Abstract:
Higher education faces the challenge of high student attrition, which is especially disconcerting
if associated with low participation rates, as is the case in South Africa. Recently,
the use of learning analytics has increased, enabling institutions to make data-informed
decisions to improve teaching, learning, and student success. Most of the literature thus far
has focused on “at-risk” students. The aim of this paper is twofold: to use learning analytics
to define a different group of students, termed the “murky middle” (MM), early enough in
the academic year to provide scope for targeted interventions; and to describe the learning
strategies of successful students to guide the design of interventions aimed at improving
the prospects of success for all students, especially those of the MM. We found that it was
possible to identify the MM using demographic data that are available at the start of the
academic year. The students in the subgroup were cleanly defined by their grade 12 results
for physical sciences. We were also able to describe the learning strategies that are associated
with success in first-year biology. This information is useful for curricular design,
classroom practice, and student advising and should be incorporated in professional development
programs for lecturers and student advisors.
