Visualizing Student Opinion Through Text Analysis

• Published in: IEEE transactions on education 2019-11, Vol.62 (4), p.305-311
• Main Authors: Cunningham-Nelson, Samuel, Baktashmotlagh, Mahsa, Boles, Wageeh
• Format: Article
• Language: English
• Subjects: Case Studies; Comparative Analysis; Computational Linguistics; Computer Software; Course Evaluation; Data Analysis; Data visualization; Decision Making; Dirichlet problem; Education; Electrical engineering; Engineering Education; Feedback (Response); Foreign Countries; Learning Processes; Machine learning; research to practice; Resource management; Sentiment analysis; Student Attitudes; Student Satisfaction; student satisfaction comments; Teachers; teaching evaluations; Teaching Methods; Text analysis; undergraduate; Undergraduate Students; Visualization
• ISSN: 0018-9359; 1557-9638
• DOI: 10.1109/TE.2019.2924385

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.