SETSum: Summarization and Visualization of Student Evaluations of Teaching
Yinuo Hu, Shiyue Zhang, Viji Sathy, Abigail Panter, Mohit Bansal
Abstract
Student Evaluations of Teaching (SETs) are widely used in colleges and universities. Typically SET results are summarized for instructors in a static PDF report. The report often includes summary statistics for quantitative ratings and an unsorted list of open-ended student comments. The lack of organization and summarization of the raw comments hinders those interpreting the reports from fully utilizing informative feedback, making accurate inferences, and designing appropriate instructional improvements. In this work, we introduce a novel system, SETSUM, that leverages sentiment analysis, aspect extraction, summarization, and visualization techniques to provide organized illustrations of SET findings to instructors and other reviewers. Ten university professors from diverse departments serve as evaluators of the system and all agree that SETSUM help them interpret SET results more efficiently; and 6 out of 10 instructors prefer our system over the standard static PDF report (while the remaining 4 would like to have both). This demonstrates that our work holds the potential of reforming the SET reporting conventions in the future.
BibTeX
@inproceedings{hu-etal-2022-setsum,
title = "{SETS}um: Summarization and Visualization of Student Evaluations of Teaching",
author = "Hu, Yinuo and
Zhang, Shiyue and
Sathy, Viji and
Panter, Abigail and
Bansal, Mohit",
editor = "Hajishirzi, Hannaneh and
Ning, Qiang and
Sil, Avi",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: System Demonstrations",
month = jul,
year = "2022",
address = "Hybrid: Seattle, Washington + Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-demo.9/",
doi = "10.18653/v1/2022.naacl-demo.9",
pages = "71--89"
}