OPTIMAL ALGORITHM FOR PREDICTING STUDENTS ACADEMIC PERFORMANCE

Afolashade Kuyoro,Prof Nicolae Goga,Dr Samuel Okolie,Dr Oludele Awodele

doi:10.24297/ijct.v4i1b.3061

Abstract

Machine learning has been successfully applied to numerous domains such as pattern recognition, image recognition, fraud detection, medical diagnosis, banking, bioinformatics, commodity trading, computer games and various control applications. Recently, this paradigm is been employed to enhance and evaluate higher education tasks. The focus of this work is on identifying the optimal algorithm suitable for predicting first-year tertiary students academic performance based on their family background factors and previous academic achievement. One thousand five hundred (1,500) enrolment records of students admitted into computer science programme Babcock University, Nigeria between 2001 and 2010 was used. The studentsâ€™ first year academic performance was measured by Cumulative Grade Point Average (CGPA) at the end of the first session and the previous academic achievement was measured by SSCE grade score and UME score. Waikato Environment for Knowledge Analysis (WEKA) was used to generate 10 classification models( five decision tree algorithmsÂ -Random forest, Random tree, J48, Decision stump and REPTree and five rule induction algorithms â€“JRip, OneR, ZeroR, PART, and Decision table) Â and a multilayer perceptron, an artificial neural network function. These algorithms were compared using 10-fold cross validation and hold-out method considering accuracy level, confusion matrices and CPU time to determine the optimal model. This work will be taken further by designing a framework of predictive system based on the rules generated from the optimal model.

Highlights

Machine learning has proven to be of great value in various application domains
It is especially useful in data mining problems where large databases contain valuable implicit regularities that can only be discovered automatically; in poorly understood domains where humans might not have the knowledge needed to develop effective algorithms such as face recognition from images; and in domains where the program must dynamically adapt to changing conditions. (Schaffer, 1994) Machine Learning (ML) techniques embody some of the facets of the human mind that allow us solve complex problems at speeds which outperform even the fastest computers (Schank, 1982)
This study focus is on identifying optimal machine learning algorithm suitable for predicting first-year tertiary students academic performance based on their family background factors and previous academic achievement

Summary

Introduction

Machine learning has proven to be of great value in various application domains. It is especially useful in data mining problems where large databases contain valuable implicit regularities that can only be discovered automatically; in poorly understood domains where humans might not have the knowledge needed to develop effective algorithms such as face recognition from images; and in domains where the program must dynamically adapt to changing conditions. Machine learning is finding larger and wider applications in higher education learning. It is showing an increasing trend in institutional research. This has to do with the growing interest in knowledge management and in moving from data to information and to knowledge discovery

Methods

Results

Conclusion