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Demonstrating the potential of text mining for analyzing school inspection reports: a sentiment analysis of 17,000 Ofsted documents

Demonstrating the potential of text mining for analyzing school inspection reports: a sentiment analysis of 17,000 Ofsted documents
Demonstrating the potential of text mining for analyzing school inspection reports: a sentiment analysis of 17,000 Ofsted documents

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.

School inspection, sentiment analysis, text mining
1743-727X
Bokhove, Christian
7fc17e5b-9a94-48f3-a387-2ccf60d2d5d8
Sims, Samuel
958f3090-b223-4832-8794-883afc83cca8
Bokhove, Christian
7fc17e5b-9a94-48f3-a387-2ccf60d2d5d8
Sims, Samuel
958f3090-b223-4832-8794-883afc83cca8

Bokhove, Christian and Sims, Samuel (2020) Demonstrating the potential of text mining for analyzing school inspection reports: a sentiment analysis of 17,000 Ofsted documents. International Journal of Research and Method in Education. (doi:10.1080/1743727X.2020.1819228).

Record type: Article

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.

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Bokhove Sims IJRME Accepted Pure - Accepted Manuscript
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Accepted/In Press date: 4 August 2020
e-pub ahead of print date: 17 September 2020
Published date: 17 September 2020
Keywords: School inspection, sentiment analysis, text mining

Identifiers

Local EPrints ID: 443270
URI: http://eprints.soton.ac.uk/id/eprint/443270
ISSN: 1743-727X
PURE UUID: 8d64c3fb-4b0f-4c19-8159-6a450a14b2b9
ORCID for Christian Bokhove: ORCID iD orcid.org/0000-0002-4860-8723

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Date deposited: 19 Aug 2020 16:32
Last modified: 13 Aug 2022 04:06

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Author: Samuel Sims

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