Using an Enhanced Feed-Forward Neural Network Technique for Prediction of Students' Performance

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International Scientific Academy of Engineering & Technology.


The newly admitted students for the undergraduate programmes in the institutions of higher learning sometimes experience some academic adjustment that is associated with stress; many factors have been attributed to this, which most times, results in the high percentage of failure and low Grade Point Average (GPA). Computing the earlier academic achievements for these sets of students would make one to be abreast of their level of knowledge academically, in order to be well-informed of their areas of weakness and strength. In this paper, an enhancement of Feed-forward Neural Network for the creation of a network model to predict the students' performance based on their historical data is proposed. In the course of experimentations with Matlab software, two network models are created using the existing and enhanced feed-forward neural network techniques. The ability of these models to generalize is measured using simulation methods. The enhanced nefwork model consistently shows a high degree of accuracy and predicts well. The performance of students predicted as outstanding, can also be supported financially in the form of scholarship; while those that are found to be academically weak can be encouraged and rightly counseled at the early stage of their studies.


Conference proceedings


data partitioning, neural networks, predictive model, sfudents' performance