Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

10-2017

Abstract

In academic institutions it is normal practice that at the end of each term,students are required to complete a questionnaire that is designed to gather students’perceptions of the instructor and their learning experience in the course. This questionnaire comprises of Likert-scale questions and qualitative questions.One of the important goals of this exercise is to enable the instructor and the senior management to examine the feedback and then enhance students’ learning experience. In most universities, including our own, a lot of attention is paid to the quantitative feedback, which is summarized and statistical comparisons are computed, analysed and presented. However, the qualitative comments given by the students are not fully tapped. Capturing and analysing the qualitative feedback data, at the individual course, school and university-level, can provide valuable insights on teaching practices and curriculum. In this paper,we propose a conceptual framework for student feedback analysis that provides the necessary structure for implementing a prototype tool for mining student comments. We then discuss the application of the tool to analyse feedback from selected courses.

Keywords

Student feedback analysis, framework, learning analytics, topics, sentiments, text analytics, clustering

Discipline

Higher Education | Numerical Analysis and Scientific Computing

Research Areas

Learning and Information Systems Education

Publication

FIE 2017: Proceedings of 47th Annual Frontiers in Education Conference, Indianapolis, Indiana, October 18-21

First Page

1

Last Page

8

ISBN

9781509059201

Identifier

10.1109/FIE.2017.8190703

Publisher

IEEE

City or Country

Piscataway, NJ

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/FIE.2017.8190703

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