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Education and Information Technologies

Erschienen in:

03.06.2020

Learner modeling in cloud computing

verfasst von: Sameh Ghallabi, Fathi Essalmi, Mohamed Jemni, Kinshuk

Erschienen in: Education and Information Technologies | Ausgabe 6/2020

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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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Titel: Learner modeling in cloud computing
verfasst von: Sameh Ghallabi
Fathi Essalmi
Mohamed Jemni
Kinshuk
Publikationsdatum: 03.06.2020
Verlag: Springer US
Erschienen in: Education and Information Technologies / Ausgabe 6/2020
Print ISSN: 1360-2357
Elektronische ISSN: 1573-7608
DOI: https://doi.org/10.1007/s10639-020-10185-5

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