Abstract
In this paper, we report on developments in the Mastery Learning (ML) curriculum and
assessment model that has been successfully implemented in a metropolitan university for
teaching first-year mathematics. Initial responses to ML were positive; however, we ask
whether the nature of the ML tests encourages a focus on shallow learning of procedures,
and whether the structure of the assessment regime provides sufficient motivation for
learning more complex problem solving. We analysed assessment data, as well as student
reports and survey responses in an attempt to answer these questions.
Mary Coupland, Danica Solina, & Gregory E. Cave
Mastery Learning: Improving The Model