@InProceedings{terkik-EtAl:2016:COLING,
  author    = {Terkik, Andamlak  and  Prud'hommeaux, Emily  and  Ovesdotter Alm, Cecilia  and  Homan, Christopher  and  Franklin, Scott},
  title     = {Analyzing Gender Bias in Student Evaluations},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {868--876},
  abstract  = {University students in the United States are routinely asked to provide
	feedback on the quality of the instruction they have received. Such feedback is
	widely used by university administrators to evaluate teaching ability, despite
	growing evidence that students assign lower numerical scores to women and
	people of color, regardless of the actual quality of instruction. In this
	paper, we analyze students’ written comments on faculty evaluation forms
	spanning eight years and five STEM disciplines in order to determine whether
	open-ended comments reflect these same biases. First, we apply sentiment
	analysis techniques to the corpus of comments to determine the overall affect
	of each comment. We then use this information, in combination with other
	features, to explore whether there is bias in how students describe their
	instructors. We show that while the gender of the evaluated instructor does not
	seem to affect students’ expressed level of overall satisfaction with their
	instruction, it does strongly influence the language that they use to describe
	their instructors and their experience in class.},
  url       = {http://aclweb.org/anthology/C16-1083}
}