Classifying Student Academic Performance: A Hybrid Approach
Nowadays, organizations are overwhelmed with a large amount of electronic data that require proper management to discover previously hidden knowledge. Having a set of non-transformed data may be a huge waste as specific processes onto the data would result in the discovery of valuable knowledge. Thi...
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| 主要な著者: | , , , , |
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| フォーマット: | Conference or Workshop Item |
| 出版事項: |
2007
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| オンライン･アクセス: | http://eprints.utp.edu.my/1182/1/IMECS_19March08.pdf http://eprints.utp.edu.my/1182/ |
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| 要約: | Nowadays, organizations are overwhelmed with a large amount of electronic data that require proper management to discover previously hidden knowledge. Having a set of non-transformed data may be a huge waste as specific processes onto the data would result in the discovery of valuable knowledge. This paper discusses the development of a predictive model to classify undergraduate students’ class of graduation: first class, second upper division, second class lower division, or third class. Techniques used to support the classification are implemented in using back propagation feed forward neural network with Bayes probability.
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