Text analytics approach to extract course improvement suggestions from students' feedback - Swapna Gottipati; Venky Shankararaman; Jeff Rongsheng Lin (The Asia-Pacific Society for Computers in Education (APSCE), 2018)
Abstract In academic institutions, it is normal practice that at the end of each term, students are required to complete a questionnaire that is designed to gather students' perceptions of the instructor and their learning experience in the course. Students' feedback includes numerical answers to Likert scale questions and textual comments to open-ended questions. Within the textual comments given by the students are embedded suggestions. A suggestion can be explicit or implicit. Any suggestion provides useful pointers on how the instructor can further enhance the student learning experience. However, it is tedious to manually go through all the qualitative comments and extract the suggestions. In this paper, we provide an automated solution for extracting the explicit suggestions from the students' qualitative feedback comments. The implemented solution leverages existing text mining and data visualization techniques. It comprises three stages, namely data pre-processing, explicit suggestions extraction and visualization. We evaluated our solution using student feedback comments from seven undergraduate core courses taught at the School of Information Systems, Singapore Management University. We compared rule-based methods and statistical classifiers for extracting and summarizing the explicit suggestions. Based on our experiments, the decision tree (C5.0) works the best for extracting the suggestions from students' qualitative feedback.