Analyzing Research Trends in Self-Assessment within Korean EFL Education Using LDA Topic Modeling

This study analyzes research trends in self-assessment within the Korean EFL context using Latent Dirichlet Allocation (LDA) topic modeling, a data-driven approach to uncover thematic patterns in textual datasets. By examining 98 journal articles published between 1998 and 2023, five major themes were identified: student-centered evaluation in writing, self-assessment in classroom and language learning contexts, the role of teachers in assessment, proficiency and motivation in English learning, and skill-specific self-evaluation such as reading and listening. The findings highlight the diverse applications of self-assessment in fostering engagement, tracking progress, and enhancing metacognitive awareness. Teacher involvement is essential in supporting effective practices, while the alignment between self-assessment and formal evaluations reveals its diagnostic value. Furthermore, the study emphasizes self-assessment’s ability to create collaborative and learner-centered environments, particularly in EFL settings. This research provides a structured perspective on self-assessment trends, identifying areas for further exploration.