Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

12-2017

Abstract

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%.

Keywords

student feedback, teaching evaluation, implicit suggestions, text analytics, text mining, classification techniques

Discipline

Higher Education | Numerical Analysis and Scientific Computing

Research Areas

Learning and Information Systems Education

Publication

Proceedings of 25th International Conference on Computers in Education (ICCE 2017:, Christchurch, New Zealand, December 4-8

First Page

261

Last Page

269

ISBN

9789869401265

Publisher

Asia-Pacific Society for Computers in Education

City or Country

Taoyuan, Taiwan

Copyright Owner and License

Authors

Download

Share

COinS