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Knowledge Mining for Adaptive Multimedia Web-Based Educational Platform
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Author(s): Leyla Zhuhadar (University of Louisville, USA), Olfa Nasraoui (University of Louisville, USA)and Robert Wyatt (Western Kentucky University, USA)
Copyright: 2008
Pages: 53
Source title:
Technology Enhanced Learning: Best Practices
Source Author(s)/Editor(s): Miltiadis D. Lytras (Effat University, Saudi Arabia), Dragan Gasevic (Athabasca University, Canada), Patricia Ordóñez de Pablos (Universidad de Oviedo, Spain)and Weihong Huang (Kingston University, UK)
DOI: 10.4018/978-1-59904-600-6.ch010
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Abstract
This chapter introduces an Adaptive Web-Based Educational platform that maximizes the usefulness of the online information that online students retrieve from the Web. It shows in a data driven format that information has to be personalized and adapted to the needs of individual students; therefore, educational materials need to be tailored to fit these needs: learning styles, prior knowledge of individual students, and recommendations. This approach offers several techniques to present the learning material for different types of learners and for different learning styles. User models (user profiles) are created using a combination of clustering techniques and association rules mining. These models represent the learning technique, learning style, and learning sequence, which can help improve the learning experience on the Web site for new users. Furthermore, the user models can be used to create an intelligent system that provides recommendations for future online students whose profile matches one of the mined profiles that represents the discovered user models.
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