EAI Endorsed Transactions on e-Learning 17(15): e4

Research Article

A traditional-learning time predictive approach for e-learning systems in challenging environments

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  • @ARTICLE{10.4108/eai.29-11-2017.153391,
        author={K. M. Belise},
        title={A traditional-learning time predictive approach for e-learning systems in challenging environments},
        journal={EAI Endorsed Transactions on e-Learning},
        volume={17},
        number={15},
        publisher={EAI},
        journal_a={EL},
        year={2017},
        month={11},
        keywords={challenging environment, context, e-learning, offline learning, online learning, traditional learning, prediction, recommender, content filtering, collaborative filtering},
        doi={10.4108/eai.29-11-2017.153391}
    }
    
  • K. M. Belise
    Year: 2017
    A traditional-learning time predictive approach for e-learning systems in challenging environments
    EL
    EAI
    DOI: 10.4108/eai.29-11-2017.153391
K. M. Belise1
  • 1: Faculty of Science, Department of Mathematics and Computer Science, LIFA, Po. Box. 67 Dschang, Cameroon

Abstract

The explosion of world-wide-web has offered people a large number of online courses, e-classes and e-schools. Such e-learning applications contain a wide variety of learning materials which can confuse the choices of learner to select. Although the area of recommender systems has made a significant progress over the last several years to address this problem, the issue remained fairly unexplored for challenging environments. This paper proposes an approach to predict traditional-learning times for recommender systems in such environments.