Leveraging machine learning to predict academic success: a literature review and bibliometric trend analysis

This study synthesizes research on the use of machine learning (ML) to predict academic success through a three-scope approach. First, a systematic review of 33 review studies from Web of Science (WOS), Scopus, and Taylor & Francis identifies prevailing methods, levels of prediction, and recurrent gaps in the literature. The synthesis of 33 review studies reveals a consolidation around supervised learning approaches and prediction-focused frameworks, with recurring gaps related to real-world implementation and pedagogical integration. Second, a qualitative synthesis of the 10 most cited WOS articles provides in-depth insight into influential algorithmic strategies, data sources, and emerging emphases on explainability, human-centered modeling, and early intervention. Third, a bibliometric analysis of 520 WOS articles maps publication trends, country-level productivity, keyword co-occurrence, citation patterns, and international collaboration networks. Results indicate growing publication activity between 2018 and 2025, with research thematically clustering around methodological foundations in educational data mining, model development and prediction strategies, and contextual applications primarily in higher education, with limited chronological differentiation across themes. China leads in publication volume, while major collaboration hubs are concentrated in Australia, India, and Saudi Arabia. Persistent challenges include inconsistent terminology, data heterogeneity, and limited real-world deployment of ML models.