LEARNING OUTCOME EFFECTIVENESS: OPEN-SOURCE VERSUS COMMERCIALLY-LICENSED DATA MINING SOFTWARE TOOLS

Abstract

Data Analytics, Data Mining, and Machine-Learning have become increasingly prevalent in higher-education, in both on-ground and online curricula. However, few studies have thoroughly and objectively studied the effectiveness of specific Data Mining software (both commercially-licensed and open-source), on learning outcomes. The current study used the Independent Samples T-Test to objectively compare two popular data mining software tools. Specifically, undergraduate, and graduate Information Systems students enrolled in an Introduction to Data Mining course were tested on the use of an open-source data mining tool (i.e., R Programming Language), and on a commercially-licensed data mining tool (i.e., XLMiner from Frontline Solvers). The current study assessed the impact of both tools on undergraduate and graduate student learning outcomes. The findings of this study will be of interest to higher-education faculty who are using, or who may consider using, data mining and machine-learning software in their undergraduate or graduate Information Systems Curriculum.