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

• Main Authors: Ajiboye, Adeleke Raheem, Ruzaini, Abdullah Arshah, Qin, Hongwu
• Format: Conference or Workshop Item
• Language: English
• Published: 2015
• Subjects: QA76 Computer software
• Online Access: http://umpir.ump.edu.my/id/eprint/14580/6/fskkp-2015-ruzaini-Using%20an%20enhanced%20feed-forward%20neural.pdf; http://umpir.ump.edu.my/id/eprint/14580/

The newly admitted students for the undergraduate programmes in the institutions of higher learning sometimes experi ence some academic adjustment that is associated with stress; many factors have been attributed to this, which most times, results in the high percentage of fail ure and low Grade Point Average (GPA). Computing the earlier academjc achievements for these sets of students would make one to be abreast of their level of knowledge academically, in order to be well-infonned of their areas of weakness and strength. In this paper, an enhancement of Feed-forward Neural Network fo r 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 crea ted using the existing and enhanced feed-forward neural network techniques. The abiliry of these models to generalize is measured using simulation methods. The enhanced network model consistently shows a high degree of accuracy and predicts well. The performance of students predicted as outstanding, can also be supponed financially in the fom1 of scholarship; while those that are found to be academically weak can be encouraged and rightly counseled at the early stage of their studies.