LAUSR

The purpose of this study is to use machine learning and exploratory data analysis to interrogate patterns of metrics from a national-level student survey. Analysis of over 1.8 million returns detected long-term stability of the predictors of student satisfaction, with survey items relating to course management and teaching being consistently most influential. All metric outputs increased over the survey period; however, the rates of increase of several dimensions including Overall Satisfaction decreased markedly in the most recent years to a point of levelling off. There was also a growing similarity in an institution of outcomes at a national level. This study contributes new insights into the influential survey instrument, through rigorous determination of the most influential survey items, descriptions of the changes in variability between institutions, and exploration of the importance of patterns of outliers at the extremes of the metric outputs. We also identify a rapidly growing spike in total satisfaction at a broad course level and highlight how this is inconsistent with a customer satisfaction model. We conclude by considering the challenges of the use of national-level student surveys for the management of student satisfaction in higher education.