju nastave fizike. (shrink)

In this article we focus on the concept of concept in conceptual change. We argue that (1) theories of higher learning must often employ two different notions of concept that should not be conflated: psychological and scientific concepts. The usages for these two notions are partly distinct and thus straightforward identification between them is unwarranted. Hence, the strong analogy between scientific theory change and individual learning should be approached with caution. In addition, we argue that (2) research in psychology and (...) cognitive science provides a promising theoretical basis for developing explanatory mechanistic models of conceptual change. Moreover, we argue that (3) arguments against deeper integration between the fields of psychology and conceptual change are not convincing, and that recent theoretical developments in the cognitive sciences might prove indispensable in filling in the details in mechanisms of conceptual change. (shrink)

In this article, we propose that gestures play an important role in the connection between sensorimotor experience and language. Gestures may be the link between bodily experience and verbal expression that advocates of embodied cognition have postulated. In a developmental sequence of communicative action, gestures, which are initially similar to action sequences, substantially shorten and represent actions in metonymic form. In another process, action sequences are based on kinesthetic schemata that themselves find their metaphoric expression in language. Again, gestures enact (...) kinesthetic schemata that are correlated with verbal expressions. Examples from a large database are used to illustrate the various processes by means of which language arises when students conduct school science investigations. (shrink)

It is often assumed that engaging in a one‐on‐one dialogue with a tutor is more effective than listening to a lecture or reading a text. Although earlier experiments have not always supported this hypothesis, this may be due in part to allowing the tutors to cover different content than the noninteractive instruction. In 7 experiments, we tested the interaction hypothesis under the constraint that (a) all students covered the same content during instruction, (b) the task domain was qualitative physics, (c) (...) the instruction was in natural language as opposed to mathematical or other formal languages, and (d) the instruction conformed with a widely observed pattern in human tutoring: Graesser, Person, and Magliano's 5‐step frame. In the experiments, we compared 2 kinds of human tutoring (spoken and computer mediated) with 2 kinds of natural‐language‐based computer tutoring (Why2‐Atlas and Why2‐AutoTutor) and 3 control conditions that involved studying texts. The results depended on whether the students' preparation matched the content of the instruction. When novices (students who had not taken college physics) studied content that was written for intermediates (students who had taken college physics), then tutorial dialogue was reliably more beneficial than less interactive instruction, with large effect sizes. When novices studied material written for novices or intermediates studied material written for intermediates, then tutorial dialogue was not reliably more effective than the text‐based control conditions. (shrink)

Despite the accumulation of substantial cognitive science research relevant to education, there remains confusion and controversy in the application of research to educational practice. In support of a more systematic approach, we describe the Knowledge-Learning-Instruction (KLI) framework. KLI promotes the emergence of instructional principles of high potential for generality, while explicitly identifying constraints of and opportunities for detailed analysis of the knowledge students may acquire in courses. Drawing on research across domains of science, math, and language learning, we illustrate the (...) analyses of knowledge, learning, and instructional events that the KLI framework affords. We present a set of three coordinated taxonomies of knowledge, learning, and instruction. For example, we identify three broad classes of learning events (LEs): (a) memory and fluency processes, (b) induction and refinement processes, and (c) understanding and sense-making processes, and we show how these can lead to different knowledge changes and constraints on optimal instructional choices. (shrink)

This paper explores the relation between scientific knowledge and common sense intuitions as a complement to Hoyningen-Huene’s account of systematicity. On one hand, Hoyningen-Huene embraces continuity between these in his characterization of scientific knowledge as an extension of everyday knowledge, distinguished by an increase in systematicity. On the other, he argues that scientific knowledge often comes to deviate from common sense as science develops. Specifically, he argues that a departure from common sense is a price we may have to pay (...) for increased systematicity. I argue that to clarify the relation between common sense and scientific reasoning, more attention to the cognitive aspects of learning and doing science is needed. As a step in this direction, I explore the potential for cross-fertilization between the discussions about conceptual change in science education and philosophy of science. Particularly, I examine debates on whether common sense intuitions facilitate or impede scientific reasoning. While contending that these debates can balance some of the assumptions made by Hoyningen-Huene, I suggest that a more contextualized version of systematicity theory could supplement cognitive analysis by clarifying important organizational aspects of science. (shrink)

