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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| Published in | Computers & operations research Vol. 24; no. 4; pp. 367 - 377 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Elsevier Ltd
01.04.1997
Elsevier Science Pergamon Press Inc |
| Subjects | |
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| Abstract | 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. |
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| AbstractList | 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. Using data from key first-year courses, the development of subject-specific models to identify enrolled students at risk of failure is considered. The primary technique considered was neural networks, with its results compared with logistic regression and multiple discriminant analysis. The 3 different modeling approaches were developed by 3 different analysts to achieve the benefits accruing from the independent M-Competition. The quality of forecasts achieved were found to be significantly improved on earlier studies, presumably because of the subject-specific nature of the models. |
| Author | Flitman, A.M. |
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| Cites_doi | 10.1016/0305-0548(94)90088-4 10.1111/j.1540-5915.1992.tb00425.x 10.1177/0013164491513023 10.1002/for.3980010202 10.1016/0169-2070(93)90043-M |
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| Keywords | Logistic regression Discriminant analysis Prediction Network Neural network Risk analysis Student School failure |
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| References | Fisher, Resnick (BIB2) 1990; 55 Lipperman (BIB8) 1987 Graham (BIB9) 1991 Chatfield (BIB1) 1993; 9 Wasserman (BIB11) 1993 Hertz, Krogh, Palmer (BIB10) 1991 Salchenberger, Cinar, Lash (BIB7) 1992; 23 Hardgrave, Wilson, Walstrom (BIB5) 1994; 213 Makridakis (BIB6) 1982; 1 Baird (BIB3) 1975; 35 Remus, Wong (BIB4) 1982; 16 Baird (10.1016/S0305-0548(96)00060-3_BIB3) 1975; 35 Hertz (10.1016/S0305-0548(96)00060-3_BIB10) 1991 Fisher (10.1016/S0305-0548(96)00060-3_BIB2) 1990; 55 Remus (10.1016/S0305-0548(96)00060-3_BIB4) 1982; 16 Lipperman (10.1016/S0305-0548(96)00060-3_BIB8) 1987 Graham (10.1016/S0305-0548(96)00060-3_BIB9) 1991 Makridakis (10.1016/S0305-0548(96)00060-3_BIB6) 1982; 1 Hardgrave (10.1016/S0305-0548(96)00060-3_BIB5) 1994; 213 Wasserman (10.1016/S0305-0548(96)00060-3_BIB11) 1993 Chatfield (10.1016/S0305-0548(96)00060-3_BIB1) 1993; 9 Salchenberger (10.1016/S0305-0548(96)00060-3_BIB7) 1992; 23 |
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| Snippet | 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... Using data from key first-year courses, the development of subject-specific models to identify enrolled students at risk of failure is considered. The primary... |
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| SubjectTerms | 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 |
| Title | Towards analysing student failures: neural networks compared with regression analysis and multiple discriminant analysis |
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