Abstract
Summer melt is a phenomenon when college-intending students fail to enroll in the fall after high school graduation. Previous research on summer melt utilized surveys, typically consisting of Likert scale questions and open-ended response questions. Open-ended responses can elicit more information from students, but they have not been fully analyzed due to the cost, time, and complexity of theme extraction with manual coding. In the present study, we applied the topic modeling approach to extract topics and relevant themes, and evaluated model performance by comparing model-generated topics and categories with the human-identified topics and themes. Results showed that the topic model allows for extracting similar topics as the survey questions that were investigated, but only extracted part of the themes classified by the human. Discussion and implications focus on potential improvements in automated topic and theme classification from open-ended survey responses.
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