Changes in University Students’ Explanation Models of DC Circuits

Kokkonen, Tommi; Mäntylä, Terhi

doi:10.1007/s11165-016-9586-y

Cited by 11 publications

(14 citation statements)

References 34 publications

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“…When students consider voltage, they often see it as a consequence rather than a cause of electric current (Cohen, Eylon, & Ganiel, 1983), have problems differentiating between voltage and current, or they do not see why both concepts are needed (Koponen & Huttunen, 2013). It is shown that even university students in physics have difficulties understanding relations between concepts (Kokkonen & Mäntylä, 2017), and that textbooks in electrical engineering at university level tend to contain 'conceptual gaps' where important conceptual features are missing in the treatment of electric circuits (Sangam & Jesiek, 2015).…”

Section: Student Conceptions About Electric Circuitsmentioning

confidence: 99%

Ski lifts, bowling balls, pipe system or waterfall? Lower secondary students’ understanding of analogies for electric circuits.

Mogstad¹,

Bungum²

2020

NorDiNa

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Electric circuits are challenging for students to understand, and a wide range of analogies are developed in order to support their learning. This article investigates how lower secondary students understand four analogies presented in teaching material for science for Norwegian schools. The analogies compare electric circuits to a ski lift, a water pipe system, a waterfall and moving bowling balls, respectively. Data in the study consist of group interviews with 12 students in lower secondary school, about how they understand the analogies. Results show that students are able to reason about continuity and the concept of current in circuits based on all the analogies, but that the concept of voltage remains a challenge. It seems from the results that analogies relating voltage to energy transfer as an effect of height difference in a gravitational field are constructive, despite the need for the more abstract concept of field. In addition, the results demonstrate that weaknesses in how the analogies are presented may cause major problems for students in building a fruitful understanding. This kind of weaknesses are prevalent in the teaching material studied.

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Section: Student Conceptions About Electric Circuitsmentioning

confidence: 99%

Ski lifts, bowling balls, pipe system or waterfall? Lower secondary students’ understanding of analogies for electric circuits.

Mogstad¹,

Bungum²

2020

NorDiNa

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“…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%

“…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%

“…For example, Kokkonen and Mäntylä (2017) proposed model change, refinement, and elaboration as the possible change mechanisms related to student's explanation models. Elaboration refers to adding relations to the existing structure, while refinement means simplifying the structure via conglomerating different models into one.…”

Section: Mechanisms Of Changementioning

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.…”

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confidence: 99%

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Models as Relational Categories

Kokkonen

2017

Sci & Educ

Self Cite

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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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The Nature of Scientific Explanation: Examining the perceptions of the nature, quality, and “goodness” of explanation among college students, science teachers, and scientists

2022

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Issues regarding scientific explanation have been of interest to philosophers from Pre‐Socratic times. The notion of scientific explanation is of interest not only to philosophers, but also to science educators as is clearly evident in the emphasis given to K‐12 students' construction of explanations in current national science education reform efforts. Nonetheless, there is a dearth of research on conceptualizing explanation in science education. Using a philosophically guided framework—the Nature of Scientific Explanation (NOSE) framework—the study aims to elucidate and compare college freshmen science students', secondary science teachers', and practicing scientists' scientific explanations and their views of scientific explanations. In particular, this study aims to: (1) analyze students', teachers', and scientists' scientific explanations; (2) explore the nuances about how freshman students, science teachers, and practicing scientists construct explanations; and (3) elucidate the criteria that participants use in analyzing scientific explanations. In two separate interviews, participants first constructed explanations of everyday scientific phenomena and then provided feedback on the explanations constructed by other participants. Major findings showed that, when analyzed using NOSE framework, participant scientists did significantly “better” than teachers and students. Our analysis revealed that scientists, teachers, and students share a lot of similarities in how they construct their explanations in science. However, they differ in some key dimensions. The present study highlighted the need articulated by many researchers in science education to understand additional aspects specific to scientific explanation. The present findings provide an initial analytical framework for examining students' and science teachers' scientific explanations.

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Changes in University Students’ Explanation Models of DC Circuits

Cited by 11 publications

References 34 publications

Ski lifts, bowling balls, pipe system or waterfall? Lower secondary students’ understanding of analogies for electric circuits.

Ski lifts, bowling balls, pipe system or waterfall? Lower secondary students’ understanding of analogies for electric circuits.

Models as Relational Categories

The Nature of Scientific Explanation: Examining the perceptions of the nature, quality, and “goodness” of explanation among college students, science teachers, and scientists

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