Research ArticleJournal of Scientometric ResearchVol. 13 | Issue 1 | 2024 | pp. 201–216Open access
Scientific Productivity and Collaboration Networks in Lifelong Learning: A Longitudinal Bibliometric Analysis (1963-2022)
- 1*ORCID,
- 2,
- 3,
- 4
- 1 Centre for Adult and Continuing Education, School of Education, Pondicherry University, Kalapet, Puducherry, INDIA
- 2 School of Education, Pondicherry University, Kalapet, Puducherry, INDIA
- 3 Department of Education, The Gandhigram Rural Institute (Deemed to be University), Gandhigram, Dindigul, Tamil Nadu, INDIA
- 4 Department of Library and Information Science, Pondicherry University, Kalapet, Puducherry, INDIA
Published in Journal of Scientometric Research
Correspondence: Kannan Thamizhiniyan
Centre for Adult and Continuing Education, School of Education, Pondicherry University, Kalapet, Puducherry, INDIA
Email: drthamizhiniyank@gmail.com
Copyright: © 2024 Manuscript Technomedia. This is an open access article.
- Published:
- Apr 8, 2024
- Received:
- Feb 11, 2023
- Accepted:
- Jan 1, 2024
How to cite
Thamizhiniyan, K., Chellamani, K., Begum, A. H. J., & Naseema, S. (2024). Scientific Productivity and Collaboration Networks in Lifelong Learning: A Longitudinal Bibliometric Analysis (1963-2022). Journal of Scientometric Research, 13(1), 201–216. https://doi.org/10.5530/jscires.13.1.17
Abstract
In the post-pandemic era, lifelong learning (LLL) emerged as the key to professional development and the core competency of all disciplines. Even globally, there is a dearth of evidence based bibliometric analysis, notably on LLL. This study addresses this gap by examining the data retrieved from the Elsevier Scopus database. A systematic search method was adopted to retrieve 1806 publications from 790 journals from 1963 to 2022. The R package, Biblioshiny, was used for data analysis, including productivity/performance analysis, citation analysis, and collaboration network analysis of social structure. The findings showed that the number of publications has significantly increased over time. A large number of studies were published in 2022. Overall, 85 countries contributed to LLL. Among them, the United States was the most productive with 787 publications, and the United Kingdom was the country with 4731 citations. Learning was the trending topic, and skill development was an emerging theme in LLL. The results will aid the stakeholders in identifying largely unexplored areas of research that need more attention and funding. This study outlines not only the current scientific developments but also the potential future of LLL research. This study will also be used as a resource for researchers and teachers in LLL. Future research directions in this area of knowledge are also outlined.
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Article metadata
| Title | Scientific Productivity and Collaboration Networks in Lifelong Learning: A Longitudinal Bibliometric Analysis (1963-2022) |
|---|---|
| Authors | Kannan Thamizhiniyan; Kathirkamanathan Chellamani; Abdul Huq Jahitha Begum; Sheriff Naseema |
| Affiliations | Centre for Adult and Continuing Education, School of Education, Pondicherry University, Kalapet, Puducherry, INDIA; School of Education, Pondicherry University, Kalapet, Puducherry, INDIA; Department of Education, The Gandhigram Rural Institute (Deemed to be University), Gandhigram, Dindigul, Tamil Nadu, INDIA; Department of Library and Information Science, Pondicherry University, Kalapet, Puducherry, INDIA |
| Corresponding author | drthamizhiniyank@gmail.com |
| Journal | Journal of Scientometric Research |
| Volume / Issue | Vol. 13, Issue 1 (2024) |
Also in this issue
- Exploring the Role of Social Media in Mental Health Research: A Bibliometric and Content Analysispp. 1–8
- Usability Testing: A Bibliometric Analysis Based on WoS Datapp. 9–24
- Exploring the Landscape of Autonomous Vehicles Research: A Scientometric Analysis in the Context of Urban Transportation Planningpp. 25–42
- Bibliometric Analysis of Recent Trends in Machine Learning for Online Credit Card Fraud Detectionpp. 43–57
- Comparing Research Topics through Metatags Analysis: A Multi-module Machine Algorithm Approaches Using Real World Data on Digital Humanitiespp. 58–70
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