Extracting implicit suggestions from students’ comments: A text analytics approach

• Main Authors: SHANKARARAMAN, Venky, GOTTIPATI, Swapna, LIN, Jeff Rongsheng, GAN, Sandy
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2017
• Subjects: student feedback; teaching evaluation; implicit suggestions; text analytics; text mining; classification techniques; Higher Education; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/3833; https://ink.library.smu.edu.sg/context/sis_research/article/4835/viewcontent/Sugestions_ICCE_2017_Camera_V1.pdf
• Institution: Singapore Management University

At the end of each course, students are required to give feedback on the course and instructor. This feedback includes quantitative rating using Likert scale and qualitative feedback as comments. Such qualitative feedback can provide valuable insights in helping the instructor enhance the course content and teaching delivery. However, the main challenge in analysing the qualitative feedback is the perceived increase in time and effort needed to manually process the textual comments. In this paper, we provide an automated solution for analysing comments, specifically extracting implicit suggestions from the students’ qualitative feedback comments. The implemented solution leverages existing text mining and data visualization techniques and comprises three stages namely data pre-processing, implicit 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. The experiments show that the proposed solution generated suggestions from the comments with the F-Score of 78.1%.