Design of a Learning Analytics System for Academic Advising in Nigerian Universities

Learning analytics (LA) leverages learner-related data to generate reliable and factual information for the purpose of enhancing decision making in higher education, workplace and schools. Therefore, we have envisioned the need for a learning analytics system for a consortium of Nigerian universities that could serve as a tool for academic advising. The system should be able to offer advice at several layers of granularity such as individual, department, university and nation with the cardinal objective of facilitating student retention, student progression and cost saving. This paper reports the operational framework of the proposed LA system in the form of an n-tier layered architecture, which was designed after comprehensive requirements analysis. The design of the system encompasses mechanisms that address open issues confronting LA systems such as interoperability, security, scalability, amongst others. Besides addressing open problems, the study offers practical steps for the application of LA in an African clime, which so far, is not common in the literature. The proposed LA system for academic advising is presented as feasible and relevant for the advancement of higher education in Nigeria. Keywords-Academic Advising; Learner-related Data; Higher Education; Layers of Granularity; Learning Analytics; User Requirements; MapReduce Framework