Assessing gender inequality from large scale online student reviews

Career growth in academia is often dependent on student reviews of university professors. A growing concern is how evaluation of teaching has been affected by gender biases throughout the reviewing process. However, pinpointing the exact causes and consequential effects of this form of gender inequality has been a hard task.;Current work focusses on university-wide student reviewing system, that depends on objective responses on a Likert scale to measure various aspects of an instructor's quality. Through our work, we access online student review data which are not limited by geographies, universities, or disciplines.;Thereafter, we come up with a systematic approach to assess the various ways in which gender inequality is apparent from the student reviews. We also suggest a possible way in which bias related to the gender of a professor could be detected from both objective numerical measures and subjective opinions in reviews. Finally, we assess a logistic regression learning algorithm to find the most important factors that can help in identifying gender inequality.