Improving Personalization In E-Learning Systems

Improving Personalization In E-Learning Systems

Assma Bezza, Amar Balla, Farhi Marir
Copyright: © 2014 |Volume: 4 |Issue: 2 |Pages: 10
ISSN: 2155-5605|EISSN: 2155-5613|EISBN13: 9781466657113|DOI: 10.4018/ijtem.2014070107
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MLA

Bezza, Assma, et al. "Improving Personalization In E-Learning Systems." IJTEM vol.4, no.2 2014: pp.75-84. http://doi.org/10.4018/ijtem.2014070107

APA

Bezza, A., Balla, A., & Marir, F. (2014). Improving Personalization In E-Learning Systems. International Journal of Technology and Educational Marketing (IJTEM), 4(2), 75-84. http://doi.org/10.4018/ijtem.2014070107

Chicago

Bezza, Assma, Amar Balla, and Farhi Marir. "Improving Personalization In E-Learning Systems," International Journal of Technology and Educational Marketing (IJTEM) 4, no.2: 75-84. http://doi.org/10.4018/ijtem.2014070107

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Abstract

Individual learners have different requirements and characteristics, and as a result learning content should be able to be personalized and adaptable to the e-learner' profile. Little research work undertaken to tackle this issue, and it has been limited to ad-hoc work on personalizing, and adapting learning content in e-Learning. This paper presents two methods for modeling user profile and for personalizing and adapting a given content to match that profile: inductive (without user intervention) and deductive (with user intervention). These methods will be used as a base to review and classify research work undertaken on personalizing content in the domain of knowledge management and e-learning systems. Based on these reviews, especially those undertaken in personalizing knowledge content in knowledge management systems, the paper proposes a comprehensive approach for personalizing learning content.

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