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.

摘要

许多国家教育系统都设有中央监察局，负责访问、评估和报告学校的表现。检查员做出的判断通常在学校承担责任的方式中发挥作用，并以其自身的权利构成重要的数据来源。因此，研究人员对检查报告非常感兴趣。然而，国家学校监察机构出具的大量检查报告给分析人员带来了挑战。我们展示了使用文本挖掘——非结构化文本数据的自动化处理和分析——来分析自世纪之交以来英国国家学校监察局发布的完整学校检查报告语料库。更准确地说，我们报告了情感分析的结果，比较在每次检查中授予的不同等级和不同总检查员的检查报告的基调。在此过程中，我们希望证明文本挖掘方法可以对大量检查报告提供代表性分析的效率，使其成为小规模手动分析的有用补充。为希望使用文本挖掘技术的研究人员提供了资源和参考资料。