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Predicting Student Performance Using Artificial Neural Network: in the Faculty of Engineering and Information Technology

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dc.contributor.author Abu-Naser, Samy S.
dc.contributor.author Zaqout, Ihab S.
dc.contributor.author Abu Ghosh, Mahmoud
dc.contributor.author Atallah, Rasha R.
dc.contributor.author Eman Alajrami
dc.date.accessioned 2/28/2015
dc.date.accessioned 2/28/2015
dc.date.accessioned 2019-05-27T16:31:21Z
dc.date.available 2/28/2015
dc.date.available 2019-05-27T16:31:21Z
dc.date.issued 2015
dc.identifier.citation ,8(2), pp. 221-228 en_US
dc.identifier.uri http://dstore.alazhar.edu.ps/xmlui/handle/123456789/391
dc.description.abstract In this paper an Artificial Neural Network (ANN) model, for predicting the performance of a sophomore student enrolled in engineering majors in the Faculty of Engineering and Information Technology in Al-Azhar University of Gaza was developed and tested. A number of factors that may possibly influence the performance of a student were outlined. Such factors as high school score, score of subject such as Math I, Math II, Electrical Circuit I, and Electronics I taken during the student freshman year, number of credits passed, student cumulative grade point average of freshman year, types of high school attended and gender, among others, were then used as input variables for the ANN model. A model based on the Multilayer Perceptron Topology was developed and trained using data spanning five generations of graduates from the Engineering Department of the Al-Azhar University, Gaza. Test data evaluation shows that the ANN model is able to correctly predict the performance of more than 80% of prospective students. en_US
dc.language.iso en_US en_US
dc.subject Artificial Neural Networks en_US
dc.subject Student performance en_US
dc.subject Education en_US
dc.subject ANN en_US
dc.title Predicting Student Performance Using Artificial Neural Network: in the Faculty of Engineering and Information Technology en_US
dc.type Article en_US


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