ABSTRACT

This article develops a machine learning methodology to analyse the relationship between university accreditation and student experience. It is applied to 98 university accreditations conducted by the Quality Assurance Agency (QAA) in the UK in 2012–2018, and 263,025 university ratings in three categories posted by students on the website whatuni.com. Natural Language Processing (NLP) is used to extract features from the accreditation reports. These features are explanatory variables in automated linear regression models where the dependent variable is the student experience, as measured by the student ratings. It finds that the Institutional Reviews in 2012–2014 and the Higher Education Reviews in 2014–2016 misinform the public about the student experience, while the Enhancement-Led Institutional Reviews in Scotland in 2014–2018 provide sound guidance. These findings should lead to a deep reflection on how the university accreditation system functions in the UK. They also contribute to the ongoing debates on student engagement in HE quality assurance and whether student experience is a reliable measure of university quality. Finally, it is shown that machine learning models are useful tools to compare accreditation reports and can assist prospective students in choosing the university.

摘要

本文开发了一种机器学习方法来分析大学认证与学生体验之间的关系。它适用于 2012-2018 年英国质量保证局 (QAA) 进行的 98 项大学认证，以及学生在 whatuni.com 网站上发布的三个类别的 263,025 项大学评级。自然语言处理 (NLP) 用于从认证报告中提取特征。这些特征是自动线性回归模型中的解释变量，其中因变量是学生体验，由学生评分来衡量。它发现 2012-2014 年的机构审查和 2014-2016 年的高等教育审查在学生体验方面误导了公众，而 2014-2018 年苏格兰以增强为主导的机构审查提供了良好的指导。这些发现应该引发对英国大学认证系统如何运作的深刻反思。他们还为正在进行的关于学生参与高等教育质量保证以及学生体验是否是衡量大学质量的可靠指标的辩论做出了贡献。最后，它表明机器学习模型是比较认证报告的有用工具，可以帮助未来的学生选择大学。