Analysis on the Research Trends of KOHS Journal Papers using Topic Modeling

Recently, there has been growing educational interest in competency. Global organizations, such as the United Nations (UN) and Organization for Economic Co-operation and Development (OECD), which are leading the discourse on education reform, are undertaking the lead in spreading awareness regarding competency education. Since 2015, the number of published articles on competency education has been rapidly increasing. This paper aims to provide significant implications for creating a sustainable future of competency education. A topic modeling method was used to empirically analyze latent topics and international research trends in 26,532 articles published on competency-based education (CBE). As a result of the analysis, 15 topics were derived, including “approach to competency development.” In addition, five topics including “learning skills” and “teacher training” were found to be hot topics with the increasing article publication. The rapidly changing modern society is calling for a transformation in education. We hope that the results of this study paves the way for further research exploring new directions for education, such as competency education.

Download Full-text

Analysis of Research Trends in Online Discussion Using Topic Modeling

Institute of Brain-based Education Korea National University of Education ◽

10.31216/bdl.20210028 ◽

2021 ◽

Vol 11 (3) ◽

Keyword(s):

Topic Modeling ◽

Online Discussion ◽

Research Trends

Download Full-text

A topic modeling analysis of Korea’s T&I research trends in the 2010s

Babel ◽

10.1075/babel.00228.lee ◽

2021 ◽

Author(s):

Changsoo Lee

Keyword(s):

Topic Modeling ◽

Research Output ◽

Research Trends ◽

Korean Language ◽

Modeling Analysis ◽

Trends Over Time ◽

New Research ◽

Topic Structure ◽

Manual Classification

Abstract The present study aims to demonstrate the relevance of topic modeling as a new research tool for analyzing research trends in the T&I field. Until now, most efforts to this end have relied on manual classification based on pre-established typologies. This method is time- and labor-consuming, prone to subjective biases, and limited in describing a vast amount of research output. As a key component of text mining, topic modeling offers an efficient way of summarizing topic structure and trends over time in a collection of documents while being able to describe the entire system without having to rely on sampling. As a case study, the present paper applies the technique to analyzing a collection of abstracts from four Korean Language T&I journals for the 2010s decade (from 2010 to 2019). The analysis proves the technique to be highly successful in uncovering hidden topical structure and trends in the abstract corpus. The results are discussed along with implications of the technique for the T&I field.

Download Full-text