Combining BI and Analytics in Higher Ed

Abstract

Data analytics in higher education offers distinct potential to analyze, comprehend, and model educational processes. As a result, the approaches and procedures supporting data analytics in higher education have resulted in distinct, highly correlated terminologies such as Learning Analytics (LA), Academic Analytics (AA), and Educational Data Mining (EDM), where the results of one may serve as the input for another. This study aims to provide IS educators and researchers with an overview of the present state of research and theoretical perspectives on educational data analytics. The document presents a comprehensive set of concepts and a cohesive framework for data analytics within higher education. By examining the framework, scholars may uncover novel settings and domains of investigation. The Gestalt-like process of integrating the framework (total) with the articulation of data analytics (parts) may prove beneficial for educational stakeholders in making decisions about individual students, student groups, curricula, schools, and educational systems.