The Epistemic Generativity of Using a Model of a Big Idea
Datos Bibliográficos
| ID | 21392735 |
|---|---|
| Autores | Jonathan T Shemwell (0000-0001-8681-0378, University of Alabama Tuscaloosa Alabama USA), Daniel K Capps (0000-0002-2017-9218, University of Georgia, University of Johannesburg Athens Georgia USA, autor de correspondencia) |
| Año | 2026 |
| Volumen | 110 |
| Número | 3 |
| Páginas | 803-825 |
| Fecha de publicación | 2026-05-01 |
| Peer Reviewed | Sí |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | Science Education (JOURNAL) |
| Identificadores de la revista | ISSN: 0036-8326 • E-ISSN: 1098-237X |
| Editorial | Wiley (PUBLISHER • GB) |
| DOI | 10.1002/sce.70037 |
| OpenAlex | W4417010720 |
| Idioma | EN |
| Referencias citadas | 86 |
Science instruction should involve learners in generating and warranting ideas, what we call epistemic generation. In modeling instruction, epistemic generation should be achieved by coordinating a model structure with the experienced world in reciprocal directions denoted as developing models and using models. In the former, a model structure is shaped from experience. In the latter, experience is shaped by a model structure. Focusing on this latter direction, the present study combats the perception that using models is not a generative practice but merely the dutiful application of others' ideas. Featuring an energy model as an example, the article explains, and illustrates with qualitative evidence, how the model supported epistemic generation by ninth‐grade students using it to analyze and explain the biological process of aerobic cellular respiration. Because the model represented energy, a big idea governing the domain of cellular respiration, it also incorporated three characteristics attributable to big ideas that contributed to its generativity: abstraction; mechanistic meaning, and representational efficiency. Presenting cases of small‐group work, the article traces how epistemic generativity was supported by the energy model's structure, general affordances of models, and these three characteristics
Affordance · Domain (mathematical analysis) · Generative grammar · Generativity · Ontology · Philosophy of science · Process (computing) · Reciprocal · Science education · Cancer and biochemical research · Educational Strategies and Epistemologies · Science Education and Pedagogy
Modelling-based Teaching in Science Education
Creating Scientific Concepts
Schema induction and analogical transfer
Cognitive load theory, learning difficulty, and instructional design
Developing a learning progression for scientific modeling
How Efficiency Shapes Human Language
Analogical problem solving
Learning and transfer
Examining Classroom Science Practice Communities
Disciplinary authority and accountability in scientific practice and learning
Enhancing the quality of argumentation in school science
On the role of analogies and metaphors in learning science
Guiding Principles for Fostering Productive Disciplinary Engagement
Structure‐Mapping
Different Bodies, Different Minds
Science and Statistics
Bootstrapping the Mind
‘Models of’ versus ‘Models for’
Beyond the scientific method
Toward a Psychology of Human Agency
Models as Epistemic Artifacts for Scientific Reasoning in Science Education Research
Moving beyond the model as a copy problem
Evaluating a learning progression for the solar system
Making semantic waves
Strategies in the interfield discovery of the mechanism of protein synthesis
Models and the locus of their truth
Strategies for Discovering Mechanisms
A Parser as an Epistemic Artifact
Thinking about Mechanisms
Understanding (with) Toy Models
Supporting and Promoting Argumentation Discourse in Science Education
Force Dynamics in Language and Cognition
| Velocidad de citación | historical |
|---|---|
| Altamente citado | No |