Machine Learning for Detecting Gender Bias at Chalmers

Abstract

This thesis studies gender bias in course evaluations through the lens of machine learning and NLP. Different methods are used to examine and explore the data and find differences in what students write about courses depending on the gender of the examiner. The data is also examined using more traditional statistical methods to get an understanding of how the students’ impressions of the courses are related to the gender of the examiner. Other aspects related to gender and gender bias are also examined, such as how the proportion of female students relates to the gender of the examiner and whether male or female examiners give different grades to their students. Student grades and teaching language are also factors that are being examined to see whether there is any bias against female examiners or students that is easily detectable in the data. The main findings are that courses with female examiners seem to get lower overall impression scores than those with male examiners. Courses taught in Swedish also receive lower scores, compared to the English courses. No clear patterns as to what words are used when writing comments about a course with a male or female examiner were found. When trying to predict the author gender the patterns were clearer, finding that men write more words directly related to the course and women write more words related to communication.