Nurturing the African higher education teaching and learning ecology using data, information and knowledge from our physical, digital and biological learning spaces

Abstract

The purpose of this chapter is to explore and conceptualise a business intelligence (BI) model that can capture, monitor and analyse big and small data from the physical, digital and biological learning spaces of a higher education institution (HEI). Using enterprise architecture (EA), BI and the emerging domain of learning analytics, the platform and its users can potentially identify and predict the causes of both negative and positive effects or event triggers to enable the real-time monitoring and support of students, lecturers, administrators and alumni. If well-supported, this information and knowledge could significantly improve the quality of teaching and learning and the overall higher education (HE) experience. The chapter draws on the work of others, discussions, observations and reflections and proposes; firstly, a red cross early alert model for students enrolled in HE, and, secondly, a framework for a University BI system with key big and small data opportunities for four end-users. The chapter then poses analytical questions and answers on the past, present and future to provide information and insight into the best- and worst-case scenarios for different HE processes during the Fourth Industrial Revolution (4IR). This conceptual research study is the start of a continuous cycle of reflections, the assessment of challenges, the pragmatic planning of interventions and highlighting benchmark successes during the formal documentation of the findings.