Comparative Analysis of Three Data Mining Tools in the Allocation of Senior Secondary Students into Departments for Career Path

Abstract

Abstract: Choosing a career path in life begins with studying the right subject combination at the secondary school level. The Nigerian educational system of 6-3-3-4 which makes a student to spend 6 years in pry education, 3 years in junior secondary school, 3 years in senior secondary school and 4 years in the university makes it mandatory for such a student to choose a department of study at the point of entry into the senior secondary school (SSS). It is at this point that a decision is made consciously or unconsciously by the students about life career as the choice of department determines the choice of course of study at the university level later. Students and their parents/guardians often take this decision without any scientific guide which in most cases leads to a wrong choice of career path. In this study, three data mining tools, WEKA, RapidMiner and Orange were employed with three algorithms each to determine the most appropriate tool with the best result in the allocation of senior secondary school (SSS) students to various departments of study. The best algorithm methodology that classified the dataset in WEKA was Random Forest with an accuracy of 100% predicting 308 students correctly. In RapidMiner the best algorithm methodology was Naïve Bayes with an accuracy of 82.9% correctly predicting 73 students. Thereafter, Orange gave the best algorithm methodology to be Random Forest at 98.7333% predicting 304 students accurately. Our study shows that the optimum algorithm suited for the application software implementation to allocate SS1 Students into Department was Random Forest, having highest rates in the Accuracy. Though Orange has additional feature of being able to visualize the output of all the three results in one interface at a glance, and also shows outcome visualizations in various plots and graphs, WEKA’s highest predictive measure of 100% places it above all and makes it the tool of choice with Random Forest being the best algorithm.