Photo by https://unsplash.com/@charlesdeluvio. A photo of an illusion. Two men are sitting next to one another at a table and have laptops in front of them. The man on the right has his left hand lifted, as though pointing at something on his screen. Although it's only the back of his head that you can see, he looks a bit frustrated.

2021-24 - Mining Text for Bias in Student Evaluations of Teaching

Mining Text for Bias in Written Comments of Student Evaluations of Teaching 

 

This research proposal, a collaborative effort between UC Riverside, UC Santa Cruz, and UC Davis, has four specific aims: 1) develop a predictive model that efficiently and automatically scans written course comments, and determines the proportions reflecting student satisfaction levels that are positive, mixed, or negative; 2) pilot an implementation of the predictive model at UC Riverside by integrating it into the iEval student teaching evaluation system to assess both practical and cultural implications of augmenting written comments with a summary report showing the proportions of positive, mixed, or negative comments; 3) use the predictive model to investigate the degree of bias in written comments with respect to the gender, ethnicity, and rank of the instructor, and compare the findings to a parallel bias study of the corresponding numerical scores; and 4) evaluate the efficacy of UC Santa Cruz’s recent revision of instructional evaluation questions as an intervention for reducing bias in comments.