Evaluating e-learning environment by using data mining techniques

Abstract

Learning is an inevitable life-long process for human beings and today, the concept of continuous learning gets much more acceptance. The demanding nature of today‟s life conditions force people and corporations to invest in life-long education. It is important to make this continuous learning process more affordable and accessible to larger groups of people. At this point, online learning seems to be an attractive solution. This fact brings about the core research question guiding this study as: what are the most significant factors influencing e-learning effectiveness? The goal of this study is to develop e-learning effectiveness models and to understand main contributors of e-learning systems. Corporate e-learning programs are studied in the scope of this study. Learner achievement, learner‟s course program completion duration, completion or withdrawal decision are selected as key measures of e-learning effectiveness. The effect of learner demographics, learner‟s e-learning experience, course characteristics and perceived usefulness factors is analyzed via appropriate data mining methods using SPSS 17.0 (Statistical Package for the Social Sciences) tool. Most of the independent factors (demographics, experience, course program) are discovered to have power at different levels for explaining variance in e-learning effectiveness. Course program characteristics like content, existence of certification are explored having a strong influence on the success of e-learning process.