Using Topic Modeling in Gifted Education Research: Drawing Insights From Open-Ended Survey Responses.

This article focuses on introducing topic modeling, specifically Latent Dirichlet Allocation (LDA), as an efficient statistical method for analyzing open-ended survey responses in educational research. Using a dataset of 477 students' open-ended responses about career interests from a federally funded Javits grant, the authors demonstrate how LDA can identify latent thematic topics—such as Scientific Exploration, Digital Technology, Human Services, Business/Sports/Media, and Arts/Literature—and relate these themes to students' demographics and giftedness measures. The article discusses the advantages of topic modeling, including efficiency, objectivity, and the ability to integrate qualitative and quantitative data, while also addressing limitations like neglect of word order, challenges with short texts, and reliance on researcher judgment. Practical guidance for implementation and considerations for future research in gifted education are provided, highlighting topic modeling's potential to enrich understanding of diverse student experiences through systematic analysis of textual data.