Toward Predictive Models for E-Learning: What Have We Learning So Far?

Toward Predictive Models for E-Learning: What Have We Learning So Far?

Maria Alexandra Rentroia-Bonito (Technical University of Lisbon, Portugal) and Joaquim Armando Pires Jorge (Technical University of Lisbon, Portugal)
Copyright: © 2004 |Pages: 15
DOI: 10.4018/978-1-93177-792-6.ch013


Currently, developing courseware for e-learning initiatives remains much of a black art. While we are mastering the process of authoring interactive media, we know little about the many factors that affect the e-learning experience. This can drastically limit return on invested efforts for organizations. Indeed, authoring multimedia content is a very expensive endeavor as compared to the traditional approach. A better understanding of the process could yield new approaches and insights to achieve a more ambitious goal: predictive models for e-learning. The reviewed literature highlights a lack of reliable results describing the interplay between e-learning context, web usability, cognitive styles, motivation, learner performance and satisfaction. Clearly, more research is needed to better understand and predict learner performance during an e-learning experience. The expected results of such an integrated approach would assist developers to design better e-learning experiences. This chapter proposes a holistic framework covering the interplay among Business-Process, People and Information-Systems issues. This could serve to guide future research.

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