ABSTRACT
The lineage of quantitative research from classical statistics to learning analytics represents several transformations from classical statistics through artificial intelligence, machine learning, and data mining. This chapter discusses learning analytics as a tool for quality assurance with the concept of data mining. Taking the concepts of learning and discovery to the next level, learning analytics collects the data, analyzes the data, and investigates emerging patterns, revealing patterns yielding pathways to informed decision making. Various quality assurance models for higher education have been designed with high expectations and hopes that they will eventually gain universal adoption. The chapter looks at the convergence of Big Data, data mining, and learning analytics, emphasizing the elements of quality assurance that these forces seek to uncover. Access to data across institutional departments provides administrators with an overarching perspective that enables insight into how strategic decisions made at the administrative level eventually affect the student.
