Dimensions of Causal Understanding: the Role of Complex Causal Models in Students' Understanding of Science

Perkins, David N.; Grotzer, Tina A.

doi:10.1080/03057260508560216

Cited by 130 publications

(132 citation statements)

References 55 publications

Supporting

Mentioning

112

Contrasting

Unclassified

Order By: Relevance

“…Within science education, students' Brepertoire^of different causal modeling styles has been recognized as one major hurdle in acquiring science concepts and models and consequently in using these model types in novel situations (Perkins and Grotzer 2005). That is, students might find modeling with constraint equations familiar after being exposed to modeling dynamic systems with Newton's laws or electric circuits with Ohm's law (Perkins and Grotzer 2005).…”

Section: Cognitive Aspects Of Generative Modelingmentioning

confidence: 99%

“…That is, students might find modeling with constraint equations familiar after being exposed to modeling dynamic systems with Newton's laws or electric circuits with Ohm's law (Perkins and Grotzer 2005). Similarly, Collins (2011) has identified various epistemic forms, which denote structures and functional or causal relationships.…”

Section: Cognitive Aspects Of Generative Modelingmentioning

confidence: 99%

“…Epistemic forms are kinds of generic relational patterns that guide the inquiry process and consequently concept formation as well (Collins 2011). Initially, however, students are largely unaware of sophisticated modeling styles (and/or epistemic forms) and are inclined towards linear and/or sequential causality (for example, A causes B, which causes C and so forth) instead of more complex patterns such as constraint-based interactions (Kokkonen and Nousiainen 2016;Kokkonen and Mäntylä 2017;Perkins and Grotzer 2005). Goldwater and Gentner (2015) argued that while cognitive science has found several experiences that lead people to infer causal structures, gaining abstract, generic knowledge about the relations requires both exposure to particular causal phenomena and applying this knowledge across domains.…”

Section: Cognitive Aspects Of Generative Modelingmentioning

confidence: 99%

“…While for example, Ohm's law, U = RI, typically has a causal reading (voltage causes current), Kirchhoff's current law, ∑I k = 0, may be best described as a constraining equation. There are also various relational patterns, which refer to the ways in which the variables are linked to each other (such as linear causality, common cause, feedback loops, and cyclic causalities) (for examples, see Kokkonen and Mäntylä 2017;Perkins and Grotzer 2005;Rottman et al 2012).…”

Section: Models As Relational Categoriesmentioning

confidence: 99%

“…Answers arguing for the centrality of models in science and/or equating models with scientific knowledge are inadequate since they identify external reasons for the importance of models instead of cognitive reasons based on the psychology of learning. Some studies have examined the cognitive processes 2 underlying MBL (see , Clement 1989;Perkins and Grotzer 2005;Kokkonen and Mäntylä 2017;Nersessian 1995), but only a few have attempted to develop a cognitively justified approach to understanding concept learning within MBL, despite concept learning being among the most important aims of MBL (Louca and Zacharia 2012). Moreover, constructing, using, and learning from models is affected by the modeling tool (and/or representational format) used.…”

mentioning

confidence: 99%

See 4 more Smart Citations

Models as Relational Categories

Kokkonen

2017

Sci & Educ

View full text Add to dashboard Cite

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.

show abstract

Section: Cognitive Aspects Of Generative Modelingmentioning

confidence: 99%

Section: Cognitive Aspects Of Generative Modelingmentioning

confidence: 99%

Section: Cognitive Aspects Of Generative Modelingmentioning

confidence: 99%

Section: Models As Relational Categoriesmentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Models as Relational Categories

Kokkonen

2017

Sci & Educ

View full text Add to dashboard Cite

show abstract

How Laypeople Understand the Economy

Leiser¹,

Krill²

2017

Economic Psychology

View full text Add to dashboard Cite

The Development of Scientific Thinking

Lehrer

Schauble

2015

Handbook of Child Psychology and Developmental Science

View full text Add to dashboard Cite

We introduce three views of science that ground different perspectives on the long‐term development of scientific thinking: science‐as‐reasoning, science‐as‐conceptual change, and science‐as‐practice. An emphasis on science as an integrated set of epistemic practices is receiving increasing attention. For example, new science education standards in the United States highlight the importance of students' participation in scientific practices, which are communally recognized and supported ways of generating, revising, and critiquing scientific knowledge. Based on reviews of the history, philosophy, and psychology of science, we argue that the defining practice of science is modeling, and that it is useful to conceive of other scientific practices in relation to this foundational one. Accordingly, we trace the ontogeny of representational and material practices of modeling and situate seven related scientific practices, such as constructing and interpreting data, or developing explanations and arguments, as motivated by modeling. This perspective identifies fruitful areas for additional research that can inform the field's understanding of how to describe and support the development of scientific thinking.

show abstract

Dimensions of Causal Understanding: the Role of Complex Causal Models in Students' Understanding of Science

Cited by 130 publications

References 55 publications

Models as Relational Categories

Models as Relational Categories

How Laypeople Understand the Economy

The Development of Scientific Thinking

Contact Info

Product

Resources

About