If folk science means individuals having well worked out mechanistic theories of the workings of the world, then it is not feasible. Laypeople’s explanatory understandings are remarkably coarse, full of gaps, and often full of inconsistencies. Even worse, most people overestimate their own understandings. Yet recent views suggest that formal scientists may not be so different. In spite of these limitations, science somehow works and its success offers hope for the feasibility of folk science as well. The success of science (...) arises from the ways in which scientists learn to leverage understandings in other minds and to outsource explanatory work through sophisticated methods of deference and simplification of complex systems. Three studies ask whether analogous processes might be present not only in laypeople but also in young children and thereby form a foundation for supplementing explanatory understandings almost from the start of our first attempts to make sense of the world. (shrink)

This work uses microgenetic study of classroom learning to illuminate (1) the role of pre-instructional student knowledge in the construction of normative scientific knowledge, and (2) the learning mechanisms that drive change. Three enactments of an instructional sequence designed to lead to a scientific understanding of thermal equilibration are used as data sources. Only data from a scaffolded student inquiry preceding introduction of a normative model were used. Hence, the study involves nearly autonomous student learning. In two classes, students developed (...) stable and socially shared explanations (“causal schemes”) for understanding thermal equilibration. One case resulted in a near-normative understanding, while the other resulted in a non-normative “alternative conception.” The near-normative case seems to be a particularly clear example wherein the constructed causal scheme is a composition of previously documented naïve conceptions. Detailed prior description of these naive elements allows a much better than usual view of the corresponding details of change during construction of the new scheme. A list of candidate mechanisms that can account for observed change is presented. The non-normative construction seems also to be a composition, albeit of a different structural form, using a different (although similar) set of naïve elements. This article provides one of very few high-resolution process analyses showing the productive use of naïve knowledge in learning. (shrink)

This article aims to contribute to the literature on conceptual change by engaging in direct theoretical and empirical comparison of contrasting views. We take up the question of whether naïve physical ideas are coherent or fragmented, building specifically on recent work supporting claims of coherence with respect to the concept of force by Ioannides and Vosniadou [Ioannides, C., & Vosniadou, C. (2002). The changing meanings of force. Cognitive Science Quarterly 2, 5–61]. We first engage in a theoretical inquiry on the (...) nature of coherence and fragmentation, concluding that these terms are not well‐defined, and proposing a set of issues that may be better specified. The issues have to do with contextuality, which concerns the range of contexts in which a concept (meaning, model, theory) applies, and relational structure, which is how elements of a concept (meaning, model, or theory) relate to one another. We further propose an enhanced theoretical and empirical accountability for what and how much one needs to say in order to have specified a concept. Vague specification of the meaning of a concept can lead to many kinds of difficulties.Empirically, we conducted two studies. A study patterned closely on Ioannides and Vosniadou's work (which we call a quasi‐replication) failed to confirm their operationalizations of “coherent.” An extension study, based on a more encompassing specification of the concept of force, showed three kinds of results: (1) Subjects attend to more features than mentioned by Ioannides and Vosniadou, and they changed answers systematically based on these features; (2)We found substantial differences in the way subjects thought about the new contexts we asked about, which undermined claims for homogeneity within even the category of subjects (having one particular meaning associated with “force”) that best survived our quasi‐replication; (3) We found much reasoning of subjects about forces that cannot be accounted for by the meanings specified by Ioannides and Vosniadou. All in all, we argue that, with a greater attention to contextuality and with an appropriately broad specification of the meaning of a concept like force, Ioannides and Vosniadou's claims to have demonstrated coherence seem strongly undermined. Students' ideas are not random and chaotic; but neither are they simply described and strongly systematic. (shrink)

