Theorising Machine Learning as an Alternative Pathway for Higher Education in Africa
Kehdinga George Fomunyam
Abstract
Machine learning technology is currently a new frontier for higher education globally, and the African higher education system needs to change in tandem with this technological trend in order to combat challenges faced by the system. These challenges include lack of institutional research to discover new knowledge, unfavorable methods of instruction, especially the language conflict, access to education for marginalized and isolated communities, high dropout rates, depleted infrastructure and unavailability of resources, overpopulated classrooms, and a biased grading system. This paper discusses alternative machine learning solutions to these challenges faced by the African higher education system, in order to ensure that students develop the skills needed to thrive in this digital era. Findings reveal three key technological solutions that can provide alternative solutions to these challenges, and they include customized/personalized learning, predictive analytics and digital administrative management, and virtual assistance. This paper concludes that for Africa, Catching up with the world goes beyond adopting these new innovations to facilitate learning. Recommendations include rethinking the content of the African curriculum, developing an unbiased education system, and adopting a suitable medium of instruction.