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Personalized Learning Objects Recommendation Based on the Semantic-Aware Discovery and the Learner Preference Pattern

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Journal of Educational Technology & Society Volume 10, Number 3, ISSN 1176-3647 e-ISSN 1176-3647


With vigorous development of the Internet, especially the web page interaction technology, distant E-learning has become more and more realistic and popular. Digital courses may consist of many learning units or learning objects and, currently, many learning objects are created according to SCORM standard. It can be seen that, in the near future, a vast amount of SCORM-compliant learning objects will be published and distributed cross the Internet. Facing huge volumes of learning objects, learners may be lost in selecting suitable and favorite learning objects. In this paper, an adaptive personalized recommendation model is proposed in order to help recommend SCORM-compliant learning objects from repositories in the Internet. This model adopts an ontological approach to perform semantic discovery as well as both preference-based and correlation-based approaches to rank the degree of relevance of learning objects to a learner's intension and preference. By implementing this model, a tutoring system is able to provide easily and efficiently suitable learning objects for active learners. (Contains 1 table and 12 figures.)


Wang, T.I., Tsai, K.H., Lee, M.C. & Chiu, T.K. (2007). Personalized Learning Objects Recommendation Based on the Semantic-Aware Discovery and the Learner Preference Pattern. Journal of Educational Technology & Society, 10(3), 84-105. Retrieved January 25, 2020 from .

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    Yevgen Biletskiy, Michael Wojcenovic & Hamidreza Baghi, University of New Brunswick, Canada

    Interdisciplinary Journal of E-Learning and Learning Objects Vol. 5, No. 1 (Jan 01, 2009) pp. 169–180

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