Causal reasoning is one of our most central cognitive competencies, enabling us to adapt to our world. Causal knowledge allows us to predict future events, or diagnose the causes of observed facts. We plan actions and solve problems using knowledge about cause-effect relations. Without our ability to discover and empirically test causal theories, we would not have made progress in various empirical sciences. In the past decades, the important role of causal knowledge has been discovered in many areas of cognitive (...) psychology. Despite the ubiquity of causal reasoning, textbooks of cognitive psychology have neglected this growing field. The goal of The Oxford Handbook of Causal Reasoning is to fill this gap. The handbook brings together the leading researchers in the field of causal reasoning and offers state-of-the-art presentations of theories and research. It provides introductions of competing theories of causal reasoning, and discusses its role in various cognitive functions and domains. The final section presents research from neighboring fields. 1. Causal Reasoning: An Introduction Michael R. Waldmann Part I: Theories of Causal Cognition 2. Associative Accounts of Causal Cognition Mike E. Le Pelley, Oren Griffiths, and Tom Beesley 3. Rules of Causal Judgment: Mapping Statistical Information Onto Causal Beliefs José C. Perales, Andrés Catena, Antonio Cándido, and Antonio Maldonado 4. The Inferential Reasoning Theory of Causal Learning: Toward a Multi- Process Propositional Account Yannick Boddez, Jan De Houwer, and Tom Beckers 5. Causal Invariance as an Essential Constraint for Creating a Causal Representation of the World: Generalizing the Invariance of Causal Power Patricia W. Cheng and Hongjing Lu 6. The Acquisition and Use of Causal Structure Knowledge Benjamin Margolin Rottman 7. Formalizing Prior Knowledge in Causal Induction Thomas L. Griffiths 8. Causal Mechanisms Samuel G. B. Johnson and Woo-kyoung Ahn 9. Force Dynamics and Causation Phillip Wolff and Robert Thorstad 10. Mental Models and Causation P. N. Johnson- Laird and Sangeet S. Khemlani 11. Pseudocontingencies Klaus Fiedler and Florian Kutzner 12. Singular Causation David Danks 13. Cognitive Neuroscience of Causal Reasoning Joachim T. Operskalski and Aron K. Barbey Part II: Basic Cognitive Functions 14. Visual Impressions of Causality Peter White 15. Goal-Directed Actions Bernhard Hommel 16. Planning and Control Magda Osman 17. Reinforcement Learning and Causal Models Samuel J. Gershman 18. Causation and the Probability of Causal Conditionals David E. Over 19. Causal Models and Conditional Reasoning Mike Oaksford and Nick Chater 20. Concepts as Causal Models: Categorization Bob Rehder 21. Concepts as Causal Models: Induction Bob Rehder 22. Causal Explanation Tania Lombrozo and Nadya Vasilyeva 23. Diagnostic Reasoning Björn Meder and Ralf Mayrhofer 24. Inferring Causal Relations by Analogy Keith J. Holyoak and Hee-Seung Lee 25. Causal Argument Ulrike Hahn, Roland Bluhm, and Frank Zenker 26. Causality in Decision- Making York Hagmayer and Philip M. Fernbach Part III: Domains of Causal Reasoning 27. Intuitive Theories Tobias Gerstenberg and Joshua B. Tenenbaum 28. Space, Time, and Causality Marc J. Buehner 29. Causation in Legal and Moral Reasoning David A. Lagnado and Tobias Gerstenberg 30. The Role of Causal Knowledge in Reasoning About Mental Disorders Woo-kyoung Ahn, Nancy S. Kim, and Matthew S. Lebowitz 31. Causality and Causal Reasoning in Natural Language Torgrim Solstad and Oliver Bott 32. Social Attribution and Explanation Denis Hilton Part IV: Development, Phylogeny, and Culture 33. The Development of Causal Reasoning Paul Muentener and Elizabeth Bonawitz 34. Causal Reasoning in Non-Human Animals Christian Schloegl and Julia Fischer 35. Causal Cognition and Culture Andrea Bender, Sieghard Beller, and Douglas L. Medin. (shrink)

