Date of Award

May 2016

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Electrical Engineering and Computer Science

Advisor(s)

Reza Zafarani

Keywords

data mining, data science, gender bias, gender inequality, machine learning, teaching reviews

Subject Categories

Engineering

Abstract

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

gender inequality.

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Open Access

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