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


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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.

Item Type: Book Chapter
Additional Information: Paper presented at SHAKE-UP: New Perspectives in Business Research and Education: New Zealand Applied Business Education Conference (NZABE) 2010, held 27-28 September, 2010, in Napier, New Zealand
Uncontrolled Keywords: Student outcomes, completions, pass rates, examination, predictors, predictive efficiency
Subjects: L Education > LB Theory and practice of education > LB2300 Higher Education
Divisions: Schools > Centre for Business, Information Technology and Enterprise > School of Business and Adminstration
Schools > Centre for Business, Information Technology and Enterprise > School of Information Technology
Schools > School of Communication
Depositing User: Christo Potgieter
Date Deposited: 15 Dec 2010 21:46
Last Modified: 21 Jul 2023 02:28

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