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Intelligent Interactive Multimedia Systems and Services 2017Toward a Personalized Recommender System for Learning Activities in the Context of MOOCs

Intelligent Interactive Multimedia Systems and Services 2017: Toward a Personalized Recommender... [ Massive Open Online Courses have brought a revolution in the field of e-learning. However, the lack of support and personalization drives learners to lose their motivation and surrender the learning process. One of issues that MOOC should address in personalization of learning according to learners needs to reinforce motivation. The potential of learning activities to motivate learners in enhancing learning cannot be denied. Therefore, we focus on adapting learning activities to learners through a recommender system in order to suit individual learners’ diverse needs. In this paper we outline a set of dimensions that distinguish, describe and categorize learning activities based on existing categorizations. We propose a classification of these recommended learning activities according to Bloom’s taxonomy. These learning activities are integrated into and overall a rule based recommender system with modular architecture. ] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Intelligent Interactive Multimedia Systems and Services 2017Toward a Personalized Recommender System for Learning Activities in the Context of MOOCs

Part of the Smart Innovation, Systems and Technologies Book Series (volume 76)
Editors: De Pietro, Giuseppe; Gallo, Luigi; Howlett, Robert J.; Jain, Lakhmi C.

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References (10)

Publisher
Springer International Publishing
Copyright
© Springer International Publishing AG 2018
ISBN
978-3-319-59479-8
Pages
575–583
DOI
10.1007/978-3-319-59480-4_57
Publisher site
See Chapter on Publisher Site

Abstract

[ Massive Open Online Courses have brought a revolution in the field of e-learning. However, the lack of support and personalization drives learners to lose their motivation and surrender the learning process. One of issues that MOOC should address in personalization of learning according to learners needs to reinforce motivation. The potential of learning activities to motivate learners in enhancing learning cannot be denied. Therefore, we focus on adapting learning activities to learners through a recommender system in order to suit individual learners’ diverse needs. In this paper we outline a set of dimensions that distinguish, describe and categorize learning activities based on existing categorizations. We propose a classification of these recommended learning activities according to Bloom’s taxonomy. These learning activities are integrated into and overall a rule based recommender system with modular architecture. ]

Published: May 28, 2017

Keywords: Learning activities; E-learning; MOOC; Personalization; Recommender system

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