Publication Type

Conference Proceeding Article

Publication Date

12-2017

Abstract

At theend of each course, students are required to give feedback on the course andinstructor. This feedback includes quantitative rating using Likert scale andqualitative feedback as comments. Such qualitative feedback can providevaluable insights in helping the instructor enhance the course content andteaching delivery. However, the main challenge in analysing the qualitativefeedback is the perceived increase in time and effort needed to manually processthe textual comments. In this paper, we provide an automated solution foranalysing comments, specifically extracting implicit suggestions from thestudents’ qualitative feedback comments. The implemented solution leveragesexisting text mining and data visualization techniques and comprises three stages namely datapre-processing, implicit suggestions extraction and visualization. We evaluatedour solution using student feedback comments from seven undergraduate corecourses taught at the School of Information Systems, Singapore ManagementUniversity. The experiments show that the proposed solution generatedsuggestions from the comments with the F-Score of 78.1%.

Keywords

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

Discipline

Digital Communications and Networking | Programming Languages and Compilers

Research Areas

Data Management and Analytics

Publication

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

First Page

1

Last Page

9

City or Country

Christchurch, New Zealand

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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