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| Artificial Neural Network Prediction of the Academic Warning of Students in the Faculty of Engineering and Information Technology in Al-Azhar University-Gaza |
Sabreen R. Qwaider, Samy S. Abu Naser, and Ihab S. Zaqout
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Abstract:
In this research, an Artificial Neural Network (ANN) model was developed and tested for predicting that students from the Faculty of Engineering and Information Technology at Al-Azhar University in Gaza would receive an academic warning or expelled. Through the dataset obtained from the Faculty of Engineering and Information Technology, the factors - columns - that may affect the ANN model such as the number of semesters completed by the student, the student's GPA for the last 3 semesters, were identified as input variables for the JustNN tool environment used in building Sample. The results column was also selected, which was used in the internal comparison with the model results to see the extent of convergence in the results from the resulting accuracy ratio. The network model has been developed and trained by relying on the topology of multi-layered perception on this data that includes x generations of students of the Faculty of Engineering and Information Technology at the university. The resulting accuracy rate was 99.42%, which confirms that the ANN model is able to predict, with very high accuracy, the probability that any of the students will have an expulsion or academic warnings in the future. This prediction will benefit the university from an academic point of view to direct students to raise their GPA, or to direct them to one major instead of another.
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