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.
