Modeling for E-Learning Systems

Computer-based instruction is touted as an effective tool to support knowledge dissemination within predefined learning environments. Indeed, many see it as a way to overcome geographical or social barriers to knowledge transmission and educational institutions. However, its domain of application has traditionally been restricted to basic skills and educational contexts. Recently, dynamic and complex business environments shaped by technological changes and the downsizing trend of the ’90s placed new constraints on the underlying assumptions (Fuglseth, 2003). Organizations are now pushing for skill flexibility, demanding specialized knowledge and requiring faster learning curves from employees. Many advocate Internet-based education materials as one way to meet those challenges (Bernardes & O’Donoghue, 2003; Karoulis et al., 2004; Storey et al., 2002; Strazzo & Wentling, 2001). However, this raises important questions concerning both effectiveness and efficiency of such tools and materials. Indeed, developing interactive multimedia-based courseware remains pretty much a black art, consuming enormous resources. So far, there is a lack of established models to predict the performance and evaluate how adequately courseware can meet user needs. In fact, developing courseware should take into account the target constituency requirements, organizational context, and the stated educational or training goals. Developing the wrong training materials can lead to costly investments in creating and maintaining content to match the increasing expectations on e-learning. Perhaps this can explain the recent rash of failed e-learning projects—current results do not measure up to business and individual expectations yet. A better understanding of the many factors affecting e-learning performance would allow individuals and organizations to achieve the expected benefits. In so doing, development teams need methods, techniques, and tools to evaluate in advance which features are needed to achieve higher outcomes, namely, performance and satisfaction. Thus, the need to develop predictive models to improve learning effectiveness is in order. This overview includes four sections. “Background” presents a proposed e-learning theoretical framework to guide our analysis based upon the reviewed literature. “Key Issues” section describes main issues arising from the proposed elearning conceptual framework. “Future Trends” describes our vision on how to approach e-learning initiatives and future trends. Finally, we present a general conclusion.