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The major point of departure from Hammond
et al
is that no single factor
stands out as the only contributor, but always there are multiple contributory
factors and that while they are difficult to ferret out they can be identified
and such identification is a milestone towards remedial and corrective action.
•
Furthermore, South African universities like a number of universities abroad, are
embracing the use of student data analytics as an important tool that can be used in a
variety of ways to achieve the objectives of student retention and success.
•
We wish to argue in this paper that student data analytics can be used wisely also for
the specific objectives of identifying students at risk from multiple data sources in line
with the view of Hammond et al so as to put measures in place to ameliorate the risk.
•
We postulate that student profiling using a combination of pre university entrance data
and early performance assessment data can be used to identify students at risk with a
high degree of accuracy and early enough for remedial and corrective action to be
designed and implemented, and its impact to be evaluated.
•
This significance of this proposal is that the remedial and corrective action planning
and implementation is responsive to specific risk profiles of students at risk, thus
highlighting the power of student data analytics to provide university with the data
that forms the basis for targeted remedial and corrective action for students at risk.