Bibliometric Analysis of Adaptive Learning Literature from 2011-2019: Identifying Primary Concepts and Keyword Clusters

EasyChair Preprint 10720

Abstract

This paper presents a comprehensive bibliometric analysis of the adaptive learning literature from 2011 to 2019 in the social sciences domain. The study utilizes the Scopus database to identify relevant sources and employs cluster analysis based on keyword co-occurrence to categorize the primary concepts. The research focuses on understanding the key areas and trends within adaptive learning, shedding light on its development and impact over the specified period.
The relevance of this analysis lies in the increasing importance of adaptive learning in modern education systems, especially in the context of integrating innovative technologies and addressing the challenges posed by the digital society. As the demand for quality education and skilled teaching staff grows, there is a need to explore and implement more student-centered approaches, such as adaptive learning, to enhance the learning experience and improve educational outcomes.
The outputs of this research provide valuable insights into the main themes and areas of interest within the adaptive learning field during the selected timeframe. By identifying primary concepts and keyword clusters, the study offers a comprehensive overview of the key topics, theories, and technologies that have shaped the development of adaptive learning. This analysis can serve as a valuable resource for researchers, educators, and policymakers seeking to understand the current landscape of adaptive learning and explore potential avenues for future research and innovation.

Keyphrases: Artificial Intelligence, Bibliometric review, VOSviewer, adaptive learning, adaptive systems

@booklet{EasyChair:10720,
  author    = {Liliia Fadieieva},
  title     = {Bibliometric Analysis of Adaptive Learning Literature from 2011-2019: Identifying Primary Concepts and Keyword Clusters},
  howpublished = {EasyChair Preprint 10720},
  year      = {EasyChair, 2023}}