NSW Branch: Gender and Cultural Bias In Student Evaluations of Teaching at Universities by A/Prof Yanan Fan

  • 6 Aug 2019
  • 6:00 PM - 7:30 PM
  • New Law School Seminar 100, The University of Sydney

Gender and Cultural Bias In Student Evaluations of Teaching at Universities

There can be no doubt that men and women are evaluated differently in the workplace, particularly in places where one gender dominates the other. The precise manner in which this difference is translated into a disadvantage for women varies depending on the workplace. In higher education, few women make it to the upper echelons of the academic hierarchy, here we investigate the role of conscious or unconscious bias on performance assessment. While large scale opinion surveys can be difficult to obtain in general, surveys of student opinions have been carried out for many years to evaluate teachers, this gives us a glimpse at how biases can effect assessment of individuals. With unprecedented access to institution-wide teaching evaluations at a large top public university in Australia, we analysed more than 500,000 student evaluations of teaching over the period 2010-2016. We find that SET scores are effected by a number of factors, including gender of the teacher, cultural background of the teacher, and personal characteristics of the student, this effect is large and statistically significant in some faculties. This study has implications for the wider society, in the sense that with over 40% of the population now going to the university, and most of the these students end up on boards and management teams in companies, they carry with them these unconscious biases.

Biography

Yanan Fan is an Associate Professor of Statistics at the School of Mathematics and Statistics UNSW. Her research is focussed primarily on the development of Bayesian models and computational methodology to solve real world problems. Her current interest include developing Bayesian semiparametric models; computational methods for large spatial data arising from medical images; approximate Bayesian inference methods and various applications of Bayesian methodology to real world problems.

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