SETSum: Summarization and Visualization of Student Evaluations of Teaching
This addresses the inefficiency in interpreting student feedback for instructors and reviewers in educational settings, though it is incremental as it builds on existing techniques.
The authors tackled the problem of disorganized and unsummarized student evaluations of teaching (SETs) by developing SETSum, a system that uses sentiment analysis, aspect extraction, summarization, and visualization to provide organized illustrations, with 6 out of 10 instructors preferring it over standard reports.
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 helps 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 to reform the SET reporting conventions in the future. Our code is available at https://github.com/evahuyn/SETSum