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<p>This study conducts a detailed bibliometric analysis of the Learning Analytics (LA) and Educational Data Mining (EDM) research from 2014 to 2023 as well as content analyses based on the abstracts of the selected papers. It explores the evolution of the LA &amp; EDM fields, their diverse applications, and the data sources employed in the research. 356 publications with explicit focus on LA and EDM applications were selected for the review. Using bibliometric tools, we analyzed keywords, collaborations, and citations to map the research landscape and identify key academic networks. Additionally, ChatGPT 4.0 as a large language model (LLM) was employed for text analysis to systematically categorize LA and EDM applications and the data sources utilized. For this purpose, a semi-automated method has been developed and applied to evaluate the abstracts of the review papers. As a result, this publication provides a structured synthesis of recent developments in LA and EDM research, offering a robust foundation to identify research gaps and explore deeper topics for future studies. Furthermore, with the developed three-step semi-automated LLM analysis approach, the paper offers researchers a new method to conduct content analysis.</p>

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Learning analytics and educational data mining applications: bibliometric and ChatGPT-based analysis of research publications from 2014 to 2023

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月4日

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  • Maria Kurday,
  • Gergana Vladova

摘要

This study conducts a detailed bibliometric analysis of the Learning Analytics (LA) and Educational Data Mining (EDM) research from 2014 to 2023 as well as content analyses based on the abstracts of the selected papers. It explores the evolution of the LA & EDM fields, their diverse applications, and the data sources employed in the research. 356 publications with explicit focus on LA and EDM applications were selected for the review. Using bibliometric tools, we analyzed keywords, collaborations, and citations to map the research landscape and identify key academic networks. Additionally, ChatGPT 4.0 as a large language model (LLM) was employed for text analysis to systematically categorize LA and EDM applications and the data sources utilized. For this purpose, a semi-automated method has been developed and applied to evaluate the abstracts of the review papers. As a result, this publication provides a structured synthesis of recent developments in LA and EDM research, offering a robust foundation to identify research gaps and explore deeper topics for future studies. Furthermore, with the developed three-step semi-automated LLM analysis approach, the paper offers researchers a new method to conduct content analysis.