Machine Learning in Education: A Bibliometric Review of Research Trends and Future Directions

Abstract

Machine Learning (ML) in Education is the application of Machine Learning technology in an educational context; the main objective of this study is to update the current knowledge frontiers around investigations related to research trends on Machine Learning in Education and, identify key research topics and analyze their evolution over time. Bibliometric Analysis has been applied in this article, analyzing 472 academic articles related to Machine Learning in Education from Scopus after several data cleaning and preparation steps. The R package "Bibliometrix" was mainly used to analyze this content. Our study has two parts, and the performance analysis contains five categories (Annual Scientific Production, Most Relevant Sources, Most Productive Authors, Most Cited Publications, and Most Relevant Keywords). Science mapping includes country collaboration analysis and thematic Analysis. We analyzed the thematic map by dividing the entire bibliographic dataset into four quadrants to present the thematic evolution over time. This study is one of the most comprehensive bibliometric reviews analyzing Machine Learning in Education related studies. We explain how the results will benefit the understanding of academic research interests to improve the quality of future research on Islamic Education.