Using an Enhanced Feed-Forward Neural Network Technique for Prediction of Students' Performance
No Thumbnail Available
Date
2015-05
Journal Title
Journal ISSN
Volume Title
Publisher
International Scientific Academy of Engineering & Technology.
Abstract
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.
Description
Conference proceedings
Keywords
data partitioning, neural networks, predictive model, sfudents' performance