학술논문
So Many Responses, so Little Time: A Machine-Learning Approach to Analyzing Open-Ended Survey Data
Document Type
Journal Articles
Reports - Research
Reports - Research
Author
Source
Subject
Language
English
ISSN
1041-6099
1536-0725
1536-0725
Abstract
Open-ended responses to surveys can be highly beneficial to higher education institutions, providing clarity and context that quantitative data can sometimes lack. However, analyzing open-ended responses typically takes time and manpower most institutional assessment offices do not have to spare. This study focused on finding a potential solution to this problem by utilizing natural language processing, a type of artificial intelligence (AI), specifically to analyze open-ended responses from the National Survey of Student Engagement (NSSE). The study utilized a subset of AI called Natural Language Processing (NLP), which focuses on how machines understand and translate language.