Learning Analytics in Higher Education

Learning Analytics in Higher Education

Rushil Raghavjee, Prabhakar Rontala Subramaniam, Irene Govender
Copyright: © 2021 |Pages: 34
DOI: 10.4018/978-1-7998-2983-6.ch015
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Abstract

It is known that big data has penetrated several if not all spheres of life. In higher education, the ability to take these large amounts of data and process it into something meaningful for academic decision making is commonly referred to as learning analytics. This chapter provides an overview of learning analytics and its importance, as well as identifying academic data sources, techniques used for learning analytics and prediction, and data visualisation techniques used to present analysis for better understanding and eventual decision making. It also includes a discussion of learning analytics frameworks for research and some identified research challenges.
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Background

Currently, higher education is seen as an essential component not only for individuals but for national economic performance (Pinheiro, Wangenge-Ouma, Balbachevsky, & Cai, 2015). Barr (2004) and Murphy (2010) both indicated that there is an increase in enrollments at UK higher education institutions and thus increased dependence on support for funding. In South Africa, the situation is similar with government support and funding being inadequate (Badat, 2016) as more students are unable to fund their education (Akoojee & Nkomo, 2007). This has resulted in protest action such as the ‘fees must fall movement’ where students are turning to the government to provide for the cost of education, study accommodation and better infrastructure.

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