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Motivation to E-Learn: A Quantitative Design Technique

Motivation to E-Learn: A Quantitative Design Technique

M. A. Rentroia-Bonito (Technical University of Lisbon, Portugal), J. A. Jorge (Technical University of Lisbon, Portugal), and C. Ghaoui (John Moores University, UK)
Copyright: © 2009 |Pages: 9
DOI: 10.4018/978-1-59904-480-4.ch004
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Abstract

One of e-learning challenges is to promote effectiveness in order to fully get expected benefits. Achieving effectiveness will contribute to its establishment as a credible way to support educational endeavours. To address this complex and multidisciplinary challenge, development teams need proper design techniques to build effective learning experiences. The literature does not show solid quantitative approaches to support learning-centered design, where student needs and their immediate and broader contexts are taken into account. This work explores a variable called “motivation-to-e-learn,” a key component to design technology-supported learning experiences. Our goal is to identify what motivation-related variables are critical for student engagement in learning online. This will be the basis for a specific, bottom-up and quantitative design technique. To this end, we further explored the importance of a set of motivation- to-e-learn variables building on previous results in real instructional settings. From this activity, an exploratory two-factor structure emerged which explains 96% of motivation to e-learn construct. We discuss our results, together with their implications for learning-support design and future work. Our contribution is a step towards quantitatively understanding and cost-effectively improving the link among learning-design process, supporting systems and students into an effective and harmonious whole.
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Conceptual Framework

The complexities associated to technology-assisted learning require holistic views that address context, process, system, and individual-related specificities. These holistic views could be supported by learning-centered design, which focus on students, instructors, institutions, and society needs (Andersen, 2004; Costabile et al., 2006). As can be seen in Figure 1, at operational level, tasks link learning-design processes to supporting systems. Learning tasks are performed by students to achieve goals. Further, the functional and non-functional characteristics of supporting systems reflect strategic and design options regarding skills and system development. The structure of the experience is given by the learning-design process and supporting system. This process-system fit influences the quality of interactions between students and systems thus influencing their motivation to engage and perform learning tasks (Walker, 1992; Organ & Bateman, 1991; Gagne & Deci, 2005). That is why a better understanding of motivation to e-learn could help development teams to structurally design cost-effective learning experiences.

Figure 1.

Conceptual framework

978-1-59904-480-4.ch004.f01

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