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Learner modeling in cloud computing

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Abstract

With the emergence of technology, the personalization of e-learning systems is enhanced. These systems use a set of parameters for personalizing courses. However, in literature, these parameters are not based on classification and optimization algorithms to implement them in the cloud. Cloud computing is a new model of computing where standard and virtualized resources are provided as a service through the Internet. This paper proposes an approach that allows learner modeling in the cloud where these parameters are integrated. The suggested approach is based on the support vector machine algorithm, which analyzes the learners’ traces to find the best classification of learners through selected parameters with a low cost. An experimentation is conducted to validate this approach. This experimentation is based on the produced traces for learner modeling. The obtained results show that this approach represents the learner model with low operation costs compared to classic systems (no cloud).

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Authors and Affiliations

  1. The Research Laboratory of Technologies of Information and Communication & Electrical engineering (LaTICE), Tunis, Tunisia

    Sameh Ghallabi, Fathi Essalmi & Mohamed Jemni

  2. ICT Department, The Arab League Educational, Cultural and Scientific Organization, Tunis, Tunisia

    Mohamed Jemni

  3. College of Information, University of North Texas, Denton, TX, USA

    Kinshuk

Authors
  1. Sameh Ghallabi
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  2. Fathi Essalmi
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  3. Mohamed Jemni
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  4. Kinshuk
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Each author contributed evenly to this paper. All authors read and approved the final manuscript.

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Correspondence to Sameh Ghallabi.

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The authors declare that they have no competing interests.

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The datasets generated and/or analyzed during the current study are not publicly available due to privacy reasons but are available from the corresponding author on reasonable request.

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Ghallabi, S., Essalmi, F., Jemni, M. et al. Learner modeling in cloud computing. Educ Inf Technol 25, 5581–5599 (2020). https://doi.org/10.1007/s10639-020-10185-5

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