Semester of Graduation
Spring 2022
Degree
Master of Science in Computer Science (MSCS)
Department
The Division of Computer Science and Engineering
Document Type
Thesis
Abstract
Online Professor Reputation (OPR) systems, such as RateMyProfessors.com (RMP), are frequently used by college students to post and access peer evaluations of their pro- fessors. However, recent evidence has shown that these platforms suffer from major bias problems. Failing to address bias in online professor ratings not only leads to negative expectations and experiences in class, but also poor performance on exams. To address these concerns, in this thesis, we study bias in OPR systems from a software design point of view. At the first phase of our analysis, we conduct a systematic literature review of 23 interdisciplinary studies on bias problems affecting OPR systems. Our objective is to systematically categorize and synthesize existing evidence and identify features of OPR systems which enable offline patterns of bias to flourish online. In the second phase, we propose several preventive and corrective software design strategies to mitigate bias in OPR systems. Our objective is to highlight evidence-based design tactics that software engineers can use to develop OPR systems that are immune to bias by design.
Recommended Citation
Tatum, Haley, "Rethinking the Design of Online Professor Reputation Systems" (2022). LSU Master's Theses. 5515.
https://repository.lsu.edu/gradschool_theses/5515
Committee Chair
Mahmoud, Anas
DOI
10.31390/gradschool_theses.5515
Included in
Graphics and Human Computer Interfaces Commons, Other Computer Sciences Commons, Software Engineering Commons, Systems Architecture Commons