Demonstrating the Potential of Text Mining for Analyzing School Inspection Reports: A Sentiment Analysis of 17,000 Ofsted Documents

Autor/inn/en	Bokhove, Christian; Sims, Sam
Titel	Demonstrating the Potential of Text Mining for Analyzing School Inspection Reports: A Sentiment Analysis of 17,000 Ofsted Documents
Quelle	In: International Journal of Research & Method in Education, 44 (2021) 4, S.433-445 (13 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Bokhove, Christian) ORCID (Sims, Sam)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1743-727X
DOI	10.1080/1743727X.2020.1819228
Schlagwörter	Reports; Data Analysis; Inspection; Institutional Evaluation; School Effectiveness; Foreign Countries; Content Analysis; Secondary Schools; United Kingdom (England) + Suchen Sie Ihr Suchwort? Abschlussbericht; Berichten; Auswertung; Prüfverfahren; Schuleffizienz; Ausland; Inhaltsanalyse; Sekundarschule
Abstract	Many national education systems incorporate a central inspectorate tasked with visiting, evaluating and reporting on the performance of schools. The judgements produced by inspectors often play a part in the way that schools are held to account and constitute an important source of data in their own right. Inspection reports are therefore of great interest to researchers. However, the sheer quantity of inspection reports produced by national school inspectorates creates challenges for analysts. We demonstrate the use of text mining -- automated processing and analysis of unstructured textual data -- to analyse the complete corpus of school inspection reports released by the English national schools inspectorate since the turn of the century. More precisely, we report the results of a sentiment analysis, comparing the tone of inspection reports across the different grades awarded in each inspection and across different Chief Inspectors. In doing so, we hope to demonstrate the efficiency with which text mining approaches can provide representative analysis of very large volumes of inspection reports, making them a useful complement to smaller-scale, manual analyses. Resources and references are provided for researchers looking to use text mining techniques. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01