Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

10-2015

Abstract

Universities collect qualitative and quantitative feedback from students upon course completion in order to improve course quality and students’ learning experience. Combining program-wide and module-specific questions, universities collect feedback from students on three main aspects of a course namely, teaching style, content, and learning experience. The feedback is collected through both qualitative comments and quantitative scores. Current methods for analyzing the student course evaluations are manual and majorly focus on quantitative feedback and fall short of an in-depth exploration of qualitative feedback. In this paper, we develop student feedback mining system (SFMS) which applies text analytics and opinion mining approach to provide instructors a quantified and exhaustive analysis of the qualitative feedback from students.

Keywords

Student feedback, education data mining, topics, sentiments, text analytics, clustering

Discipline

Computer Sciences | Higher Education

Research Areas

Learning and Information Systems Education

Publication

2015 IEEE Frontiers in Education Conference: El Paso, Texas, October 21-24: Proceedings

First Page

1658

Last Page

1666

ISBN

9781479984541

Identifier

10.1109/FIE.2015.7344296

Publisher

IEEE

City or Country

Piscataway, NJ

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/FIE.2015.7344296

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