Text Mining and Topic Modeling in Education: Revealing Insights From Educational Textual Data

Abstract

This book chapter explores the transformative potential of text mining and topic modeling in the field of education. With the exponential growth of digital educational content, the need for effective analysis and understanding of large-scale textual data has become crucial. The chapter provides an overview of text mining techniques, covering data preprocessing and information retrieval. It delves into topic modeling algorithm, Latent Dirichlet Allocation (LDA), and its applications in extracting latent themes from educational texts. The chapter highlights the diverse applications of text mining in education, such as analyzing student essays, academic publications, and online discussions. Leveraging sentiment analysis and opinion mining, it enables educators and administrators to gauge learner emotions and attitudes. Ethical considerations, including data privacy and bias, are also discussed, emphasizing the responsible use of text-mining technologies in educational contexts. In conclusion, “Text Mining and Topic Modeling in Education” serves as a valuable resource for educators, researchers, and policymakers, facilitating data-driven decision-making and fostering innovation in education. By empowering stakeholders with powerful analytical tools, this chapter propels education toward evidence-based practices and a more informed, equitable future. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.