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Are rankings and accreditation related? Examining the dynamics of higher education in Poland
Quality Assurance in Education Pub Date : 2020-06-27 , DOI: 10.1108/qae-03-2020-0032
Krzysztof Rybinski

This paper aims to analyse the relationship between two measures of university quality, the outcome and other characteristics of a mandatory accreditation and the university position in the national ranking.,Natural language processing (NLP) models are used to calculate the sentiment indicators for 1,850 accreditation reports from the Polish Accreditation Agency. The sentiment indicators, accreditation frequency and outcomes for 203 HEIs are used in correlation analysis, automated linear regressions and quantile regressions with the university position in the Polish Perspektywy rankings as the outcome variable.,High/low frequency of accreditation visits, excellent/poor accreditation outcomes and low/high frequency of negative inclination words in the accreditation report are followed by high/low university rankings. Quantile regressions reveal that these relationships vary with the quality of the university.,Publishers of university rankings may consider adding the accreditation features to the set of indicators used in such rankings. The machine learning methodology presented allows cross-country inconsistencies to be identified in the approaches used by accreditation agencies in Europe. The authors of the accreditation reports should be aware they can be mined by machine learning models and this should be considered when the reports are drafted.,This is a novel application of NLP models for analysing the relationship between the accreditation and rankings of universities. In other research, the author has applied NLP models to test whether quality assurance agency (QAA) accreditation in the UK can predict how students rate their university on whatuni.com website.

中文翻译:

排名和认证是否相关?考察波兰高等教育的动态

本文旨在分析两种大学质量衡量指标,强制性认证的结果和其他特征与大学在国家排名中的位置之间的关系。自然语言处理(NLP)模型用于计算1,850个认证的情感指标波兰认证局的报告。203个HEI的情绪指标,认可频率和结果被用于相关性分析,自动线性回归和分位数回归,并将波兰Perspektywy排名中的大学排名作为结果变量。,高/低频率的认证访问,良好/不良的认证认证报告中的结果和负倾斜字的频率高/低依次为高/低大学排名。分位数回归显示这些关系随大学的质量而变化。大学排名的发布者可以考虑将资格认证功能添加到此类排名中使用的一组指标中。提出的机器学习方法可以在欧洲认证机构使用的方法中识别跨国不一致之处。认证报告的作者应意识到可以通过机器学习模型进行挖掘,并且在起草报告时应予以考虑。这是NLP模型在分析认证与大学排名之间的关系中的一种新颖应用。在其他研究中,作者应用NLP模型来测试英国的质量保证机构(QAA)认证是否可以预测学生对whatuni的评价。
更新日期:2020-06-27
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