원문정보
초록
영어
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.
목차
1. Introduction
2. Literature Review
3. The Artificial Neural Networks
4. Methodology
4.1. The Input Variables
4.2. The Output Variable
5. Design of the Neural Networks
5.1. Network Architecture
5.2. The Back-propagation Training Algorithm
6. Evaluation of Neural Network
7. Conclusion
References