Date of Award
Master of Science (MS)
Electrical Engineering and Computer Science
data mining, data science, gender bias, gender inequality, machine learning, teaching reviews
Career growth in academia is often dependent on student reviews of university profes-
sors. A growing concern is how evaluation of teaching has been affected by gender biases
throughout the reviewing process. However, pinpointing the exact causes and consequen-
tial 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 qual-
ity. 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 re-
gression learning algorithm to find the most important factors that can help in identifying
Sinha, Souradeep, "Assessing gender inequality from large scale online student reviews" (2016). Dissertations - ALL. 485.