The Development of Scientific Thinking

Lehrer, Richard; Schauble, Leona

doi:10.1002/9781118963418.childpsy216

Cited by 79 publications

(95 citation statements)

References 207 publications

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“…Hence, models are seen as tools for thinking, reasoning, and creating scientific knowledge. Lehrer and Schauble (2015) also argued for viewing modeling as a practice rather than merely a tool for making predictions. This view highlights the role of models in knowledge generation, as the models are subject to open-ended revision influenced not only by new phenomena but also by, for example, new instrumentation.…”

Section: Generative Modeling In Science Educationmentioning

confidence: 99%

“…In this kind of approach, modeling can be viewed as an epistemic practice for gaining knowledge, and models as tools for knowledge construction. Hence, models are given more autonomous status both in theory formation and in inquiry in general (see, e.g., Koponen and Tala 2014;Lehrer and Schauble 2015;Passmore et al 2009). This is an interesting viewpoint as it assigns models a central role in the creative process of generating scientific knowledge (Koponen and Tala 2014).…”

Section: Approaches Towards Mblmentioning

confidence: 99%

“…In the elementary school context, Lehrer and Schauble (2015) have noted that inquiry and external representations are intertwined as evolving external representations can act as recourses and facilitate different inferences and bring about novel questions (Lehrer and Schauble 2015). Even simple models, such as scale models, are important in communicating what is being modeled (Lehrer et al 2001;Lehrer and Schauble 2015).…”

Section: Generative Modeling In Science Educationmentioning

confidence: 99%

“…Even simple models, such as scale models, are important in communicating what is being modeled (Lehrer et al 2001;Lehrer and Schauble 2015). As students become more knowledgeable and competent in using different kinds of models and acquire a richer repertoire of representations, there is a shift towards more abstract models (Lehrer et al 2001;Lehrer and Schauble 2015).…”

Section: Generative Modeling In Science Educationmentioning

confidence: 99%

“…MBL also helps to engage students in epistemically Bauthentic^scientific activities (Gilbert 2004;Koponen 2007;Lehrer and Schauble 2015). Indeed, models and modeling capture much of what is essential in constructing, communicating, and accepting scientific knowledge (Frigg and Hartmann 2017;Giere 1988;Gilbert 2004;Koponen 2007).…”

mentioning

confidence: 99%

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Models as Relational Categories

Kokkonen

2017

Sci & Educ

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Model-based learning (MBL) has an established position within science education. It has been found to enhance conceptual understanding and provide a way for engaging students in authentic scientific activity. Despite ample research, few studies have examined the cognitive processes regarding learning scientific concepts within MBL. On the other hand, recent research within cognitive science has examined the learning of so-called relational categories. Relational categories are categories whose membership is determined on the basis of the common relational structure. In this theoretical paper, I argue that viewing models as relational categories provides a well-motivated cognitive basis for MBL. I discuss the different roles of models and modeling within MBL (using ready-made models, constructive modeling, and generative modeling) and discern the related cognitive aspects brought forward by the reinterpretation of models as relational categories. I will argue that relational knowledge is vital in learning novel models and in the transfer of learning. Moreover, relational knowledge underlies the coherent, hierarchical knowledge of experts. Lastly, I will examine how the format of external representations may affect the learning of models and the relevant relations. The nature of the learning mechanisms underlying students' mental representations of models is an interesting open question to be examined. Furthermore, the ways in which the expert-like knowledge develops and how to best support it is in need of more research. The discussion and conceptualization of models as relational categories allows discerning students' mental representations of models in terms of evolving relational structures in greater detail than previously done.

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Section: Generative Modeling In Science Educationmentioning

confidence: 99%

Section: Approaches Towards Mblmentioning

confidence: 99%

Section: Generative Modeling In Science Educationmentioning

confidence: 99%

Section: Generative Modeling In Science Educationmentioning

confidence: 99%

mentioning

confidence: 99%

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Models as Relational Categories

Kokkonen

2017

Sci & Educ

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What Do Young Science Students Need to Learn About Variables?

Kuhn

2016

Science Education

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Have the Next Generation Science Standards fulfilled a goal of specifying the objectives of precollege science education in clear and exact enough terms to make them readily implementable? Using students’ understanding of the concept of a variable as a case in point, the author suggests that the standards, despite their seeming precision and clarity, leave critical questions not fully answered regarding what K–12 students need to master to be judged knowledgeable regarding scientific practices. This is especially the case given that scientific practices entail not only procedural knowledge but also epistemic understanding, i.e., the purposes and ends toward which practices are conducted. If the teacher does not fully appreciate the epistemic component and lacks deep understanding of procedures, engaging students in related activities is less likely to seem worthwhile to the teacher and in turn to students, leaving teachers tempted to focus attention on content‐defined learning, where goals are clearer.

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Can Engaging in Science Practices Promote Deep Understanding of Them?

et al. 2017

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It is now widely accepted, and indeed emphasized in the Next Generation Science Standards, that science education should encompass scientific practice as well as science content. By participating in an intellectual community engaged in the broad range of activities that constitute scientific inquiry, rather than simply mastering isolated science procedures, it is hoped students will come to better understand and appreciate the norms, goals, and values that govern the conduct of science. We put this expectation to empirical test by engaging a secondary school science class in an extended problem‐based activity that included design of investigations, multivariable causal analysis, and argumentation. Compared to students in a nonparticipating control group, in delayed assessments involving new content, participating students showed more advanced investigation, analysis, and argumentation skills, but also superior epistemological understanding regarding science as entailing the evaluation of claims in relation to available evidence.

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The Development of Scientific Thinking

Cited by 79 publications

References 207 publications

Models as Relational Categories

Models as Relational Categories

What Do Young Science Students Need to Learn About Variables?

Can Engaging in Science Practices Promote Deep Understanding of Them?

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