A Descriptive Learning Analytics: An Online Learning Programmes and Load-shedding Conundrum

Abstract

Electricity load-shedding in South Africa has become a new challenge for students pursuing online learning following the Covid-19 outbreak. This article thus investigated the way load-shedding affects students in pursuing the Advanced Certificate in Teaching (ACT) programmes. Pre-Covid-19 and during load-shedding data between 2018 and 2022 was gathered and analysed using a descriptive learning analytics tool. The research took place at a South African university where the researcher is currently employed. Learning analytics data has been available for years, but it is now being effectively harnessed to improve learning and teaching through relatively recent developments in learning analytics tools. However, the load-shedding conundrum works against this objective of harnessing learning and teaching. Research has also shown that in the least developed countries, effective e-learning practices face significant barriers, primarily stemming from insufficient infrastructure, economic challenges, low levels of computer literacy, and the added challenge of power outages. Using a constructivist paradigm, the study employed a mixed qualitative approach, which included desktop historical data and semi-structured interviews. Three participants were purposely selected because of their role in overseeing ACT students situated in both rural and urban areas. The study revealed that numerous programmes were discontinued, enrollment declined, ACT Blended shifted to e-learning, persisting load-shedding conundrum affected e-learning, a low throughput rate, and digital illiteracy contributed to an increased rate of student dropouts. This research recommends that universities should embrace the evolution of e-learning through the digitalisation of learning processes to enhance efficiency. Keywords: Load-shedding, E-learning, Learning Analytics Tools, Declined Enrolment, Low Throughput Rate vs High Distinction Module Pass Rate