Abstract
With the majority of scholarship on Learning Analytics derived from residential institutions in predominantly WEIRD (Western, Educated, Industrialised, Rich and Democratic) countries, this chapter provides a perspective on learning analytics from a South African ODL context. The value of this chapter is found in the problems encountered while moving through the learning analytic cycle, such as ready access to data with some of the processes needed to purify and integrate that data somehow lacking. Although demographic and educational data is more easily accessed, data pertinent to the interaction of the students with university digital systems (i.e. LMS data) is still deeply buried in access logs and not easily extractable (currently). The chapter discusses a need to implement a university-wide, ‘business’ or ‘middle’ layer within the analytical process which might facilitate the retrieval of data from institutional databases (with the necessary permissions/ethical clearances) and present it in a useful manner to all the parties that require it. Finally, the chapter recommends a facility to enable effective data presentation for a range of stakeholders such as students, lecturers, and chairs of departments.
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28 August 2022
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Notes
- 1.
Where students have unlimited access to the system but often without success.
- 2.
A traditional student is commonly defined as a younger student, under the age of 25 years, often enrolling directly from high school, and attending university full-time with no major life or work responsibilities (e.g., full-time job or dependents) (Daiva, 2017; Dimmick, 2013; Tilley, 2014). A non-traditional student, by contrast, is a student who is often older, a commuter student and attends university or college on a part-time basis (due to occupational, social and/or family commitments) (Holmberg, 1995; Kasworm, 1990). While the distinction is clear, it must be noted that ‘traditional’ students entering UNISA’s systems do not have access to full-time, face-to-face classes, and residences.
- 3.
While learning analytics and machine learning, are fundamentally different processes, increasingly machine learning is being seen as a part of learning analytics.
- 4.
In South Africa, “matric” (otherwise known as “matriculation”) is a term commonly used to refer to the final year of high school and the qualification received upon graduating from high school.
- 5.
Operationalised as the number of modules registered for (in total).
- 6.
Developed by Ross Quinlan, the C4.5 is an algorithm used to generate decision trees.
- 7.
The rationale behind the choice of cohort was to allow for the maximum possible time for students to complete their 360- and 480-credit qualifications (i.e. eight and ten years, respectively) and therefore provide as complete data as possible (see UNISA, 2011).
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Fynn, A., Adamiak, J., Young, K. (2022). A Global South Perspective on Learning Analytics in an Open Distance E-learning (ODeL) Institution . In: Prinsloo, P., Slade, S., Khalil, M. (eds) Learning Analytics in Open and Distributed Learning. SpringerBriefs in Education(). Springer, Singapore. https://doi.org/10.1007/978-981-19-0786-9_3
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