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Academic Achievement Prediction Model Using Neural Networks

Normaziah, Abdul Rahman (2002) Academic Achievement Prediction Model Using Neural Networks. Masters thesis, Universiti Utara Malaysia.

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Abstract

This study aims to develop the academic achievement prediction (ACP) model using Neural Networks. It is capable of predicting the student's result in Programming I (C Language) subject for Kolej Agama Sultan Zainal Abidin, Kuala Terengganu. This model allows the system administrator to train and normalize data as well as trains. Once the model has been established by the administrator, the future student achievement can be forecast by the model. The system can predict the result of Programming I subject based on the student's background during the Sijil Pelajaran Malaysia (SPM) examination. A neural network technique, using Multi Layer Perceptron (MLP) and back propagation algorithm was employed. A total of 248 data samples from Information Technology and Multimedia Diploma students were collected, trained and tested using this model. A training prediction of 90 % accuracy and testing prediction of 83.33% accuracy were achieved using this model. The analysis of the data shows a reasonably strong correlation between the input variables, which consist of age, gender, school location, subject stream and result for a certain subjects: English, Mathematics, Science, Physics and Additional Mathematics, with the targeted output variable. The results also indicate that neural network has a potential to be used for education planning.

Item Type: Thesis (Masters)
Uncontrolled Keywords: Neural Networks, Academic Achievement
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty and School System > Sekolah Siswazah
Depositing User: Mrs Hafiza Mohd Akhir
Date Deposited: 07 Nov 2009 02:21
Last Modified: 24 Jul 2013 12:08
URI: http://etd.uum.edu.my/id/eprint/716

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