So Many Responses, So Little Time: A Machine‐Learning Approach to Analyzing Open‐Ended Survey Data

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.