Managing “at risk” business students: Statistical analysis of student data profiles

Abstract

In this paper a multivariate model is developed to predict success or failure, helping to identify potentially at-risk students, based on the first two assessments. A robust discriminant or regression function will allow identification of students who obtained scores on these assessments that would put them at risk of failing the course. This paper uses stepwise discriminant analysis and multiple regression analysis to explore the data profiles for two closely related degree-level qualifications in Business Studies at the same institution. Evidence for the Business Studies qualifications suggests that a robust prediction model is possible. Therefore one could reflect on implications for more efficient teaching and assessment policies and practices. In line with the theme of the NZABE conference to question fundamentals”, one could ask whether exams are really required?! Although various kinds of changes seem possible, there are many pitfalls that still need to be explored.