Corporate E-Learning Success Model Development by Using Data Mining Methodologies

Abstract

The dynamic and more 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, e-learning seems to be more convenient way of learning than formal education especially for working adults because of their time and place constraints and their need for flexibility. The crucial concern is whether the e-learning process is useful or not and under what conditions it brings more value to adult learners. Thus, the core research question guiding this study is: What are the most significant factors influencing corporatee-learning success? The study aims to answer this question by developing e-learning success models via data mining. After a number of data preprocessing activities, a combination of descriptive and predictive data mining methodologies are applied on the data set. Most of the independent factors (learner demographics, learner experience, and course characteristics) are discovered to have power at different levels for explaining variance in e-learning success. Course program characteristics like content type, existence of certification are explored having a strong influence on the success of e-learning process.