‘Models of’ versus ‘Models for’

Gouvea, Julia; Passmore, Cynthia

doi:10.1007/s11191-017-9884-4

Cited by 133 publications

(111 citation statements)

References 55 publications

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“…According to Giere, agents intend to use models to represent a part of the world for some purpose. Building on Giere's interpretation of the function of models, Gouvea and Passmore [41] describe models as knowledgegenerating tools used by an agent for a particular epistemic goal. The agent decides what to model and why, and they evaluate and refine their models according to their goal.…”

Section: A Models and Modeling In Science Educationmentioning

confidence: 99%

“…In this context, valuable models are not necessarily those whose external representations are highly realistic or map isomorphically onto reality. Rather, they are those that succeed in their intended uses, such as making accurate predictions [5,37,41]. The process through which an agent creates, uses, evaluates, or revises a model is called modeling.…”

Section: A Models and Modeling In Science Educationmentioning

confidence: 99%

“…And, in the future, the final version of the PLIC may shed light on how students compare model predictions with experimental data. However, external expressions of models are not themselves models [37,41], and matching data to predictions is only one of many aspects of modeling [5]. Therefore, each of these instruments [24][25][26] provides only partial measures of students' modeling ability.…”

Section: Relevant Existing Assessmentsmentioning

confidence: 99%

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Characterizing lab instructors’ self-reported learning goals to inform development of an experimental modeling skills assessment

Dounas-Frazer

Rios

Pollard

et al. 2018

Phys. Rev. Phys. Educ. Res.

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The ability to develop, use, and refine models of experimental systems is a nationally recognized learning outcome for undergraduate physics lab courses. However, no assessments of students' model-based reasoning exist for upper-division labs. This study is the first step toward development of modeling assessments for optics and electronics labs. We interviewed 35 lab instructors about the ways they incorporate modeling in their courses, and we used their self-reported learning goals and activity designs to identify test contexts and objectives that are likely relevant across many institutional settings. The study design was informed by the Modeling Framework for Experimental Physics, which conceptualizes modeling as consisting of multiple subtasks: making measurements, constructing system models, comparing data to predictions, proposing causes for discrepancies, and enacting revisions to models or apparatus. We found that each modeling subtask was identified by multiple instructors as an important learning outcome for their course. Based on these results, we argue that test objectives should include probing students' competence with most modeling subtasks, and test items should be designed to elicit students' justifications for choosing particular modeling pathways. In addition to discussing these and other implications for assessment, we also identify future areas of research related to the role of modeling in optics and electronics labs. *

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Section: A Models and Modeling In Science Educationmentioning

confidence: 99%

Section: A Models and Modeling In Science Educationmentioning

confidence: 99%

Section: Relevant Existing Assessmentsmentioning

confidence: 99%

See 1 more Smart Citation

Characterizing lab instructors’ self-reported learning goals to inform development of an experimental modeling skills assessment

Dounas-Frazer

Rios

Pollard

et al. 2018

Phys. Rev. Phys. Educ. Res.

View full text Add to dashboard Cite

show abstract

“…Interpreted data are compared to predictions, and domain-specific criteria are used to determine whether the level of agreement is good enough. Applying accepted scientific criteria for determining what counts as "good enough" is part of the modeling process (Giere 2009, Gouvea & Passmore 2017. If the agreement is good enough, then there is no need to revise models or apparatus.…”

Section: Description Of the Modeling Framework For Experimental Physicsmentioning

confidence: 99%

The Modelling Framework for Experimental Physics: description, development, and applications

Dounas-Frazer

Lewandowski

2018

Eur. J. Phys.

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The ability to construct, use, and revise models is a crucial experimental physics skill. Many existing frameworks describe modeling in science education at introductory levels. However, most have limited applicability to the context of upperdivision physics lab courses or experimental physics. Here, we discuss the Modeling Framework for Experimental Physics, a theoretical framework tailored to labs and experimentation. A key feature of the Framework is recursive interaction between models and apparatus. Models are revised to account for new evidence produced by apparatus, and apparatus are revised to better align with the simplifying assumptions of models. Another key feature is the distinction between the physical phenomenon being investigated and the measurement equipment used to conduct the investigation. Models of physical systems facilitate explanation or prediction of phenomena, whereas models of measurement systems facilitate interpretation of data. We describe the Framework, provide a chronological history of its development, and summarize its applications to research and curricular design. Ultimately, we argue that the Modeling Framework is a theoretically sound and well-tested tool that is applicable to multiple physics domains and research purposes. In particular, it is useful for characterizing students' approaches to experimentation, designing or evaluating curricula for lab courses, and developing instruments to assess students' experimental modeling skills.Submitted to: Eur. J. Phys.

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“…Instead, classroom-audience students more often prioritized the esthetics of their models, limiting their opportunities for domain-specific reasoning. In this way, students' aims and criteria for modeling may have shaped their opportunities for learning (Barzilai & Chinn, 2018;Gouvea & Passmore, 2017;Pluta, Chinn, & Duncan, 2011).…”

Section: Implications and Conclusionmentioning

confidence: 99%

Engaging students in computational modeling: The role of an external audience in shaping conceptual learning, model quality, and classroom discourse

Pierson

Clark

2018

Science Education

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Research suggests that designing for an external audience may support conceptual understanding by offering students increased opportunities to reframe perspectival thinking in ways that support domain‐specific reasoning. While this argument is theoretically compelling, to our knowledge, it has not been empirically tested in terms of comparing the conceptual growth of students designing computational models for an external audience to the conceptual growth of students designing computational models for a classroom audience of their teacher and peers. In a constructionist agent‐based computational modeling environment, we compare the conceptual understanding, artifact quality, and discourse of 6th grade students designing models of tides primarily for an external audience of younger students (i.e., designing for 5th graders) to the conceptual understanding of 6th grade students designing models for a classroom audience. We found that students who designed for an external audience of younger children displayed greater conceptual growth about the mechanisms that cause tidal bulges as evidenced by students’ pre–post assessments, models, and user guides/reports. Our analysis of classroom discourse suggests that designing for an audience of younger children may have facilitated domain‐specific reasoning in whole‐class discussions by creating opportunities for shifts between students’ own perspectives and the perspectives of their anticipated audience.

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‘Models of’ versus ‘Models for’

Cited by 133 publications

References 55 publications

Characterizing lab instructors’ self-reported learning goals to inform development of an experimental modeling skills assessment

Characterizing lab instructors’ self-reported learning goals to inform development of an experimental modeling skills assessment

The Modelling Framework for Experimental Physics: description, development, and applications

Engaging students in computational modeling: The role of an external audience in shaping conceptual learning, model quality, and classroom discourse

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