In ensuring inclusive and equitable quality education as well as promoting lifelong learning opportunities for all as enshrined in the United Nations Sustainable Development Goal 4 (SDG-4), various technology-based pedagogical approaches have been adopted. The pedagogical methods include mobile learning, blended learning, e-learning, etc. However, there has been less emphasis in the literature on the potential of learning analytics (LA) for enhanced educational outcomes in the present fourth industrial revolution (Industry 4.0). In Industry 4.0, production systems (educational system inclusive) are heavily technology-driven. An artificial intelligence (AI) technique such as the LA focuses on eliciting hidden but useful patterns in learners-related data to provide critical educational stakeholders with precise information about the learners’ behaviours for appropriate and proactive decision-making. The aim is to minimise students’ failure rate and maximize progression rate. Using the Nigerian educational system as a case study, this vision paper proposes an LA framework that leverages big data and predictive analytics for predicting student attrition rate, measuring student progression rate, and establishing associations between courses, among others. We also proffer solutions to challenges faced by LA systems.

Authors:
Aderinsola Kayode, Durban University of Technology, South Africa & Trinity University, Nigeria
Emmanuel Okewu, University of Lagos, Nigeria
Fulufhelo Netswera, Durban University of Technology, South Africa

About the Presenter(s)
Dr Aderinsola Kayode is a University Postdoctoral Fellow or Instructor at Durban University of Technology, South Africa in South Africa

See this presentation on the full schedule – Monday Schedule

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