What Do Economic Education Scholars Study? Insights from Machine Learning

Autor/inn/en	Fernandez, Jose M.; Yetter, Erin A.; Holder, Kim
Titel	What Do Economic Education Scholars Study? Insights from Machine Learning
Quelle	In: Journal of Economic Education, 52 (2021) 2, S. 156-172Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Fernandez, Jose M.) ORCID (Yetter, Erin A.) ORCID (Holder, Kim)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0022-0485
DOI	10.1080/00220485.2021.1887027
Schlagwörter	Forschungsbericht; Economics Education; Educational Research; Artificial Intelligence; Scholarship; Indexing; Citations (References); Periodicals + Suchen Sie Ihr Suchwort? Wirtschaftskunde; Bildungsforschung; Pädagogische Forschung; Künstliche Intelligenz; Scholarships; Stipendium; Indexierung; Sacherschließung; Citations; Zitat; Periodical; Journal; Zeitschrift; Fachzeitschrift; Periodikum
Abstract	The authors of this article use text mining techniques to uncover hidden or latent topics in economic education. The common use of JEL codes only identifies the academic setting for each paper but does not identify the underlying economic concept the paper addresses. An unsupervised machine learning algorithm called Latent Dirichlet Allocation is utilized to identify 15 hidden topics in economic education scholarly work. The text mining model identifies economic education topics by finding correlations in word usage across different documents. The authors show that these newly identified research topics explain more variation in citation counts than the commonly adopted JEL codes. Moreover, specific journals display preferences for certain topics within economic education research. (As Provided).
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Begutachtung	Peer reviewed
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01