Title

Analyzing Educational Comments for Topics and Sentiments: A Text Analytics Approach

Publication Type

Conference Proceeding Article

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

Data Management and Analytics; Learning and Information Systems Education

Publication

Frontiers in Education Conference 45th , October 2015, El Paso, Texas

First Page

1

Last Page

9

ISBN

9781479984541

Identifier

10.1109/FIE.2015.7344296

Publisher

IEEE

City or Country

Piscataway, NJ

Additional URL

http://dx.doi.org/10.1109/FIE.2015.7344296

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