Benchmarking for the effective use of student evaluation data

The purpose of this paper is to examine current approaches to interpretation of student evaluation data and present an innovative approach to developing benchmark targets for the effective and efficient use of these data.

This article discusses traditional approaches to gathering and using student feedback across the tertiary sector. The limitations of the customary use of the statistical mean as a quality measure of performance are presented and examined. An alternative method of interpreting student evaluation data is proposed and examples given.

The traditional use of the statistical mean to interpret student evaluation data has limitations. Focusing on data at the macro level provides subject teaching staff and managers with a clearer indication of student satisfaction. The use of a percentage satisfied and percentage dissatisfied metric to classify and rank subjects is presented as an efficient alternative to the traditional approach, while recognising the value of the statistical mean to interpret data at the micro level.

In light of the important role student feedback plays in determining university ranking, prioritising staff development and its potential function as an academic performance indicator, the effective interpretation of student evaluation data is critical. As economic factors become increasingly important to higher education providers, the role of evaluation data obtained from students will continue to gain traction. The identification of methods to fully capitalise on the value of these data, such as the one proposed in this article, is therefore crucial.