Performance evaluation in teaching: Dissecting student evaluations in higher education

Abstract

Numerous studies have highlighted the significant role of Student Evaluations of Teaching (SETs) as a key metric for assessing teaching quality in Higher Education (HE). Building upon these insights, our study introduces an innovative four-tiered model, derived from diverse research, to examine the reliability of SETs. This model addresses biases associated with SETs, delving into both statistical anomalies and cognitive biases, with particular emphasis on often-overlooked hidden context and timing factors. We reveal that these biases can distort SET scores, leading to potentially inaccurate representations of both individual and comparative academic performances. The implications of our research are significant for those influencing HE policy-making and performance evaluation. We echo previous calls for a more expansive approach to teaching effectiveness, essential for genuine insight into teaching quality. By adopting this perspective, HE can design better-informed strategies, ensuring policies and practices reflect the diverse nature of teaching excellence.