This inaugural handbook documents the distinctive research field that utilizes history and philosophy in investigation of theoretical, curricular and pedagogical issues in the teaching of science and mathematics. It is contributed to by 130 researchers from 30 countries; it provides a logically structured, fully referenced guide to the ways in which science and mathematics education is, informed by the history and philosophy of these disciplines, as well as by the philosophy of education more generally. The first handbook to cover the (...) field, it lays down a much-needed marker of progress to date and provides a platform for informed and coherent future analysis and research of the subject. -/- The publication comes at a time of heightened worldwide concern over the standard of science and mathematics education, attended by fierce debate over how best to reform curricula and enliven student engagement in the subjects There is a growing recognition among educators and policy makers that the learning of science must dovetail with learning about science; this handbook is uniquely positioned as a locus for the discussion. -/- The handbook features sections on pedagogical, theoretical, national, and biographical research, setting the literature of each tradition in its historical context. Each chapter engages in an assessment of the strengths and weakness of the research addressed, and suggests potentially fruitful avenues of future research. A key element of the handbook’s broader analytical framework is its identification and examination of unnoticed philosophical assumptions in science and mathematics research. It reminds readers at a crucial juncture that there has been a long and rich tradition of historical and philosophical engagements with science and mathematics teaching, and that lessons can be learnt from these engagements for the resolution of current theoretical, curricular and pedagogical questions that face teachers and administrators. (shrink)

The backward induction controversy in game theory flared up and then practically ended within a decade – the 1990s. The protagonists, however, did not converge on an agreement about the source of the controversy. Why was this the case, if opposing sides had access to the same modelling techniques and empirical facts? In this paper I offer an explanation for this controversy and its unsettled end. The answer is not to be found in the modelling claims made by the opposing (...) protagonists, but in the tacit metaphors they operate under. Aristotle defined metaphor as giving a 'thing a name that belongs to something else' (Poetica, 1457b). The meaning of metaphors has not changed much since then – in contrast to models which are comparatively new, and still not well-understood, scientific tools. The controversy of backward induction in game theory provides a test bed for the explanatory power of metaphors. This paper frames the controversy in terms of metaphor choice to provide a common framework for the protagonists. This results in the identification of three different domains – mathematical logic, game theory and the world – each connected to the other via different metaphors. The controversy around backward induction is placed in, and tentatively explained by, this framework. (shrink)

Historians and philosophers of science have, in recent decades, offered evidence in support of several influential models of conceptual change in science. These models have often drawn on and in turn driven research on conceptual change in childhood and in science education. This nexus of reciprocal influences is held together by several largely unexamined analogies and by several assumptions concerning analogy itself. In this chapter, we aim to shed some light on these hidden premises and subject them to critical scrutiny. (...) Our critical survey suggests the following hypothesis: conceptual change in both childhood and science is driven by the epistemic subject’s quest for coherence. (shrink)

This chapter will discuss the role of thought experiments in science and in science teaching. The constructive and destructive roles played by thought experiments in the construction of scientific theories can be used in science teaching to help students to understand the processes of science. In addition, they have potential to be used as a teaching tool for developing students’ conceptual understanding. The use of thought experiments can also increase students’ interest in science and help them in understanding situations beyond (...) their everyday experiences. It has been reported elsewhere that the use of thought experiments in science teaching may be challenging for both teachers and students. Despite the recent increase in research activities with respect to thought experiments in science education, further systematic research work is still needed for the most effective methods to be discovered of how best to use thought experiments in science teaching. Of particular importance will be studies that focus on science teachers’ understanding of thought experiments and their actual use in a classroom environment. (shrink)

The state of geoscience education, in terms of numbers of teachers, students taught, and perceived importance, has been lagging behind the other science disciplines for decades. Part of the reason for this is that geology is seen as a “derivative” science as compared to its “experimental” counterparts (for instance, physics and chemistry). However, with current global issues facing the populations of the world (climate change, scarcity of clean water, increasing fossil fuel usage), being geoscience literate is a must. We will (...) show that, in fact, the geological sciences have their own philosophical structure, being both historical and hermeneutic, and it is the structure that makes the teaching of the geosciences for addressing such global issues advantageous. In addition, we will explore the use of historical controversies as a pedagogical tool for geoscience instruction. The history of geology is rife with controversy and the use of such a strategy has been shown to be effective for developing students’ interest in the content, sharpening critical thinking skills, as well as emphasizing the nature of science. This chapter consolidates the knowledge base by describing the structure of the geosciences in terms of its philosophical, theoretical, and cognitive frameworks. It highlights four geoscience controversies in terms of these frameworks, all the while reviewing the literature for the use of HPS in geoscience teaching. Finally it contains recommendations for possible future directions for geoscience education research within this context. (shrink)