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

Ajiboye, Adeleke Raheem and Ruzaini, Abdullah Arshah and Qin, Hongwu (2015) Using an Enhanced Feed-Forward Neural Network Technique for Prediction of Students' Performance. In: Proceedings of 3rd International Conference on Computer Science and Data Mining (ICCSDM 2015), 20-21 May 2015 , Dubai, UAE. pp. 22-27..

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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.

Item Type: Conference or Workshop Item (Speech)
Uncontrolled Keywords: data panitioning; neural networks; predictive model; students' performance
Subjects: Q Science > QA Mathematics > QA76 Computer software
Faculty/Division: Faculty of Computer System And Software Engineering
Depositing User: Mrs. Neng Sury Sulaiman
Date Deposited: 05 Apr 2017 04:08
Last Modified: 18 May 2018 01:35
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