Personalized Learning in the Online Learning from 2011 to 2021: A Bibliometric Analysis
Hoa-Huy Nguyen, Viet Anh Nguyen
Abstract
This paper has analyzed research trends on personalized learning by bibliometric analysis method through a study of 928 articles from the Scopus database. The following issues are investigated: (1) Development scale, growth trajectory and geographical distribution of the research; (2) Outstanding authors and works on Personalized Learning; (3) Outstanding magazines and books on the topic; (4) Key themes found in these documents, and (5) Prominent methods/technologies used for personalized learning. Research results show that personalized learning is a fascinating topic in education and has been overgrown in recent years. Many researches on personalized learning comes from countries such as the United States and China. Our bibliometric analysis has revealed the main themes in the research works on Personalized Learning, such as artificial intelligence, learning style, and learning technology. The research has observed cognitive aspects of learners like knowledge level, learning style, preferences, etc. In most cases, the recommended tools and methods combined the content-based filtering, collaborative filtering, ontological approaches, etc. In addition, future research goals, difficulties, and concerns are highlighted in our work by examining the trends in several personalized learning elements.