A Systematic Analysis of Machine Learning Studies in Education

Machine learning has been transforming education and changing learning, teaching, and administration processes. However, studies analyzing the existing body of work and emerging research foci are lacking. To fill in the re-search gap, this paper presents a bibliometric analysis of articles on machine learning in education that were indexed byWeb of Science Core Citation In-dices from 1979 to 2023. The study investigates publication patterns (articles per year and journals) and key research areas. A keyword co-occurrence analysis was conducted to identify the clusters of keywords which often co-exist in articles. The analysis revealed six clusters which correspond to the main research themes: profiling and prediction, assessment, intelligent tutoring systems, MOOCs, natural language processing, and prediction in distance learning. It is discussed that the newly emerging and rapidly developing research area focuses merely on applications of the technology, while ethical, pedagogical, socio-cultural, and administrative is-sues regarding machine learning in education need further attention.