Visualizing Student Opinion Through Text Analysis
Open accessAbstract
Contribution: An automated methodology that provides visualizations of students' free text comments from course satisfaction surveys. Focusing on sentiment, these visualizations reveal learning and teaching aspects of the course that either may require improvement or are performing well. They provide educators with a simple, systematic way to monitor their courses and make pedagogically sound decisions on teaching strategies. Background: Student course satisfaction surveys often solicit free text comments. This feedback can provide invaluable insights for educators, but because these comments often contain a large amount of data, they cannot easily be acted upon. Existing visualization methods are not suitable for this application, and needed additional capabilities. Research Questions: How can large quantities of student satisfaction data be summarized and visualized? How can these visualizations be used to learn meaningful information about courses? What are the recurring themes across semesters? Methodology: Several methods based on machine learning and text analysis techniques were used to visualize student satisfaction comments. The latent Dirichlet allocation (LDA) statistical method was used to identify aspects of student opinion of a course. The sentiment of the student comments were also identified. This information was then presented visually for educators in a case study that gives examples of these visualizations. Findings: The visualization methods explored provide educators with an overview of aspects and their associated sentiment. The summary visualizations allow easy comparison to be made between courses, or between teaching periods in the same course.
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Teaching analytics and teacher dashboards to visualise SET data: Implication to theory and practice
Ndukwe, I. G. (2021) · Otago University Research Archive (University of Otago)
Brownlie, E. K., Evans, A., & Horlin, C. (2022) · doi:10.31219/osf.io/y5wqz
Visualizing Commenters Opinions Through Topic Analysis
Soni, A., Rasool, A., Dubey, A., & Khare, N. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-5224-1_27
Developing Video Resources in Engineering Education
Dart, S., Cunningham, S., & Gregg, A. (2022) · CRC Press eBooks · doi:10.1201/9781003263180-5
Intelligent Tutoring Systems for Multidisciplinary Education
Bhatnagar, S., & Agrawal, R. (2024) · Innovations in Sustainable Technologies and Computing · doi:10.1007/978-981-97-1111-6_8
Feedback Intent Analysis using Machine Learning: A Case Study on Value Added Courses
Singh, S., Satsangee, D., & Singh, L. (2025) · International Conference on Information Systems and Computer Networks (ISCON) · doi:10.1109/iscon65210.2025.11340822
“The research study [17] introduces an automated methodology for creating visual representations of student’s free text comments from course satisfaction surveys highlighting the areas of the course that may require improvement.”
Wiktor, S., & Dorodchi, M. (2026) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3770762.3772606
Reframing student feedback: from evaluation to dialogue
Bengtsson, M. (2026) · Teaching in Higher Education · doi:10.1080/13562517.2026.2694024
Falcón, S. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-1042-8.ch006
An analytics-driven outranking method for bias mitigation in survey-based performance assessment
Barrera-Jimenez, F., Barrigüete, J. L. A., Segura, M., García, A. O., Álvarez-Fernández, C., & Ortiz-Lozano, J. M. (2026) · Decision Analytics Journal · doi:10.1016/j.dajour.2026.100756
Imam, M. (2026) · Smart innovation, systems and technologies · doi:10.1007/978-3-032-11456-3_19
- Authors
- Cunningham-Nelson, S., Baktashmotlagh, M., & Boles, W.
- Affiliations
- The University of Queensland (AU)
- Queensland University of Technology (AU)
- Venue
- IEEE Transactions on Education
- Published
- 2019-11
- Volume
- 62
- Issue
- 4
- Pages
- 305-311
- ISSN
- 0018-9359 (ISSN-L)
- 1557-9638
- OpenAlex
- W2958619243
- Cited by
- 75
- References
- 23
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 12 · 2023: 20 · 2022: 17 · 2021: 7 · 2020: 6
- FWCI
- 3.46
- Type
- journal article
- Language
- en
- Open access
- green
- eprints.qut.edu.au
- Licence
- Locations
- IEEE Transactions on Education (publishedVersion)
- QUT ePrints (Queensland University of Technology) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Education › Evaluation of Teaching Practices
- Keywords
- student satisfaction survey, course satisfaction, free-text comments, sentiment analysis, latent Dirichlet allocation, topic modeling, course evaluation, teaching methods
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