Digital Transformation in Higher Education Institutions: Bibliometric Analysis

Javier Muñoz Acuña,Felipe Hernández-Perlines,Alejandro Vega-Muñoz,Guido Salazar-Sepúlveda,Nicolás Contreras-Barraza, Jorge L. Vinueza-Martínez, Mario Torres-Alcayaga

Research Square (Research Square)(2023)

引用 0|浏览7
暂无评分
摘要
The purpose of this article is to carry out a bibliometric analysis of the advances in digital transformation in higher education institutions worldwide. This was done using a search vector on Digital Transformation in Higher Education TS= ((Digital NEAR/0 transform) AND (Higher NEAR/0 education)), without restricted time parameters, performing the extraction on October 15, 2022, of the Web of Science (WoS) database, obtaining a total of 384 records, between the years 2014 and October 2022. Within this universe, most of the documents extracted were articles (206, 59.2%), followed by records (132, 37, 9%) and reviews (10, 2.9%). These publications were analyzed per year (Price's Law); concentration by journals (Bradford Law); concentration by authors (Lotka's Law); concentration of citations per article (Hirsch Index); and concentration of keywords (Zipf's Law). Regarding the results, we observed an exponential increase in the scientific production on DT2HE in the last 9 years, with a concentration of the scientific discussion on DT2HE, in 14 journals that published more than 2 articles each, on DT2HE in the period studied; a production distributed in 50 closely related countries, forming a global community of knowledge about DT2HE, concentrated in Russia, Spain, Germany and Portugal. Regarding citations, there are two prolific authors: both with 115 citations each. Finally, three groups of keywords were identified: the first group is related to the goals of modernization in higher education, the second, with the effects on students produced by DT2HE, and a third group, which emphasizes the DT2HE process itself.
更多
查看译文
关键词
bibliometric analysis,digital transformation,higher education institutions,higher education
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要