Modelling Students at Risk



Using a sample of several hundred students we model progression in a first-year econometrics course. Our primary interest is in determining the usefulness of these models in the identification of ‘students at risk’. This interest highlights the need to distinguish between students who drop the course and those who complete but who ultimately fail. Such models allow identification and quantification of the factors that are most important in determining student progression and thus make them a potentially useful aid in educational decision making. Our main findings are that Tertiary Entrance Rank (TER), mathematical aptitude, being female and attendance in tutorials are all good predictors of success but amongst these factors only attendance is significant in discriminating between students who fail and those who discontinue. Also, there are differences across degree programs and, in particular, students in Combined Law are very likely to pass but, if they are at risk, they are much more likely to discontinue than to fail.