Evolution, topics and relevant research methodologies in business intelligence and data analysis in the academic management of higher education institutions. A literature review

Abstract

In the digital era, Business Intelligence (BI) and data analytics have become essential for optimizing academic management in higher education institutions. This bibliometric study analyzed 755 Scopus-indexed publications (2019–2023) using RStudio, Biblioshiny, and Microsoft Excel to elucidate key themes, influential authors, and emerging research trends. Learning analytics, educational data mining, and BI applications such as dropout prediction systems, tailored distance education strategies, and machine learning models for institutional performance predominate in the field. High-impact journals, including the British Journal of Educational Technology and the Journal of Learning Analytics, play crucial roles with contributions from scholars such as Christothea Herodotou and Bart Rienties. Thematic analysis revealed ten clusters emphasizing predictive modeling, educational innovation, and online learning. Geographic trends highlight the predominance of research in the United States and Europe, underscoring the necessity for greater inclusivity in underrepresented regions such as Africa and South America. While quantitative methodologies prevail, this study emphasizes the significance of qualitative approaches to capture nuanced impacts and ethical considerations, including privacy, equity, and bias mitigation. Future research must adopt interdisciplinary methodologies to address systemic challenges, foster context-sensitive, equitable BI solutions that drive innovation, and enhance decision making across diverse educational environments.

Keywords

• Academic analytics
• Bibliometric analysis
• Bibliometrix
• Business intelligence
• Data analytics
• Educational data mining