In 2013, the Student Centre at The University of Tasmania began researching the development of distance learning support. A survey was designed as part of this endeavour, which attracted 1002 responses. The survey's broad context focused on the primary drawbacks and benefits of distance education in general and sought to identify emergent themes…

In this paper, a dropout prediction method for e-learning courses, based on three popular machine learning techniques and detailed student data, is proposed. The machine learning techniques used are feed-forward neural networks, support vector machines and probabilistic ensemble simplified fuzzy ARTMAP. Since a single technique may fail to…

Previous studies conducted on dropouts within online courses have found inconsistent factors affecting attrition. A literature review was performed, focusing on eight main studies. These studies were performed at both national and international universities. The methodology, participants, research question, and results varied by study. Overall,…

Student retention is an important issue in distance education. In the literature of higher education literature, however, information on student retention in online courses is incomplete. Few empirical studies have been conducted. Many articles reference the issue and then provide descriptions of best practices to address retention. Seldom,…

This document contains 35 papers from the program development section of an international conference on vocational education and training (VET) for lifelong learning in the information era. The following are among the papers included: "Using Quality Indicators to Create World-Class Curricula: From Concept to Application" (Curtis Finch,…