(BNG 2023) Leveraging Data Science to Analyze and Interpret Qualitative Data from Student Evaluations of Teaching
Aug-01-2023
Qualitative data from student evaluations of teaching (SET) could be immensely helpful in improving the teaching and learning experience. However, qualitative data are rarely used, because it takes substantial time, effort, and expertise to interpret and extract useful information. We are designing a tool to be used alongside Explorance BLUE that makes use of natural language processing and machine learning to provide semi-automated analysis of qualitative SET data. This presentation will showcase our approach to construct our tool, including thematic analysis, building and fine-tuning topic models, and visualizing the data. If we are successful in creating this tool, we have an opportunity to revolutionize the method by which students and instructors give and receive qualitative feedback, and how teaching is evaluated at higher education institutions
Presented by : Sharonna Greenberg, Caroline Junkins, Pratheepa Jeganatha, Keyu Hong, Rashmi Panse, and Amanda Ferguson, McMaster University