Gather-Narrow-Extract: A Framework for Studying Local Policy Variation Using Web-Scraping and Natural Language Processing

Autor/in	Anglin, Kylie L.
Titel	Gather-Narrow-Extract: A Framework for Studying Local Policy Variation Using Web-Scraping and Natural Language Processing
Quelle	In: Journal of Research on Educational Effectiveness, 12 (2019) 4, S.685-706 (22 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Anglin, Kylie L.) Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1934-5747
DOI	10.1080/19345747.2019.1654576
Schlagwörter	Natural Language Processing; Educational Policy; Web Sites; Decision Making; School Districts; Guides; Educational Improvement; Policy Analysis; Computational Linguistics; Classification; Information Retrieval; Dictionaries; Elementary Secondary Education + Suchen Sie Ihr Suchwort? Natürliche Sprache; Politics of education; Bildungspolitik; Web-Design; Decision-making; Entscheidungsfindung; School district; Schulbezirk; Handbuch; Leitfaden; Teaching improvement; Unterrichtsentwicklung; Politikfeldanalyse; Linguistics; Computerlinguistik; Classification system; Klassifikation; Klassifikationssystem; Dictionary; Wörterbuch
Abstract	Education researchers have traditionally faced severe data limitations in studying local policy variation; administrative data sets capture only a fraction of districts' policy decisions, and it can be expensive to collect more nuanced implementation data from teachers and leaders. Natural language processing and web-scraping techniques can help address these challenges by assisting researchers in locating and processing policy documents located online. School district policies and practices are commonly documented in student and staff manuals, school improvement plans, and meeting minutes that are posted for the public. This article introduces an end-to-end framework for collecting these sorts of policy documents and extracting structured policy data: The researcher gathers all potentially relevant documents from district websites, narrows the text corpus to spans of interest using a text classifier, and then extracts specific policy data using additional natural language processing techniques. Through this framework, a researcher can describe variation in policy implementation at the local level, aggregated across state- or nationwide populations even as policies evolve over time. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01