‘Scientific Literacy’

An Exercise in Model Building

Dados Bibliográficos

ID22042808
AutoresJohn Mitchell O’Toole (0000-0003-0648-9233, University of Newcastle Australia, autor correspondente), Karina McKoy (School of Education, Faculty of Education and Arts, University of Newcastle, Callaghan, NSW 2308, Australia), Karina S McKoy (University of Newcastle Australia), Margaret Freestone (0000-0002-6616-3772, University of Newcastle Australia), Judy-Anne Osborn (School of Mathematical and Physical Sciences, Faculty of Science, University of Newcastle, Callaghan, NSW 2308, Australia), Judy-anne H Osborn (University of Newcastle Australia)
Ano2020
Volume10
Fascículo8
Páginas204
Data de publicação2020-08-07
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoEducation Sciences (JOURNAL)
Identificadores do periódicoISSN: 2227-7102 • E-ISSN: 2227-7102
EditoraMDPI AG (PUBLISHER • IT)
DOI10.3390/educsci10080204
OpenAlexW3048188401
IdiomaEN
Citações recebidas2
Referências citadas41

‘Literacy’ and ‘science’ are power words and the interaction between them is of potential interest to people working at other boundaries between text and content, such as that characterising wider disciplinary literacy. ‘Scientific literacy’ has a deep enough literature base to support an attempt to build a model of these interactions. If robust, such a model could synthesise existing literature and resolve differences within a narrower range of journals. This quantitative review suggests such a model based on a wide review of previous literature and then challenges it by comparing publication patterns in premium international journals dealing specifically with research in science education. The emergent model comprises interaction between Use of, Engagement with and Access to science and its application revealed changes in publication patterns both within and between the five science education research journals surveyed. The use of power words can obscure, rather than clarify, discussions that lead to curriculum and pedagogical decisions. Robust models can resolve multiple components of a complex field and make it easier to understand for newcomers, easier to explain when change seems necessary to those more deeply involved, and then expedite the prediction of fruitful areas for further work

Curriculum · Data science · Discipline · Engineering ethics · Literacy · Mathematics education · Pedagogy · Science education · Scientific Literacy · Social science · Sociology · Computer Science · Education and Critical Thinking Development · Educational Strategies and Epistemologies · Engineering · Psychology · Science Education and Pedagogy