OPTIMAL ALGORITHM FOR PREDICTING STUDENTS ACADEMIC PERFORMANCE - Kuyoro, Afolashade; Goga, Prof. Nicolae; Awodele, Dr. Oludele; Okolie, Dr. Samuel (INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 2013)
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 students’ 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 algorithms -Random forest, Random tree, J48, Decision stump and REPTree and five rule induction algorithms –JRip, OneR, ZeroR, PART, and Decision table) 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.