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

Abstract

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.

Thematic Analysis of Indonesian Physics Education Research Literatures Using Machine Learning

“…Our topic weights were representation of each word in each topic. Our findings reported spectrum of topic weights between 8% and 5.3% that was also reported as acceptable findings by the previous studies [11,18]. The order of topic number is arranged based on this weights that represents how blended the topic is within literatures.…”

“…Figure 5 informed us that eight topics should be the best choice within our data corpus. The coherence value between 0.4 -0.6 has been recommended by the previous study [11,18] as sufficient measure to interpret the results. Face validity is a procedure to qualitatively evaluate the identified topics to the PER experts that are familiar with the PER publications and their state of the PER results.…”

“…Recently, natural language processing (NLP), a subfield of machine learning studies, has proposed method of thematic analysis to extract our understanding on textual data from large collection of literature. Recent studies by Odden et al have performed this kind of analysis towards physics education research conference (PERC) proceeding [11] and 100 years journey of Science Education journal [18]. In this study, we extend these efforts to cope with the context of Indonesian PER literatures.…”

Catholic Education and the Study of Science: The Mysticism of Scientific Pursuit

“…Biology was the product of the transformation of Natural History that occurred in the 19th century (Farber 1982). Disciplines such as physics, engineering, medicine, and chemistry became highly popular, and the universities, in turn, developed curricula aimed at training applied scientists (Odden et al 2021). This trend has accelerated, in the 21st century, with the transformation of some countries into a technologically advanced civilization and the consequent increase in the market demand for highly specialized and qualified expertise (Enders and Christine 2008).…”

Developing a natural language processing approach for analyzing student ideas in calculus-based introductory physics

“…In the past few years, there have been several studies exploring the utility of NLP in physics education [7,8]. LDA has been utilized to characterize topics in Physics Education Research (PER) articles over the last several decades as a way to understand the scope and breadth of the field [9,10].…”