How has Science Education changed over the last 100 years? An analysis using natural language processing

Bibliographic Data

ID21392792
AuthorsTor Ole B Odden (0000-0003-1635-9491, Department of Physics, Center for Computing in Science Education University of Oslo Oslo Norway, corresponding author), Alessandro Marin (0000-0002-5524-3451, Department of Physics, Center for Computing in Science Education University of Oslo Oslo Norway), John L Rudolph (0000-0001-9103-9653, Department of Curriculum and Instruction University of Wisconsin–Madison Madison Wisconsin USA)
Year2021
Volume105
Issue4
Pages653-680
Publication date2021-07-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueScience Education (JOURNAL)
Journal identifiersISSN: 0036-8326 • E-ISSN: 1098-237X
PublisherWiley (PUBLISHER • GB)
DOI10.1002/sce.21623
OpenAlexW3160589955
LanguageEN
Citations received12
References cited38

For well over a century, the journal Science Education has been publishing articles about the teaching and learning of science. These articles represent more than just a repository of past work: they have the potential to offer insights into both the history of science education as well as well as the dynamics of field‐specific change. It can be difficult, however, for educators, researchers, reformers, and policymakers to grasp the nuances of over 100 years of scholarship given the overwhelming amount of textual material. To address this problem, we have used latent Dirichlet allocation, an automated machine‐learning algorithm from the field of natural language processing, to perform an automated literature review and classification of the corpus of work in Science Education . Using this technique, we have classified research in the journal into 21 distinct topics, falling into three thematic groups: science content topics, teaching‐focused topics, and student‐focused topics. We have also quantified the rise and fall of these topics and groups over time, and used them to begin to extract insight into the development of the field, including the effects of national policy changes on topics of interest to the research community, the interrelationships between different research topics, and the effects of intellectual cross‐pollination. Based on this analysis, we argue that this technique shows great promise for even larger‐scale analyses of educational literature and other textual data

Data science · Field (mathematics) · Latent Dirichlet allocation · Mathematics education · Political science · Publishing · Scholarship · Science education · Social science · Sociology · Topic model · Artificial Intelligence · Climate Change Communication and Perception · Computational and Text Analysis Methods · Computer Science · Psychology · scientometrics and bibliometrics research