Towards analysing student failures: Neural networks compared with regression analysis and multiple discriminant analysis

Towards analysing student failures: Neural networks compared with regression analysis and multiple discriminant analysis

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Article ID: iaor19972348
Country: United Kingdom
Volume: 24
Issue: 4
Start Page Number: 367
End Page Number: 377
Publication Date: Apr 1997
Journal: Computers and Operations Research
Authors:
Keywords: neural networks, statistics: regression, forecasting: applications
Abstract:

Using data from key first year courses, this article considers the development of subject-specific models to identify enrolled student at-risk of failure. The primary technique considered was neural networks, with it’s results compared with logistic regression and multiple discriminant analysis. The three different modelling approaches were developed by three different analysis to achieve the benefits accruing from the independent M-Competition. The authors have found the quality of forecasts achieved to be significantly improved on earlier studies, presumably because of the subject specific nature of the models.

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