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

Using data from key first year courses, this article considers the development of subject-specific models to identify enrolled students at-risk of failure. The primary technique considered was neural networks, with it's results compared with logistic regression and multiple discriminant analysi...

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Bibliographic Details
Published inComputers & operations research Vol. 24; no. 4; pp. 367 - 377
Main Author Flitman, A.M.
Format Journal Article
LanguageEnglish
Published Oxford Elsevier Ltd 01.04.1997
Elsevier Science
Pergamon Press Inc
Subjects
Applied sciences
Artificial intelligence
Comparative studies
Computer science; control theory; systems
Connectionism. Neural networks
Discriminant analysis
Exact sciences and technology
Failure
Forecasting
Neural networks
Operational research and scientific management
Operational research. Management science
Planning. Forecasting
Regression analysis
Students
Logistic regression
Discriminant analysis
Prediction
Network
Neural network
Risk analysis
Student
School failure
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Summary:Using data from key first year courses, this article considers the development of subject-specific models to identify enrolled students 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 analysts to achieve the benefits accruing from the independent M-Competition. We have found the quality of forecasts achieved to be significantly improved on earlier studies, presumably because of the subject specific nature of the models.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
content type line 14
ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/S0305-0548(96)00060-3