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<p>This study applied Blei et al.’s (Journal of Machine Learning Research 3:993–1022, 2003) Latent Dirichlet Allocation (LDA) topic modeling to analyze 1,727 research articles published in <i>Early Childhood Education Journal</i> (ECEJ) from 2000 to 2023. LDA is a probability-based analysis model effective for discovering hidden topics within large sets of textual data. The purpose of this study was to identify underlying research trends and thematic shifts in ECEJ publications during this period by examining trends in publication numbers, keyword frequencies, and topic emergence. The analysis results revealed a significant increase in published papers during the 2020–2023 period, with 2023 recording the highest number of publications. Keyword frequency analysis identified “young children,” “teachers,” and “preschools” as the three most frequently used keywords, underscoring a consistent focus on children in their early years, their educators, and educational settings. Trends in research topics highlighted an enduring focus on “Reading and Young Children’s Literature,” the most dominant topic over the past 24&#xa0;years, reflecting the connections to federal and state educational policies promoting early reading and literacy. Additionally, the analysis demonstrated the impact of the COVID-19 pandemic on research priorities, with “Teacher Perceptions and Distance Education during COVID-19” emerging as the second most prevalent topic since 2000. The article concludes by discussing implications for future directions in early childhood research.</p>

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An Analysis of Publications in Early Childhood Education Journal Through LDA Topic Modeling: Exploring Research Trends From 2000 to 2023

  • 期刊论文

  • 发表日期：

    2025年6月12日

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  • Keon-Ryeong Park,
  • Kyunghwa Lee,
  • Sukyoung Park

摘要

This study applied Blei et al.’s (Journal of Machine Learning Research 3:993–1022, 2003) Latent Dirichlet Allocation (LDA) topic modeling to analyze 1,727 research articles published in Early Childhood Education Journal (ECEJ) from 2000 to 2023. LDA is a probability-based analysis model effective for discovering hidden topics within large sets of textual data. The purpose of this study was to identify underlying research trends and thematic shifts in ECEJ publications during this period by examining trends in publication numbers, keyword frequencies, and topic emergence. The analysis results revealed a significant increase in published papers during the 2020–2023 period, with 2023 recording the highest number of publications. Keyword frequency analysis identified “young children,” “teachers,” and “preschools” as the three most frequently used keywords, underscoring a consistent focus on children in their early years, their educators, and educational settings. Trends in research topics highlighted an enduring focus on “Reading and Young Children’s Literature,” the most dominant topic over the past 24 years, reflecting the connections to federal and state educational policies promoting early reading and literacy. Additionally, the analysis demonstrated the impact of the COVID-19 pandemic on research priorities, with “Teacher Perceptions and Distance Education during COVID-19” emerging as the second most prevalent topic since 2000. The article concludes by discussing implications for future directions in early childhood research.