Academic Rating Index: 4IR Supervision Model

Managing the postgraduate supervision relationship and measuring the cumulative impact of a supervisor’s postgraduate outputs and performance are major challenges for many universities. There is a desire to use technology to enhance the quality of academic services, but current efforts are narrowly focused on enhancing journal quality through metrics like the h-index, SciVal, and the i10-index. There are few technological tools available to gauge the quality of supervision services. However, an analysis of the literature to date raises questions regarding the caliber of academic supervision in various settings and across a range of learning environments. This research presents the academic rating index (ARI), a critical digital supervision model that combines Fourth Industrial Revolution (4IR) techniques with an academic supervision theoretical framework. The chapter highlights the ARI’s capabilities to manage supervision relationships and measure the cumulative impact of supervisors’ postgraduate outputs and performance, while contributing to the UN’s sustainable development goal four (4) of quality education. The ARI is rooted in quantitative algorithms, coding, and programming methodology. Theoretically, the ARI advances service quality and performance measurement models, which transforms manual modeling to 4IR-inspired modeling. In practice, the ARI serves as a data warehouse and data mining innovation that helps universities to achieve the optimum academic service relative to excellence quality standards or constraints. The ARI serves as a supervision quality benchmarking tool for universities, a reflexive tool for supervisors, and a voice and microscope for postgraduate students. As this paper indicates, the use of 4IR techniques elevates the ARI beyond the existing library of supervision models.