research

Using mirror and other internal surveys in order to improve student experience

Abstract

This article is the first stage of a project which considers how best to use the data collected from mirror surveys and other internal student surveys to enhance the student experience, with a subsidiary aim of thereby enhancing National Student Survey (NSS) scores. The second stage, which is underway at present, combines the theoretical basis and debate explored in this article with detailed statistical analysis of internal and external survey results, to provide a greater evidential basis for decision-making and strategic planning. The research was supported as a 2011-12 Learning Development Project, at City University London, and is intended to inform educational discussion and strategy. The interim findings discussed below are readily transferable to other disciplines and other universities. Universities have put a great deal of effort into improving student satisfaction, but not always with measurable results. Throughout the existence of the NSS, universities have experienced significant variance between student satisfaction as represented by internal measures and the levels of satisfaction reported in the NSS. This has been the case even when the internal measures take the form of mirror surveys, i.e. surveys which mirror or closely resemble the questions on the current version of the NSS. Although general morale factors and events beyond a university’s control may play a strong role in the scores, they do not necessarily explain the differences, especially where the internal questions are based on those from the NSS. Both measures may be an accurate representation of student satisfaction but measuring subtly different factors, or other influences may be operating. By examining this issue, this project aims to enable better planning for the future and the development of appropriate, tailored responses to issues. The interim findings reflect examples of best practice and next steps for the strategic use of such data, including free-text comments

    Similar works