The Epistemic Generativity of Using a Model of a Big Idea

Dados Bibliográficos

ID21392735
AutoresJonathan 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 correspondente)
Ano2026
Volume110
Fascículo3
Páginas803-825
Data de publicação2026-05-01
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoScience Education (JOURNAL)
Identificadores do periódicoISSN: 0036-8326 • E-ISSN: 1098-237X
EditoraWiley (PUBLISHER • GB)
DOI10.1002/sce.70037
OpenAlexW4417010720
IdiomaEN
Referências citadas86

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